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A Study of Mobile App Use for Teaching and Research in Higher Education

• Original research
• Open access
• Published: 05 June 2022
• Volume 28 , pages 1271–1299, ( 2023 )

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• Annika Hinze   ORCID: orcid.org/0000-0002-7383-1134 1 ,
• Nicholas Vanderschantz 2 ,
• Claire Timpany 2 ,
• Sally Jo Cunningham 1 ,
• Sarah-Jane Saravani 3 &
• Clive Wilkinson 3  

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The exponential growth in the use of digital technologies and the availability of mobile software applications (apps) has been well documented over the past decade. Literature on the integration of mobile technology into higher education reveals an increasing focus on how mobile devices are used within the classroom environment, both physical and online, rather than on how mobile applications may be used for either teaching or the research process. Our study surveyed staff and higher degree research students at a New Zealand university using an online questionnaire to gain insight into the use of mobile apps for tertiary teaching and research, seeking information, particularly on which apps were used for which tasks and what obstacles hindered their use. The online survey used 29 questions and ran in 2016/2017. 269 participants completed the survey, nearly 20% of the potential sample. We found that mobile apps were used by academics and students for both teaching and research, primarily in the form of document and data storage and exchange, and communication. Very little app use was recorded for in-class activities (teaching) or in-field activities (research). Apps use resulted from personal motivation rather than institutional planning. Both students and academics reported that institutional support and flexibility would likely provide motivation and lead to increased app use for both research and teaching.

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1 Introduction

Mobile learning has been claimed as the future of learning (Bowen & Pistilli, 2012 ) yet surprisingly little specific empirical investigation of mobile application use in tertiary settings is available in the literature. While digital devices are prevalent in the higher education environment, the use and uptake of mobile apps for tertiary teaching and research by academic staff has only begun to be studied (Lai & Smith, 2018 ; Shraim & Crompton, 2015 ).

1.1 Technology Availability to Students

The 2019 ECAR survey of Undergraduate Students and Information Technology found that students see technology as a means for better engagement with study material, instructors and peers in the classroom (Galanek, & Gierdowski, 2019 ). The 2020 survey found that 75% of students who connect to campus WiFi are using two or more devices (Gierdowski et al., 2020 ). The 2018 survey reveals 95% of students have access to smartphones and 91% to laptops (Galanek et al., 2018 ). The downloading of mobile software applications (apps) in recent years shows a similar pattern of increase, rising from 84 billion downloads from the Apple App Store/Google Play in 2016 to 105 billion in 2018 (Sensor Tower). The third most popular Apple App Store category in May 2019, was education at 8.52% (Statista, 2019 ). Studies of higher education students in Southeast Asian universities reveal even higher percentages, for example, 100% of Hong Kong undergraduates in a 2018 study possessed mobile phones, of whom 85% also used apps for their academic studies (Shuk Han Wai et al., 2016 ). Thus previously held concerns that not all students will have access to a smartphone is not supported by the wealth of recent research investigating technology availability (Anderson, 2015 ).

For some time there has been the suggestion that technological advancement of mobile devices and the increased availability of mobile apps may prove central to academic teaching and research (Hahn, 2014 ; Canuel & Chrichton, 2015 ; MacNeill, 2015 ). Specific empirical investigation that discusses mobile app use as opposed to mobile device or more generally information technology use in tertiary teaching or research is extremely limited. Of the few specific discussions of mobile app use in academia, we identify library studies that have been conducted on the selection, use or development of mobile apps (Wong, 2012 ; Hennig, 2014 ; van Arnhem, 2015 ). These studies have often had a focus on the delivery of information or data about library services. Practitioner research in library and education have also included work describing apps and app features for research or teaching—an example being apps for ethnographic field research (van Arnhem, 2015 ). Work has investigated undergraduate student perceptions of mobile apps and mobile devices. An early study of tertiary student use of mobile note-taking software by undergraduate students (Schepman et al., 2012 ) saw widespread positive perception and adoption of these mobile tools by students. Studies exploring the impact the integration of mobile computing devices is having on higher education teaching and learning reveal an increasing engagement with content, collaboration with classmates and information creation and sharing outside the formal learning spaces (Bell et al., 2019 ; Compton & Burke, 2018 ; Gikas & Grant, 2013 ). Systematic literature reviews (Burch & Mohammed, 2019 ; Singh & Hardaker, 2014 ) and reports or investigations of academics’ perspectives of technology use in tertiary classrooms (Galanek & Gierdowski, 2019 ) provide insights into the broad picture but have provided little advice regarding app use for research or teaching.

1.2 Technology Use in Academia

Research on the integration of mobile technology in higher education is focussed on how mobile devices are used within the classroom environment, rather than on their application to the research process (Morris et al., 2016 ; Pedro et al., 2018 ; Schepman et al., 2012 ; Shuk Han Wai et al., 2016 ). MacNeill ( 2015 ) outlines techniques and strategies for the use of apps to support learning, teaching and research. The perspectives are self-reflective and provide insights into tools that have been trialled by the author with recommendations for educators to dedicate time to explore the wealth of available applications for teaching and research inside and outside the classroom. In the higher education classroom, mobile devices in higher education can provide new opportunities for information gathering and use, content access, communication, collaboration and reflection (Beddall-Hill et al., 2011 ; Bowen & Pistilli, 2012 ).

Lai and Smith ( 2018 ) identify a paucity of research on technology use in higher education. We identified two previous surveys of mobile technology use in tertiary teaching and learning. They focussed either on how socio-demographic factors influenced the perception of teaching staff (Lai & Smith, 2018 ), or the perceptions of the pedagogical affordances for mobile devices in teaching (Shraim & Compton, 2015 ). The survey of 308 tertiary teaching staff by Lai and Smith ( 2018 ) found that while many of the respondents were positive about the benefits that mobile technology could provide for their teaching, many felt they lacked the confidence to apply the technology effectively. “When implementing a mobile application in curriculum, instructors need to clearly state the goals of using the application to make sure the students understand the purpose of using the application for coursework, how it is connected to the curriculum, and how it will improve their learning” (Chen et al., 2013 , p.339). Other surveys of academics on their use of apps and mobile devices have focussed on teaching. The survey of faculty members use of mobile devices for teaching by Shraim and Crompton ( 2015 ) found that there are positive perceptions of the opportunities that mobile devices provide for teaching, but were focussed on the opportunities that the device itself provided (mobile connectivity, linking of formula and informal teaching, increasing enjoyment and connecting to real-world problems), rather than apps. The most important finding related to app use was the concerns that academics held about finding time to select appropriate apps and develop their teaching plans to incorporate them (Shraim & Crompton, 2015 ). Their scope was wider than app use, but only asked academics about their app use in their teaching, not their research.

