academic activities research

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• Published: 01 September 2020

Not everything helps the same for everyone: relevance of extracurricular activities for academic achievement

• Álvaro Balaguer   ORCID: orcid.org/0000-0002-8727-4690 1 ,
• Edgar Benítez   ORCID: orcid.org/0000-0001-7632-5109 2 ,
• Aranzazu Albertos   ORCID: orcid.org/0000-0002-3590-8364 1 &
• Sonia Lara 1  

Humanities and Social Sciences Communications volume  7 , Article number:  79 ( 2020 ) Cite this article

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Participation in organized Extracurricular Activities has contributed to improve academic achievement. However, this does not happen in the same way; it depends on sex, age, or parental educational level. Our objective is to know the importance of these factor interactions’ in the explanation of academic achievement. The sample consisted of 1148 adolescents, aged between 12 and 18 years, 52% of whom were female. Participants completed the Extracurricular Activities questionnaire, and academic and sociodemographic data were collected. The results show that differences in academic achievement depend on the adolescent stage. In early adolescence, girls improve in academic achievement, as well as with better parental education, reading of books and activity duration. On the contrary, in the middle and late adolescence, academic achievement improves with not participating in collective sports and reduced activity breadth, although parental educational level follows the same trend as in the early adolescence. These results reinforce the evolutionary hypothesis of specialization in the choice of activities throughout adolescence. In this sense, some proposals for schools that advocate for greater integration of curricular and non-curricular elements are discussed.

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Introduction.

The class “extracurricular activities” (EA) constitutes a positive youth developmental asset (Durlak et al., 2010 ; Eccles and Gootman, 2002 ; Eisman et al., 2016 ; Farb and Matjasko, 2012 ; Mueller et al., 2011 ) and covers a broad range of categories that share some essential elements (Hansen et al., 2010 ). Indeed, competent adults supervise these activities, which often involve peer interaction. They also have regular attendance schedules, offer practical learning opportunities and enable young people to spend time engaged in their own interests (Hansen and Larson, 2007 ).

EA does not form part of the school curriculum and, unlike formal education, student participation is voluntary. Adolescents often develop meaningful relationships with their peers and their instructors. This fact creates an appropriate context in which to develop identity, initiative, and social skills (Hirsch et al., 2011 ; Larson et al., 2006 ). These aspects help in theorizing about organized activities and contribute to different processes of adolescent development.

Moreover, investigations of the types of activities that involve youth in their free time have identify the relevant components that promote positive development in supervised/structured environments or, specifically, in extracurricular programs or activities. Positive youth development programs incorporate manifold elements that promote such development, with importance directed toward adult–adolescent relationships that tend to involve young people over time (Roth et al., 1998 ).

Type of activity

Studies of different types of EA address various development experiences (Eccles and Gootman, 2002 ), such as sports, performing arts, service clubs, and faith-based youth groups (Vandell et al., 2015 ). Research shows differences between sports and artistic EA, based on the results they promote in youth (Hansen et al., 2003 ; Im et al., 2016 ). Thus, evidence shows that artistic activities improve adolescent adjustment, as well as the participants’ self-knowledge, self-discipline, and artistic talents (Hansen et al., 2003 ).

Sports EA not only prevent risk behaviors, but also improve the social and academic abilities of youth (Darling, 2005 ). However, collective sports contribute to a lower level of academic, social, or preventive skills promotion than other types of EA (Hansen et al., 2010 ; Wilson et al., 2010 ).

Bartko and Eccles ( 2003 ) classify adolescents very involved in sports, spending more time with friends, and performing other EA in different groups, with high participation rates in school clubs, tasks, and reading for pleasure. For Im et al. ( 2016 ), participation in sports and artistic activities predicts an increase of self-efficacy in academic competence, so EA provides a context in which students can face and overcome challenges and increase the level of their skill, thus building trust.

Based on the approach of Mahoney et al. ( 2005 ), Reading books for pleasure would not be an organized extracurricular activity. In fact,

“these activities are generally voluntary, have regular and scheduled meetings, maintain developmentally based expectations and rules for participants in the activity setting (and sometimes beyond it), involve several participants, offer supervision and guidance from adults, and are organized around developing particular skills and achieving goals” (Mahoney et al., 2005 , p. 4).

Nonetheless, Reading books it is very relevant for youth development, relating to greater participation in a wider breadth of activities, as well as greater engagement in both extracurricular and curricular tasks (Bartko and Eccles, 2003 ).

Activity engagement

For EA to function as more effective assets requires youth engagement (Weiss et al., 2005 ) though factors such as breadth, intensity and duration (Bohnert et al., 2010 ; Busseri and Rose-Krasnor, 2009 ; Busseri et al., 2006 ). The benefits of participation in EA, both in the curricular field and in the personal development of the young person, depend on the characteristics of their participation experience. In turn, such experiences depend on their engagement, whether psychological (e.g. duration) or behavioral (e.g. breadth, intensity). However, a consensus on the conceptualization of participation should specify the aspects of behavioral and psychological engagement (Eisman et al., 2016 ).

Breadth of activities

Breadth refers to the number of EA that involve youth. The study of Breadth provides a broad description of the range of student skills and interests (Bohnert et al., 2010 ; Busseri and Rose-Krasnor, 2009 ; Busseri et al., 2006 ; Linver et al., 2009 ).

The range of activities refers to the participation in different activities in a certain period and may refer to the total number of activities in which a young person engages (Busseri and Rose-Krasnor, 2009 ) or the total number of different types of activities (Eccles and Barber, 1999 ). Contrary to participation in a single activity, a wide range of participation allows young people greater diversity of learning experiences, supportive interactions with adults, and broad networks of peers (Bohnert et al., 2010 ; Eccles and Barber, 1999 ; Vandell et al., 2015 ).

Duration of activities

The duration or consistency of participation in EA refers to the maintenance over time. “Dosage has been conceptualized in terms of consistency and continuity over time, measured as the proportion of periods in which youth engage in activities” (Pierce et al., 2013 ; in Vandell et al., 2015 ).

Longitudinal studies have measured consistency of participation among both elementary and middle-school children (see National Institute of Child Health and Human Development Early Child Care Research Network, 2004 ; Pierce et al., 2013 ) and in adolescents (see Mahoney et al., 2003 ; Zaff et al., 2003 ). The participation in EA over the years could increase the benefits for youth development, as a result of greater exposure to such opportunities (Eisman et al., 2016 ; Tudge et al., 2009 ).

Differences in adolescents’ activity participation

Sex differences in adolescents’ activity participation.

Sex relates to participation in EA, with girls showing more behavioral engagement in EA except in sports (Denault and Poulin, 2009b ; Eccles et al., 2003 ; Kleiner et al., 2004 ). Indeed, boys are more likely than girls to participate in sports, while girls are more likely than boys to participate in classes and clubs (Kleiner et al., 2004 ) or artistic EA (Luthar et al., 2006 ). The impact of EA on positive development is higher for boys (Pierce et al., 2010 ; Urban et al., 2009 ). No evidence indicates differences in participation according to sex, rather than age (Eisman et al., 2016 ). In any case, evidence of gender-related differences in the effects of EA is scarce.

Regarding risk behaviors, according to Eccles et al. ( 2003 ), participation in a competition sports team in late adolescence for both sexes is associated with higher alcohol consumption, the opposite of participation in artistic activities.

Stage differences in adolescents’ activity participation

Eisman et al. ( 2016 ) argue that the stage of development of the young person determines the importance of each type of engagement. In early adolescence, people tend to participate in a wider range of EA. Indeed, the breadth of participation could be more relevant in early adolescence than in late adolescence, because exploring different interests and strengthening bonds with peers characterizes early adolescence (Bohnert et al., 2010 ; Busseri and Rose-Krasnor, 2009 ). Also, as mentioned, the breadth of participation enables more learning opportunities and broader networks with different groups of peers and supportive adults (Vandell et al., 2015 ).

The young persons’ persistence in an activity depends on different factors, such as the activity itself, personal characteristics, and interests, and other environmental aspects, such as family circumstances, peers, and school or community characteristics (Vandell et al., 2015 ). Accordingly, as progress is made in developmental stages, adolescents develop greater control over their use of time (Fredricks and Eccles, 2010 ) and refine their personal interests. Consequently, in middle-to-late adolescence, young people participate in fewer activities, but with more intensity (Busseri et al., 2006 ; Denault and Poulin, 2009a ) and interest.

Indeed, leaving a type of activity may reflect the progression of development. Thereby, young people begin testing different EA, then focus on a smaller number of them as their interests become more defined (Badura et al., 2016 ; Rose-Krasnor et al., 2006 ), ranging from participating in a wide range of activities to a small number over time (Denault and Poulin, 2009a ; Eccles and Barber, 1999 ; Rose-Krasnor et al., 2006 ). In fact, participation in EA cannot change throughout adolescence (Eisman et al., 2016 ; Zaff et al., 2003 ) or change constantly along this stage (Farb and Matjasko, 2012 ), despite disagreements about whether such participation increases (Mahoney et al., 2003 ) or decreases (Denault and Poulin, 2009b ).

Parental education level differences in adolescents’ activity participation

Taking account of the context in which young people live is essential to understand the characteristics of their participation in EA. Research on differences in participation have been focus more in terms of socioeconomic factors (Luthar et al., 2006 ; Pedersen, 2005 ; Quinn, 1999 ;Vandell et al., 2015 ) than in differences related to parental education (Kingdon et al., 2017 ).

Parental education level is associated with participation in EA, in that youth whose parents have a higher educational level tend to participate more in activities than those whose parents have lower educational levels (Bartko and Eccles, 2003 ; Eisman et al., 2016 ; Feldman and Matjasko, 2007 ; Linver et al., 2009 ; Vandell et al., 2015 ), and parental education level also predicts their children’s duration in activities (Eisman et al., 2016 ).

EA associations with academic achievement

The relationship between participation in EA and improvements in academic achievement have been extensively studied. How adolescents decide to manage their free time is a protective factor related to academic achievement in higher grades, as well as to recovering from low GPAs (Eccles et al., 2003 ; Linver et al., 2009 ; Peck et al., 2008 ; Roth et al., 2010 ).

Participation in EA also relates to furthering the adolescent’s permanence in the educational system by improving their behavior and school attendance (Simpkins et al., 2004 ). Participation in EA improves other relevant aspects of curricular success, such as lower school dropout rates (Mahoney, 2000 ) and the school climate, in terms of associations with friendship and prosocial behavior with peers, as well as less aggressive behavior toward them (Eccles and Templeton, 2002 ).

Participants in sports activities experience lower academic achievement than those who participate in artistic activities (Eccles et al., 2003 ). Reading books is a protective factor in preventing school failure, and it improves academic achievement in early (Kingdon et al., 2017 ) and late adolescence (e.g. Horbec, 2012 ; Whitten et al., 2019 ). Adolescents reading books during their extracurricular schedule has been related to higher academic aspirations (McGaha and Fitzpatrick, 2010 ). Conversely, lack of reading has been related to difficulties in the transition to college (Rubin, 2008 ).

Most of the research that associating EA with academic achievement shows the latter as an outcome that participation improves (Eisman et al., 2016 ; Linver et al., 2009 ). Although both breadth and duration have been related to academic achievement (Palma et al., 2014 ), behavioral engagement (i.e. breadth) shows more predictive ability (Barber et al., 2005 ; Eisman et al., 2016 ; Fredricks and Eccles, 2006 ). However, duration—evaluated as consistent participation in a wide range of EA—has also predicted higher grades and academic achievement in studies that control for sex and parental education (Darling et al., 2005 ; Zaff et al., 2003 ).

The traditional controversy over whether breadth contributes to improving academic achievement (Linver et al., 2009 ) has given way to recent findings that indicate the relation of breadth to improvement in early adolescence (Eisman et al., 2016 ; Roth et al., 2010 ). However, the same would not happen in late adolescence, when duration is the more consistent predictor (Eisman et al., 2016 ).

Regarding the relationship between participation in EA and academic achievement in terms of sex and age, younger girls show the highest academic achievement (Kingdon et al., 2017 ; OECD, 2015 ), depending on the type of activity. For instance, an association has been found between reading books and academic achievement in early adolescence (i.e., schoolchildren between 12 and 14 years old) (Kingdon et al., 2017 ).

Aims and hypothesis

Despite the abundant literature on the effects of different EA on academic performance, empirical evidence is lacking for the relationship between extracurricular variables, such as breadth and duration, and academic achievement in Hispanic (including Spanish) contexts. Therefore, we aimed to investigate in such a context two groups of noncurricular factors—EA and sociodemographic variables—and a key curricular variable, academic achievement. In particular, we consider sex, age (distinguishing between early middle and late adolescence) and parental education level as sociodemographic variables.

On the other hand, as variables of EA, we take into account the breadth—understood as Busseri and Rose-Krasnor ( 2009 ) state it—and duration of participation, the type of activity (i.e., individual sport, collective sport and arts), and add an informal activity traditionally related to academic achievement, the reading of books. Associations of EA and sociodemographic variables with academic achievement were analized.

The hypotheses of the investigation are:

Types of activities (i.e., reading of books, artistic, and individual or collective sports) are associated with academic achievement, controlling for sex, age, and parental education level.

Duration of EA is associated with academic achievement, controlling for sex, age, and parental education level.

Breadth of EA is associated with academic achievement, controlling for sex, age, and parental education level.

Participants

Participants were recruited at 10 schools randomly selected from among all secondary schools in the province of Zaragoza, in Spain. We requested participation from students in grades 7, 9, and 11 (around 12, 14, and 16 years old, respectively). In total, 1148 students completed the survey, with balanced distributions by sex (50.2% female) and age (distribution of ages can be found in Table 1 ).

Instruments

EA (Hermoso et al., 2010 ) contains descriptive data about performing organized activities after school hours. From among adolescents who participated in such organized activities, data for a series of dummy variables were collected, covering the number of courses underway, the type of activity (sports and/or arts), and other items related to students’ perceptions of participation. Experts assessed and critiqued the questionnaire, and it fulfilled the requirements of external, internal, and content validity.

Sociodemographic variables data on sex, age, and parental education level were collected.

Academic achievement attends to a self-report of the previous course GPA.

