survey of research on learning styles

Automatic learning styles prediction: a survey of the State-of-the-Art (2006–2021)

• Published: 02 February 2022
• Volume 9 , pages 587–679, ( 2022 )

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• Monica Raleiras   ORCID: orcid.org/0000-0003-4052-0759 1 ,
• Amir Hossein Nabizadeh   ORCID: orcid.org/0000-0003-2613-216X 2 , 3 &
• Fernando A. Costa   ORCID: orcid.org/0000-0001-9604-5542 4  

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Learning systems, whether traditional or computerized ones, often have a teacher-based design and use a “one-size-fits-all” approach. This approach ignores learners' differences and preferences, which results in demotivating learners and decreasing their engagement with a course. Subsequently, it increases their dropout rate (i.e. withdrawing from a course). One of the solutions to tackle these issues is delivering learning materials to learners considering their learning styles. Learning style indicates the way that each learner prefers to receive learning materials and interact with a learning environment. To this end, in this paper, we present a comprehensive review on methods that are proposed for the automatic prediction of learning styles. We start by explaining the methods and algorithms for the learning style prediction and categorize them into literature-based and data-driven approaches and describe their pros and cons. We also explore the data and learning attributes used by these approaches for the prediction task. In addition, we present online and offline evaluation approaches to assess the prediction methods and describe their advantages and disadvantages. Finally, we discuss the main challenges and possible research directions in the area of learning style prediction approaches, which need to be considered to enhance the quality of research in this area.

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This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT), under project GameCourse, Portugal - PTDC/CCI-CIF/30754/2017, and within the scope of INESC-ID (UIDB/50021/2020), and UIDEF (UIBD/04107/2020).

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A. Learning objects and behavioral patterns used by data-driven methods.

Table 6 summarizes LO and behavioral patterns used by DD methods It highlights the relationships among LO/behavioral patterns and various LS categories. In this table, G represents Global while Sq presents Sequential categories. Processing dimension is represented by A for Active and R for Reflective categories, while Input dimension is shown by Vi for Visual and Ve for Verbal categories. Finally, S and I indicate Sensing and Intuitive categories from the Perceive dimension, respectively. In this table, All presents a particular LO/pattern that was used for all categories of FSLSM.

B. Summarized studies.

See Table 7 .

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Raleiras, M., Nabizadeh, A.H. & Costa, F.A. Automatic learning styles prediction: a survey of the State-of-the-Art (2006–2021). J. Comput. Educ. 9 , 587–679 (2022). https://doi.org/10.1007/s40692-021-00215-7

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Survey of research on learning styles.

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A number of studies conducted during the last decade have found that students’ achievement increases when teaching methods match their learning styles—biological and developmental characteristics that affect how they learn.

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ORIGINAL RESEARCH article

Investigating the influence of gamification on motivation and learning outcomes in online language learning.

• 1 Institute for Media and Communication, Department of Language, Literature and Media I, Faculty of Humanities, University of Hamburg, Hamburg, Hamburg, Germany
• 2 Guangzhou College of Commerce, Guangzhou, China
• 3 The University of Sydney, Darlington, New South Wales, Australia

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This study investigates the influence of gamification integration on language learning achievement among Chinese students while probing the mediating role of learners' motivation. Furthermore, it extends the boundaries of this investigation by exploring the moderating effect of digital literacy as a psychological predisposition. Data is collected through surveys from Chinese students enrolled in linguistic programs, employing a stratified random sampling technique and analyzed via SmartPLS SEM. The findings affirm the significant and positive impact of gamification integration on language learning achievement. The study introduces a moderated mediation model where learners' motivation serves as the mediator, and digital literacy acts as a moderator, further accentuating the significant impact of this integrated approach. This research advances our theoretical understanding of language learning, validating gamification's effectiveness as a motivational tool, and introduces digital literacy as a critical factor, providing deeper insights into personalized language learning experiences.

Keywords: Gamification Integration, Learners' motivation, Learning style preference, Language learning outcomes, online language learning

Received: 18 Sep 2023; Accepted: 19 Apr 2024.

Copyright: © 2024 Shen, Lai and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Minjie Lai, Guangzhou College of Commerce, Guangzhou, China

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Key facts about Americans and guns

Guns are deeply ingrained in American society and the nation’s political debates.

The Second Amendment to the United States Constitution guarantees the right to bear arms, and about a third of U.S. adults say they personally own a gun. At the same time, in response to concerns such as rising gun death rates and  mass shootings , President Joe Biden has proposed gun policy legislation that would expand on the bipartisan gun safety bill Congress passed last year.

Here are some key findings about Americans’ views of gun ownership, gun policy and other subjects, drawn primarily from a Pew Research Center survey conducted in June 2023 .

Pew Research Center conducted this analysis to summarize key facts about Americans and guns. We used data from recent Center surveys to provide insights into Americans’ views on gun policy and how those views have changed over time, as well as to examine the proportion of adults who own guns and their reasons for doing so.

The analysis draws primarily from a survey of 5,115 U.S. adults conducted from June 5 to June 11, 2023. Everyone who took part in the surveys cited is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the  ATP’s methodology .

Here are the  questions used for the analysis on gun ownership , the questions used for the analysis on gun policy , and  the survey’s methodology .

Additional information about the fall 2022 survey of parents and its methodology can be found at the link in the text of this post.

Measuring gun ownership in the United States comes with unique challenges. Unlike many demographic measures, there is not a definitive data source from the government or elsewhere on how many American adults own guns.

The Pew Research Center survey conducted June 5-11, 2023, on the Center’s American Trends Panel, asks about gun ownership using two separate questions to measure personal and household ownership. About a third of adults (32%) say they own a gun, while another 10% say they do not personally own a gun but someone else in their household does. These shares have changed little from surveys conducted in 2021  and  2017 . In each of those surveys, 30% reported they owned a gun.

These numbers are largely consistent with rates of gun ownership reported by Gallup , but somewhat higher than those reported by NORC’s General Social Survey . Those surveys also find only modest changes in recent years.

The FBI maintains data on background checks on individuals attempting to purchase firearms in the United States. The FBI reported a surge in background checks in 2020 and 2021, during the coronavirus pandemic. The number of federal background checks declined in 2022 and through the first half of this year, according to FBI statistics .

About four-in-ten U.S. adults say they live in a household with a gun, including 32% who say they personally own one,  according to an August report based on our June survey. These numbers are virtually unchanged since the last time we asked this question in 2021.

There are differences in gun ownership rates by political affiliation, gender, community type and other factors.

• Republicans and Republican-leaning independents are more than twice as likely as Democrats and Democratic leaners to say they personally own a gun (45% vs. 20%).
• 40% of men say they own a gun, compared with 25% of women.
• 47% of adults living in rural areas report personally owning a firearm, as do smaller shares of those who live in suburbs (30%) or urban areas (20%).
• 38% of White Americans own a gun, compared with smaller shares of Black (24%), Hispanic (20%) and Asian (10%) Americans.

Personal protection tops the list of reasons gun owners give for owning a firearm.  About three-quarters (72%) of gun owners say that protection is a major reason they own a gun. Considerably smaller shares say that a major reason they own a gun is for hunting (32%), for sport shooting (30%), as part of a gun collection (15%) or for their job (7%). 

The reasons behind gun ownership have changed only modestly since our 2017 survey of attitudes toward gun ownership and gun policies. At that time, 67% of gun owners cited protection as a major reason they owned a firearm.

Gun owners tend to have much more positive feelings about having a gun in the house than non-owners who live with them. For instance, 71% of gun owners say they enjoy owning a gun – but far fewer non-gun owners in gun-owning households (31%) say they enjoy having one in the home. And while 81% of gun owners say owning a gun makes them feel safer, a narrower majority (57%) of non-owners in gun households say the same about having a firearm at home. Non-owners are also more likely than owners to worry about having a gun in the home (27% vs. 12%, respectively).

Feelings about gun ownership also differ by political affiliation, even among those who personally own firearms. Republican gun owners are more likely than Democratic owners to say owning a gun gives them feelings of safety and enjoyment, while Democratic owners are more likely to say they worry about having a gun in the home.

Non-gun owners are split on whether they see themselves owning a firearm in the future. About half (52%) of Americans who don’t own a gun say they could never see themselves owning one, while nearly as many (47%) could imagine themselves as gun owners in the future.