Mobile devices provide opportunities to undertake research and fieldwork while enabling the collection, manipulation and sharing of data in real-time (Beddall-Hill et al., 2011 ). To date, the investigation of digital tools for research has focused on opportunities and challenges such as technical issues (e.g. battery life, data security or data inaccuracies) and considerations such as the preparation of future researchers to leverage the capacity of digital tools for research (Carter et al., 2015 ; Davidson et al., 2016 ; Garcia et al., 2016 ; Raento et al., 2009 ). The benefits of using mobile devices for research are described by Chen ( 2011 ), as including; immediacy of response, better enablement of longitudinal research, capturing of location information for context and the inclusion of an additional touchpoint to provide a more well-rounded research picture. Carlos ( 2012 ) suggests that mobile devices and mobile applications provide three main benefits for use in research: ready availability and familiarity, easy use, and always-on internet connections. A counter perspective is provided by McGeeney ( 2015 ) who observed a number of constraints for using mobile apps, compared to Web browsers. They found lower response rates, increased costs, and usability issues such as limited navigation and data entry options in mobile survey tools. Similarly, it is suggested that with mobile apps the time and effort required to learn how to use an app effectively can result in lower response rates than web-based data collection (Pew Research Center, 2015).

1.3 Institutional Expectations and Support

Many Institutions and academic libraries encourage mobile device use in educational contexts (Canuel et al., 2016 ; Hanbridge et al., 2018 ; Morris et al., 2016 ). Academics are encouraged to provide learning experiences that include “mobile-friendly content, multi-device syncing, and anywhere/anytime access” (EDUCAUSE, 2019 , p. 8). However, the 2019 Horizon Report has identified a need for sustained support and professional development to take advantage of the new teaching opportunities afforded by digital devices (EDUCAUSE, 2019 ). While academics are largely confident with mobile technologies, they need greater awareness of how these technologies can be incorporated effectively to take full advantage of the affordances mobile devices can offer in teaching ((Shraim & Compton, 2015 ). Several studies found that a lack of faculty training was a source for faculty dissatisfaction with classroom technology (Galanek & Gierdowski, 2019 ) and (mobile) IT integration into teaching (Burch & Mohammed, 2019 ; Shraim & Crompton, 2015 ). A number of academic libraries promote the use of mobile software to academics through digital or technological literacy training (Canuel & Chrichton, 2015 ; Hennig, 2014 ). However, research in the area of mobile application in academic libraries almost exclusively focused on the delivery of library services to mobile devices (Aher et al., 2017 ; Breeding, 2019 ; Singh Negi, 2014 ) or the integration of responsive design in web-based service (Kim, 2013 ; Tidal, 2017 ). A sample scan of university library websites indicates that it has become increasingly common for research university libraries to include guidance and instruction on the use of mobile apps for research. Such guidance usually takes the form of a brief preamble followed by a list of the various apps with a brief description of the features, functions and purpose of the app with links to the vendor website. Contextually, little indication is provided as to how or why such a list was curated or, more importantly, how the library supports the integration into learning of such mobile apps through training or instruction. A notable exception is the service offered by Stony Brook University Library, which assists in selecting and using mobile apps for research (Saragossi et al., 2018 , p. 202).

1.4 Research Questions and Focus

We identified a number of shortcomings in the existing literature on app use in tertiary contexts. Research on technology use in teaching and learning rarely focuses on (the experience of) app use, but rather on device capabilities and opportunities of technology use. Previous surveys predominantly analysed the undergraduate students’ perceptions of app use in teaching. Use of apps for academic research is little discussed beyond app use for specific projects, and general technology benefits or issues. While many tertiary institutions actively encourage academics to use mobile apps (and other technology) for teaching, the impact of such expectations on the academic experience is not well studied.

The research reported here attempts to understand more widely how apps are being used in tertiary teaching and research, including what are the perceived benefits and barriers. To provide insights into how mobile apps may be used by students and staff in teaching and research a university-wide survey on mobile app use in a tertiary setting was conducted. The survey design was guided by the following research questions:

RQ1: Are academics using mobile apps for tertiary teaching and research at University of Waikato? RQ2: Which apps are used by academics for which teaching and research tasks? RQ3: What is the experience of app use by academics: what obstacles/opportunities do they identify?

The survey was made available to staff and higher-degree students (collectively referred to as academics) across the University to capture their perspective on mobile app use for teaching and research.

This article presents our study data, and analyses these with respect to the three research questions posed above. The remainder of this article is structured as follows, in Sect.  2 we introduce our method, an online survey of staff and higher degree students at a New Zealand university. Section  3 provides results and analysis of the responses to this survey. We discuss the findings in light of our research questions in Sect.  4 and conclude this article in Sect.  5 . Initial analysis results were presented elsewhere (Hinze et al. 2017a , b ), and primarily focussed on the responses from higher degree research students. The results reported in this paper cover all responses to the survey including those of higher degree students and staff.

We performed an online survey of staff and higher degree students of the University of Waikato in New Zealand. The survey was designed to get a university-wide view of how mobile apps were being used for teaching, research, and learning purposes. The survey was performed over two consecutive years in order to capture the widest sample of participants.

2.1 Context of Study Environment

This New Zealand University is typical of western universities offering qualifications across multiple academic divisions including, but not limited to; the arts, computing, education, management, and the sciences. The majority of staff and students work on campus yet mobile and electronic learning is supported at all learning levels. The university provides Google apps for email, file storage, and word processing. A number of digital resources and technologies are supported depending on the needs of researchers and teachers in academic disciplines. A well-resourced library supports students and staff with print and electronic holdings. There are no required or mandated mobile apps at this university.

2.2 Data Collection

A location-restricted online, self-administered survey tool was developed in the Qualtrics Survey Software. The survey was made available to participants at the University of Waikato in New Zealand from 3rd to 19th August 2016 and again from 31st August to 6th October 2017. The potential sample size was approximately 820 enrolled masters or PhD thesis students and 580 staff (including academics, researchers, and research administrators). All responses were anonymous.

2.3 Participant Recruitment

Higher-degrees students and staff from across the university were invited to participate. We engaged the University’s research office to forward invitations to all departmental administrators, with whom we personally followed up with to distribute the survey invitation to all the University’s academic staff and researchers via email. We further followed up these email invitations with in-person invitations by one of the research team at Faculty and School meetings. The higher-degree students were engaged by the School of Graduate Research through email and social media. Our study had a potential pool of 1400 staff and higher-degrees students.