This study was carried out in accordance with the recommendations of the Council of the British Educational Research Association in its second edition of the Ethical Guidelines for Educational Research ( 2011 ). Subjects received no compensation for participating in the study. Compliance with these ethical standards was guaranteed at all time.

The objectives and characteristics of the study were explained to the principals and counselors, who agreed to participate. Afterwards, they transferred the study objectives and questionnaires to the tutors from the different groups. Prior to completion, families were informed by a letter of the purpose of the study and procedure, and participants’ anonymity was ensured. In the same letter, the volunteers were informed of participation and the possibility of excluding from the activity those children whose families did not consent to participation, given that the data was collected during class time.

Data analyses

The age of the participants was dichotomized at the median, which resulted in one group between 12 and 14 years of age and the other between 15 and 18 years of age. For the evaluation of parental education level, three categories were considered: primary basic studies, middle studies, and university studies. The participant was assigned to one according to the highest level either parent had reached. For sports activities, participants were asked if the child was currently involved in individual or collective sports activities.

For the identification of artistic activities, if they were currently participating in dance, theater, music, or plastic-arts activities, any positive response on any of these activities was considered an affirmative response for this type of activity in its entirety. Also, they were asked directly if they were currently dedicating their time to reading books. The time devoted to EA was determined by asking the respondents to report during how many courses they had carried out these activities.

Finally, regarding the breadth of EA, the definition of Busseri and Rose-Krasnor ( 2009 ) was adopted, which proposes breadth as the total number of activities in which a young person engages. The response variable related to academic achievement was obtained by asking for the final grade in the immediately preceding course, reported on a scale from 1 to 8. However, for the descriptive analysis, this variable was categorized as “deficient” (below minimum approval level), “sufficient” (between minimum approval level and up to 5 units), or outstanding (higher than 5 units).

For the evaluation of the proposed hypotheses, a multilevel model of main effects was evaluated, where covariates and evaluation factors were taken as fixed effects, and the school to which the student belonged was categorized as a random effect. The estimation method was through restricted maximum likelihood with the Satterthwaite method for calculating the degrees of freedom. The residuals of the model were evaluated for their normality by means of skewness and kurtosis criteria. A significance value of p  < 0.05 was used. For post hoc comparisons, t -tests protected by Fisher were used in cases where the factor had more than two levels; for the rest of the cases, the F -test was considered sufficient. Due to the differential effect of the age of the respondents on the confounding and response variables, the proposed model was estimated independently for each age group.

In total, 549 (47.8%) males and 599 (52.2%) females were recruited, a greater proportion of whom were 12–14 years of age, with parents who had a high educational level (see Table 1 ). Regarding the response variable “academic achievement”, the three categories presented show a similar distribution; however, girls show better achievement than boys by more than 10 percentage points in the superior category.

The analysis of variance (Anova) of the multilevel model, proposed for the evaluation of academic achievement as a dependent variable, shows that the responses related to the evaluated variables corroborate the differentiated behavior by age group. For the younger group (12–14 years old), sex, reading books, and duration of EA are significant factors associated with academic achievement, contrary to the results for the older group (15–18 years old) for whom collective sports activities and breadth of EA was significantly related to academic achievement. Only parental education level presented a similar effect for both age groups (Table 2 ).

The analysis of means showed that for the sex-related factor, between 12 and 14 years of age, the girls had better academic achievement (see Table 3 and Fig. 1a ). For the level of education of the parents, those young people whose parents only reported levels of primary education showed the lowest academic achievement, compared to those with both medium levels and high levels of parental education. Regarding collective sports activities, the practice of these activities by the older students affected positively to academic achievement, as Fig. 1b shows. On the contrary, the effect of reading books was found among the younger ones as a promoting factor of academic achievement, also shown in Fig. 1b . Finally, for the variables of time and breadth, a differential in behavior was found again. For the younger students, the time spent most affected their academic achievement, by increasing it. For the older students, breadth was a predictor of academic achievement—in this case, the low level of dispersion is the weaker association, compared to the medium and high levels that present the best values (see Fig. 1c ).

a Sex and parental education level; b breadth and duration of EA; and c collective sports activity and reading of books.

The main contribution of this paper is the study of these characteristics in Hispanic, concretely, Spanish contexts, which is not very common in the scientific literature. Our results indicate that adolescents’ choices of EA participation are associated with academic achievement, as other investigations have shown (Badura et al., 2016 ; Bartko and Eccles, 2003 ; Eisman et al., 2016 ; Linver et al., 2009 ), but this association differs according to sex, age, type of activity, and parental education level. As the previous literature shows, boys participate more in sports activities while girls do more in artistic activities (Darling, 2005 ; Kleiner et al., 2004 ; Luthar et al., 2006 ), and participation is higher in early adolescence than in late adolescence (Denault and Poulin, 2009b , 2009a ; Rose-Krasnor et al., 2006 ).

Regarding the control variables in the prediction of academic achievement, the youngest girls have the highest grades, as has been found previously (Badura et al., 2016 ; OECD, 2015 ). On the other hand, a higher level of parental education is associated with higher academic achievement, in both early and late adolescence (Bartko and Eccles, 2003 ). The study by Roth et al. ( 2010 ) produces the same result, as well as an association with a smaller number of behavioral problems, but only when youths with high levels of participation were compared with peers who did not attend EA. Kingdon et al. ( 2017 ) found that reading and involvement of mothers are protective factors against the decrease in academic achievement in boys, as well as a predictor of future achievements, especially among low-income groups.

Addressing the first hypothesis (i.e., type of activity is associated with academic achievement), academic achievement relates negatively with collective sport EA in late adolescence. This is in line with the previous literature where sports activities relate to lower academic achievement than other types of EA, such as artistic activities (Badura et al., 2016 ; Darling, 2005 ; Darling et al., 2005 ; Eccles et al., 2003 ; Hansen et al., 2010 ; Wilson et al., 2010 ). However, this relationship was not significant in the study by Im et al. ( 2016 ). Linver et al. ( 2009 ) showed that students who participated only in sports EA had more positive results in academic achievement than those with little or no participation in organized activities, but worse academic achievement than those who participate in sports plus other activities, such as school, community, or religious groups.

We did not obtain a significant association between artistic EA and academic achievement, contrary to the results obtained in other studies, where artistic activities, not sports, provide greater reinforcement for academic achievement (Im et al., 2016 ), but only in girls (Luthar et al., 2006 ) or in late adolescents (Eccles et al., 2003 ).

Moreover, our work confirms the results of previous research by finding an association between reading books and academic achievement in early adolescence, namely, schoolchildren between 12 and 14 years old, the same age segment that Kingdon et al. ( 2017 ) reports. However, unlike our results, other research has found the same relation in late adolescence (e.g. Horbec, 2012 ; Whitten et al., 2019 ).

Regarding the second and third hypotheses (duration and breadth are associated with academic achievement), our results are in line with previous research, in which breadth (Fredricks, 2012 ; Fredricks and Eccles, 2006 ; Linver et al., 2009 ; Mahoney et al., 2006 ) and Duration (Darling, 2005 ; Darling et al., 2005 ; Eccles et al., 2003 ; Fredricks and Eccles, 2006 ; Zaff et al., 2003 ) relate to improvement of academic achievement. Nevertheless, our results show that duration is better predictor than breadth, contrary to previous literature (Barber et al., 2005 ; Eisman et al., 2016 ; Fredricks and Eccles, 2006 ).

However, we found that duration of EA is a predictor of academic achievement only in early adolescence, as Roth et al. ( 2010 ) and Eisman et al. ( 2016 ) show. On the other hand, in late adolescence, breadth is an important factor that enhances academic achievement as Eisman et al. ( 2016 ) show, but contrary to the results of Roth et al. ( 2010 ). Our findings confirm the evolutionary hypothesis that Eisman et al. ( 2016 ) present. In this sense, the stage of development of the young person would indicate the importance of the type of engagement (behavioral or psychological). Thus, in early adolescence, breadth would be more important, as those young people are more likely to seek a wide range of EA participation experiences (Busseri and Rose-Krasnor, 2009 ), to test which are more deserving of the investment of their free time. Thus, in late adolescence, they have already acquired greater control over time itself (Fredricks and Eccles, 2010 ), allowing more intense on specific activities and reducing the range of breadth (Busseri et al., 2006 ; Denault and Poulin, 2009b ).

While the literature reports that participating in EA has a positive relationship with academic achievement, this is only true to a certain extent, since too long a duration has a negative impact (Fredricks, 2012 ; Fredricks and Eccles, 2010 ; Palma et al., 2014 ). Indeed, the overprogramming hypothesis (OSH) suggests a point at which the resulting participation becomes counterproductive in terms of benefits to the young person (Fredricks and Eccles, 2010 ; Mahoney et al., 2006 ). An explanation for this could be that much time invested in EA interferes with time that could be devoted to the family, curricular tasks, or other moments that the young person needs (Fredricks and Eccles, 2010 ; Mahoney and Vest, 2012 ; Palma et al., 2014 ), regardless of whether this occurs in early, middle, or late adolescence.

After all, the positive effects of participation in EA are manifest. Mahoney et al. ( 2005 ) reveal that participation in activities—voluntary, school and extracurricular—increases participation and school achievement because it facilitates the acquisition of interpersonal skills and positive social norms, membership in prosocial peer groups, and strengthening of emotional and social connections with the school itself.

Both the curricular and extracurricular schedules can trigger differential benefits in young people, nourishing the student in a beneficial way throughout adolescent development, through the interrelation of both contexts.

Limitations and future directions

According to Eisman et al. ( 2016 ), limitations of research on participation in EA and association with academic achievement may include a selection bias toward adolescents who start high school with higher levels of self-acceptance. They are more likely to participate (or be selected) in organized activities due to the high levels of skill required to participate, together with higher academic achievement.

Another limitation is that data on the intensity of participation have not been collected. It would be interesting to check in future studies how differences in intensity of participation over time relate to academic achievement by determining the proportion of time when participation occurs, as Vandell et al. ( 2015 ) shows. In any case, the main limitations of the investigation are the instrument used is a self-report and that it is a cross-sectional study, so longitudinal studies are required to make a causal explanation possible.

In fact, it should be added that the quantitative design prevents drawing broader conclusions. A multi-method design are more consistent and would enrich the information around the students’ perceptions, especially regarding the reasons why the activities are chosen. And, in this sense, would also deepen into the reasons why these activities are not chosen, as well as whether the offer suit all groups of adolescents.

An additional limitation is that no more complex models were evaluated (i.e., models with interactions). However, an attempt to correct that limitation included doing independent analyses for each of the age groups, but other factors can interact, such as sex with the other variables. But for statistical robustness, we do not consider that model. A longitudinal study that asks about behavior in recent years could try to correct that bias.

Besides, future research should also be directed to investigate the level of participation of students in these activities—performance, persistence, concentration, autonomy, etc.—and its relationship with their characteristics as students and their academic and social success in school.

In addition, this model is proposed for students with a common academic background. Therefore, students whose academic trajectories were not in the courses of the ordinary curricular approach were taken out of the study. We refer to students enrolled in curricular diversification or alternative curricular proposals for being unable to follow the ordinary academic trajectory.

Data availability

The datasets analyzed during the current study are available on the following repository link: https://zenodo.org/record/3689261#.XlgI32hKiUk

Code availability

The code is the following: EDI FEB 12 2020.

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Empowering students to develop research skills

February 8, 2021

This post is republished from   Into Practice ,  a biweekly communication of Harvard’s  Office of the Vice Provost for Advances in Learning

Terence D. Capellini, Richard B Wolf Associate Professor of Human Evolutionary Biology, empowers students to grow as researchers in his Building the Human Body course through a comprehensive, course-long collaborative project that works to understand the changes in the genome that make the human skeleton unique. For instance, of the many types of projects, some focus on the genetic basis of why human beings walk on two legs. This integrative “Evo-Devo” project demands high levels of understanding of biology and genetics that students gain in the first half of class, which is then applied hands-on in the second half of class. Students work in teams of 2-3 to collect their own morphology data by measuring skeletons at the Harvard Museum of Natural History and leverage statistics to understand patterns in their data. They then collect and analyze DNA sequences from humans and other animals to identify the DNA changes that may encode morphology. Throughout this course, students go from sometimes having “limited experience in genetics and/or morphology” to conducting their own independent research. This project culminates in a team presentation and a final research paper.

The benefits: Students develop the methodological skills required to collect and analyze morphological data. Using the UCSC Genome browser  and other tools, students sharpen their analytical skills to visualize genomics data and pinpoint meaningful genetic changes. Conducting this work in teams means students develop collaborative skills that model academic biology labs outside class, and some student projects have contributed to published papers in the field. “Every year, I have one student, if not two, join my lab to work on projects developed from class to try to get them published.”

“The beauty of this class is that the students are asking a question that’s never been asked before and they’re actually collecting data to get at an answer.”

The challenges:  Capellini observes that the most common challenge faced by students in the course is when “they have a really terrific question they want to explore, but the necessary background information is simply lacking. It is simply amazing how little we do know about human development, despite its hundreds of years of study.” Sometimes, for instance, students want to learn about the evolution, development, and genetics of a certain body part, but it is still somewhat a mystery to the field. In these cases, the teaching team (including co-instructor Dr. Neil Roach) tries to find datasets that are maximally relevant to the questions the students want to explore. Capellini also notes that the work in his class is demanding and hard, just by the nature of the work, but students “always step up and perform” and the teaching team does their best to “make it fun” and ensure they nurture students’ curiosities and questions.

Takeaways and best practices

• Incorporate previous students’ work into the course. Capellini intentionally discusses findings from previous student groups in lectures. “They’re developing real findings and we share that when we explain the project for the next groups.” Capellini also invites students to share their own progress and findings as part of class discussion, which helps them participate as independent researchers and receive feedback from their peers.
• Assign groups intentionally.  Maintaining flexibility allows the teaching team to be more responsive to students’ various needs and interests. Capellini will often place graduate students by themselves to enhance their workload and give them training directly relevant to their future thesis work. Undergraduates are able to self-select into groups or can be assigned based on shared interests. “If two people are enthusiastic about examining the knee, for instance, we’ll match them together.”
• Consider using multiple types of assessments.  Capellini notes that exams and quizzes are administered in the first half of the course and scaffolded so that students can practice the skills they need to successfully apply course material in the final project. “Lots of the initial examples are hypothetical,” he explains, even grounded in fiction and pop culture references, “but [students] have to eventually apply the skills they learned in addressing the hypothetical example to their own real example and the data they generate” for the Evo-Devo project. This is coupled with a paper and a presentation treated like a conference talk.