Among those who currently do not own a gun:

• 61% of Republicans and 40% of Democrats who don’t own a gun say they would consider owning one in the future.
• 56% of Black non-owners say they could see themselves owning a gun one day, compared with smaller shares of White (48%), Hispanic (40%) and Asian (38%) non-owners.

Americans are evenly split over whether gun ownership does more to increase or decrease safety. About half (49%) say it does more to increase safety by allowing law-abiding citizens to protect themselves, but an equal share say gun ownership does more to reduce safety by giving too many people access to firearms and increasing misuse.

Republicans and Democrats differ on this question: 79% of Republicans say that gun ownership does more to increase safety, while a nearly identical share of Democrats (78%) say that it does more to reduce safety.

Urban and rural Americans also have starkly different views. Among adults who live in urban areas, 64% say gun ownership reduces safety, while 34% say it does more to increase safety. Among those who live in rural areas, 65% say gun ownership increases safety, compared with 33% who say it does more to reduce safety. Those living in the suburbs are about evenly split.

Americans increasingly say that gun violence is a major problem. Six-in-ten U.S. adults say gun violence is a very big problem in the country today, up 9 percentage points from spring 2022. In the survey conducted this June, 23% say gun violence is a moderately big problem, and about two-in-ten say it is either a small problem (13%) or not a problem at all (4%).

Looking ahead, 62% of Americans say they expect the level of gun violence to increase over the next five years. This is double the share who expect it to stay the same (31%). Just 7% expect the level of gun violence to decrease.

A majority of Americans (61%) say it is too easy to legally obtain a gun in this country. Another 30% say the ease of legally obtaining a gun is about right, and 9% say it is too hard to get a gun. Non-gun owners are nearly twice as likely as gun owners to say it is too easy to legally obtain a gun (73% vs. 38%). Meanwhile, gun owners are more than twice as likely as non-owners to say the ease of obtaining a gun is about right (48% vs. 20%).

Partisan and demographic differences also exist on this question. While 86% of Democrats say it is too easy to obtain a gun legally, 34% of Republicans say the same. Most urban (72%) and suburban (63%) dwellers say it’s too easy to legally obtain a gun. Rural residents are more divided: 47% say it is too easy, 41% say it is about right and 11% say it is too hard.

About six-in-ten U.S. adults (58%) favor stricter gun laws. Another 26% say that U.S. gun laws are about right, and 15% favor less strict gun laws. The percentage who say these laws should be stricter has fluctuated a bit in recent years. In 2021, 53% favored stricter gun laws, and in 2019, 60% said laws should be stricter.

About a third (32%) of parents with K-12 students say they are very or extremely worried about a shooting ever happening at their children’s school, according to a fall 2022 Center survey of parents with at least one child younger than 18. A similar share of K-12 parents (31%) say they are not too or not at all worried about a shooting ever happening at their children’s school, while 37% of parents say they are somewhat worried.

Among all parents with children under 18, including those who are not in school, 63% see improving mental health screening and treatment as a very or extremely effective way to prevent school shootings. This is larger than the shares who say the same about having police officers or armed security in schools (49%), banning assault-style weapons (45%), or having metal detectors in schools (41%). Just 24% of parents say allowing teachers and school administrators to carry guns in school would be a very or extremely effective approach, while half say this would be not too or not at all effective.

There is broad partisan agreement on some gun policy proposals, but most are politically divisive,   the June 2023 survey found . Majorities of U.S. adults in both partisan coalitions somewhat or strongly favor two policies that would restrict gun access: preventing those with mental illnesses from purchasing guns (88% of Republicans and 89% of Democrats support this) and increasing the minimum age for buying guns to 21 years old (69% of Republicans, 90% of Democrats). Majorities in both parties also  oppose  allowing people to carry concealed firearms without a permit (60% of Republicans and 91% of Democrats oppose this).

Republicans and Democrats differ on several other proposals. While 85% of Democrats favor banning both assault-style weapons and high-capacity ammunition magazines that hold more than 10 rounds, majorities of Republicans oppose these proposals (57% and 54%, respectively).

Most Republicans, on the other hand, support allowing teachers and school officials to carry guns in K-12 schools (74%) and allowing people to carry concealed guns in more places (71%). These proposals are supported by just 27% and 19% of Democrats, respectively.

Gun ownership is linked with views on gun policies. Americans who own guns are less likely than non-owners to favor restrictions on gun ownership, with a notable exception. Nearly identical majorities of gun owners (87%) and non-owners (89%) favor preventing mentally ill people from buying guns.

Within both parties, differences between gun owners and non-owners are evident – but they are especially stark among Republicans. For example, majorities of Republicans who do not own guns support banning high-capacity ammunition magazines and assault-style weapons, compared with about three-in-ten Republican gun owners.

Among Democrats, majorities of both gun owners and non-owners favor these two proposals, though support is greater among non-owners. 

Note: This is an update of a post originally published on Jan. 5, 2016 .

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Katherine Schaeffer is a research analyst at Pew Research Center

About 1 in 4 U.S. teachers say their school went into a gun-related lockdown in the last school year

Striking findings from 2023, for most u.s. gun owners, protection is the main reason they own a gun, gun violence widely viewed as a major – and growing – national problem, what the data says about gun deaths in the u.s., most popular.

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A Study of Students' Learning Styles, Discipline Attitudes and Knowledge Acquisition in Technology-Enhanced Probability and Statistics Education

Many modern technological advances have direct impact on the format, style and efficacy of delivery and consumption of educational content. For example, various novel communication and information technology tools and resources enable efficient, timely, interactive and graphical demonstrations of diverse scientific concepts. In this manuscript, we report on a meta-study of 3 controlled experiments of using the Statistics Online Computational Resources in probability and statistics courses. Web-accessible SOCR applets, demonstrations, simulations and virtual experiments were used in different courses as treatment and compared to matched control classes utilizing traditional pedagogical approaches. Qualitative and quantitative data we collected for all courses included Felder-Silverman-Soloman index of learning styles, background assessment, pre and post surveys of attitude towards the subject, end-point satisfaction survey, and varieties of quiz, laboratory and test scores. Our findings indicate that students' learning styles and attitudes towards a discipline may be important confounds of their final quantitative performance. The observed positive effects of integrating information technology with established pedagogical techniques may be valid across disciplines within the broader spectrum courses in the science education curriculum. The two critical components of improving science education via blended instruction include instructor training, and development of appropriate activities, simulations and interactive resources.

Introduction

Modern scientific, biomedical and humanitarian college curricula demand the integration of contemporary information technology tools with proven classical pedagogical approaches. This paradigm shift of blending established instructional instruments with novel technology-based instruction is fueled by the rapid advancement of technology, the ubiquitous use of the Internet in various aspects of life and by economic and social demands ( Ali, 2008 ; Dinov, 2008 ; Santos et al., 2008 ). The proliferation of diverse contemporary methods for teaching with technology is coupled with the need for scientific assessment of these new strategies for enhancing student motivation and improving quality of the learning process and extending the time of knowledge retention ( Dinov et al., 2008 ; Orlich et al., 2009 ).

The Statistics Online Computational Resource (SOCR) is a national center for statistical education and computing located at the University of California, Los Angles (UCLA). The goals of the SOCR project are to design, implement, validate and widely distribute new interactive tools and educational materials. SOCR efforts are focused on producing new and expanding existing Java applets, web-based course materials and interactive aids for technology enhanced instruction and statistical computing ( Che et al., 2009a ; Dinov, 2006 ; Dinov and Christou, 2009 ). Many SOCR resources are useful for instructors, students and researchers. All of these resources are freely available and anonymously accessible over the Internet ( www.SOCR.ucla.edu ).

SOCR is composed of four major components: computational libraries, interactive applets, hands-on activities and instructional resources. External programs typically use the SOCR libraries for statistical computing ( Ho et al., 2010 ) ( Sowell et al., 2010 ) ( Che et al., 2009b ). The interactive SOCR applets are further subdivided into six suites of tools: Distributions, Experiments, Analyses, Games, Modeler and Charts ( www.SOCR.ucla.edu ). Dynamic Wiki pages, http://wiki.stat.ucla.edu/socr/ , contain the hands-on activities and include a variety of specific instances of demonstrations of the SOCR applets. The SOCR instructional plans are collections composed of lecture notes, documentations, tutorials and guidelines about statistics education, e.g., the Probability and Statistics EBook: http://wiki.stat.ucla.edu/socr/index.php/EBook .