The survey was done in two stages (same target group, self-selected participants, initial and repeat attempt to engage participants), we present in this article the aggregated result of both stages. 288 survey entries were received, out of which 19 contained no further data and were excluded from the analysis. The survey was thus completed by 269 participants, or nearly 20% of the potential sample of university staff and higher-degree students.

2.4 Survey Tool

Our online tool was a 24-item survey that incorporated a combination of Likert scale tools, radio button responses, and free text questions. This tool was conceptualised in three sections which (1) requested demographic data, (2) surveyed previous experience and use of mobile apps, and (3) reviewed device and operating system use. The survey invited reflection by the participants on their use of mobile apps and whether they believed that their use or lack of use had influenced research or teaching practice. The survey also required participants to give information regarding their reasons for non-use in cases where participants indicated that they had not used, and were not intending to use, mobile apps. To review the survey questions please refer to Appendix 1.

2.5 Definitions Used in the Survey

In the survey we included the following definitions for clarity for the participants:

Mobile app—is a software application developed primarily, although not exclusively, for use on small computing devices, such as smartphones or tablets. Examples include WhatsApp, Evernote, and Flipboard. Other examples might include mobile app versions of programs such as Dropbox or EndNote.

Academic purposes—includes all teaching and/or research activities engaged in while a member of the University community.

2.6 Data Analysis

The results were analysed using default and cross-tabulation report functions provided by the Qualtrics software before manual manipulation, tabulation, and analysis using Excel. We have undertaken basic descriptive statistical analysis (means testing and T test for cohort comparison) and provide tables, graphs, mean values and probability values (where appropriate) along with our reporting in the Results section.

3 Results and Analysis

We present our results structured by the three research questions. After demographic information in Sects. 3.1 , 3.2 , 3.3 address the first question ( are academics using mobile apps for tertiary teaching and research) , while Sects. 3.4 , 3.5 address the second question ( which apps are used for tasks ), and finally Sects. 3.6 , 3.7 , 3.8 address the third question ( academic experience of app use: obstacles and opportunities ).

3.1 Demographic Attributes

The university staff and postgraduate students at the time of the two instances of the survey was reasonably stable at about 1400 (580 academic staff and 820 higher-degree research students), which forms the potential participant pool. 269 of these 1400 responded to our invitation, with most of our study participants being academic staff (N = 163), followed by doctoral students (N = 83), see Fig.  1 .

Participant roles (multiple selections possible)

Out of the 269 participants, 141 were female (52%) and 125 were male (46%); 2 did not specify gender (1%), and 1 selected other.

63% of the participants were younger than 40 years old, see Fig.  2 . The participants represent a range of schools and faculties, as shown in Fig.  3 . The other university areas mentioned by participants were administration and technical support. Five participants selected two options.

Participant age

Participants by school/faculty (multiple selections possible)

3.2 Use of Mobile Apps

With 172, the majority of the 269 participants (64%) had used mobile apps for academic purposes such as teaching or research, see Fig.  4 for details. We note that the percentages among Academic staff and doctoral students were comparable at 67% and 69%, respectively, while only 25% of Master’s students had used apps for research. Four participants provided no data (Fig. 5 ).

Prior use of apps for academic purposes (multiple roles possible)

Academic use of mobile apps by participant age range

Of the 172 participants who had used mobile apps for academic purposes, the age cohort that showed the strongest engagement were the 21–30 year-olds (71%). This was followed by the group of 31–40 year-olds (64%). If broken down by gender, 62% of the 141 female participants and 67% of the 125 male participants had used apps for academic purposes ( p  = 0.3975, i.e., there was no significant gender difference in app use), see Fig.  6 .

Academic mobile app usage by participant gender

Out of the participants who had used apps for academic purposes, most (19%) were in the Faculty of Computing and Mathematical Sciences, followed closely by both the Faculty of Education (18%) and science and engineering (18%); details are shown in Fig.  7 .

Academic mobile app usage by participant school/faculty

We surveyed the 172 participants who had used mobile apps for academic purposes to inquire which types of devices they used mobile apps with (multiple selections were possible). 303 responses were collected. The majority (79%) used smartphones, followed by iPad and Android tablet devices (together 70%), details see Fig.  8 . The named other devices were laptops and PCs, and one sporting device.

Type of mobile device used (multiple selections possible)

90 of the 172 app users gave details about operating systems with 114 selections; for details see Fig.  9 . Under ‘Other’ participants listed ChromeOS and Microsoft system (surface tablet). As expected based on mobile phone ownership data, Android and iOS emerged as the preferred operating systems.

Operating system used on mobile device (multiple selections possible)

Finally, we also asked if participants had been involved in the development of any mobile apps that might be used for academic purposes, and to explain their purpose. We received 60 answers: 50 no, 5 n/a, and the 5 positive answers: driving support (1), for teaching (2), indigenous language learning (1), and a personal digital library (1).

3.3 Purpose of Mobile App Usage

In order to investigate the mobile app use-cases in the tertiary environment, we asked participants about the situations that they had used these. Participants could select either or both teaching/supervision, and/or research. Ninety-five (56%) of the 171 respondents to this question had used a mobile app for teaching/supervision purposes; 146 (85%) had used one for research purposes. Of these, 70 (41%) selected that they had used apps for both (see Fig.  10 a, top).

Mobile app usage: ( a) by purpose (top), ( b) by gender (bottom)

More female participants are using apps than male participants (see Fig.  10 b, bottom). For teaching, there was not significantly more male respondents using apps than female respondents ( p  = 0.96). For research, more female participants were found to be usings apps than male participants, though this was still not significant ( p  = 0.69). We further note that female respondents tended to use apps for research or for teaching only (63.2% of 87 female compared to 54.7% of 84 male). Conversely more male respondents used apps across both categories (marked in gray). However, the difference between male and female use of apps for both purposes was not significant ( p  = 0.59). The majority of the participants who had used apps for teaching or supervision were academic staff (86 of 95). A small number of participants who had used mobile apps for teaching identified as doctoral students (15 of 95), none as Master’s students, 7 as Other (multiple selections possible). 88 Academics, 55 Doctoral students, 2 Master’s students and 18 Others reported using mobile apps for research purposes.

We observe that higher percentages of academic staff used a mobile app for teaching and supervision purposes compared to research purposes (see Fig.  11 ). Conversely, doctoral students were more likely to use apps for research purposes than for teaching/supervision purposes. Quite predictably, Master’s students and other participants were more likely to use mobile apps for research.