Bottom line:  Capellini’s top advice for professors looking to help their own students grow as researchers is to ensure research projects are designed with intentionality and fully integrated into the syllabus. “You can’t simply tack it on at the end,” he underscores. “If you want this research project to be a substantive learning opportunity, it has to happen from Day 1.” That includes carving out time in class for students to work on it and make the connections they need to conduct research. “Listen to your students and learn about them personally” so you can tap into what they’re excited about. Have some fun in the course, and they’ll be motivated to do the work.

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Research Impact : Outputs and Activities

• Outputs and Activities
• Establishing Your Author Name and Presence
• Enhancing Your Impact
• Tracking Your Work
• Telling Your Story
• Impact Frameworks

What are Scholarly Outputs and Activities?

Scholarly/research outputs and activities represent the various outputs and activities created or executed by scholars and investigators in the course of their academic and/or research efforts.

One common output is in the form of scholarly publications which are defined by Washington University as:

". . . articles, abstracts, presentations at professional meetings and grant applications, [that] provide the main vehicle to disseminate findings, thoughts, and analysis to the scientific, academic, and lay communities. For academic activities to contribute to the advancement of knowledge, they must be published in sufficient detail and accuracy to enable others to understand and elaborate the results. For the authors of such work, successful publication improves opportunities for academic funding and promotion while enhancing scientific and scholarly achievement and repute."

Examples of activities include: editorial board memberships, leadership in professional societies, meeting organizer, consultative efforts, contributions to successful grant applications, invited talks and presentations, admininstrative roles, contribution of service to a clinical laboratory program, to name a few. For more examples of activities, see Washington University School of Medicine Appointments & Promotions Guidelines and Requirements or the "Examples of Outputs and Activities" box below. Also of interest is Table 1 in the " Research impact: We need negative metrics too " work.

Tracking your research outputs and activities is key to being able to document the impact of your research. One starting point for telling a story about your research impact is your publications. Advances in digital technology afford numerous avenues for scholars to not only disseminate research findings but also to document the diffusion of their research. The capacity to measure and report tangible outcomes can be used for a variety of purposes and tailored for various audiences ranging from the layperson, physicians, investigators, organizations, and funding agencies. Publication data can be used to craft a compelling narrative about your impact. See Quantifying the Impact of My Publications for examples of how to tell a story using publication data.

Another tip is to utilize various means of disseminating your research. See Strategies for Enhancing Research Impact for more information.

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Committee on Physical Activity and Physical Education in the School Environment; Food and Nutrition Board; Institute of Medicine; Kohl HW III, Cook HD, editors. Educating the Student Body: Taking Physical Activity and Physical Education to School. Washington (DC): National Academies Press (US); 2013 Oct 30.

Educating the Student Body: Taking Physical Activity and Physical Education to School.

• Hardcopy Version at National Academies Press

4 Physical Activity, Fitness, and Physical Education: Effects on Academic Performance

Key messages.

• Evidence suggests that increasing physical activity and physical fitness may improve academic performance and that time in the school day dedicated to recess, physical education class, and physical activity in the classroom may also facilitate academic performance.
• Available evidence suggests that mathematics and reading are the academic topics that are most influenced by physical activity. These topics depend on efficient and effective executive function, which has been linked to physical activity and physical fitness.
• Executive function and brain health underlie academic performance. Basic cognitive functions related to attention and memory facilitate learning, and these functions are enhanced by physical activity and higher aerobic fitness.
• Single sessions of and long-term participation in physical activity improve cognitive performance and brain health. Children who participate in vigorous- or moderate-intensity physical activity benefit the most.
• Given the importance of time on task to learning, students should be provided with frequent physical activity breaks that are developmentally appropriate.
• Although presently understudied, physically active lessons offered in the classroom may increase time on task and attention to task in the classroom setting.

Although academic performance stems from a complex interaction between intellect and contextual variables, health is a vital moderating factor in a child's ability to learn. The idea that healthy children learn better is empirically supported and well accepted ( Basch, 2010 ), and multiple studies have confirmed that health benefits are associated with physical activity, including cardiovascular and muscular fitness, bone health, psychosocial outcomes, and cognitive and brain health ( Strong et al., 2005 ; see Chapter 3 ). The relationship of physical activity and physical fitness to cognitive and brain health and to academic performance is the subject of this chapter.

Given that the brain is responsible for both mental processes and physical actions of the human body, brain health is important across the life span. In adults, brain health, representing absence of disease and optimal structure and function, is measured in terms of quality of life and effective functioning in activities of daily living. In children, brain health can be measured in terms of successful development of attention, on-task behavior, memory, and academic performance in an educational setting. This chapter reviews the findings of recent research regarding the contribution of engagement in physical activity and the attainment of a health-enhancing level of physical fitness to cognitive and brain health in children. Correlational research examining the relationship among academic performance, physical fitness, and physical activity also is described. Because research in older adults has served as a model for understanding the effects of physical activity and fitness on the developing brain during childhood, the adult research is briefly discussed. The short- and long-term cognitive benefits of both a single session of and regular participation in physical activity are summarized.

Before outlining the health benefits of physical activity and fitness, it is important to note that many factors influence academic performance. Among these are socioeconomic status ( Sirin, 2005 ), parental involvement ( Fan and Chen, 2001 ), and a host of other demographic factors. A valuable predictor of student academic performance is a parent having clear expectations for the child's academic success. Attendance is another factor confirmed as having a significant impact on academic performance ( Stanca, 2006 ; Baxter et al., 2011 ). Because children must be present to learn the desired content, attendance should be measured in considering factors related to academic performance.

• PHYSICAL FITNESS AND PHYSICAL ACTIVITY: RELATION TO ACADEMIC PERFORMANCE

State-mandated academic achievement testing has had the unintended consequence of reducing opportunities for children to be physically active during the school day and beyond. In addition to a general shifting of time in school away from physical education to allow for more time on academic subjects, some children are withheld from physical education classes or recess to participate in remedial or enriched learning experiences designed to increase academic performance ( Pellegrini and Bohn, 2005 ; see Chapter 5 ). Yet little evidence supports the notion that more time allocated to subject matter will translate into better test scores. Indeed, 11 of 14 correlational studies of physical activity during the school day demonstrate a positive relationship to academic performance ( Rasberry et al., 2011 ). Overall, a rapidly growing body of work suggests that time spent engaged in physical activity is related not only to a healthier body but also to a healthier mind ( Hillman et al., 2008 ).

Children respond faster and with greater accuracy to a variety of cognitive tasks after participating in a session of physical activity ( Tomporowski, 2003 ; Budde et al., 2008 ; Hillman et al., 2009 ; Pesce et al., 2009 ; Ellemberg and St-Louis-Deschênes, 2010 ). A single bout of moderate-intensity physical activity has been found to increase neural and behavioral concomitants associated with the allocation of attention to a specific cognitive task ( Hillman et al., 2009 ; Pontifex et al., 2012 ). And when children who participated in 30 minutes of aerobic physical activity were compared with children who watched television for the same amount of time, the former children cognitively outperformed the latter ( Ellemberg and St-Louis-Desêhenes, 2010 ). Visual task switching data among 69 overweight and inactive children did not show differences between cognitive performance after treadmill walking and sitting ( Tomporowski et al., 2008b ).

When physical activity is used as a break from academic learning time, postengagement effects include better attention ( Grieco et al., 2009 ; Bartholomew and Jowers, 2011 ), increased on-task behaviors ( Mahar et al., 2006 ), and improved academic performance ( Donnelly and Lambourne, 2011 ). Comparisons between 1st-grade students housed in a classroom with stand-sit desks where the child could stand at his/her discretion and in classrooms containing traditional furniture showed that the former children were highly likely to stand, thus expending significantly more energy than those who were seated ( Benden et al., 2011 ). More important, teachers can offer physical activity breaks as part of a supplemental curriculum or simply as a way to reset student attention during a lesson ( Kibbe et al., 2011 ; see Chapter 6 ) and when provided with minimal training can efficaciously produce vigorous or moderate energy expenditure in students ( Stewart et al., 2004 ). Further, after-school physical activity programs have demonstrated the ability to improve cardiovascular endurance, and this increase in aerobic fitness has been shown to mediate improvements in academic performance ( Fredericks et al., 2006 ), as well as the allocation of neural resources underlying performance on a working memory task ( Kamijo et al., 2011 ).

Over the past three decades, several reviews and meta-analyses have described the relationship among physical fitness, physical activity, and cognition (broadly defined as all mental processes). The majority of these reviews have focused on the relationship between academic performance and physical fitness—a physiological trait commonly defined in terms of cardiorespiratory capacity (e.g., maximal oxygen consumption; see Chapter 3 ). More recently, reviews have attempted to describe the effects of an acute or single bout of physical activity, as a behavior, on academic performance. These reviews have focused on brain health in older adults ( Colcombe and Kramer, 2003 ), as well as the effects of acute physical activity on cognition in adults ( Tomporowski, 2003 ). Some have considered age as part of the analysis ( Etnier et al., 1997 , 2006 ). Reviews focusing on research conducted in children ( Sibley and Etnier, 2003 ) have examined the relationship among physical activity, participation in sports, and academic performance ( Trudeau and Shephard, 2008 , 2010 ; Singh et al., 2012 ); physical activity and mental and cognitive health ( Biddle and Asare, 2011 ); and physical activity, nutrition, and academic performance ( Burkhalter and Hillman, 2011 ). The findings of most of these reviews align with the conclusions presented in a meta-analytic review conducted by Fedewa and Ahn (2011) . The studies reviewed by Fedewa and Ahn include experimental/quasi-experimental as well as cross-sectional and correlational designs, with the experimental designs yielding the highest effect sizes. The strongest relationships were found between aerobic fitness and achievement in mathematics, followed by IQ and reading performance. The range of cognitive performance measures, participant characteristics, and types of research design all mediated the relationship among physical activity, fitness, and academic performance. With regard to physical activity interventions, which were carried out both within and beyond the school day, those involving small groups of peers (around 10 youth of a similar age) were associated with the greatest gains in academic performance.

The number of peer-reviewed publications on this topic is growing exponentially. Further evidence of the growth of this line of inquiry is its increased global presence. Positive relationships among physical activity, physical fitness, and academic performance have been found among students from the Netherlands ( Singh et al., 2012 ) and Taiwan ( Chih and Chen, 2011 ). Broadly speaking, however, many of these studies show small to moderate effects and suffer from poor research designs ( Biddle and Asare, 2011 ; Singh et al., 2012 ).

Basch (2010) conducted a comprehensive review of how children's health and health disparities influence academic performance and learning. The author's report draws on empirical evidence suggesting that education reform will be ineffective unless children's health is made a priority. Basch concludes that schools may be the only place where health inequities can be addressed and that, if children's basic health needs are not met, they will struggle to learn regardless of the effectiveness of the instructional materials used. More recently, Efrat (2011) conducted a review of physical activity, fitness, and academic performance to examine the achievement gap. He discovered that only seven studies had included socioeconomic status as a variable, despite its known relationship to education ( Sirin, 2005 ).

Physical Fitness as a Learning Outcome of Physical Education and Its Relation to Academic Performance

Achieving and maintaining a healthy level of aerobic fitness, as defined using criterion-referenced standards from the National Health and Nutrition Examination Survey (NHANES; Welk et al., 2011 ), is a desired learning outcome of physical education programming. Regular participation in physical activity also is a national learning standard for physical education, a standard intended to facilitate the establishment of habitual and meaningful engagement in physical activity ( NASPE, 2004 ). Yet although physical fitness and participation in physical activity are established as learning outcomes in all 50 states, there is little evidence to suggest that children actually achieve and maintain these standards (see Chapter 2 ).

Statewide and national datasets containing data on youth physical fitness and academic performance have increased access to student-level data on this subject ( Grissom, 2005 ; Cottrell et al., 2007 ; Carlson et al., 2008 ; Chomitz et al., 2008 ; Wittberg et al., 2010 ; Van Dusen et al., 2011 ). Early research in South Australia focused on quantifying the benefits of physical activity and physical education during the school day; the benefits noted included increased physical fitness, decreased body fat, and reduced risk for cardiovascular disease ( Dwyer et al., 1979 , 1983 ). Even today, Dwyer and colleagues are among the few scholars who regularly include in their research measures of physical activity intensity in the school environment, which is believed to be a key reason why they are able to report differentiated effects of different intensities. A longitudinal study in Trois-Rivières, Québec, Canada, tracked how the academic performance of children from grades 1 through 6 was related to student health, motor skills, and time spent in physical education. The researchers concluded that additional time dedicated to physical education did not inhibit academic performance ( Shephard et al., 1984 ; Shephard, 1986 ; Trudeau and Shephard, 2008 ).

Longitudinal follow-up investigating the long-term benefits of enhanced physical education experiences is encouraging but largely inconclusive. In a study examining the effects of daily physical education during elementary school on physical activity during adulthood, 720 men and women completed the Québec Health Survey ( Trudeau et al., 1999 ). Findings suggest that physical education was associated with physical activity in later life for females but not males ( Trudeau et al., 1999 ); most of the associations were significant but weak ( Trudeau et al., 2004 ). Adult body mass index (BMI) at age 34 was related to childhood BMI at ages 10-12 in females but not males ( Trudeau et al., 2001 ). Longitudinal studies such as those conducted in Sweden and Finland also suggest that physical education experiences may be related to adult engagement in physical activity ( Glenmark, 1994 ; Telama et al., 1997 ). From an academic performance perspective, longitudinal data on men who enlisted for military service imply that cardiovascular fitness at age 18 predicted cognitive performance in later life (Aberg et al., 2009), thereby supporting the idea of offering physical education and physical activity opportunities well into emerging adulthood through secondary and postsecondary education.