In this study, we investigate the effects of learning styles, teaching with technology, interactive simulations and quantitative measures of student performance in IT-blended probability and statistics classes. We also collected attitude data towards the subject at the beginning and the end of the quarter to determine the initial and final state of the students' perception of the subject of probability and statistics, as a quantitative discipline. The first specific research question we address is whether there is significant evidence that technology enhanced instruction facilitates knowledge retention, boosts motivation and improves student satisfaction (and if so, what is the practical size of the IT-instruction effect). The second question we tackle is whether there are learning-style specific effects that may influence the quantitative outcomes of traditional or IT-enhanced instruction.

The rapid technological advancements in recent years have led to the development of diverse tools and infrastructure of integrating science, education and technology. This in turn has expanded the variety of novel methods for learning and communication. Such recent studies ( Blasi and Alfonso, 2006 ; Dinov et al., 2008 ; Schochet, 2008 ) have demonstrated the power of this new paradigm of IT-based blended instruction. In the field of statistics educational research, there are a number of excellent examples of combining new pedagogical approaches with technological infrastructure that improve student motivation and enhance the learning process ( Kreijns et al., 2007 ; Meletiou-Mavrotheris et al., 2007 ).

The list of new technologies for delivering dynamic, linked, interactive and multidisciplinary learning content is large and quickly growing. Examples of such new IT resources include common web-places for course materials ( BlackBoard, 2008 ; Moodle, 2008 ), complete online courses ( UCLAX, 2008 ), Wikis ( SOCRWiki, 2008 ), interactive video streams ( LPB, 2008 ; YouTube, 2008 ), audio-visual classrooms, real-time educational blogs ( EduBlogs, 2008 ; TechEdBlogs, 2008 ), web-based resources for blended instruction ( WikiBooks, 2008 ), virtual office hours with instructors ( VOH, 2008 ), collaborative learning environments ( SAKAI, 2008 ), test-banks and exam-building tools ( MathNetTestBank, 2008 ) and resources for monitoring and assessment of learning ( ARTIST, 2008 ; WebWork, 2008 ).

The Felder-Silverman-Soloman Index of Learning Styles ( Felder, 1998 ; Felder, 2003 ) is a self-scoring instrument that assesses student learning preferences on a four dimensional scale – Sensing/Intuiting, Visual/Verbal, Active/Reflective and Sequential/Global. There are web-based and paper versions of the ILS, which may be utilized in various types of courses ( http://www.ncsu.edu/felder-public/ILSpage.html ).

The ILS allows instructors who assess the overall behavior of each class, adapt their teaching style to cover as much of the spectrum on each of the four dimensional axes as possible. Of course, this requires a commitment of time, resources and willingness to modify course curricula. Appropriate pedagogical utilization of the ILS may optimize and enhance the instructional process – i.e., targeted, enriched and stimulating learning environment may impact majority of students ( Felder, 1998 ). Each student completed the online ILS questionnaire consisting of 44 questions at the beginning of their Fall 2006 classes. Based on the answers they provided they received a score from −11 to 11 for each one of the four ILS categories: S1: Active-reflective (a score closer to +11 indicates that the student is more reflective than active); S2: Sensing-intuitive; S3: Visual-verbal; and S4: Global-sequential. We studied the overall students' quantitative performance against several independent variables (the four categories S1, S2, S3, S4, and students' attitudes towards probability and statistics).

There are different types of frameworks for describing learning styles of students, trainees and more generally – learners. Most of these define a learning style as some description of the perception, attitude and behavior on the part of the learner, which determines the individual's preferred way of acquiring new knowledge ( Cassidy, 2004 ; Honey and Mumford, 1982 ; Knowles and Smith, 2005 ; Sims, 1995 ). Individual learning styles are indirect reflections of various cognitive and psychological factors. A learning style typically indicates an individual's approach to responding to new learning stimuli. A very comprehensive and comparative review of many classical and contemporary models and theories of learning styles is available in ( Cassidy, 2004 ). This study provides a detailed description of the commonalities and differences of many learning style instruments based on their measurements, appropriate use and interpretation. It provides a broader appreciation of learning styles and discusses various instruments for measuring learning styles. One example of an interactive learning style survey is VARK ( VARK, 2008 ), which is a questionnaire that provides users with a profile of their learning preferences based on self-assessment of preferences to take-in or give-out information.

In the past, we have conducted several experiments where we studied student behaviors, learning preferences and comprehension based on IT enhanced curricula. One prior large-scale study ( Dinov et al., 2008 ) assessed the effectiveness of SOCR as an IT tool for enhancing undergraduate probability and statistics courses using different designs, and different classroom environments. We observed good outcomes in student satisfaction and use of technology in all three SOCR-treatment courses, compared to control sections exposed to classical instruction. In SOCR-treated courses, we found improved overall performance in the class, compared to matched traditional instruction. The treatment effect was very statistically significant, as the SOCR-treatment groups consistently performed better than the control group for all sections and across all assignments. The practical size of the observed IT-treatment effect was 1.5–3% improvement, modulated by a statistically significant p-value < 0.001 using a conservative non-parametric test. In this prior study, there were no statistically significant group differences in the overall quantitative assessment between the treatment and control groups, which could have been due to limited statistical power or lack of control for the learning styles. Yet, pooling the results across all courses involved in the experiment we saw a consistent trend of improvement in the SOCR treatment group ( Dinov et al., 2008 ).

In this manuscript, we report on the use of the ILS and various other categorical measurements to evaluate the associations between student performance, learning style and attitude towards the subject in several undergraduate probability and statistics courses.

Here we report on our findings of using the SOCR resources as instruments for IT-blended instruction in several courses. UCLA Institutional Review Board (IRB) approval was obtained to collect the appropriate data and conduct this study (IRB 05-396B/08-28-2006). Individual classes varied somewhat in their intrinsic designs, but generally, our courses included beginning of the quarter quizzes, ILS assessment, standard quarter-wide learning evaluation quantitative measures (exams, quizzes, homework, etc.), beginning and ending attitude towards the subject surveys. These common characteristics of our design are described below, and the course-specific design traits are presented in the Results section. At the end, Table 22 summarizes our study-design and research-findings in three probability and statistics courses.

Summary of our study-design and research-findings on the effects of learning styles, attitudes and technology-enhanced education in probability and statistics courses (see text).

Beginning of the quarter quiz

Each Fall 2006 course administered an entry subject-specific quiz (first day of classes) to assess background knowledge within the student population. The questions on these quizzes aimed at determining the appropriate elementary knowledge of probability and statistics, according to the course-specific prerequisites. These quizzes consisted of less than 10 questions and required about 15 minutes to complete. Example questions of an entry quiz are included in Box 1 (see Appendix). As these quizzes were given only to the treatment groups, see below, the results were only used to predict students' overall quantitative performance of their corresponding class. The goal of the quiz was to assess students' prior knowledge of probability and statistics. It was an attempt to find a predictor for students' future performance in the course. However, as shown later in the paper, the quiz scores did not show an association with the overall students' performance.

Box 1. Example questions of a beginning of the quarter quiz for assessing background student knowledge

Learning Style Assessment

The ILS index is based on a model of learning, where students' learning styles are defined by their answers to four classes of questions:

• Information processing: active (through engagement in physical activity and discussion), or reflective (through self-examination);
• Type of preferential information perception: sensory (sights, sounds, physical sensations), or intuitive (possibilities, insights, hunches);
• Preferred external information sensory channel: visual (pictures, diagrams, graphs, demonstrations), or auditory (words, sounds);
• Understanding process: sequential (continual steps), or global (generative/holistic approach).

The ILS allows instructors who assess the overall behavior of each class, and perhaps adapt their teaching style to cover as much of the spectrum on each of the four dimensional axes as possible. Of course, this requires a commitment of time, resources and willingness to modify existent course curricula. If the ILS assessment is appropriately utilized in class, it is reasonable to assume that the instructional process is generally as optimal as possible – i.e., the learning environment is enriched and stimulating for most students in the class ( Felder, 1998 ).

Exam scores

All students enrolled in the traditional (control) or IT-enhanced (SOCR-treatment) groups took the same types of quantitative assessments including homework, laboratory assignments and exams. There were small variations between the gradebook allocations between the two separate studies/instructors, however, the control vs. treatment effects were only analyzed within class type and within each instructor. Instructors ensured that exams given to pairs of control-treatment courses were comparable and consistent (but not identical).