User roles for mobile app users

Only 6 participants reported being asked by their lecturer or supervisor to use mobile apps for academic purposes (50 reported having not been asked, 214 provided no answer). They named the following app purposes: document sharing, storage, referencing, communication; bookshelf app for recommended lecture text; conference presentation app; google drive and dropbox for backups of theses, and app examples to explore for research on interactive tour guides.

3.4 Apps for Teaching/Supervision

Ninety-five participants reported using mobile apps for academic purposes for teaching or supervision related activities. Unsurprisingly, the majority of these participants reported themselves as teaching staff. At this university, it is not atypical for staff to work across roles in a university, and for some higher degrees students to be contributing to teaching initiatives at various levels and therefore some doctoral students and participants in the ‘Other’ category had also used apps for teaching purposes. These 95 participants were asked to select from a shortlist of possible academic-related apps (see Fig.  12 ) the mobile apps that they used for teaching or supervision purposes. Also shown in Fig.  15 , the participants were asked if these apps were used by themselves or by students under their supervision. There was a substantial number of Other options named, including Google Drive (8), Google Docs (5), Facebook (4), Google Sheet (3), Kahoot (3), and Kindle (3) and a further 11 programmes named twice, and 65 programmes named once showing that a diverse range of apps were used (not shown in Fig.  12 ).

Apps used for teaching/supervision (multiple selections possible)

Mobile apps for teaching purposes were reported as being used by 95 participants, the specific purposes for using apps for teaching are elaborated on in Fig.  13 . The aspects teaching staff most engaged in were sharing or storing documents, as well as communication with colleagues. Other tasks mentioned were communication with students, in-class surveys, or keeping up with recent blogs.

Use of mobile apps in teaching practice (multiple selections possible)

There were 95 participants that had used a mobile app for teaching/supervision, of which 71 had requested their students to do the same. These participants were asked to state the purpose for making this request; results are summarised in Fig.  14 . The responses in the ‘Other’ category included quizzes, vocabulary practise, feedback, class activities, creative practice. Figure  14 shows that the primary reason for asking students to use mobile apps was for the purposes of communicating with others, sharing documents, followed by accessing course information.

Mobile apps recommended to students (multiple selections possible)

3.5 Apps for Research

Of the 172 participants who had used mobile apps for academic purposes, 146 did so for research purposes (85%), one participant provided no answer. This group of 146 participants were asked what academic-related mobile apps they had used for research purposes from a list of possibilities provided. The results, summarised in Fig.  15 , show the file-hosting app Dropbox was extremely popular and used by 62% of researchers (N = 91). There was a substantial number of participants (65) who provided ‘other’ options, with many participants naming up to 6 or 7 apps, including Google apps (N = 22, among which were Drive: 12, Docs: 3, Keep: 3, Slides: 2, Gmail: 3), Mendeley (N = 5), Skype (4), voice recording (3), Twitter (3). Participants also mention apps that had been written by themselves or their students.

Mobile apps used for research (multiple selections possible)

Participants were also asked what research purposes they used mobile apps for (see Fig.  16 ). Storage and sharing of documents, as well as searching and note-taking were the main reasons for researchers using mobile apps. Only 22 ‘Other’ answers were collected, mostly naming different uses such as reading (6), recording of various data, such as interviews (2) and notes on whiteboards (1), and app development (2).

Purpose of mobile app use for research (multiple selections possible)

3.6 Impact of Apps on Academic Experience

All participants who had indicated that they used mobile apps for academic purposes were asked to respond to questions on their use of mobile apps for their teaching/supervision or research, their knowledge of apps, and their use of mobile apps. The response required from participants was on a 5-point Likert scale from strongly agree (1) to strongly disagree (5), see Fig.  17 . The factors that participants reported to most strongly agree with was “my research or teaching benefited from the use of mobile apps” (mean = 1.72) and they “had no problems finding a suitable app for my research or teaching” (mean = 2.40). The attitude statement that participants most strongly disagreed with was “I experienced difficulties in using mobile apps” (mean = 3.60). Other responses regarding the attitude towards app use were; “the outcome of my research or teaching was impacted by the use of mobile apps” (mean = 2.49), “my research or teaching practice was conducted differently as a result of using mobile apps” (mean = 2.53), and “I know where to go to get help with mobile apps” (mean = 2.62).

Attitude to mobile app use: data out of 100% = 172 participants

Only 20% of participants experienced difficulties when using mobile apps in an academic setting. 45 to 60% of participants knew where to seek help and where to find suitable apps (vs 15–25% who did not; 7% no answer). A similar observation holds for the perceived impact of using apps for research and teaching both in terms of change of practice and outcomes.

However, nearly 90% of mobile app users responded that they felt they had benefited from, or felt neutral about, the inclusion of mobile apps in their academic activity (2% slightly disagreed, 8% no answer).

3.7 Experience of Users

The survey provided an opportunity for participants to provide any further comments they wished on mobile app usage in an academic setting. 60 participants provided comments, 18 from participants who had not used apps for academic purposes, and 42 from participants who had experience with such apps. Participants will be referred to by identifiers P1 to P269. Many of the concerns voiced were brought up by non-users and users alike. We, therefore, do not discuss their comments separately but indicate which category a participant falls into next to the identifier (P U for users and P N for non-users).

In comments provided by non-users, distrust in app/technical reliability were expressed, such as by participant P N 124: “Technology moves so fast that planned obsolescence is commonplace. New apps have a track record of failure in their first years: this does not look good to students if suddenly the app for their course falls over”. Similarly, P N 146 comments “I wish people would switch their bloody mobile phones off, and get a life really.”, and P N 231 “I do not have a mobile”.

Participants also discussed mobile app usefulness from a pedagogical viewpoint , stating that “[…] we have gone into more and more web-based teaching, and moodle etc. However, I have seen that … students who will end up as designers in some companies do not gain much from these approaches. In my judgement and experience … use of white board and limited amount of notes uploaded will work well, with [a] lot of laboratory type hands-on elements. I strongly believe that if we [lose] the 'human touch" in [the] classroom setting, it will gradually and negatively affect the quality of the graduates we produce” (P N 128).

Several users commented that they are planning to do more or feel still at the beginning of their journey and wish for more support : P U 233: “I've been reluctant because of time, planning and other flexibility related restrictions it places”, P U 254: “Most of the learning on this is on my own. more exposure is needed through seminar etc.” P U 87: “Would be great to get some training on this)”. Some expressed reservations about institutional support, for example, P U 108: “Help with mobile apps seems to be largely found in internet searches of forum posts and vendor provided documentation”.