Castelli and colleagues (2007) investigated younger children (in 3rd and 5th grades) and the differential contributions of the various subcomponents of the Fitnessgram ® . Specifically, they examined the individual contributions of aerobic capacity, muscle strength, muscle flexibility, and body composition to performance in mathematics and reading on the Illinois Standardized Achievement Test among a sample of 259 children. Their findings corroborate those of the California Department of Education ( Grissom, 2005 ), indicating a general relationship between fitness and achievement test performance. When the individual components of the Fitnessgram were decomposed, the researchers determined that only aerobic capacity was related to test performance. Muscle strength and flexibility showed no relationship, while an inverse association of BMI with test performance was observed, such that higher BMI was associated with lower test performance. Although Baxter and colleagues (2011) confirmed the importance of attending school in relation to academic performance through the use of 4th-grade student recall, correlations with BMI were not significant.

State-mandated implementation of the coordinated school health model requires all schools in Texas to conduct annual fitness testing using the Fitnessgram among students in grades 3-12. In a special issue of Research Quarterly for Exercise and Sport (2010), multiple articles describe the current state of physical fitness among children in Texas; confirm the associations among school performance levels, academic achievement, and physical fitness ( Welk et al., 2010 ; Zhu et al., 2010 ); and demonstrate the ability of qualified physical education teachers to administer physical fitness tests ( Zhu et al., 2010 ). Also using data from Texas schools, Van Dusen and colleagues (2011) found that cardiovascular fitness had the strongest association with academic performance, particularly in mathematics over reading. Unlike previous research, which demonstrated a steady decline in fitness by developmental stage ( Duncan et al., 2007 ), this study found that cardiovascular fitness did decrease but not significantly ( Van Dusen et al., 2011 ). Aerobic fitness, then, may be important to academic performance, as there may be a dose-response relationship ( Van Dusen et al., 2011 ).

Using a large sample of students in grades 4-8, Chomitz and colleagues (2008) found that the likelihood of passing both mathematics and English achievement tests increased with the number of fitness tests passed during physical education class, and the odds of passing the mathematics achievement tests were inversely related to higher body weight. Similar to the findings of Castelli and colleagues (2007) , socioeconomic status and demographic factors explained little of the relationship between aerobic fitness and academic performance; however, socioeconomic status may be an explanatory variable for students of low fitness ( London and Castrechini, 2011 ).

In sum, numerous cross-sectional and correlational studies demonstrate small-to-moderate positive or null associations between physical fitness ( Grissom, 2005 ; Cottrell et al., 2007 ; Edwards et al., 2009; Eveland-Sayers et al., 2009 ; Cooper et al., 2010 ; Welk et al., 2010 ; Wittberg et al., 2010 ; Zhu et al., 2010 ; Van Dusen et al., 2011 ), particularly aerobic fitness, and academic performance ( Castelli et al, 2007 ; Chomitz et al., 2008 ; Roberts et al., 2010 ; Welk et al., 2010 ; Chih and Chen, 2011 ; London and Castrechini, 2011 ; Van Dusen et al., 2011 ). Moreover, the findings may support a dose-response association, suggesting that the more components of physical fitness (e.g., cardiovascular endurance, strength, muscle endurance) considered acceptable for the specific age and gender that are present, the greater the likelihood of successful academic performance. From a public health and policy standpoint, the conclusions these findings support are limited by few causal inferences, a lack of data confirmation, and inadequate reliability because the data were often collected by nonresearchers or through self-report methods. It may also be noted that this research includes no known longitudinal studies and few randomized controlled trials (examples are included later in this chapter in the discussion of the developing brain).

Physical Activity, Physical Education, and Academic Performance

In contrast with the correlational data presented above for physical fitness, more information is needed on the direct effects of participation in physical activity programming and physical education classes on academic performance.

In a meta-analysis, Sibley and Etnier (2003) found a positive relationship between physical activity and cognition in school-age youth (aged 4-18), suggesting that physical activity, as well as physical fitness, may be related to cognitive outcomes during development. Participation in physical activity was related to cognitive performance in eight measurement categories (perceptual skills, IQ, achievement, verbal tests, mathematics tests, memory, developmental level/academic readiness, and “other”), with results indicating a beneficial relationship of physical activity to all cognitive outcomes except memory ( Sibley and Etnier, 2003 ). Since that meta-analysis, however, several papers have reported robust relationships between aerobic fitness and different aspects of memory in children (e.g., Chaddock et al., 2010a , 2011 ; Kamijo et al., 2011 ; Monti et al., 2012 ). Regardless, the comprehensive review of Sibley and Etnier (2003) was important because it helped bring attention to an emerging literature suggesting that physical activity may benefit cognitive development even as it also demonstrated the need for further study to better understand the multifaceted relationship between physical activity and cognitive and brain health.

The regular engagement in physical activity achieved during physical education programming can also be related to academic performance, especially when the class is taught by a physical education teacher. The Sports, Play, and Active Recreation for Kids (SPARK) study examined the effects of a 2-year health-related physical education program on academic performance in children ( Sallis et al., 1999 ). In an experimental design, seven elementary schools were randomly assigned to one of three conditions: (1) a specialist condition in which certified physical education teachers delivered the SPARK curriculum, (2) a trained-teacher condition in which classroom teachers implemented the curriculum, and (3) a control condition in which classroom teachers implemented the local physical education curriculum. No significant differences by condition were found for mathematics testing; however, reading scores were significantly higher in the specialist condition relative to the control condition ( Sallis et al., 1999 ), while language scores were significantly lower in the specialist condition than in the other two conditions. The authors conclude that spending time in physical education with a specialist did not have a negative effect on academic performance. Shortcomings of this research include the amount of data loss from pre- to posttest, the use of results of 2nd-grade testing that exceeded the national average in performance as baseline data, and the use of norm-referenced rather than criterion-based testing.

In seminal research conducted by Gabbard and Barton (1979) , six different conditions of physical activity (no activity; 20, 30, 40, and 50 minutes; and posttest no activity) were completed by 106 2nd graders during physical education. Each physical activity session was followed by 5 minutes of rest and the completion of 36 math problems. The authors found a potential threshold effect whereby only the 50-minute condition improved mathematical performance, with no differences by gender.

A longitudinal study of the kindergarten class of 1998–1999, using data from the Early Childhood Longitudinal Study, investigated the association between enrollment in physical education and academic achievement ( Carlson et al., 2008 ). Higher amounts of physical education were correlated with better academic performance in mathematics among females, but this finding did not hold true for males.

Ahamed and colleagues (2007) found in a cluster randomized trial that, after 16 months of a classroom-based physical activity intervention, there was no significant difference between the treatment and control groups in performance on the standardized Cognitive Abilities Test, Third Edition (CAT-3). Others have found, however, that coordinative exercise ( Budde et al., 2008 ) or bouts of vigorous physical activity during free time ( Coe et al., 2006 ) contribute to higher levels of academic performance. Specifically, Coe and colleagues examined the association of enrollment in physical education and self-reported vigorous- or moderate-intensity physical activity outside school with performance in core academic courses and on the Terra Nova Standardized Achievement Test among more than 200 6th-grade students. Their findings indicate that academic performance was unaffected by enrollment in physical education classes, which were found to average only 19 minutes of vigorous- or moderate-intensity physical activity. When time spent engaged in vigorous- or moderate-intensity physical activity outside of school was considered, however, a significant positive relation to academic performance emerged, with more time engaged in vigorous- or moderate-intensity physical activity being related to better grades but not test scores ( Coe et al., 2006 ).

Studies of participation in sports and academic achievement have found positive associations ( Mechanic and Hansell, 1987 ; Dexter, 1999 ; Crosnoe, 2002 ; Eitle and Eitle, 2002 ; Stephens and Schaben, 2002 ; Eitle, 2005 ; Miller et al., 2005 ; Fox et al., 2010 ; Ruiz et al., 2010 ); higher grade point averages (GPAs) in season than out of season ( Silliker and Quirk, 1997 ); a negative association between cheerleading and science performance ( Hanson and Kraus, 1998 ); and weak and negative associations between the amount of time spent participating in sports and performance in English-language class among 13-, 14-, and 16-year-old students ( Daley and Ryan, 2000 ). Other studies, however, have found no association between participation in sports and academic performance ( Fisher et al., 1996 ). The findings of these studies need to be interpreted with caution as many of their designs failed to account for the level of participation by individuals in the sport (e.g., amount of playing time, type and intensity of physical activity engagement by sport). Further, it is unclear whether policies required students to have higher GPAs to be eligible for participation. Offering sports opportunities is well justified regardless of the cognitive benefits, however, given that adolescents may be less likely to engage in risky behaviors when involved in sports or other extracurricular activities ( Page et al., 1998 ; Elder et al., 2000 ; Taliaferro et al., 2010 ), that participation in sports increases physical fitness, and that affiliation with sports enhances school connectedness.

Although a consensus on the relationship of physical activity to academic achievement has not been reached, the vast majority of available evidence suggests the relationship is either positive or neutral. The meta-analytic review by Fedewa and Ahn (2011) suggests that interventions entailing aerobic physical activity have the greatest impact on academic performance; however, all types of physical activity, except those involving flexibility alone, contribute to enhanced academic performance, as do interventions that use small groups (about 10 students) rather than individuals or large groups. Regardless of the strength of the findings, the literature indicates that time spent engaged in physical activity is beneficial to children because it has not been found to detract from academic performance, and in fact can improve overall health and function ( Sallis et al., 1999 ; Hillman et al., 2008 ; Tomporowski et al., 2008a ; Trudeau and Shephard, 2008 ; Rasberry et al., 2011 ).

Single Bouts of Physical Activity

Beyond formal physical education, evidence suggests that multi-component approaches are a viable means of providing physical activity opportunities for children across the school curriculum (see also Chapter 6 ). Although health-related fitness lessons taught by certified physical education teachers result in greater student fitness gains relative to such lessons taught by other teachers ( Sallis et al., 1999 ), non-physical education teachers are capable of providing opportunities to be physically active within the classroom ( Kibbe et al., 2011 ). Single sessions or bouts of physical activity have independent merit, offering immediate benefits that can enhance the learning experience. Studies have found that single bouts of physical activity result in improved attention ( Hillman et al., 2003 , 2009 ; Pontifex et al., 2012 ), better working memory ( Pontifex et al., 2009 ), and increased academic learning time and reduced off-task behaviors ( Mahar et al., 2006 ; Bartholomew and Jowers, 2011 ). Yet single bouts of physical activity have differential effects, as very vigorous exercise has been associated with cognitive fatigue and even cognitive decline in adults ( Tomporowski, 2003 ). As seen in Figure 4-1 , high levels of effort, arousal, or activation can influence perception, decision making, response preparation, and actual response. For discussion of the underlying constructs and differential effects of single bouts of physical activity on cognitive performance, see Tomporowski (2003) .

Information processing: Diagram of a simplified version of Sanders's (1983) cognitive-energetic model of human information processing (adapted from Jones and Hardy, 1989). SOURCE: Tomporowski, 2003. Reprinted with permission.

For children, classrooms are busy places where they must distinguish relevant information from distractions that emerge from many different sources occurring simultaneously. A student must listen to the teacher, adhere to classroom procedures, focus on a specific task, hold and retain information, and make connections between novel information and previous experiences. Hillman and colleagues (2009) demonstrated that a single bout of moderate-intensity walking (60 percent of maximum heart rate) resulted in significant improvements in performance on a task requiring attentional inhibition (e.g., the ability to focus on a single task). These findings were accompanied by changes in neuroelectric measures underlying the allocation of attention (see Figure 4-2 ) and significant improvements on the reading subtest of the Wide Range Achievement Test. No such effects were observed following a similar duration of quiet rest. These findings were later replicated and extended to demonstrate benefits for both mathematics and reading performance in healthy children and those diagnosed with attention deficit hyperactivity disorder ( Pontifex et al., 2013 ). Further replications of these findings demonstrated that a single bout of moderate-intensity exercise using a treadmill improved performance on a task of attention and inhibition, but similar benefits were not derived from moderate-intensity exercise that involved exergaming ( O'Leary et al., 2011 ). It was also found that such benefits were derived following cessation of, but not during, the bout of exercise ( Drollette et al., 2012 ). The applications of such empirical findings within the school setting remain unclear.

Effects of a single session of exercise in preadolescent children. SOURCE: Hillman et al., 2009. Reprinted with permission.

A randomized controlled trial entitled Physical Activity Across the Curriculum (PAAC) used cluster randomization among 24 schools to examine the effects of physically active classroom lessons on BMI and academic achievement ( Donnelly et al., 2009 ). The academically oriented physical activities were intended to be of vigorous or moderate intensity (3–6 metabolic equivalents [METs]) and to last approximately 10 minutes and were specifically designed to supplement content in mathematics, language arts, geography, history, spelling, science, and health. The study followed 665 boys and 677 girls for 3 years as they rose from 2nd or 3rd to 4th or 5th grades. Changes in academic achievement, fitness, and blood screening were considered secondary outcomes. During a 3-year period, students who engaged in physically active lessons, on average, improved their academic achievement by 6 percent, while the control groups exhibited a 1 percent decrease. In students who experienced at least 75 minutes of PAAC lessons per week, BMI remained stable (see Figure 4-3 ).

Change in academic scores from baseline after physically active classroom lessons in elementary schools in northeast Kansas (2003–2006). NOTE: All differences between the Physical Activity Across the Curriculum (PAAC) group ( N = 117) and control (more...)

It is important to note that cognitive tasks completed before, during, and after physical activity show varying effects, but the effects were always positive compared with sedentary behavior. In a study carried out by Drollette and colleagues (2012) , 36 preadolescent children completed two cognitive tasks—a flanker task to assess attention and inhibition and a spatial nback task to assess working memory—before, during, and after seated rest and treadmill walking conditions. The children sat or walked on different days for an average of 19 minutes. The results suggest that the physical activity enhanced cognitive performance for the attention task but not for the task requiring working memory. Accordingly, although more research is needed, the authors suggest that the acute effects of exercise may be selective to certain cognitive processes (i.e., attentional inhibition) while unrelated to others (e.g., working memory). Indeed, data collected using a task-switching paradigm (i.e., a task designed to assess multitasking and requiring the scheduling of attention to multiple aspects of the environment) among 69 overweight and inactive children did not show differences in cognitive performance following acute bouts of treadmill walking or sitting ( Tomporowski et al., 2008b ). Thus, findings to date indicate a robust relationship of acute exercise to transient improvements in attention but appear inconsistent for other aspects of cognition.