Pre, post and satisfaction Surveys

The pre- and post-attitude surveys were conducted at the beginning and the end of the quarter, respectfully. These were designed to inform us of students' mental position and emotion towards the subject of probability and statistics. The ordinal responses ranged from 1 (strongly disagree), through 4 (neither disagree nor agree) to 7 (strongly agree). These surveys took less than 10 minutes each. Box 2 (see Appendix) shows an example of our attitude questionnaire. Paired comparisons of these responses are indicative of potential alterations on the learners' philosophical and behavioral positions towards the educational discipline.

Box 2: The pre and post attitude survey

Finally, we conducted a satisfaction survey (sat) at the end of the quarter that aimed at comparing the treatment and control classes to other similar courses. Box 3 (see Appendix) shows example questions that were included in this questionnaire. Responses on these 10-minute surveys could be used for comparing the treatment and control groups, as well as for evaluating the relation of the two pedagogical approaches to analogous types of classical or IT-based instruction.

Box 3: End-of-quarter satisfaction survey

SOCR Treatment

The SOCR treatment involved three types of strategies blending traditional and technology-enhanced pedagogical strategies. First , demonstrations of interactive SOCR applets, simulations, virtual experiments and tools were shown during lecture (3 hrs/week). These demos were interleaved with the standard (control-type) instructional materials. Second , the discussions and laboratory classes (1–2 hrs/week), lead by teaching assistants, provided hands-on activities where students tested the applets and discussed of the probability and statistics concepts demonstrated by the corresponding interactive SOCR resources. Third , all homework assignments and projects required the use of the interactive SOCR web tools. In their assignments, students were asked to include and interpret snapshots of the final states of the appropriate SOCR applets or simulations they used to complete their projects. The control (traditional) treatment classes used classical instruction based on standard lecture/discussion format without hands-on technology demos or requirements for using web applets for completing papers and assignments. However, the controlled classes were shown graphs, results and computer outputs during class time.

Statistics 13 classes

In the Fall of 2006 we had two distinct SOCR-treatment Statistics 13 courses (Dinov and Christou), which were compared against classical instruction, by the same instructors (Fall 2005 and Winter 2006, respectively). The general description of the course and the section-specific results and findings for this sequence are discussed below.

Statistical Methods for the Life and Health Sciences (UCLA Stats 13) is an introductory course on statistical methods for the life and health sciences. Most enrolled students are bound for medical, graduate and professional schools after completing their undergraduate curricula. Brief outline of the course is available online at http://www.registrar.ucla.edu/archive/catalog/2005-07/catalog/catalog05-07-7-98.htm and the section-specific information is listed below. Each of the two sections in this study had about 90 students that received five hours of instruction a week – three lectures, one discussion and one laboratory. For discussion and laboratory, each section was split into three sub-sections conducted by teaching assistants. All students were assessed using the same gradebook schema and grade distribution. SOCR tools were used in lecture for demonstration, motivation and data analysis, as well as for projects, labs and homework.

Statistics 13.1 (Dinov)

The complete course description, coverage, assignments, class-notes, grading schema and all course related materials are available online at http://courses.stat.ucla.edu/06F/stat13_1 .

Table 1 (see Appendix for all tables ) depicts the distribution of the students in Stats 13.1 at the end of the quarter. The Fall 2006 (Fall 2005) classes started with a total enrollment of 90 students in the beginning of the quarter. There were significant differences between the treatment (2006) and control (2005) groups in the students' seniority-rankings ( p-value < 0.001 ), but no significant differences in the majors distributions.

Stats 13.1 student demographics (treatment and control groups). The values in table represent the enrollment in the Fall 2006 vs. (Fall 2005) classes, respectively.

A uniform grading schema was used in both the Fall 2005 (control) and the Fall 2006 (SOCR treatment) Stats 13 classes (Dinov). The lowest homework project was automatically dropped (only the top 7 homework scores counted). A standard letter-grade mapping was used based on quantitative overall average (e.g., 93%+ for A; 90-93% for A − ; 87–90% for B + ; 83–87% for B, etc.). Homework accounted for 20%, labs for 10%, midterm-exam for 30%, research term paper for 5% and the final exam for 35% of the final grade.

All students in the Fall 2006 Stats 13.1 class were exposed to SOCR enhanced instruction (treatment group) and all students in the Fall 2005 Stats 13.1 class (same instructor, Dinov) were subjected to the standard instructional curriculum using Stata ( STATA, 2008 ) (control group). Only the student demographics and the quantitative measures of learning (exam scores, homework, etc.) were comparable between the control and treatment groups. The ILS and attitude surveys were only available for the Fall 2006 class (treatment group).

Tables 2 , ​ ,3, 3 , ​ ,4, 4 , and ​ and5 5 contain the results of the background quiz, the pre and post attitude surveys and the end-of-quarter satisfaction survey for Stats 13.1 (Dinov). There were no statistically significant differences in the responses to the 10 questions in the pre and post survey attitude effects ( Table 3 ). With exception of question e (considering statistics as a possible major/minor), the end-of-the-quarter satisfaction survey showed a consistent trend, Table 4 and Figure 1 , which may indicate an increase in the motivation and improved experiences in the treatment group (compared to unrelated other classes that did not use technology). The results of Part B of the final survey, Table 5 , did not indicate any significant trends or unexpected effects.

Plot of the end-of-quarter satisfaction survey frequencies of responses to the 11 questions (see Box 3 and Table 4 ).

Results of the background knowledge quiz (see Box 1 ). Left side shows seven standard summary statistics, and the right side illustrates a histogram plot of all scores.

Results of the Pre vs. Post surveys (see Box 2 ).

Part A results of the satisfaction survey (see Box 3 ).

Part B results of the satisfaction survey (see Box 3 ).

When comparing the quantitative learning outcomes, by type of assessment, between the treatment (Fall 2006) and control (Fall 2005) groups, we discovered mixed results. These mixed results may be partially accounted for by the fact that Statistics 13 has a required lab hour for the control group using Stata during the laboratories and lectures. This provided the control group with some exposure to technology-based instruction. The quantitative results from Dinov's Stats 13 classes are shown in Table 6 . For example, there was no statistical difference between the outcomes on the final exam, whereas there were significant differences between the treatment and the control groups in the overall and laboratory grades. The SOCR-treatment group (Fall 2006) had performed statistically significantly better than the control group, even though the practical size of the effect was within 3–5 percentage points. There were also trends of improvement on the midterm and homework scores for the treatment group; however these did not reach statistically significant levels.

Quantitative Results measuring student learning in the two Stats 13 classes (Dinov, Fall 2005, Fall 2006).

Figure 2 shows the results of the ILS assessment of the Stats 13.1 (Fall'06) SOCR-treatment section. These graphs represent the histograms for each of the 4 ILS categories. There is some evidence suggesting the majority of the SOCR treatment group students were more reflective (rather than active, S1), more intuitive (rather than sensing, S2), more verbal (rather than visual, S3), and more sequential (rather than global, S4). As the ILS survey was conducted in the beginning of classes, these self-identification results do not reflect the experience of the (treatment group) students during the quarter. There was significant evidence demonstrating the presence of positive S1, S2, S3 and S4 effects (test for proportion using Binomial distribution, with Ho: p <0 = p >0 , Ha: p <0 ≠ p >0 , p-values < 0.001 ).

Histogram plots of the 4-dimensional ILS responses for the Stats 13.1 treatment group (Fall'06).

The results of the (quantitative) performance regression on ILS, pre and post attitudes and satisfaction variable (see Methods section) are shown in Table 7 .

Regression results for the ILS effects on overall quantitative performance for the Stats 13.1 (Dinov).

Excluding the constant term, the only variable that was a significant predictor of overall performance at the 5% level was the initial (pre) attitude towards the discipline. The effect of the background quiz was borderline. None of the ILS spectrum variables played a significant role in explaining overall student performance. From these results, it appears as if the students' initial demeanor and affection for using technology was the only indicative factor on their overall quantitative performance in the Statistics 13 class.

Statistics 13.2 (Christou) Most students enrolled in Statistics 13 are bound for medical, graduate and professional schools. Table 8 shows a summary of the student populations enrolled in the control (Winter 2006) and SOCR-treatment (Fall 2006) groups. Again, there were significant differences between the treatment and control groups in the students' seniority-rankings (p-value < 0.001), but no significant differences in the majors distributions.