Participants expressed that guidance on choosing apps was needed as “It would also be great if there was some sort of online resource on the uni website that lists and briefly explains some of the apps that might be useful when conducting research” (P U 39), and the concern that “There is simply not the capacity in ITS to support mobile app usage” (P N 124). Similarly, non-users wished for more support: P N 108: “Help with mobile apps seems to be largely found in internet searches of forum posts and vendor provided documentation”,

Some participants considered app use inconvenient , claiming “In many instances and situations a well thought out website enhanced for use on mobile will be more useful and less cumbersome than an app. I despise having to download and constantly update several apps, plus they come with intrusive permissions” (P71). Or participants felt that apps were “only useful where use of a real computer is impossible”. The context within which apps could be integrated into the learning environment caused some uncertainty, with several comments highlighting this reservation, “It is sometimes challenging to find the most appropriate app to meet a specific teaching purpose” and “The challenge will be to develop apps or modify existing apps to suit the purpose of the user and the context of the user”.

Finally, some participants expressed a dislike or unfamiliarity with/for phones and technology in general: “I wish people would switch their bloody mobile phones off, and get a life really [..]” (P N 146) and a distrust in apps as they expressed concern that “they need to be reliable enough that researchers can be confident that they will not suffer data losses if they use just apps” (P U 105). Similarly, worries about the hardware were expressed: “Our devices need updating. Phones are personally owned and my ipad is too old for some of the apps I want to use.” (P U 155).

Some comments seemed to be expressions of undisclosed fears that were channelled into the reasons given. For example, P U 104 raised the issue that “One can only move as fast as students are able. One can only do so much introducing of new technology—you can get to a point where you have built a learning task for example on a particular resource and then find that half the class cannot even access it”.

A theme that was detected in the responses received reinforced the mobile nature of both tertiary education and academic publishing today. This can be specifically seen in the discussion of mobile and on-the-go teaching, learning, and research. Participants listed the importance of being able to collect data, take notes, as well as communicate with peers, participants, and users in a variety of situations. One participant noted, “I've largely found it useful for mobility rather than anything else.” Another participant, whose complaint we noted earlier regarding screen size making viewing information less pleasurable for them compared to a computer, did note “at least information is available and accessible when on the move”. A further PhD student stated that “mobile apps are great. If you are in tedious work meetings you can work on easy bits of your thesis and people just think you are diligently taking notes”.

Similar numbers of participants believed their work-life was or was not impacted by mobile apps as participants who believed their teaching or research practices were different today because of their mobile app use. Investigation of the impact of technologies including mobile devices and applications on traditional pedagogies and research practices and processes warrants further empirical investigation.

Significant discussion related to use of apps for teaching rather than research. With some being enthusiastic: “We are moving into the new generation Apps is the tool to connect with the students. / Let’s not hesitate. We need to be engaging successfully to create a sense of new age.” (P U 81), while others are quite reserved about technology use, including “ web based teaching, and moodle” (P N 128). P U 78 described challenges: “It is sometimes challenging to find the most appropriate app to meet a specific teaching purpose”. P N 203 teaches online papers and comments “it would be great to have a way for students to access discussion groups and to have virtual communication through a mobile app”.

Several participants explicitly wished for apps that allowed access to library resources such as eBook readers (P U 33, P U 7), library search (P U 42, P U 120), and a personal library (P U 202). Some participants were very enthusiastic about the potential of apps in the academic environment, such as “We are moving into the new generation of Apps is the tool to connect with the students. Let’s not hesitate. We need to be engaging successfully to create a sense of new age” (P U 81) and “Apps greatly increases my ability to store quotes and research links” (P U 67). Conversely, some participants used the open feedback option to comment on the shortcomings of their personal phones (P N 169: “I find the real estate of my mobile device is too small [..] my tablet is too slow”), on perceived shortcomings of innovation management (P N 182: “endless workshops”) or even expressed fears about the motivation of the survey (P N 166: “The outcome of studies like this can be deeply political”); conveying a sense of fear about potentially being forced to use apps for research and teaching.

3.8 Experience of Non-Users

In Sect.  4.2 we report that 35% of the participants had not used mobile apps for academic purposes (N = 93). More than half of these non-users (N = 50/93) indicated that they did not intend using mobile apps for academic purposes in the future. Forty-four percent (41/93) of these non-users reported that they do plan to use apps in the future. We asked non-responders what their reasons for non-use of mobile apps in academic contexts were. Forty-seven people responded to this question. Nearly half of the 47 participants reported they lacked knowledge about how they might use mobile apps for their purposes. Further to this approximately one-third of the participants confessed their disinterest in apps, while approximately a third considered them to be irrelevant for their teaching or research needs. Eight participants reported a lack of apps for their purposes and 7 participants discussed the perceived lack of support from the university. Other opinions suggested that computers and large screen devices serve their needs better than mobile devices for academic purposes. One academic responder twice noted planned obsolescence as a factor hindering their use of mobile apps in the academic context. Some participants also named specific fields for which they believed mobile apps or small screen devices would not be suitable.

We noted earlier that 44% of non-users had indicated they might use mobile apps in the future. We asked these non-users to select what factors might influence their future use (multiple selections were possible). 40 participants responded to this question (1 provided no data) resulting in 145 selections (see Fig.  18 ). Non-users were most interested in mobile apps that supported them to share or communicate with others (selected 23 times), see Fig.  19 . The option ‘Other’ included participant sign-up, reading, engaging with students in and out of lectures (3), and the possibility of so far unforeseen usages (3).

Reasons for intended non-use of mobile apps (multiple selections possible)

Non-users intended future use of mobile apps

The 41 participants who reported not using mobile apps were asked how helpful the six factors shown in Fig.  20 might be in facilitating the uptake of mobile app usage for academic purposes. This question was posed as a 5-point Likert scale from very helpful (1) to very unhelpful (5), for which 38 of 41 people responded. Responses show that “more appropriate apps” (mean = 1.54) and “easier to use apps” (mean = 1.55) were the factors most likely to facilitate uptake with app non-users. This was followed by; “more practical support” (mean = 1.58), “more institutional support (mean = 1.66), “more information about apps” (mean = 1.66) and better access to appropriate devices (mean = 1.81).

Factors facilitating uptake of mobile apps

Of the six factors posed, the two that were defined as very helpful and helpful were factors relating to “easier to use apps” and “more appropriate apps”.