Academic Learning Time and On- and Off-Task Behaviors

Excessive time on task, inattention to task, off-task behavior, and delinquency are important considerations in the learning environment given the importance of academic learning time to academic performance. These behaviors are observable and of concern to teachers as they detract from the learning environment. Systematic observation by trained observers may yield important insight regarding the effects of short physical activity breaks on these behaviors. Indeed, systematic observations of student behavior have been used as an alternative means of measuring academic performance ( Mahar et al., 2006 ; Grieco et al., 2009 ).

After the development of classroom-based physical activities, called Energizers, teachers were trained in how to implement such activities in their lessons at least twice per week ( Mahar et al., 2006 ). Measurements of baseline physical activity and on-task behaviors were collected in two 3rd-grade and two 4th-grade classes, using pedometers and direct observation. The intervention included 243 students, while 108 served as controls by not engaging in the activities. A subgroup of 62 3rd and 4th graders was observed for on-task behavior in the classroom following the physical activity. Children who participated in Energizers took more steps during the school day than those who did not; they also increased their on-task behaviors by more than 20 percent over baseline measures.

A systematic review of a similar in-class, academically oriented, physical activity plan—Take 10!—was conducted to identify the effects of its implementation after it had been in use for 10 years ( Kibbe et al., 2011 ). The findings suggest that children who experienced Take 10! in the classroom engaged in moderate to vigorous physical activity (6.16 to 6.42 METs) and had lower BMIs than those who did not. Further, children in the Take 10! classrooms had better fluid intelligence ( Reed et al., 2010 ) and higher academic achievement scores ( Donnelly et al., 2009 ).

Some have expressed concern that introducing physical activity into the classroom setting may be distracting to students. Yet in one study it was sedentary students who demonstrated a decrease in time on task, while active students returned to the same level of on-task behavior after an active learning task ( Grieco et al., 2009 ). Among the 97 3rd-grade students in this study, a small but nonsignificant increase in on-task behaviors was seen immediately following these active lessons. Additionally, these improvements were not mediated by BMI.

In sum, although presently understudied, physically active lessons may increase time on task and attention to task in the classroom setting. Given the complexity of the typical classroom, the strategy of including content-specific lessons that incorporate physical activity may be justified.

It is recommended that every child have 20 minutes of recess each day and that this time be outdoors whenever possible, in a safe activity ( NASPE, 2006 ). Consistent engagement in recess can help students refine social skills, learn social mediation skills surrounding fair play, obtain additional minutes of vigorous- or moderate-intensity physical activity that contribute toward the recommend 60 minutes or more per day, and have an opportunity to express their imagination through free play ( Pellegrini and Bohn, 2005 ; see also Chapter 6 ). When children participate in recess before lunch, additional benefits accrue, such as less food waste, increased incidence of appropriate behavior in the cafeteria during lunch, and greater student readiness to learn upon returning to the classroom after lunch ( Getlinger et al., 1996 ; Wechsler et al., 2001 ).

To examine the effects of engagement in physical activity during recess on classroom behavior, Barros and colleagues (2009) examined data from the Early Childhood Longitudinal Study on 10,000 8- to 9-year-old children. Teachers provided the number of minutes of recess as well as a ranking of classroom behavior (ranging from “misbehaves frequently” to “behaves exceptionally well”). Results indicate that children who had at least 15 minutes of recess were more likely to exhibit appropriate behavior in the classroom ( Barros et al., 2009 ). In another study, 43 4th-grade students were randomly assigned to 1 or no days of recess to examine the effects on classroom behavior ( Jarrett et al., 1998 ). The researchers concluded that on-task behavior was better among the children who had recess. A moderate effect size (= 0.51) was observed. In a series of studies examining kindergartners' attention to task following a 20-minute recess, increased time on task was observed during learning centers and story reading ( Pellegrini et al., 1995 ). Despite these positive findings centered on improved attention, it is important to note that few of these studies actually measured the intensity of the physical activity during recess.

From a slightly different perspective, survey data from 547 Virginia elementary school principals suggest that time dedicated to student participation in physical education, art, and music did not negatively influence academic performance ( Wilkins et al., 2003 ). Thus, the strategy of reducing time spent in physical education to increase academic performance may not have the desired effect. The evidence on in-school physical activity supports the provision of physical activity breaks during the school day as a way to increase fluid intelligence, time on task, and attention. However, it remains unclear what portion of these effects can be attributed to a break from academic time and what portion is a direct result of the specific demands/characteristics of the physical activity.

• THE DEVELOPING bRAIN, PHYSICAL ACTIVITY, AND BRAIN HEALTH

The study of brain health has grown beyond simply measuring behavioral outcomes such as task performance and reaction time (e.g., cognitive processing speed). New technology has emerged that has allowed scientists to understand the impact of lifestyle factors on the brain from the body systems level down to the molecular level. A greater understanding of the cognitive components that subserve academic performance and may be amenable to intervention has thereby been gained. Research conducted in both laboratory and field settings has helped define this line of inquiry and identify some preliminary underlying mechanisms.

The Evidence Base on the Relationship of Physical Activity to Brain Health and Cognition in Older Adults

Despite the current focus on the relationship of physical activity to cognitive development, the evidence base is larger on the association of physical activity with brain health and cognition during aging. Much can be learned about how physical activity affects childhood cognition and scholastic achievement through this work. Despite earlier investigations into the relationship of physical activity to cognitive aging (see Etnier et al., 1997 , for a review), the field was shaped by the findings of Kramer and colleagues (1999) , who examined the effects of aerobic fitness training on older adults using a randomized controlled design. Specifically, 124 older adults aged 60 and 75 were randomly assigned to a 6-month intervention of either walking (i.e., aerobic training) or flexibility (i.e., nonaerobic) training. The walking group but not the flexibility group showed improved cognitive performance, measured as a shorter response time to the presented stimulus. Results from a series of tasks that tapped different aspects of cognitive control indicated that engagement in physical activity is a beneficial means of combating cognitive aging ( Kramer et al., 1999 ).

Cognitive control, or executive control, is involved in the selection, scheduling, and coordination of computational processes underlying perception, memory, and goal-directed action. These processes allow for the optimization of behavioral interactions within the environment through flexible modulation of the ability to control attention ( MacDonald et al., 2000 ; Botvinick et al., 2001 ). Core cognitive processes that make up cognitive control or executive control include inhibition, working memory, and cognitive flexibility ( Diamond, 2006 ), processes mediated by networks that involve the prefrontal cortex. Inhibition (or inhibitory control) refers to the ability to override a strong internal or external pull so as to act appropriately within the demands imposed by the environment ( Davidson et al., 2006 ). For example, one exerts inhibitory control when one stops speaking when the teacher begins lecturing. Working memory refers to the ability to represent information mentally, manipulate stored information, and act on the information ( Davidson et al., 2006 ). In solving a difficult mathematical problem, for example, one must often remember the remainder. Finally, cognitive flexibility refers to the ability to switch perspectives, focus attention, and adapt behavior quickly and flexibly for the purposes of goal-directed action ( Blair et al., 2005 ; Davidson et al., 2006 ; Diamond, 2006 ). For example, one must shift attention from the teacher who is teaching a lesson to one's notes to write down information for later study.

Based on their earlier findings on changes in cognitive control induced by aerobic training, Colcombe and Kramer (2003) conducted a meta-analysis to examine the relationship between aerobic training and cognition in older adults aged 55-80 using data from 18 randomized controlled exercise interventions. Their findings suggest that aerobic training is associated with general cognitive benefits that are selectively and disproportionately greater for tasks or task components requiring greater amounts of cognitive control. A second and more recent meta-analysis ( Smith et al., 2010 ) corroborates the findings of Colcombe and Kramer, indicating that aerobic exercise is related to attention, processing speed, memory, and cognitive control; however, it should be noted that smaller effect sizes were observed, likely a result of the studies included in the respective meta-analyses. In older adults, then, aerobic training selectively improves cognition.

Hillman and colleagues (2006) examined the relationship between physical activity and inhibition (one aspect of cognitive control) using a computer-based stimulus-response protocol in 241 individuals aged 15-71. Their results indicate that greater amounts of physical activity are related to decreased response speed across task conditions requiring variable amounts of inhibition, suggesting a generalized relationship between physical activity and response speed. In addition, the authors found physical activity to be related to better accuracy across conditions in older adults, while no such relationship was observed for younger adults. Of interest, this relationship was disproportionately larger for the condition requiring greater amounts of inhibition in the older adults, suggesting that physical activity has both a general and selective association with task performance ( Hillman et al., 2006 ).

With advances in neuroimaging techniques, understanding of the effects of physical activity and aerobic fitness on brain structure and function has advanced rapidly over the past decade. In particular, a series of studies ( Colcombe et al., 2003 , 2004 , 2006 ; Kramer and Erickson, 2007 ; Hillman et al., 2008 ) of older individuals has been conducted to elucidate the relation of aerobic fitness to the brain and cognition. Normal aging results in the loss of brain tissue ( Colcombe et al., 2003 ), with markedly larger loss evidenced in the frontal, temporal, and parietal regions ( Raz, 2000 ). Thus cognitive functions subserved by these brain regions (such as those involved in cognitive control and aspects of memory) are expected to decay more dramatically than other aspects of cognition.

Colcombe and colleagues (2003) investigated the relationship of aerobic fitness to gray and white matter tissue loss using magnetic resonance imaging (MRI) in 55 healthy older adults aged 55-79. They observed robust age-related decreases in tissue density in the frontal, temporal, and parietal regions using voxel-based morphometry, a technique used to assess brain volume. Reductions in the amount of tissue loss in these regions were observed as a function of fitness. Given that the brain structures most affected by aging also demonstrated the greatest fitness-related sparing, these initial findings provide a biological basis for fitness-related benefits to brain health during aging.

In a second study, Colcombe and colleagues (2006) examined the effects of aerobic fitness training on brain structure using a randomized controlled design with 59 sedentary healthy adults aged 60-79. The treatment group received a 6-month aerobic exercise (i.e., walking) intervention, while the control group received a stretching and toning intervention that did not include aerobic exercise. Results indicated that gray and white matter brain volume increased for those who received the aerobic fitness training intervention. No such results were observed for those assigned to the stretching and toning group. Specifically, those assigned to the aerobic training intervention demonstrated increased gray matter in the frontal lobes, including the dorsal anterior cingulate cortex, the supplementary motor area, the middle frontal gyrus, the dorsolateral region of the right inferior frontal gyrus, and the left superior temporal lobe. White matter volume changes also were evidenced following the aerobic fitness intervention, with increases in white matter tracts being observed within the anterior third of the corpus callosum. These brain regions are important for cognition, as they have been implicated in the cognitive control of attention and memory processes. These findings suggest that aerobic training not only spares age-related loss of brain structures but also may in fact enhance the structural health of specific brain regions.

In addition to the structural changes noted above, research has investigated the relationship between aerobic fitness and changes in brain function. That is, aerobic fitness training has also been observed to induce changes in patterns of functional activation. Functional MRI (fMRI) measures, which make it possible to image activity in the brain while an individual is performing a cognitive task, have revealed that aerobic training induces changes in patterns of functional activation. This approach involves inferring changes in neuronal activity from alteration in blood flow or metabolic activity in the brain. In a seminal paper, Colcombe and colleagues (2004) examined the relationship of aerobic fitness to brain function and cognition across two studies with older adults. In the first study, 41 older adult participants (mean age ~66) were divided into higher- and lower-fit groups based on their performance on a maximal exercise test. In the second study, 29 participants (aged 58-77) were recruited and randomly assigned to either a fitness training (i.e., walking) or control (i.e., stretching and toning) intervention. In both studies, participants were given a task requiring variable amounts of attention and inhibition. Results indicated that fitness (study 1) and fitness training (study 2) were related to greater activation in the middle frontal gyrus and superior parietal cortex; these regions of the brain are involved in attentional control and inhibitory functioning, processes entailed in the regulation of attention and action. These changes in neural activation were related to significant improvements in performance on the cognitive control task of attention and inhibition.

Taken together, the findings across studies suggest that an increase in aerobic fitness, derived from physical activity, is related to improvements in the integrity of brain structure and function and may underlie improvements in cognition across tasks requiring cognitive control. Although developmental differences exist, the general paradigm of this research can be applied to early stages of the life span, and some early attempts to do so have been made, as described below. Given the focus of this chapter on childhood cognition, it should be noted that this section has provided only a brief and arguably narrow look at the research on physical activity and cognitive aging. Considerable work has detailed the relationship of physical activity to other aspects of adult cognition using behavioral and neuroimaging tools (e.g., Boecker, 2011 ). The interested reader is referred to a number of review papers and meta-analyses describing the relationship of physical activity to various aspects of cognitive and brain health ( Etnier et al., 1997 ; Colcombe and Kramer, 2003 ; Tomporowski, 2003 ; Thomas et al., 2012 ).

Child Development, Brain Structure, and Function

Certain aspects of development have been linked with experience, indicating an intricate interplay between genetic programming and environmental influences. Gray matter, and the organization of synaptic connections in particular, appears to be at least partially dependent on experience (NRC/IOM, 2000; Taylor, 2006 ), with the brain exhibiting a remarkable ability to reorganize itself in response to input from sensory systems, other cortical systems, or insult ( Huttenlocher and Dabholkar, 1997 ). During typical development, experience shapes the pruning process through the strengthening of neural networks that support relevant thoughts and actions and the elimination of unnecessary or redundant connections. Accordingly, the brain responds to experience in an adaptive or “plastic” manner, resulting in the efficient and effective adoption of thoughts, skills, and actions relevant to one's interactions within one's environmental surroundings. Examples of neural plasticity in response to unique environmental interaction have been demonstrated in human neuroimaging studies of participation in music ( Elbert et al., 1995 ; Chan et al., 1998 ; Münte et al., 2001 ) and sports ( Hatfield and Hillman, 2001 ; Aglioti et al., 2008 ), thus supporting the educational practice of providing music education and opportunities for physical activity to children.