Stats 13.2 student demographics. The values in table represent the enrollment in the control (Winter 2006) and SOCR-treatment (Fall 2006) classes.

The final grade was computed based on three categories: homework, labs, and exams. The seven homework assignments accounted for 10% of the final grade and the six labs for another 10% of the final grade. There are five exams of which the first four were worth 15% each and the last was worth 20% of the final grade. A standard letter-grade mapping analogous to the Stats 13.1 study was used (see above).

Two classes of Statistics 13 (Winter 2006 and Fall 2006) served as control and treatment groups, respectively. The grading process was the same for both classes as described above. Both classes received three one-hour lectures per week plus one hour discussion time and one hour lab time per week. The control group was not exposed to any of the SOCR tools. The labs for the control group were done using the statistical software Stata ( STATA, 2008 ), while the treatment group used SOCR simulations and activities. The lectures of the SOCR treatment group frequently incorporated materials of SOCR. Besides these differences, everything else was kept the same for both groups.

Tables 9 , ​ ,10, 10 , ​ ,11, 11 , and ​ and12 12 contain the results of the background quiz, the pre vs. post attitude surveys and the satisfaction surveys for Stats 13.2 (Christou). With some exceptions (e.g., Q9, t-test p-value=0.03), there were no significant longitudinal attitude effects as measured by the 10 questions in the pre and post attitude survey ( Table 10 ). Compared to Stats13.1, in this section (Stats 13.2) there was a different pattern of responses to the end-of-quarter survey. In this study, we observed a more consistent trend of increase in the motivation and improved experiences in the treatment group (compared to unrelated other classes that did not use technology), Table 11 and Figure 3 . Like in the Stats 13.1 study, the results of Part B of the final survey, Table 12 , did not indicate any significant trends or unexpected effects.

Plot of the end-of-quarter satisfaction survey frequencies of responses to the 11 questions (see Box 3 and Table 11 ).

Results of the Pre vs. Post Attitude surveys (see Box 2 ).

Part A results of the satisfaction survey (see Box 3 and Figure 2 ).

Table 13 shows the results of the quantitative assessment of the control and treatment groups. However, the overall performance again favors of the SOCR-treatment group. The results of exams 2 and 5, as well as the overall evaluation, provided strong evidence suggesting the treatment group performed better on these quantitative assessments.

Quantitative Results measuring student learning in the two Stats 13 classes (Christou, Winter 2006 vs. Fall 2006).

We now present the results of the analysis of the impact of the Index of Learning Styles (ILS) on students' learning (Stats 13.2, Christou). At the beginning of the course each student completed the online ILS questionnaire consisting of 44 questions. Based on the answers they provide, each student received a score from −11 to 11 for each one of the four categories. The results of the (quantitative) performance regression on ILS, pre and post attitudes and satisfaction (see Methods) are shown on Table 14 .

Regression results for the ILS effects on overall quantitative performance for Stats 13.2 (Christou).

The variables that were significant predictors of overall performance, at the 5% level, included the active-reflective (S1) and visual-verbal (S3) ILS measures and the attitude towards the discipline (post). The fact that the global-sequential and sensing-intuitive directions of the ILS spectrum did not play a significant role in explaining overall student performance makes the interpretation of the ILS results difficult. One possibility for explaining this observed effect is that an increase of the overall student performance directly correlates with both – a shift of the learners into the active (tendency to retain and understand information by doing or applying something active) and verbal (written or spoken word explanations) spectra of the ILS space. The effects of active-reflective (S1) are consistently negative, indicating that quantitative performance is inversely correlated with this ILS measure. That is, more active users (negative scale of the active-reflective axis) seem to do better on their qualitative examinations. The post-survey and the satisfaction survey also showed significant effects on predicting the students' quantitative performance in Stats 13.2.

Figure 4 shows the results of the ILS assessment of the Stats 13.2 (Fall'06, Christou) SOCR-treatment section. These graphs represent the histograms for each of the 4 ILS categories. As with the first study (Stats 13.1), there appears to be evidence suggesting the majority of the SOCR treatment group students were more reflective, intuitive, verbal and sequential , rather than active, sensing, visual and global. Again, as the ILS data was collected in the beginning of classes, these results do not reflect the experience of the (treatment group) students during the quarter. We observed significant evidence demonstrating the presence of positive (uni-directional) S1, S2, S3 and S4 effects ( p-values < 0.001 ).

Histogram plots of the 4-dimensional ILS responses for the Stats 13.2 treatment group (Fall'06).

Statistics 100A class. Introduction to Probability Theory (Stats 100A) is the first course in a three-course sequence. The other two are Introduction to Mathematical Statistics and Regression Analysis. Most enrolled students are from Mathematics, Economics, and Computer Science majors. A description of the course can be found at http://courses.stat.ucla.edu/index.php?term=05f&lecture=26330320 . The class meets 3 times a week with the instructor and once a week for a discussion with a teaching assistant.

Majority of the students enrolled in Statistics 100A were senior mathematics majors. Table 15 shows the student demographics for the control (Winter 2006) and SOCR-treatment (Fall 2006) classes. In this study, there were no significant differences between the treatment and control groups in the students' seniority-rankings or the majors distributions.

Stats 100A student demographics. The values in table represent the enrollment in the control (Fall 2005) and SOCR-treatment (Fall 2006) classes.

The final grade for the treatment group was computed based on three categories: homework, labs, and exams. The six homeworks accounted for 10% of the final grade and the five labs for another 10% of the final grade. There were five exams of which the first four were worth 15% each and the last was worth 20% of the final grade. The final grade for the control group was computed based on 2 categories – homeworks and exams. The homeworks accounted for 10% of the grade, and of the five exams three were worth 20% and the other two were worth 15%.

Two classes of Statistics 100A (Fall 2005 and Fall 2006) served as control and treatment groups, respectively. Both classes received three one-hour lectures per week plus one hour discussion time per week. The control group was not exposed to any of the SOCR tools or materials. The lectures of the treatment group integrated SOCR simulations, demonstrations and activities with the standard curriculum. In addition, the students of the treatment group were assigned SOCR labs, whereas students from the control group did not do assigned labs. Besides these differences, everything else was kept the same for both groups.

Tables 16 , ​ ,17, 17 , ​ ,18, 18 , and ​ and19 19 contain the results of the background knowledge quiz, the pre vs. post attitude surveys, and the satisfaction survey for Stats 100A. Like in study 2 (Stats 13.2), some of the questions in the attitude survey showed statistically significant differences in the mean student responses between the pre and post surveys (e.g., Q1, t-test p-value <0.001), Table 17 . In this study we observed a more consistent trend of increase in the motivation and improved experiences in the treatment group (compared to unrelated other classes that did not use technology), Table 18 and Figure 5 . The results of Part B of the final survey, Table 19 , indicated some satisfaction with the use of SOCR materials and activities in the curriculum.

Plot of the end-of-quarter satisfaction survey frequencies of responses to the 11 questions (see Box 3 and Table 19 ).

Results of the Pre vs. Post attitude surveys (see Box 2 ).

In this study, the quantitative score comparison between the treatment and control groups show encouraging results. We compared the SOCR-treatment and control classes using two sample t-tests, Table 20 . Both of the Stats 100A courses (control and treatment) used the same grading style (exams and homework) and therefore are comparable.

Quantitative Results measuring student learning in the two Stats 100A classes (control, Fall 2005, and treatment, Fall 2006).

Figure 6 shows the results of the ILS assessment of the Stats 100A (Fall'06) SOCR-treatment section. These graphs represent the histograms for each of the 4 ILS categories. The same trend noticed in the 2 Stats 13 studies is observed here, although the shapes of the distributions are somewhat different. The majority of the SOCR treatment group students were again more reflective, intuitive, verbal and sequential , rather than active, sensing, visual and global. As in the previous 2 studies, we observed significant, albeit slightly weaker, evidence demonstrating the presence of positive (uni-directional) S1, S2, S3 and S4 effects ( p-values < 0.003 ).

Histogram plots of the 4-dimensional ILS responses for the Stats 100A treatment group (Fall'06).

We explored the overall students' performance with some independent variables (the four categories S1, S2, S3, S4, and students' attitudes towards the field of probability and statistics). The results of the (quantitative) performance regression on ILS, pre and post attitudes and satisfaction (see Methods) are shown on Table 21 . None of these variables represented significant predictors of the students' quantitative performance in Stats 100A.