4 Discussion

We here discuss our findings in light of our research questions, their implications and opportunities. Our research was motivated by three questions, which we will answer here based on our study results. We will compare and contrast our findings with the related work, giving specific relation to two related surveys of academic use of mobile technology use (Lai & Smith, 2018 ; Shraim & Crompton, 2015 ).

4.1 Answering RQ1: Are Academics Using Mobile Apps?

Our study had 269 participants from a potential pool of 1400 staff and HRD students (19.2% response rate, including 28% academics and 10%). The two related surveys had similar response rates of 24% among teaching staff (Lai & Smith, 2018 ) and 29% (Shraim & Crompton, 2015 ), with similar distributions across gender (i.e. a slight to significant majority of male respondents).

172 of our 269 participants (64%) had used mobile apps for academic purposes such as teaching or research. The percentages among academic staff and doctoral students were comparable at 67% and 69%, respectively, but much lower for Master’s students. 95 of 172 (56%) had used a mobile app for teaching/supervision purposes; 146 (85%) had used one for research purposes; and 70 (41%) had used apps for both. By contrast, Lai and Smith ( 2018 ) found that the majority (75–90% for comparable categories) of their respondents had not used any mobile technology for teaching. Shraim and Crompton ( 2015 ) did not report previous app use for academic purposes.

We found that 62% of the 141 female participants and 67% of the 125 male participants had used apps for academic purposes. By contrast, Lai and Smith ( 2018 ) found that more female teachers used mobile technologies for teaching than male teachers. They hypothesised that the reason may have been that the female teachers were younger than the male teachers in their response cohort. They also found that junior teachers are more willing to learn to use new technologies than senior teachers. We similarly found the strongest engagement with mobile technology among the 21–30 year-olds (71%), followed by the 31–40 year-olds (64%). Shraim and Crompton ( 2015 ) noted that three-quarters of their respondents were aged between 25 and 45, going so far as to suggest that older faculty chose not to respond, perhaps being less inclined to use mobile technology as part of their teaching. Some of our participants were of a generation where the technology may be seen as a hindrance or unfamiliar tool. For example, participant P N 191 stated “I think strategic training is really necessary for people like myself who is not a digital native—what are the benefits? How to develop greater usage in daily work and life?” However, very few participants who had used apps did report technical difficulties (see our discussion in Sect.  4.3 ). We conclude that the study participants who did use apps for teaching and research were proficient, while the extent to which non-users experienced difficulties is hard to gauge.

Our findings support the related literature that academics are using mobile technology and mobile apps for teaching and research. These findings imply there is a need to more deeply understand the reasons for app use/non-use by academics across tertiary institutions. From there, an exploration can be started of how appropriate support can be provided.

4.2 Answering RQ2: Which Apps are Used by Academics for Teaching and Research?

Apps for Supervision/Teaching Participants reported app use for tasks that involve sharing or storing documents, as well as for communication with colleagues and students, and some use for in-class surveys, or keeping up with recent blogs. Similarly, it was reported that teachers required students to use apps primarily for communication and information storage or delivery purposes. This is in line with many studies that suggest that mobile devices in higher education may provide new opportunities for information gathering and use, content access, communication, collaboration and reflection (Beddall-Hill et al., 2011 ; Bowen & Pistilli, 2012 ). The tool that academics most reported as being used by themselves and by students was Dropbox, a file sharing and storing app that facilitates collaboration and information dissemination. Neither of the two related surveys (Lai & Smith, 2018 ; Shraim & Crompton, 2015 ) focussed on the use of mobile apps or specific software, but rather on technology use.

However, many participants reported a lack of time, resources, and control as reasons why they have not successfully implemented mobile apps into their teaching for use by or with students. Participant P U 233 noted “I've been looking at Kahoot at the like for teaching. I've been reluctant because of time, planning and other flexibility related restrictions it places”, while P U 154 reported “at the moment, I am just using the iPad to save paper. It hasn't really impacted how I teach. I am aware that there is far more I could do with it, but I do not have a lot of control over what/how I teach.” Lack of time, resources and knowledge are well-known issues for academic use of technology that were observed in other studies as well (Ajjan & Hartshorne, 2008 ; Lai & Smith, 2018 ; Shraim & Crompton, 2015 )).

Another interesting aspect was the perception that teachers need to restrict students’ screen time (P N 187): “with my overseas students (English language learners) … I try to promote personal f2f interaction in my lessons and try to get the young students away from their screens!” While this was not a prevalent theme, it deserves consideration in future research.

Apps for Research Sixty-four percent (172 of 269) of participants had used apps for research or teaching. A number of apps were listed for participants to select from. The participant was able to select multiple apps that they had used for their research. The research team had hypothesised a number of bibliographic, file sharing, and document creation tools for participants to select from. While file hosting and sharing was reported as being used significantly by participants, it was interesting to note that social media (Twitter), communication (Skype), as well as file creation and storage solutions (Drive, Google apps, voice recording) were also listed by numerous participants. If we consider the nature of research and international connectedness that is expected in universities today, it is unsurprising that a number of these apps that allow for asynchronous collaboration and long-distance telecommunication are listed as central to the modern research framework. This is summed up by one participant (P U 206) who commented “the survey seems to focus on information management. Apps also allow easy access to communication and collaboration channels.”

Higher numbers of academics and students reported using apps in the early phases of the research process for tasks such as note-taking (64 participants), search (66), research planning (59), communication (43), data collection (60), and document sharing (73), compared to later phases of the research process such as data analysis (21), presentation (30), and publishing (16).

App use for research was not considered in the related surveys (Lai & Smith, 2018 ; Shraim & Crompton, 2015 ). To the best of our knowledge, no comparable data has been collected so far. The implications of these findings is that work to support and develop appropriate mobile applications that service academics during all phases of the teaching and research process are required.

4.3 Answering RQ3: What is the Experience of Academic App Use?

We here discuss first the experience of respondents who had used apps, and then those of respondents who did not use apps but had identified obstacles.