Effects of Regular Engagement in Physical Activity and Physical Fitness on Brain Structure

Recent advances in neuroimaging techniques have rapidly advanced understanding of the role physical activity and aerobic fitness may have in brain structure. In children a growing body of correlational research suggests differential brain structure related to aerobic fitness. Chaddock and colleagues (2010a , b ) showed a relationship among aerobic fitness, brain volume, and aspects of cognition and memory. Specifically, Chaddock and colleagues (2010a) assigned 9- to 10-year-old preadolescent children to lower- and higher-fitness groups as a function of their scores on a maximal oxygen uptake (VO 2 max) test, which is considered the gold-standard measure of aerobic fitness. They observed larger bilateral hippocampal volume in higher-fit children using MRI, as well as better performance on a task of relational memory. It is important to note that relational memory has been shown to be mediated by the hippocampus ( Cohen and Eichenbaum, 1993 ; Cohen et al., 1999 ). Further, no differences emerged for a task condition requiring item memory, which is supported by structures outside the hippocampus, suggesting selectivity among the aspects of memory that benefit from higher amounts of fitness. Lastly, hippocampal volume was positively related to performance on the relational memory task but not the item memory task, and bilateral hippocampal volume was observed to mediate the relationship between fitness and relational memory ( Chaddock et al., 2010a ). Such findings are consistent with behavioral measures of relational memory in children ( Chaddock et al., 2011 ) and neuroimaging findings in older adults ( Erickson et al., 2009 , 2011 ) and support the robust nonhuman animal literature demonstrating the effects of exercise on cell proliferation ( Van Praag et al., 1999 ) and survival ( Neeper et al., 1995 ) in the hippocampus.

In a second investigation ( Chaddock et al., 2010b ), higher- and lower-fit children (aged 9-10) underwent an MRI to determine whether structural differences might be found that relate to performance on a cognitive control task that taps attention and inhibition. The authors observed differential findings in the basal ganglia, a subcortical structure involved in the interplay of cognition and willed action. Specifically, higher-fit children exhibited greater volume in the dorsal striatum (i.e., caudate nucleus, putamen, globus pallidus) relative to lower-fit children, while no differences were observed in the ventral striatum. Such findings are not surprising given the role of the dorsal striatum in cognitive control and response resolution ( Casey et al., 2008 ; Aron et al., 2009 ), as well as the growing body of research in children and adults indicating that higher levels of fitness are associated with better control of attention, memory, and cognition ( Colcombe and Kramer, 2003 ; Hillman et al., 2008 ; Chang and Etnier, 2009 ). Chaddock and colleagues (2010b) further observed that higher-fit children exhibited increased inhibitory control and response resolution and that higher basal ganglia volume was related to better task performance. These findings indicate that the dorsal striatum is involved in these aspects of higher-order cognition and that fitness may influence cognitive control during preadolescent development. It should be noted that both studies described above were correlational in nature, leaving open the possibility that other factors related to fitness and/or the maturation of subcortical structures may account for the observed group differences.

Effects of Regular Engagement in Physical Activity and Physical Fitness on Brain Function

Other research has attempted to characterize fitness-related differences in brain function using fMRI and event-related brain potentials (ERPs), which are neuroelectric indices of functional brain activation in the electro-encephalographic time series. To date, few randomized controlled interventions have been conducted. Notably, Davis and colleagues (2011) conducted one such intervention lasting approximately 14 weeks that randomized 20 sedentary overweight preadolescent children into an after-school physical activity intervention or a nonactivity control group. The fMRI data collected during an antisaccade task, which requires inhibitory control, indicated increased bilateral activation of the prefrontal cortex and decreased bilateral activation of the posterior parietal cortex following the physical activity intervention relative to the control group. Such findings illustrate some of the neural substrates influenced by participation in physical activity. Two additional correlational studies ( Voss et al., 2011 ; Chaddock et al., 2012 ) compared higher- and lower-fit preadolescent children and found differential brain activation and superior task performance as a function of fitness. That is, Chaddock and colleagues (2012) observed increased activation in prefrontal and parietal brain regions during early task blocks and decreased activation during later task blocks in higher-fit relative to lower-fit children. Given that higher-fit children outperformed lower-fit children on the aspects of the task requiring the greatest amount of cognitive control, the authors reason that the higher-fit children were more capable of adapting neural activity to meet the demands imposed by tasks that tapped higher-order cognitive processes such as inhibition and goal maintenance. Voss and colleagues (2011) used a similar task to vary cognitive control requirements and found that higher-fit children outperformed their lower-fit counterparts and that such differences became more pronounced during task conditions requiring the upregulation of control. Further, several differences emerged across various brain regions that together make up the network associated with cognitive control. Collectively, these differences suggest that higher-fit children are more efficient in the allocation of resources in support of cognitive control operations.

Other imaging research has examined the neuroelectric system (i.e., ERPs) to investigate which cognitive processes occurring between stimulus engagement and response execution are influenced by fitness. Several studies ( Hillman et al., 2005 , 2009 ; Pontifex et al., 2011 ) have examined the P3 component of the stimulus-locked ERP and demonstrated that higher-fit children have larger-amplitude and shorter-latency ERPs relative to their lower-fit peers. Classical theory suggests that P3 relates to neuronal activity associated with revision of the mental representation of the previous event within the stimulus environment ( Donchin, 1981 ). P3 amplitude reflects the allocation of attentional resources when working memory is updated ( Donchin and Coles, 1988 ) such that P3 is sensitive to the amount of attentional resources allocated to a stimulus ( Polich, 1997 ; Polich and Heine, 2007 ). P3 latency generally is considered to represent stimulus evaluation and classification speed ( Kutas et al., 1977 ; Duncan-Johnson, 1981 ) and thus may be considered a measure of stimulus detection and evaluation time ( Magliero et al., 1984 ; Ila and Polich, 1999 ). Therefore the above findings suggest that higher-fit children allocate greater attentional resources and have faster cognitive processing speed relative to lower-fit children ( Hillman et al., 2005 , 2009 ), with additional research suggesting that higher-fit children also exhibit greater flexibility in the allocation of attentional resources, as indexed by greater modulation of P3 amplitude across tasks that vary in the amount of cognitive control required ( Pontifex et al., 2011 ). Given that higher-fit children also demonstrate better performance on cognitive control tasks, the P3 component appears to reflect the effectiveness of a subset of cognitive systems that support willed action ( Hillman et al., 2009 ; Pontifex et al., 2011 ).

Two ERP studies ( Hillman et al., 2009 ; Pontifex et al., 2011 ) have focused on aspects of cognition involved in action monitoring. That is, the error-related negativity (ERN) component was investigated in higher- and lower-fit children to determine whether differences in evaluation and regulation of cognitive control operations were influenced by fitness level. The ERN component is observed in response-locked ERP averages. It is often elicited by errors of commission during task performance and is believed to represent either the detection of errors during task performance ( Gehring et al., 1993 ; Holroyd and Coles, 2002 ) or more generally the detection of response conflict ( Botvinick et al., 2001 ; Yeung et al., 2004 ), which may be engendered by errors in response production. Several studies have reported that higher-fit children exhibit smaller ERN amplitude during rapid-response tasks (i.e., instructions emphasizing speed of responding; Hillman et al., 2009 ) and more flexibility in the allocation of these resources during tasks entailing variable cognitive control demands, as evidenced by changes in ERN amplitude for higher-fit children and no modulation of ERN in lower-fit children ( Pontifex et al., 2011 ). Collectively, this pattern of results suggests that children with lower levels of fitness allocate fewer attentional resources during stimulus engagement (P3 amplitude) and exhibit slower cognitive processing speed (P3 latency) but increased activation of neural resources involved in the monitoring of their actions (ERN amplitude). Alternatively, higher-fit children allocate greater resources to environmental stimuli and demonstrate less reliance on action monitoring (increasing resource allocation only to meet the demands of the task). Under more demanding task conditions, the strategy of lower-fit children appears to fail since they perform more poorly under conditions requiring the upregulation of cognitive control.

Finally, only one randomized controlled trial published to date has used ERPs to assess neurocognitive function in children. Kamijo and colleagues (2011) studied performance on a working memory task before and after a 9-month physical activity intervention compared with a wait-list control group. They observed better performance following the physical activity intervention during task conditions that required the upregulation of working memory relative to the task condition requiring lesser amounts of working memory. Further, increased activation of the contingent negative variation (CNV), an ERP component reflecting cognitive and motor preparation, was observed at posttest over frontal scalp sites in the physical activity intervention group. No differences in performance or brain activation were noted for the wait-list control group. These findings suggest an increase in cognitive preparation processes in support of a more effective working memory network resulting from prolonged participation in physical activity. For children in a school setting, regular participation in physical activity as part of an after-school program is particularly beneficial for tasks that require the use of working memory.

Adiposity and Risk for Metabolic Syndrome as It Relates to Cognitive Health

A related and emerging literature that has recently been popularized investigates the relationship of adiposity to cognitive and brain health and academic performance. Several reports ( Datar et al., 2004 ; Datar and Sturm, 2006 ; Judge and Jahns, 2007 ; Gable et al., 2012 ) on this relationship are based on large-scale datasets derived from the Early Child Longitudinal Study. Further, nonhuman animal research has been used to elucidate the relationships between health indices and cognitive and brain health (see Figure 4-4 for an overview of these relationships). Collectively, these studies observed poorer future academic performance among children who entered school overweight or moved from a healthy weight to overweight during the course of development. Corroborating evidence for a negative relationship between adiposity and academic performance may be found in smaller but more tightly controlled studies. As noted above, Castelli and colleagues (2007) observed poorer performance on the mathematics and reading portions of the Illinois Standardized Achievement Test in 3rd- and 5th-grade students as a function of higher BMI, and Donnelly and colleagues (2009) used a cluster randomized trial to demonstrate that physical activity in the classroom decreased BMI and improved academic achievement among pre-adolescent children.

Relationships between health indices and cognitive and brain health. NOTE: AD = Alzheimer's disease; PD = Parkinson's disease. SOURCE: Cotman et al., 2007. Reprinted with permission.

Recently published reports describe the relationship between adiposity and cognitive and brain health to advance understanding of the basic cognitive processes and neural substrates that may underlie the adiposity-achievement relationship. Bolstered by findings in adult populations (e.g., Debette et al., 2010 ; Raji et al., 2010 ; Carnell et al., 2011 ), researchers have begun to publish data on preadolescent populations indicating differences in brain function and cognitive performance related to adiposity (however, see Gunstad et al., 2008 , for an instance in which adiposity was unrelated to cognitive outcomes). Specifically, Kamijo and colleagues (2012a) examined the relationship of weight status to cognitive control and academic achievement in 126 children aged 7-9. The children completed a battery of cognitive control tasks, and their body composition was assessed using dual X-ray absorptiometry (DXA). The authors found that higher BMI and greater amounts of fat mass (particularly in the midsection) were related to poorer performance on cognitive control tasks involving inhibition, as well as lower academic achievement. In follow-up studies, Kamijo and colleagues (2012b) investigated whether neural markers of the relationship between adiposity and cognition may be found through examination of ERP data. These studies compared healthy-weight and obese children and found a differential distribution of the P3 potential (i.e., less frontally distributed) and larger N2 amplitude, as well as smaller ERN magnitude, in obese children during task conditions that required greater amounts of inhibitory control ( Kamijo et al., 2012c ). Taken together, the above results suggest that obesity is associated with less effective neural processes during stimulus capture and response execution. As a result, obese children perform tasks more slowly ( Kamijo et al., 2012a ) and are less accurate ( Kamijo et al., 2012b , c ) in response to tasks requiring variable amounts of cognitive control. Although these data are correlational, they provide a basis for further study using other neuroimaging tools (e.g., MRI, fMRI), as well as a rationale for the design and implementation of randomized controlled studies that would allow for causal interpretation of the relationship of adiposity to cognitive and brain health. The next decade should provide a great deal of information on this relationship.

• LIMITATIONS

Despite the promising findings described in this chapter, it should be noted that the study of the relationship of childhood physical activity, aerobic fitness, and adiposity to cognitive and brain health and academic performance is in its early stages. Accordingly, most studies have used designs that afford correlation rather than causation. To date, in fact, only two randomized controlled trials ( Davis et al., 2011 ; Kamijo et al., 2011 ) on this relationship have been published. However, several others are currently ongoing, and it was necessary to provide evidence through correlational studies before investing the effort, time, and funding required for more demanding causal studies. Given that the evidence base in this area has grown exponentially in the past 10 years through correlational studies and that causal evidence has accumulated through adult and nonhuman animal studies, the next step will be to increase the amount of causal evidence available on school-age children.

Accomplishing this will require further consideration of demographic factors that may moderate the physical activity–cognition relationship. For instance, socioeconomic status has a unique relationship with physical activity ( Estabrooks et al., 2003 ) and cognitive control ( Mezzacappa, 2004 ). Although many studies have attempted to control for socioeconomic status (see Hillman et al., 2009 ; Kamijo et al., 2011 , 2012a , b , c ; Pontifex et al., 2011 ), further inquiry into its relationship with physical activity, adiposity, and cognition is warranted to determine whether it may serve as a potential mediator or moderator for the observed relationships. A second demographic factor that warrants further consideration is gender. Most authors have failed to describe gender differences when reporting on the physical activity–cognition literature. However, studies of adiposity and cognition have suggested that such a relationship may exist (see Datar and Sturm, 2006 ). Additionally, further consideration of age is warranted. Most studies have examined a relatively narrow age range, consisting of a few years. Such an approach often is necessary because of maturation and the need to develop comprehensive assessment tools that suit the various stages of development. However, this approach has yielded little understanding of how the physical activity–cognition relationship may change throughout the course of maturation.

Finally, although a number of studies have described the relationship of physical activity, fitness, and adiposity to standardized measures of academic performance, few attempts have been made to observe the relationship within the context of the educational environment. Standardized tests, although necessary to gauge knowledge, may not be the most sensitive measures for (the process of) learning. Future research will need to do a better job of translating promising laboratory findings to the real world to determine the value of this relationship in ecologically valid settings.