Regression results for the ILS effects on overall quantitative performance for the Stats 100A (Christou).

General trends and analysis of results

Quantitative measurements in all three classes (two Stat 13 and one Stat 100A) showed that the treatment groups consistently outperformed the control groups. This was clear by the overall class performance, which includes all homework, labs, and exams (see Tables 6 , ​ ,13, 13 , and ​ and20). 20 ). Undoubtedly, this illustrates that SOCR significantly affected students' performance for these three introductory Statistics classes at UCLA. We also received positive feedbacks from all three classes on the use of SOCR. As shown from the result of the end-of-quarter satisfaction survey, the majority of students indicated that technology helped them understand the main concepts of the course (see Tables 4 , ​ ,11, 11 , and ​ and18, 18 , question a).

Especially for Stat 100A, which mostly includes students from Mathematical Sciences, we see an overall positive trend in their satisfaction survey. As seen in Table 19 , 97% of students indicated that SOCR made the class more interesting and 74% said SOCR made the class easier. This result demonstrates that SOCR has effects on students in the Mathematical Sciences field, and suggests that SOCR-embedded mathematics curricula improve student attitudes towards the class. Mathematics classes rarely include sections with hands-on processing and/or data exploration, like the SOCR tools and activities we used in our study. Our results illustrate that introduction of such pedagogical approaches in mathematics-oriented courses may improve motivation and enhance students' learning experiences.

In this study, we did not investigate the possible instructor-effects or the effects of the style of blending IT in the curriculum. The way in which instructors use technology in their courses can greatly affect students' experiences in the courses, and the outcome results may vary significantly based on the specific pedagogical utilization of technology. Future research studies should investigate more closely the effects of concrete implementations and use of technology in the classroom.

In this study, SOCR exposure in the treatment group included lecture and lab discussions; however the most striking differences between the treatment and control groups were the diametrically opposed laboratory sections. Thus, the magnitude of the observed differences between SOCR-treatment and control groups may have been in large aspects due to the laboratory sections conducted by teaching assistants. Laboratory assignments were written separately by each teaching assistant for their particular class. This could be another factor explaining differences in the findings between the two Statistics 13 courses (Dinov's and Christou's sections).

Table 22 depicts a summary of this meta-study. The columns in Table 22 show the data we had acquired for each of the 3 studies. Each row in this table contains references to appropriate figures and tables in the paper, and includes a brief annotation of the effect of this variable to discriminate the control and treatment groups or to predict the final quantitative outcomes (overall grades), as applicable. Notations : SSBGD= some significant between group differences (treatment vs. control), NSBGD= no significant between group differences, SUDE=significant uni-directional effects of the S1, S2, S3 & S4 ILS dimensions, NSPPD=no significant Pre vs. Post attitude differences (in treatment groups).

Novel communication and information technology tools provide the foundation for efficient, timely, interactive and graphical demonstrations of various scientific concepts in and out of the classroom. Now-a-days it is possible to conduct a complete investigative study using a web-browser and various interoperable tools for data collection, processing, visualization, analysis and interpretation. The SOCR resources provide a framework, where learners can use mouse clicks, copy and paste actions, and interactive web-based functions to go from data generation to data analysis and understanding within seconds, without demanding any special software, user-authentication or advanced hardware infrastructure.

Here, we reported on a meta-study of 3 controlled experiments of using SOCR resources vs. traditional pedagogical approaches. Qualitative and quantitative data we collected from all courses included Felder-Silverman-Soloman index of learning styles, quantitative background assessment, pre and post surveys of attitude towards the subject, end-point satisfaction survey, and varieties of examination, quiz and laboratory test scores. This study confirms the findings and significantly extends a previous report on the technology-driven improvement of the quantitative performance in probability and statistics courses ( Dinov et al., 2008 ). The results of the 10 pre- or post survey questions were not consistent between the 3 different classes (Cronbach's α = − 0.4495 ).

Students' learning styles and attitudes towards a discipline are important confounds of their final quantitative performance. We identified a marginal (within each study), yet very consistent (across all studies) effect of SOCR-treatment, which tends to increase student satisfaction (measured by post surveys) and improve quantitative performance (measured by standard assessment instruments). These observed positive effects of integrating information technology with established pedagogical techniques may also be valid across STEM disciplines ( Dinov, 2008 ; Dinov et al., 2008 ). The two critical components of improving science education via blended instruction include instructor-training and the development of appropriate curriculum- and audience-specific activities, simulations and interactive resources for data understanding. The beginning quiz taken by the treatment groups at the start of the courses was used to inform instructors about the students' level of understanding of basic concepts of probability and statistics. These results help instructors design activities specific to students' learning needs.

Simulations and virtual experiments provide powerful instructional tools that complement classical pedagogical approaches. Such tools are valuable in explaining difficult statistical concepts in probability and statistics classes. Utilizing visualization, graphical and computational simulation tools in teaching provides valuable complementary means of presenting concepts, properties and/or abstract ideas. In addition, such IT-based pedagogical instruments are appreciated and well received by students who normally operate in technological environments far exceeding these of their instructors. In our experiments, we saw effects of using SOCR simulation tools even when we did not completely stratify the student populations or control for all possible predictors (like age, major, learning style, background, attitude towards the subject, etc.) The effects we saw within each class provide marginal cues favoring technology-enhanced blended instruction. However, the results were very robust across all 3 studies and support other independent investigations ( Dinov et al., 2008 ; Kreijns et al., 2007 ). Our findings show that the students' learning styles can play important roles in their quantitative performance. Despite that, we would not blindly recommend that instructors employ technology-enhanced approaches to improve learning outcomes solely based on students' learning styles. There are advantages to broad spectrum training outside the domain of the students' preferred learning approach. For example, in multidisciplinary studies, active, visual or global learners may significantly benefit from exposure to reflective, verbal and/or sequential pedagogical styles.

Acknowledgements

This work was funded in part by NSF DUE grants 0716055 & 0442992, under the CCLI mechanism, and NIH Roadmap for Medical Research, NCBC Grant U54 RR021813. The SOCR resource is designed, developed and maintained by faculty and graduate students in the departments of Statistics, Computer Science, Laboratory of Neuro Imaging, Neurology and Biomedical Engineering at UCLA. All data-analyses and result-visualization was accomplished using SOCR Analyses, Modeler and Charts ( http://www.SOCR.ucla.edu ).

The help of our teaching assistants (Christopher Barr, Jackie Dacosta, Brandi Shantana and Judy Kong) was invaluable in the process of conducting this SOCR evaluation. We are also indebted to Juana Sanchez for her valuable feedback, PhuongThao Dinh for her insightful remarks, and Maykel Vosoughiazad for his help with the data processing. The authors are also indebted to the JOLT editors and reviewers for their constructive critiques and valuable recommendations.

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• Open access
• Published: 22 April 2024

Training nurses in an international emergency medical team using a serious role-playing game: a retrospective comparative analysis

• Hai Hu 1 , 2 , 3   na1 ,
• Xiaoqin Lai 2 , 4 , 5   na1 &
• Longping Yan 6 , 7 , 8  

BMC Medical Education volume  24 , Article number:  432 ( 2024 ) Cite this article

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Although game-based applications have been used in disaster medicine education, no serious computer games have been designed specifically for training these nurses in an IEMT setting. To address this need, we developed a serious computer game called the IEMTtraining game. In this game, players assume the roles of IEMT nurses, assess patient injuries in a virtual environment, and provide suitable treatment options.

The design of this study is a retrospective comparative analysis. The research was conducted with 209 nurses in a hospital. The data collection process of this study was conducted at the 2019-2020 academic year. A retrospective comparative analysis was conducted on the pre-, post-, and final test scores of nurses in the IEMT. Additionally, a survey questionnaire was distributed to trainees to gather insights into teaching methods that were subsequently analyzed.

There was a significant difference in the overall test scores between the two groups, with the game group demonstrating superior performance compared to the control group (odds ratio = 1.363, p value = 0.010). The survey results indicated that the game group exhibited higher learning motivation scores and lower cognitive load compared with the lecture group.

Conclusions

The IEMT training game developed by the instructor team is a promising and effective method for training nurses in disaster rescue within IEMTs. The game equips the trainees with the necessary skills and knowledge to respond effectively to emergencies. It is easily comprehended, enhances knowledge retention and motivation to learn, and reduces cognitive load.