Impact on Academic Experience The majority of our participants did not encounter any issues with finding and identifying relevant apps. Our participants also did not encounter major technical difficulties when using apps. For example, only three explicit comments called for technical support and only 20% of participants mentioned technical difficulties. Most observed that using apps influenced the way they did their teaching and research. The vast majority of mobile app users (90%) in our study felt that they had benefited from, or were neutral about, the inclusion of mobile apps in their academic activity. Both Lai and Smith ( 2018 ) and Shraim and Compton ( 2015 ) also explored teachers’ attitudes towards mobile technology use in the classroom but did not ask if teachers experienced the technology as having been helpful. Like most other publications on technology use for teaching (Hahn, 2014 ; Canuel & Chrichton, 2015 ; MacNeill, 2015 ), they asked instead about the teachers’ beliefs in the opportunity of enhanced learning, which may not align with the actual experience of using mobile apps. As a potential drawback, they named students becoming less critical, or increasing their workload (Lai & Smith, 2018 ). Given the low percentage of mobile technology use (< 25%), this feedback is largely not based on the academics’ experience. While they reported that their departments supported the use of mobile technology, it remains unclear if this describes a positive attitude or practical help (Lai & Smith, 2018 ).

Lack of support A common theme was a lack of support by the institution for mobile device and mobile app use for teaching, learning, and research purposes. Participants noted a need to be supported in identifying apps of relevance and suitability to their teaching and research. One academic participant discussed a “notable lack of support for adequate apps, a case in point being that the Uni does not provide apps suitable for reading online books” (P N 124). The results of both studies suggest that non-users may be more willing to use mobile apps if institutional support and guidance were provided. This desire for institutional support came from both academics and students, with higher degree student P U 33 reporting “it would be very beneficial to have an online list, or equivalent, of useful apps for students, varying from note-taking, referencing, data collection right through to ones specific to different fields of study. Many of the apps I now use would have been extremely useful had I known about them when I began this degree.” This reporting by academics of a need for institutional and wider support in selecting and using apps to support their pedagogy, classroom practice, and research is in line with Horizon Report Preview (EDUCAUSE, 2019 ) that calls for sustained support and professional development in order to take advantage of the new teaching practice opportunities afforded by the inclusion of digital devices within the education environment. A similar sentiment has been mirrored in other studies (Ajjan & Hartshorne, 2008 ; Chen et al., 2015 ; Lai & Smith, 2018 ; Shraim & Crompton, 2015 ).

Non-use 35% of our participants reported not having used mobile apps for academic purposes. Furthermore, approximately half these reported they had no intention to use apps in the future. Disinterest in mobile apps for teaching, or a view of mobile apps as being irrelevant to the participant, were common reasons for these responses. Some also noted a preference for desktop solutions for these tasks. This is summed up by P N 174 who noted “I don't like/prefer to use apps for academic purposes. I feel more comfortable on desktop/laptop when having to access content relating to academic needs”, while another participant stated “computers have more options than mobiles” and the perennial concern “screen size makes viewing information not as pleasurable as computer”.

Of the non-users, slightly under half suggested they might use or were willing to use mobile apps in the future for academic purposes. Non-users of apps were primarily interested in the potential ability to communicate or share with others. It is interesting to note that the communication affordances are of high interest because in both surveys the view that there is no use for apps besides for communication was a common criticism for mobile apps. Perceived potential benefits of mobile apps by non-users were features such as participant sign-up, reading and engaging with students in and out of lectures. Another feature that participants noted as a potential positive for mobile apps was the perceived convenience of managing, capturing, collecting, and storing information.

Many participants saw a need for future development, advancement, and indeed further research such as that we offer here. Almost all non-users identified “easier to use [apps]” and “more appropriate [apps]” as important or helpful. One participant summed this up “the challenge will be to develop apps or modify existing apps to suit the purpose of the user and the context of the user”, while another stated “I think [mobile apps] have some good potential for engaging students in classrooms and out of classrooms. I also don't think they are the be all, end all of engagement (i.e., necessary but not sufficient for good engagement).” There appears, in addition, to have been a perception that because software or apps are open source that they do not require coordinated technical support or training from the University. Through conducting this survey we found that there is a need to provide support and information to users for both subscription software as well as open-source alternatives.

The survey has highlighted that current users have typical usage patterns and generally feel confident with the use of mobile apps for a range of purposes. There was also a group of non-users and low-users that did not feel confident. We feel the implications of our findings are the need to support academics to locate and use mobile apps during teaching and research and the desire from academics for this support as well as for new mobile apps to meet their needs.

4.4 Limitations

This study was based at a single university in New Zealand; however, its results and recommendations for engagement and need for ongoing support are potentially widely applicable for a western tertiary education environment due to similarities in academic environments. One may expect differences in the specifics of apps used, such as the prevalence of Google tools in this sample, vs the use of OneDrive for similar tasks in universities with Microsoft contacts.

A participation rate is in keeping with typical response rates for similar online studies (Fosnacht et al., 2017 ; Nulty, 2008 ; Van Mol, 2017 ). As the participants were self-selected, it is unclear to what extent our sample accurately reflects the university situation. As our participants were self-selected, they did not necessarily constitute a representative sample of the whole university but rather reflected the feedback of people who felt strongly enough to engage in the process. While both users and non-users of mobile apps were explicitly targeted, the resulting sample consisted of predominantly mobile app users (66%). We hypothesise that non-users may have been less inclined to respond to a survey about app usage.

The study ran at the same time in two consecutive years. One notable difference was the proportion of academic vs student participants within the studies. However, there did not appear to be overall a significant variation between the results obtained in the first year vs the results from the second year, which were therefore presented here together. We noted that some participants commented also on the use of web applications. In order to keep the study results comparable, we did not change any questions. However, in future studies we would wish to include both mobile apps and web apps (i.e., software as a service), thus addressing the use of any software services away from the office or lab environment.

5 Conclusion

While students and academics use digital devices in the higher education environment, the uptake of mobile apps for tertiary teaching and research has only begun to be studied. Research on technology use in teaching and learning rarely focuses on the experience of app use by academics. The impact and experience of the institution’s expectations regarding apps by academics is not well studied. Our research attempts to understand how apps are being used in tertiary teaching and research, including what are the perceived benefits and barriers. Our study used an online survey, aiming to answer three research questions. Our study here is unique in that it has investigated students and academics’ attitudes to mobile apps in both the tertiary classroom and the research environment.

The contributions of our research presented here are the following: We conducted the first study into the experience of mobile app use for teaching and research by academics. Findings from our research are as follows: (1) Mobile apps were used by academics and students for both teaching and research, primarily in the form of document & data storage and exchange, and communication. Furthermore, the stated primary motivators for future mobile app use for both teaching and research were again the ability to communicate, collaborate and share with others. (2) Very little app use was recorded for in-class activities (teaching) or in-field activities (research). (3) Our study results and related work show that at present academics and students use mobile apps due to intrinsic personal motivations rather than institutional support or provision. There remain, consequently, opportunities for better support of mobile app use. (4) Both students and academics reported that institutional support and flexibility would likely provide motivation and lead to increased app use for both research and teaching.