From an authentic and practical to a mechanistic perspective, physically active and aerobically fit children consistently outperform their inactive and unfit peers academically on both a short- and a long-term basis. Time spent engaged in physical activity is related not only to a healthier body but also to enriched cognitive development and lifelong brain health. Collectively, the findings across the body of literature in this area suggest that increases in aerobic fitness, derived from physical activity, are related to improvements in the integrity of brain structure and function that underlie academic performance. The strongest relationships have been found between aerobic fitness and performance in mathematics, reading, and English. For children in a school setting, regular participation in physical activity is particularly beneficial with respect to tasks that require working memory and problem solving. These findings are corroborated by the results of both authentic correlational studies and experimental randomized controlled trials. Overall, the benefits of additional time dedicated to physical education and other physical activity opportunities before, during, and after school outweigh the benefits of exclusive utilization of school time for academic learning, as physical activity opportunities offered across the curriculum do not inhibit academic performance.

Both habitual and single bouts of physical activity contribute to enhanced academic performance. Findings indicate a robust relationship of acute exercise to increased attention, with evidence emerging for a relationship between participation in physical activity and disciplinary behaviors, time on task, and academic performance. Specifically, higher-fit children allocate greater resources to a given task and demonstrate less reliance on environmental cues or teacher prompting.

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• Research Skills

50 Mini-Lessons For Teaching Students Research Skills

Please note, I am no longer blogging and this post hasn’t updated since April 2020.

For a number of years, Seth Godin has been talking about the need to “ connect the dots” rather than “collect the dots” . That is, rather than memorising information, students must be able to learn how to solve new problems, see patterns, and combine multiple perspectives.

Solid research skills underpin this. Having the fluency to find and use information successfully is an essential skill for life and work.

Today’s students have more information at their fingertips than ever before and this means the role of the teacher as a guide is more important than ever.

You might be wondering how you can fit teaching research skills into a busy curriculum? There aren’t enough hours in the day! The good news is, there are so many mini-lessons you can do to build students’ skills over time.

This post outlines 50 ideas for activities that could be done in just a few minutes (or stretched out to a longer lesson if you have the time!).

Learn More About The Research Process

I have a popular post called Teach Students How To Research Online In 5 Steps. It outlines a five-step approach to break down the research process into manageable chunks.

This post shares ideas for mini-lessons that could be carried out in the classroom throughout the year to help build students’ skills in the five areas of: clarify, search, delve, evaluate , and cite . It also includes ideas for learning about staying organised throughout the research process.

Notes about the 50 research activities:

• These ideas can be adapted for different age groups from middle primary/elementary to senior high school.
• Many of these ideas can be repeated throughout the year.
• Depending on the age of your students, you can decide whether the activity will be more teacher or student led. Some activities suggest coming up with a list of words, questions, or phrases. Teachers of younger students could generate these themselves.
• Depending on how much time you have, many of the activities can be either quickly modelled by the teacher, or extended to an hour-long lesson.
• Some of the activities could fit into more than one category.
• Looking for simple articles for younger students for some of the activities? Try DOGO News or Time for Kids . Newsela is also a great resource but you do need to sign up for free account.
• Why not try a few activities in a staff meeting? Everyone can always brush up on their own research skills!

• Choose a topic (e.g. koalas, basketball, Mount Everest) . Write as many questions as you can think of relating to that topic.
• Make a mindmap of a topic you’re currently learning about. This could be either on paper or using an online tool like Bubbl.us .
• Read a short book or article. Make a list of 5 words from the text that you don’t totally understand. Look up the meaning of the words in a dictionary (online or paper).
• Look at a printed or digital copy of a short article with the title removed. Come up with as many different titles as possible that would fit the article.
• Come up with a list of 5 different questions you could type into Google (e.g. Which country in Asia has the largest population?) Circle the keywords in each question.
• Write down 10 words to describe a person, place, or topic. Come up with synonyms for these words using a tool like  Thesaurus.com .
• Write pairs of synonyms on post-it notes (this could be done by the teacher or students). Each student in the class has one post-it note and walks around the classroom to find the person with the synonym to their word.

• Explore how to search Google using your voice (i.e. click/tap on the microphone in the Google search box or on your phone/tablet keyboard) . List the pros and cons of using voice and text to search.
• Open two different search engines in your browser such as Google and Bing. Type in a query and compare the results. Do all search engines work exactly the same?
• Have students work in pairs to try out a different search engine (there are 11 listed here ). Report back to the class on the pros and cons.
• Think of something you’re curious about, (e.g. What endangered animals live in the Amazon Rainforest?). Open Google in two tabs. In one search, type in one or two keywords ( e.g. Amazon Rainforest) . In the other search type in multiple relevant keywords (e.g. endangered animals Amazon rainforest).  Compare the results. Discuss the importance of being specific.
• Similar to above, try two different searches where one phrase is in quotation marks and the other is not. For example, Origin of “raining cats and dogs” and Origin of raining cats and dogs . Discuss the difference that using quotation marks makes (It tells Google to search for the precise keywords in order.)
• Try writing a question in Google with a few minor spelling mistakes. What happens? What happens if you add or leave out punctuation ?
• Try the AGoogleADay.com daily search challenges from Google. The questions help older students learn about choosing keywords, deconstructing questions, and altering keywords.
• Explore how Google uses autocomplete to suggest searches quickly. Try it out by typing in various queries (e.g. How to draw… or What is the tallest…). Discuss how these suggestions come about, how to use them, and whether they’re usually helpful.
• Watch this video  from Code.org to learn more about how search works .
• Take a look at  20 Instant Google Searches your Students Need to Know  by Eric Curts to learn about “ instant searches ”. Try one to try out. Perhaps each student could be assigned one to try and share with the class.
• Experiment with typing some questions into Google that have a clear answer (e.g. “What is a parallelogram?” or “What is the highest mountain in the world?” or “What is the population of Australia?”). Look at the different ways the answers are displayed instantly within the search results — dictionary definitions, image cards, graphs etc.

• Watch the video How Does Google Know Everything About Me?  by Scientific American. Discuss the PageRank algorithm and how Google uses your data to customise search results.
• Brainstorm a list of popular domains   (e.g. .com, .com.au, or your country’s domain) . Discuss if any domains might be more reliable than others and why (e.g. .gov or .edu) .
• Discuss (or research) ways to open Google search results in a new tab to save your original search results  (i.e. right-click > open link in new tab or press control/command and click the link).
• Try out a few Google searches (perhaps start with things like “car service” “cat food” or “fresh flowers”). A re there advertisements within the results? Discuss where these appear and how to spot them.
• Look at ways to filter search results by using the tabs at the top of the page in Google (i.e. news, images, shopping, maps, videos etc.). Do the same filters appear for all Google searches? Try out a few different searches and see.
• Type a question into Google and look for the “People also ask” and “Searches related to…” sections. Discuss how these could be useful. When should you use them or ignore them so you don’t go off on an irrelevant tangent? Is the information in the drop-down section under “People also ask” always the best?
• Often, more current search results are more useful. Click on “tools” under the Google search box and then “any time” and your time frame of choice such as “Past month” or “Past year”.
• Have students annotate their own “anatomy of a search result” example like the one I made below. Explore the different ways search results display; some have more details like sitelinks and some do not.

• Find two articles on a news topic from different publications. Or find a news article and an opinion piece on the same topic. Make a Venn diagram comparing the similarities and differences.
• Choose a graph, map, or chart from The New York Times’ What’s Going On In This Graph series . Have a whole class or small group discussion about the data.
• Look at images stripped of their captions on What’s Going On In This Picture? by The New York Times. Discuss the images in pairs or small groups. What can you tell?
• Explore a website together as a class or in pairs — perhaps a news website. Identify all the advertisements .
• Have a look at a fake website either as a whole class or in pairs/small groups. See if students can spot that these sites are not real. Discuss the fact that you can’t believe everything that’s online. Get started with these four examples of fake websites from Eric Curts.
• Give students a copy of my website evaluation flowchart to analyse and then discuss as a class. Read more about the flowchart in this post.
• As a class, look at a prompt from Mike Caulfield’s Four Moves . Either together or in small groups, have students fact check the prompts on the site. This resource explains more about the fact checking process. Note: some of these prompts are not suitable for younger students.
• Practice skim reading — give students one minute to read a short article. Ask them to discuss what stood out to them. Headings? Bold words? Quotes? Then give students ten minutes to read the same article and discuss deep reading.

All students can benefit from learning about plagiarism, copyright, how to write information in their own words, and how to acknowledge the source. However, the formality of this process will depend on your students’ age and your curriculum guidelines.

• Watch the video Citation for Beginners for an introduction to citation. Discuss the key points to remember.
• Look up the definition of plagiarism using a variety of sources (dictionary, video, Wikipedia etc.). Create a definition as a class.
• Find an interesting video on YouTube (perhaps a “life hack” video) and write a brief summary in your own words.
• Have students pair up and tell each other about their weekend. Then have the listener try to verbalise or write their friend’s recount in their own words. Discuss how accurate this was.
• Read the class a copy of a well known fairy tale. Have them write a short summary in their own words. Compare the versions that different students come up with.
• Try out MyBib — a handy free online tool without ads that helps you create citations quickly and easily.
• Give primary/elementary students a copy of Kathy Schrock’s Guide to Citation that matches their grade level (the guide covers grades 1 to 6). Choose one form of citation and create some examples as a class (e.g. a website or a book).
• Make a list of things that are okay and not okay to do when researching, e.g. copy text from a website, use any image from Google images, paraphrase in your own words and cite your source, add a short quote and cite the source. 
• Have students read a short article and then come up with a summary that would be considered plagiarism and one that would not be considered plagiarism. These could be shared with the class and the students asked to decide which one shows an example of plagiarism .
• Older students could investigate the difference between paraphrasing and summarising . They could create a Venn diagram that compares the two.
• Write a list of statements on the board that might be true or false ( e.g. The 1956 Olympics were held in Melbourne, Australia. The rhinoceros is the largest land animal in the world. The current marathon world record is 2 hours, 7 minutes). Have students research these statements and decide whether they’re true or false by sharing their citations.

Staying Organised

• Make a list of different ways you can take notes while researching — Google Docs, Google Keep, pen and paper etc. Discuss the pros and cons of each method.
• Learn the keyboard shortcuts to help manage tabs (e.g. open new tab, reopen closed tab, go to next tab etc.). Perhaps students could all try out the shortcuts and share their favourite one with the class.
• Find a collection of resources on a topic and add them to a Wakelet .
• Listen to a short podcast or watch a brief video on a certain topic and sketchnote ideas. Sylvia Duckworth has some great tips about live sketchnoting
• Learn how to use split screen to have one window open with your research, and another open with your notes (e.g. a Google spreadsheet, Google Doc, Microsoft Word or OneNote etc.) .

All teachers know it’s important to teach students to research well. Investing time in this process will also pay off throughout the year and the years to come. Students will be able to focus on analysing and synthesizing information, rather than the mechanics of the research process.

By trying out as many of these mini-lessons as possible throughout the year, you’ll be really helping your students to thrive in all areas of school, work, and life.

Also remember to model your own searches explicitly during class time. Talk out loud as you look things up and ask students for input. Learning together is the way to go!

You Might Also Enjoy Reading:

How To Evaluate Websites: A Guide For Teachers And Students

Five Tips for Teaching Students How to Research and Filter Information

Typing Tips: The How and Why of Teaching Students Keyboarding Skills

8 Ways Teachers And Schools Can Communicate With Parents

10 Replies to “50 Mini-Lessons For Teaching Students Research Skills”

Loving these ideas, thank you

This list is amazing. Thank you so much!

So glad it’s helpful, Alex! 🙂

Hi I am a student who really needed some help on how to reasearch thanks for the help.

So glad it helped! 🙂

seriously seriously grateful for your post. 🙂

So glad it’s helpful! Makes my day 🙂

How do you get the 50 mini lessons. I got the free one but am interested in the full version.

Hi Tracey, The link to the PDF with the 50 mini lessons is in the post. Here it is . Check out this post if you need more advice on teaching students how to research online. Hope that helps! Kathleen

Best wishes to you as you face your health battler. Hoping you’ve come out stronger and healthier from it. Your website is so helpful.

Comments are closed.

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Five Ways to Make Research More Engaging

When most students (and many teachers) hear the word research, they often cringe — maybe they’ve been frustrated in the past while researching or they see research as an isolated academic task that does little to spark intrinsic motivation. As a school librarian, my goal is to transform research projects into opportunities to spark student creativity and tap into intrinsic interest while still effectively teaching foundational researching skills through collaborations with classroom teachers. Here are five ways to make research more engaging in your classroom!

1. Tackle Real-World Challenges

Research projects that reflect real world tasks encourage students to see research as a valuable life skill. For example, a 7th grade science teacher at my school wanted her students to learn more about radioactive elements. We worked together to create several real world research challenges to engage her students.

Our final prompts included one that asked them to pretend they worked at Chernobyl and to take steps to protect their families when they uncovered information about a potential accident. Another challenge asked students to go back in time and share what we know today about x-rays and their radiation with famed scientist Marie Curie. Both challenges required students to engage in more traditional research before applying critical thinking to complete the challenge.

2. Get Persuasive

Another type of real-world research encourages students to view their surroundings critically and suggest improvements in a persuasive way. As a 5th grade Language Arts teacher, I ended the year with a persuasive writing unit and, one year, I had students research effective learning spaces. They explored different types of furniture, decorations, lighting, and even layouts. They even collected and compiled their own research data by creating surveys and conducting interviews. Then, they chose a learning space at our school to evaluate based on their research and suggest changes in the form of a persuasive letter to the person in charge of the space. This encouraged students not only to research thoroughly to back up their claims, but also provided a sense of agency.

3. Start a Debate

Another way to make research more engaging is to utilize it as preparation for a debate. For example, I partnered with an 8th grade humanities teacher to craft a list of debate questions that reflected her curriculum as her year end wrap up. We had students work in groups of four to research their assigned question and then split them into pairs, with one pair supporting each side of the debate.