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Since the beginning of the twenty-first century, the deployment of international emergency medical teams in disaster-stricken regions has increased world wide [ 1 ]. To enhance the efficiency of these teams, the World Health Organization (WHO) has introduced the International Emergency Medical Team (IEMT) initiative to guarantee their competence. Adequate education and training play a vital role in achieving this objective [ 2 ].

Nurses play a vital role as IEMTs by providing essential medical care and support to populations affected by disasters and emergencies. Training newly joined nurses is an integral part of IEMT training.

Typical training methods include lectures, field-simulation exercises, and tabletop exercises [ 3 , 4 , 5 ]. However, lectures, despite requiring fewer teaching resources, are often perceived as boring and abstract. This may not be the most ideal method for training newly joined nurses in the complexities of international medical responses. However, simulation field exercises can be effective in mastering the knowledge and skills of disaster medicine responsiveness. However, they come with significant costs and requirements, such as extended instructional periods, additional teachers or instructors, and thorough preparation. These high costs make it challenging to organize simulation exercises repeatedly, making them less ideal for training newly joined nurses [ 6 ].

Moreover, classic tabletop exercises that use simple props, such as cards in a classroom setting, have limitations. The rules of these exercises are typically simple, which makes it challenging to simulate complex disaster scenarios. In addition, these exercises cannot replicate real-life situations, making them too abstract for newly joined nurses to fully grasp [ 7 , 8 ].

Recently, game-based learning has gained increasing attention as an interactive teaching method [ 9 , 10 ]. Previous studies have validated the efficacy of game-based mobile applications [ 11 , 12 ]. Serious games that align with curricular objectives have shown potential to facilitate more effective learner-centered educational experiences for trainees [ 13 , 14 ]. Although game-based applications have been used in disaster medicine education, no serious computer games have been designed specifically for training newly joined nurses in an international IEMT setting.

Our team is an internationally certified IEMT organization verified by the WHO, underscoring the importance of providing training for newly joined nurses in international medical responses. To address this need, we organized training courses for them. As part of the training, we incorporated a serious computer game called the IEMTtraining game. In this game, players assume the roles of IEMT nurses, assess patient injuries in a virtual environment, and provide suitable treatment options. This study aims to investigate the effectiveness of the IEMTtraining game. To the best of our knowledge, this is the first serious game specifically designed to train newly joined nurses in an IEMT setting.

The IEMTtraining game was subsequently applied to the training course for newly joined nurses, and this study aimed to investigate its effectiveness. To the best of our knowledge, this is the first serious game specifically designedto train newly joined nurses in an IEMT setting.

Study design

This study was conducted using data from the training records database of participants who had completed the training. The database includes comprehensive demographic information, exam scores, and detailed information from post-training questionnaires for all trainees. We reviewed the training scores and questionnaires of participants who took part in the training from Autumn 2019 to Spring 2020.

The local Institutional Review Committee approved the study and waived the requirement for informed consent due to the study design. The study complied with the international ethical guidelines for human research, such as the Declaration of Helsinki. The accessed data were anonymized.

Participants

A total of 209 newly joined nurses needed to participate in the training. Due to limitations in the size of the training venue, the trainees had to be divided into two groups for the training. All trainees were required to choose a group and register online. The training team provided the schedule and training topic for the two training sessions to all trainees before the training commenced. Each trainee had the opportunity to sign up based on their individual circumstances. Furthermore, the training team set a maximum limit of 110 trainees for each group, considering the dimensions of the training venue. Trainees were assigned on a first-come-first-served basis. In the event that a group reached its capacity, any unregistered trainees would be automatically assigned to another group.

In the fall of 2019, 103 newly joined nurses opted for the lecture training course (lecture group). In this group, instructors solely used the traditional teaching methods of lectures and demonstrations. The remaining 106 newly joined nurses underwent game-based training (game group). In addition to the traditional lectures and demonstrations, the instructor incorporated an IEMTtraining game to enhance the training experience in the game group.

The IEMTTraining game

The IEMTtraining game, a role-playing game, was implemented using the RPG Maker MV Version1.6.1 (Kadokawa Corporation, Tokyo, Tokyo Metropolis, Japan). Players assumed the roles of rescuers in a fictional setting of an earthquake (Part1 of Supplemental Digital Content ).

The storyline revolves around an earthquake scenario, with the main character being an IEMT nurse. Within the game simulation, there were 1000 patients in the scenario. The objective for each player was to treat as many patients as possible to earn higher experience points compared to other players. In addition, within the game scene, multiple nonplayer characters played the role of injured patients. The players navigate the movements of the main character using a computer mouse. Upon encountering injured persons, the player can view their injury information by clicking on them and selecting the triage tags. The player can then select the necessary medical supplies from the kit to provide treatment. Additionally, the player is required to act according to the minimum standards for IEMTs, such as registration in the IEMT coordination cell and reporting of injury information following the minimum data set (MDS) designed by the WHO [ 15 , 16 ]. This portion of the training content imposes uniform requirements for all IEMT members, hence it is necessary for IEMT nurses to learn it. All correct choices result in the accumulation of experience points. Game duration can be set by the instructor and the player with the highest experience points at the end of the game.

Measurement

We have collected the test scores of the trainees in our training database to explore their knowledge mastery. Additionally, we have collected post-training questionnaire data from the trainees to investigate their learning motivation, cognitive load, and technology acceptance.

Pre-test, post-test, and final test

All trainees were tested on three separate occasions: (1) a “pre-test”before the educational intervention, (2) a “post-test”following the intervention, and (3) a “final test”at the end of the term (sixweeks after the intervention). Each test comprised 20 multiple-choice questions (0.5 points per item) assessing the trainees’ mastery of crucial points in their knowledge and decision-making. The higher the score, the better the grade will be.

Questionnaires

The questionnaires used in this study can be found in Part 2 of the Supplemental Digital Content .

The learning motivation questionnaire used in this study was based on the measure developed by Hwang and Chang [ 17 ]. It comprises seven items rated on a six-point scale. The reliability of the questionnaire, as indicated by Cronbach’s alpha, was 0.79.

The cognitive load questionnaire was adapted from the questionnaire developed by Hwang et al [ 18 ]. It consisted of five items for assessing “mental load” and three items for evaluating “mental effort.” The items were rated using a six-point Likert scale. The Cronbach’s alpha values for the two parts of the questionnaire were 0.86 and 0.85, respectively.

The technology acceptance questionnaire, which was only administered to the game group, as it specifically focused on novel teaching techniques and lacked relevance tothe lecture group, was derived from the measurement instrument developed by Chu et al [ 19 ]. It comprised seven items for measuring “perceived ease of use” and six items for assessing “perceived usefulness.” The items were rated on a six-point Likert scale. The Cronbach’s alpha values for the two parts of the questionnaire were 0.94 and 0.95, respectively.

The lecture group received 4 hours of traditional lectures. Additionally, 1 week before the lecture, the trainees were provided with a series of references related to the topic and were required to preview the content before the class. A pre-test was conducted before the lecture to assess the trainees’ prior knowledge, followed by a post-test immediately after the lecture, and a final test 6 weeks after training.

In the game group, the delivery and requirements for references were the same as those in the lecture group. However, the training format differed. The game group received a half-hour lecture introducinggeneral principles, followed by 3 hours of gameplay. The last halfhour was dedicated to summarizing the course and addressing questions or concerns. Similar to the lecture group, the trainees in this group also completed pre-, post-, and final tests. Additionally, a brief survey ofthe teaching methods was conducted at the end of the final test (see Fig.  1 ).

General overview of the teaching procedure. Figure Legend: The diagram shows the teaching and testing processes for the two groups of trainees. Q&A: questions and answers

Data analysis

All data were analyzed using IBM SPSS Statistics (version 20.0;IBM Inc., Armonk, NY, USA). Only the trainees who participated in all three tests were included in the analysis. In total, there were 209 trainees, but 11 individuals (6 from the lecture group and 5 from the game group) were excluded due to incomplete data. Therefore, the data of 198 trainees were ultimately included in the analysis.

In addition, measurement data with a normal distribution were described as mean (standard deviation, SD). In contrast, measurement data with non-normal distributions were expressed as median [first quartile, third quartile]. Furthermore, enumeration data were constructed using composition ratios.

Moreover, a generalized estimating equation (GEE) was employed to compare the groups’ pre-, post-, and final test scores. The Mann–Whitney U test was used to compare the questionnaire scores between the two groups. The statistical significance was set at a level of 0.05.