Questions again included real-world applications, including, “Should the US government sponsor and promote pro-war propaganda during times of war?” and “Which invention was more significant, the telephone or the lightbulb?” Students chose topics that reflected their interests and were motivated to research by their competitive nature. These debates also put their research to the test and taught them to select their most effective findings to support their own arguments.

4. Lean Into Trends

In my 5th grade Language Arts class, we ended the year by researching the pros and cons of using fidget toys in classes. We explored different types of fidget toys, then interviewed teachers and students about their experiences with them. The unit culminated in students creating their own fidget toy and then writing a persuasive letter to a teacher in the next grade encouraging them to let their students use the toy next year.

5. Embrace the Makerspace

Recently, I partnered with our school’s Latin teacher to create makerspace research projects for her 5th-8th grade students. Her 5th graders researched armor and animals found in Ancient Rome before creating armor for a stuffed animal based on their research. Her 6th graders each researched a different merchant from the Ancient Roman forum (marketplace) and then created items to sell in our forum simulation. Her 7th graders chose different structures from Ancient Rome to research and then created models to use in a presentation where they convinced a panel of Ancient Roman government officials to fund their idea. Her 8th graders researched chariots in Ancient Rome and built their own life-sized chariots out of cardboard and PVC pipes to race. These hands-on activities transformed traditional research projects into collaborative, competitive, and creative endeavors.

Makerspace Madness: Making Research More Engaging

What if you could leverage research to encourage creativity? You can! Research-based makerspace projects allow space for research magic to happen. Makerspace projects can take many forms, but the ones I’ve referred to throughout the article are those where students use readily available (and inexpensive) craft supplies and recycled materials to create items based on their imagination and research.

Are you excited about dipping your toe into the wonderful world of makerspace research projects but not sure where to begin? Start small by using building challenges as bell ringers or brain breaks. These challenges help students build their creativity, collaboration, and grit and prepare them to tackle larger projects. Then, take a trip to The Dollar Tree and begin building your stash of maker supplies. My shopping list even includes mock “prices” because I often give my students a budget for projects so they have to be more intentional in planning their creations. 

Claire Reddig

After graduating from Rice University with a bachelor’s degree in English and history, Claire started her teaching career in 1998 as an Upper School English teacher. Along the way, she also earned a Masters of Education in Learning, Teaching, and Curriculum from Mizzou. In 2020 while helping her own children navigate the college search process, Claire decided to explore the wonderful world of school libraries before making the leap to become a Middle School librarian in 2023. Her background as an English teacher and department chair, debate coach, design-thinking instructor, makerspace proponent, and more all prepared her for this new adventure. Her passions include sharing reading suggestions, sparking a love of reading, collaborating with colleagues, and crafting design thinking projects that foster creativity and spread joy.

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Calculate for all schools

Your chance of acceptance, your chancing factors, extracurriculars, academic activities examples.

I'm a high school junior, and I want to get more involved in academic activities outside of my classes. Can anyone give me some examples of clubs, competitions, or other extracurriculars that have an academic focus? Thanks in advance!

You're on the right track by seeking out academic activities to diversify your extracurricular profile! There are many options to consider, and I'll provide you with a few examples to get started:

1. Math Club: This might involve participating in math competitions like the American Mathematics Competitions (AMC), Math Olympiad, or MathCounts. Clubs often practice challenging problems and discuss math concepts in-depth.

2. Science Olympiad/Science Bowl: These are competitive academic events where teams of students compete in various disciplines, from chemistry to physics to robotics. Many high schools have Science Olympiad/Science Bowl clubs that prepare for and compete in these events.

3. Debate Team: Joining a school debate team can help you develop critical thinking and public speaking skills. High school debate usually revolves around topics like politics, ethics, and current events. Some popular formats include public forum, Lincoln-Douglas, and policy debate.

4. Model United Nations (MUN): MUN simulates the real-life United Nations by having students role-play as delegates from different countries. Participants research and debate global issues, draft resolutions, and practice diplomacy and negotiation.

5. Quiz Bowl: Quiz Bowl is a trivia competition where teams of students face off against other schools in realtime, answering questions from categories like history, literature, and science. Teams practice together regularly and participate in regional and national competitions.

6. Academic Decathlon: This competitive event encompasses ten subject areas: math, science, social science, literature, economics, art, music, speech, interview, and essay. Students study a specific theme each year and compete individually and as a team across subjects.

7. Writing Club or Literary Magazine: If you have a passion for writing, consider joining or starting a writing club or contributing to your school's literary magazine. You'll have the opportunity to share your work, receive feedback, and collaborate with other writers.

8. Language Clubs: Many high schools have clubs dedicated to specific languages, where students come together to practice speaking, watch movies, cook traditional dishes, and learn about different cultures. It's an excellent opportunity for learners of a language to develop their skills outside the classroom.

9. School Newspaper or Yearbook: If you're interested in journalism or photography, you can contribute to your school's newspaper or yearbook. By taking on roles like a reporter, editor, or photographer, you'll gain valuable experience and build practical skills.

Don't forget to explore opportunities in your local community or online, where you might find academic clubs, competitions, or other resources. The key is to find activities that you are passionate about and that will help you learn and grow. Good luck!

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Office of the Vice President for Research

Research funding to ui increases, topping $680m.

External funding to support research, scholarship, and creative activities at the University of Iowa totaled $683.8 million in fiscal year 2024, which ended on June 30, 2024. This is a 22% increase over the previous year’s $561 million. 

Total external funding to the institution reached $811 million, an increase of 15% from last year. These funds support a range of activities that build on the university’s research strengths, engage students in dynamic educational experiences, and support engagement with local, regional, and global communities.  

Several sources of federal and private funding trended up during FY24, including a significant uptick in funding from the National Science Foundation (NSF) and the Department of Education. Industry-supported activities, which include clinical trials, rose 17% to $149 million, the highest in the institution’s history. The previous record was $127 million set in FY22. 

“These trends align with the institution’s efforts to diversify our funding portfolio and forge new partnerships with federal, corporate, and foundation entities,” says Marty Scholtz, vice president for research. “Their support fosters a range of high-impact faculty work, from the arts and humanities, to engineering, to the sciences.”

Federal support 

Federal funding remains the strongest supporter of the institution, totaling $315 million in FY24, or 39% of all external funding. 

The NSF’s support of UI research projects increased 56% for a record high of $18 million, including a new $1.2 million project to advance the personalization of hearing aids. The lead investigator on the project is Octav Chipara, a professor in the Department of Computer Science. 

Funding from the Department of Defense (DOD) totaled $18 million. The DOD’s social-science focused Minerva Research Institute funded a  $1.7 million project to examine the global spread of propaganda, disinformation, and manipulative content on social media.   Brian Ekdale, associate professor in the School of Journalism and Mass Communication, leads the study.

Several federally funded projects reached major milestones of longevity with continuous funding from a single grant. Fiscal year 2024 marked the 45th year of National Institutes of Health (NIH) funding for the UI’s training program in hematology . The   project, which is currently led by Steven Lentz, professor of internal medicine and the Henry Hamilton Chair in Hematology, trains young scientists for productive careers as academic scientists and physician-scientists in hematology. It received funding to train its first cohort of scientists in 1978. To date, the program has trained more than 141 individuals who have gone on to successful careers in academic medical centers, the NIH, American Red Cross, and private industry. 

Private funding

Overall, support from private foundations and individuals totaled $129 million in FY24.

Two UI researchers secured $18 million from the nonprofit Patient-Centered Outcomes Research Institute for a five-year project to help older adults with multiple chronic medical conditions better manage their high blood pressure. Korey Kennelty, the Patrick E. Keefe Professor in Pharmacy and vice chair for research and implementation science in the Department of Family Medicine, and Carri Casteel, professor in the Department of Occupational and Environmental Health and director of UI’s Injury Prevention Research Center, are dual-principal investigators on the award. Their goal is to enroll more than 900 patients and partner with 60 primary care clinics across the country. 

Private sources also supported UI writers who continue to shape the landscape of American literature. The John Simon Guggenheim Memorial Foundation awarded Guggenheim fellowships to two UI faculty in FY24. Kaveh Akbar, associate professor and director of the undergraduate English and creative writing major, and Jamel Brinkley, assistant professor in the Iowa Writers’ Workshop, secured the fellowships from a pool of nearly 3,000 applicants. Thirty-three UI faculty have received the highly prestigious award since 1978. 

Clinical trials represent the largest percentage of industry-funded projects, at $85 million.

“Many clinical trials help patients treated at UI Health Care gain access to new drugs and therapies,” says Scholtz. “We’re proud to leverage the unique strengths and capabilities of UI researchers to help advance discoveries and treatments that directly benefit people in our state and beyond.”

Serving Iowa’s communities 

The U.S. Department of Commerce, the National Oceanic and Atmospheric Administration, and the National Weather Service co-funded a project to improve community resistance to flooding in eastern Iowa. The $1 million project will support new remote sensors to collect data and monitor conditions throughout the Lower Cedar River and Maquoketa River watersheds. Nathan Young, associate director of the Iowa Flood Center, will lead the project.

To address the shortage of special education teachers in Iowa and across the country, the Department of Education awarded a $1.2 million grant to a team in the College of Education led by Allison Bruhn, professor of special education.

“We are proud of the ingenuity of UI researchers and scholars, who continue to push the bounds of research and discovery, and secure competitive funding from a wide range of sources to do this work,” says Scholtz. “This year’s records are a testament to UI researchers’ enduring commitments to secure new knowledge that benefits the people of Iowa, the nation, and the world.”

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University Closure and Suspension of Academic Activities

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LAURELS: CASE Honors, Fulbright Scholars, NSF CAREER Award

• by Dateline Staff
• July 16, 2024

The latest Laurels column in Dateline UC Davis has news on awards for teaching and research, as well as communications and other activities that support the academic mission. Read on for more information.

Dateline UC Davis welcomes news of faculty and staff awards, for publication in Laurels. Send information to [email protected].

Circle of Excellence Awards

Nearly a dozen UC Davis communication, marketing and development projects have been honored in an international awards competition by the Council for Advancement and Support of Education, or CASE. Eleven projects from UC Davis earned Circle of Excellence Awards ; they were among thousands of entries from 600 institutions in 28 countries.

The winning entries:

• Davis Day of Reflection – Healing Together, in the category Special Events (In-Person - Single-Day Events)
• The Art of UC Davis: Donor Stewardship Video, in the category Video (Fundraising and Stewardship, Long)
• IDEA$ at Work: Engaging Employees to Help Solve Budget Challenges, in the category Communications (Communications Initiatives, More Than 25 Staff)
• Annual Stewardship — You're the Gift That Keeps on Giving, in the category Fundraising (Donor Relations and Stewardship Initiatives)
• A Day in the Life of Marine Science Student Researchers , in the category Video (Student Audience, Long)
• Accessibility and Inclusivity Guide for UC Davis Communicators , in the category Leadership (Diversity, Equity and Inclusion Initiatives)
• Aggie Accord: An App for Gift Agreements at UC Davis, in the category Advancement Services (Advancement Services Initiatives, More Than 25 Staff)
• Pod of Orcas , in the category Communications (Podcasts, Occasional)
• Last Chance to Give Video: Cori and UC Davis, in the category Video (Fundraising, Flash Campaign/Giving Day)
• Parent & Family Weekend 2023 , in the category Special Events (In-Person, Multi-Day Events)
• The Learning DO — Development Officer Boot Camp, in the category Leadership (Talent Management Initiatives)

Fulbright U.S. Scholar Awards

Two faculty and one professional staff member have received a Fulbright U.S. Scholar Program award for the 2024-2025 academic year from the U.S. Department of State and the Fulbright Foreign Scholarship Board.  Fulbright Scholar Awards  are prestigious and competitive fellowships that provide unique opportunities for scholars to teach and conduct research abroad. Fulbright scholars also play a critical role in U.S. public diplomacy, establishing long-term relationships between people and nations.

The Fulbright Scholars from UC Davis are Mary Alurwar, international scholar advisor in Global Affairs, Lynna Dhanani, assistant professor in the Department of Religious Studies, and Ron Mangun, director of the Center for Mind and Brain and distinguished professor in the Department of Psychology. 

Read more on the Global Affairs website.

NSF CAREER Award

Assistant Professor of Mechanical and Aerospace Engineering Jonathon Schofield has been recognized for his research with a National Science Foundation Faculty Early Career Development, or NSF CAREER Award — an accolade that supports early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department.

With this new recognition, Schofield seeks to further develop the efficacy of prosthetics for children born without fully formed limbs.

“We're trying to offer more functional options that cater to the innate capabilities that these children have,” he said. “Then we're going to investigate how that helps with learning, and if we can allow them to feel a sense of body ownership and incorporate prosthesis into their body image.”

Read more on the College of Engineering website.

— Roxanne Demorest

Center for Neuroscience Postdoc Named 2024 Pew Latin American Fellow in the Biomedical Sciences

T. Christine Stevens Award for Leadership Development

Professor of Mathematics Jesús De Loera has received the Mathematical Association of America’s T. Christine Stevens Award for Leadership Development. The award recognizes the importance of professional development that seeks to build leadership capacity within the mathematical sciences. 

“It was a pleasant surprise,” De Loera said of receiving the award. “I have been doing a lot of mentoring both on and off campus for years, but I didn’t even know I was being nominated. So it was a bit of a surprise.” 

While he’s enthusiastic about awards, the honors come second to the lives De Loera influences daily. This year marked the 20th graduate student that’s finished a doctoral thesis with De Loera. He’s mentored over 60 undergraduate students through their senior thesis projects.    

Read more on the College of Letters and Science website.

— Greg Watry

Smoke and Tobacco-Free Policy Report Card

UC Davis earned an A+ grade for its smoke and tobacco-free policy, which prohibits smoking, chewing and vaping everywhere on campus, from the California Youth Advocacy Network .

The policy went into effect 10 years ago on the Davis campus ; UC Davis Health enacted its own ban six years earlier.

All UC campuses are smoke and tobacco-free, and all earned A+ grades on the report card.

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Cody Kitaura is the editor of Dateline UC Davis and can be reached by email or at 530-752-1932.

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