Among the data included in the analysis, 97 (48.99%) participants were in the lecture group, and 101 (51.01%)were in the game group.

The number of male trainees in the lecture and game groups was 30 (30.93%) and 33 (32.67%), respectively. The mean age of participants in the lecture group was 27.44 ± 4.31 years, whereas that of the game group was 28.05 ± 4.29 years. There were no significant differences in sex or age (Table  1 ). Regarding the test scores, no significant differences were found between the two groups in the pre- and post-tests. However, a significant difference was observed in the final test scores conducted 6 weeks later (Table 1 ).

According to the GEE analysis, the overall scores for the post-test and final test were higher compared to the pre-test scores. Additionally, there was a significant difference in the overall test scores between the two groups, with the game group demonstrating superior performance compared to the control group (odds ratio = 1.363, p value = 0.010). Further details of the GEE results can be found in Part 3 of the supplementary materials .

Table  2 presents the results of the questionnaire ratings for the two groups. The median [first quartile, third quartile] of the learning motivation questionnaire ratings were 4 [3, 4] for the lecture group and 5 [4, 5] for the game group. There were significant differences between the questionnaire ratings of the two groups ( p  < 0.001), indicating that the game group had higher learning motivation for the learning activity.

The median [first quartile, third quartile] of the overall cognitive load ratings were 3 [3, 4] and 4 [4, 5] for the game and lecture groups, respectively. There was a significant difference between the cognitive load ratings of the two groups ( p  < 0.001).

This study further compared two aspects of cognitive load: mental load and mental effort. The median [first quartile, third quartile] for the mental effort dimension were 3 [2, 3] and 4 [4, 5] for the game and lecture groups, respectively (p < 0.001). For mental load, the median [first quartile, third quartile] were 4 [3, 4] and 4 [3, 4] for the game and lecture groups, respectively. There was no significant difference in the mental load ratings between the two groups ( p  = 0.539).

To better understand the trainees’ perceptions of the use of the serious game, this study collected the feedback of the trainees in the game group regarding “perceived usefulness” and “perceived ease of use,” as shown in Table 2 . Most trainees provided positive feedback on the two dimensions of the serious game.

To the best of our knowledge, this IEMT training game is the first serious game intended for newly joined nurses of IEMTs. Therefore, this study presents an initial investigation into the applicability of serious games.

Both lectures and serious games improved post-class test scores to the same level, consistent with previous studies. Krishnan et al. found that an educational game on hepatitis significantly improved knowledge scores [ 20 ]. Additionally, our study showed higher knowledge retention in the game group after 6 weeks, in line with previous studies on serious games. In a study on sexually transmitted diseases, game-based instruction was found to improve knowledge retention for resident physicians compared to traditional teaching methods [ 21 ]. The IEMTtraining game, designed as a role-playing game, is more likely to enhance knowledge retention in newly joined nurses in the long term. Therefore, serious games should be included in the teaching of IEMT training.

This study demonstrated improved learning motivation in the game group, consistent with previous research indicating that game-based learning enhances motivation due to the enjoyable and challenging nature of the games [ 22 , 23 ]. A systematic review by Allan et al. further supports the positive impact of game-based learning tools on the motivation, attitudes, and engagement of healthcare trainees [ 24 ].

As serious games are a novel learning experience for trainees, it is worth investigating the cognitive load they experience. Our study found that serious games effectively reduce trainees’ overall cognitive load, particularly in terms of lower mental effort. Mental effort refers to the cognitive capacity used to handle task demands, reflecting the cognitive load associated with organizing and presenting learning content, as well as guiding student learning strategies [ 25 , 26 ]. This reduction in cognitive load is a significant advantage of serious gaming, as it helps learners better understand and organize their knowledge. However, our study did not find a significant difference in mental load between the two groups. Mental load considers the interaction between task and subject characteristics, based on students’ understanding of tasks and subject characteristics [ 18 ]. This finding is intriguing as it aligns with similar observations in game-based education for elementary and secondary school students [ 27 ], but is the first mention of game-based education in academic papers related to nursing training.

In our survey of the game group participants, we found that their feedback regarding the perceived ease of use and usefulness of the game was overwhelmingly positive. This indicates that the designed game was helpful to learners during the learning process. Moreover, the game’s mechanics were easily understood by the trainees without requiring them to investsignificant time and effort to understand the game rules and controls.

This study had some limitations. First, this retrospective observational study may have been susceptible to sampling bias due to the non-random grouping of trainees. It only reviewed existing data from the training database, and future research should be conducted to validate our findings through prospective studies. Therefore, randomized controlled trials are required. Second, the serious game is currently available only in China. We are currently developing an English version to better align with the training requirements of international IEMT nurses. Third, the development of such serious gamescan be time-consuming. To address this problem, we propose a meta-model to help researchers and instructors select appropriate game development models to implement effective serious games.

An IEMT training game for newly joined nurses is a highly promising training method. Its potential lies in its ability to offer engaging and interactive learning experiences, thereby effectively enhancing the training process. Furthermore, the game improved knowledge retention, increased motivation to learn, and reduced cognitive load. In addition, the game’s mechanics are easily understood by trainees, which further enhances its effectiveness as a training instrument.

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Abbreviations

World Health Organization

International Emergency Medical Team

Minimum Data Set

Generalized estimating eq.

Standard deviation

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Acknowledgements

We would like to thank all the staffs who contribute to the database. We would like to thank Editage ( www.editage.cn ) for English language editing. We also would like to thank Dr. Yong Yang for statistics help. We would like to thank The 10th Sichuan University Higher Education Teaching Reform Research Project (No. SCU10170) and West China School of Medicine (2023-2024) Teaching Reform Research Project (No. HXBK-B2023016) for the support.

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Both Hai Hu and Xiaoqin Lai contributed equally to this work and should be regarded as co-first authors.

Authors and Affiliations

Emergency Management Office of West China Hospital, Sichuan University, The street address: No. 37. Guoxue Road, Chengdu City, Sichuan Province, China

China International Emergency Medical Team (Sichuan), Chengdu City, Sichuan Province, China

Hai Hu & Xiaoqin Lai

Emergency Medical Rescue Base, Sichuan University, Chengdu City, Sichuan Province, China

Day Surgery Center, West China Hospital, Sichuan University, Chengdu City, Sichuan Province, China

Xiaoqin Lai

Department of Thoracic Surgery, West China Tianfu Hospital, Sichuan University, Chengdu City, Sichuan Province, China

West China School of Nursing, Sichuan University, Chengdu City, Sichuan Province, China

Longping Yan

West China School of Public Health, Sichuan University, Chengdu, Sichuan, China

West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China

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Contributions

HH conceived the study, designed the trial, and obtained research funding. XL supervised the conduct of the data collection from the database, and managed the data, including quality control. HH and LY provided statistical advice on study design and analyzed the data. All the authors drafted the manuscript, and contributed substantially to its revision. HH takes responsibility for the paper as a whole.

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Correspondence to Hai Hu .

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Hu, H., Lai, X. & Yan, L. Training nurses in an international emergency medical team using a serious role-playing game: a retrospective comparative analysis. BMC Med Educ 24 , 432 (2024). https://doi.org/10.1186/s12909-024-05442-x

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Received : 05 November 2023

Accepted : 17 April 2024

Published : 22 April 2024

DOI : https://doi.org/10.1186/s12909-024-05442-x

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Computer Science > Computer Vision and Pattern Recognition

Title: rethink arbitrary style transfer with transformer and contrastive learning.

Abstract: Arbitrary style transfer holds widespread attention in research and boasts numerous practical applications. The existing methods, which either employ cross-attention to incorporate deep style attributes into content attributes or use adaptive normalization to adjust content features, fail to generate high-quality stylized images. In this paper, we introduce an innovative technique to improve the quality of stylized images. Firstly, we propose Style Consistency Instance Normalization (SCIN), a method to refine the alignment between content and style features. In addition, we have developed an Instance-based Contrastive Learning (ICL) approach designed to understand the relationships among various styles, thereby enhancing the quality of the resulting stylized images. Recognizing that VGG networks are more adept at extracting classification features and need to be better suited for capturing style features, we have also introduced the Perception Encoder (PE) to capture style features. Extensive experiments demonstrate that our proposed method generates high-quality stylized images and effectively prevents artifacts compared with the existing state-of-the-art methods.

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