Many of the apps named in our study were mobile versions of web apps (such as Dropbox, Evernote, Google Drive). Some participants may even have interpreted mobile app use to mean both mobile apps and web apps (e.g., for bibliographic software Zotero and Endnote). This interplay of mobile apps and web apps (or mobile access to web apps) has not been explored for the academic context so far and should be studied in a follow-up survey. Extending this survey with consideration of software as a service (SaaS) used on mobile devices may shine a light on some of these wider-reaching applications which also facilitate teaching and research.

Our study is the first of its kind, exploring the practical experience of academics using mobile apps for teaching and research. Data such as ours can inform academic management to better support students and staff with mobile app selection and use in the academic context.

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Hinze, A., Vanderschantz, N., Timpany, C. et al. A Study of Mobile App Use for Teaching and Research in Higher Education. Tech Know Learn 28 , 1271–1299 (2023). https://doi.org/10.1007/s10758-022-09599-6

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[PhD Thesis – 2019] Imagined Futures: The impact of mlearning and access to mobile technology on the role of the teacher

The use of mobile devices in education has long been predicted and imagined. Recent technological changes and increased affordability have enabled pioneering schools and educators to embark on mobile device initiatives. In the Irish educational context, schools were able to link their use of devices to anticipated curricular reforms, but lacked national guidance on the use of those devices for teaching and learning. This study concerned itself with the impact of mobile learning and devices on relationships of learning. The literature review revealed a significant gap in the research on the use of mobile devices in certain contexts and a lack of theoretical understanding of their use. The study employed a constructivist grounded theory methodology to explore the experiences of schools, teachers and students in Ireland. A sample of two post-primary schools and seven teachers, with their students, were recruited to the study. Data were gathered using interviews, video analysis, online observations and physical observations of classes. Some methods were extensions or innovations on traditional grounded theory approaches. These data were analysed through the process of constant comparison, from which codes and categories emerged. The categories demonstrated the importance of school context, the value of teachers’ virtual classrooms and the requirement to understand teachers’ beliefs. The findings add new knowledge to the field of mobile learning, and innovation in the methods of grounded theory, and yielded insights of value to school leaders and policy makers. The grounded theories which emerged placed emphasis on understanding a teacher’s beliefs, and demonstrated that those beliefs largely shape their use of technology. They also establish that mobile devices, despite substantial new benefits to users, were not intrinsically agents of pedagogical change.

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Gumawang Jati , Yessy Purnamasari , kusumanigrum fkip , Todo F . B . Sibuea

Mobile Learning: Unlocking the Potentials for Female Education in KSA

Jihan Zayed

Out of their homes, Saudi women are imposed to be accompanied by their male guardians. Therefore, they suffer to complete their education. To overcome this obstacle, they currently use mobile learning (mLearning)-in the form of social networking apps such as WhatsApp or Twitter-to keep in touch with their instructors. Being specified for educational purposes, the present study used ClassDojo and Edmodo as alternatives to these apps for unlocking the potentials of mLearning for female education in KSA. Employing a quantitative, one-group design, a questionnaire was conducted for collecting data during the 2 nd Semester of the academic year 2017/2018. It aimed at determining the positive perceptions of 15 Saudi female students enrolled in the last level of a teacher education programme towards mLearning.

David Rosen

Piotr Grabowski

The thesis is devoted to the effectiveness of ESN use in the process of teaching and learning EFL in Primary School. It begins with the analysis of student’s needs and characteristics of ESN. In the next part, using different methods and techniques (content analysis of two ESN, questionnaires for students and teachers that used ESN) the author examines advantages and disadvantages of ESN use. The conclusions from the research show the great potential of ESN in teaching. They also suggest that in polish schools their use is insufficient to take advantage of their capabilities. The research shows also the need of simultaneous introduction of pedagogy 2.0 with the implementation of ESN into the teaching practice.

إبراهيم السويفى

Dr. Ciara Morrison-Reilly

Within Religious Education in the Irish post primary sector, there is little evidence of smartphone use for supporting mobile learning. This research aims to address this shortcoming by exploring our experience of smartphone microblogging supporting mobile learning. A participatory action research (PAR) methodology was employed. Research participants involved one teacher-researcher and a hundred and five first year post primary students of Religious Education from an Educational Training Board (ETB) school. A mixed method design was employed using both quantitative and qualitative data from pre and post online surveys, pre and post-research questionnaires, focus groups, online posts from Edmodo and the teacher-researcher’s own reflective journal. The research question was ‘What were our experiences of smartphone microblogging supporting mobile learning on ‘Images of God?’ ‘Images of God’ is a module from the Junior Certificate Religious Education syllabus. Mobile learning was defined as consisting of three aspects: the device, learner and social aspect as theorised in Koole’s (2009) Framework for the Rational Analysis of Mobile Education (FRAME) model. First, the device aspect of mobile learning examined Edmodo’s technical challenges and conveniences as well as measuring research participants’ perceptions through the Technology Acceptance Model (TAM) research instrument (Davis 1989). Second, the learner aspect of mobile learning explored students’ use of Edmodo for supporting cognitive learning, collaborative learning and deeper learning within post primary Religious Education. Third, the social aspect of mobile learning investigated Edmodo as a virtual learning community and a safe space for the students to disclose and discuss their personal images of God that included agnostic and atheist worldviews. The social aspect also provided an insight into suitable pedagogy stemming from relevant mobile learning theories for supporting smartphone microblogging. This research concluded with recommendations for practising smartphone microblogging for supporting mobile learning within post primary Religious Education.

Morteza Mellati

International Journal of Recent Research in Social Sciences and Humanities (IJRRSSH)

Enock Swanzy-Impraim

This study analyzes how the introduction of a Learning Management System (Google classroom) leads to persistent innovation in teaching and learning activities at the Ahantaman Girls' Senior High School, Ketan-Sekondi. This qualitative and quantitative case study explores the possibility of using LMS (Google classroom) for online instruction at the senior high school level. Since independence, the educational system in Ghana has undergone many reforms and restructuring to improve the standard of education both in quality and quantity. These reforms are not without political undertones. The use of computers, tablets, phablets, internet and smart phones to improve the standard of education is also growing rapidly in Ghana. However, it seems there is a big gap between the use of learning management system (Google Classroom, Edmodo, Moodle, etc.) among senior high school students in developing nations and senior high school students in developed nations. Although students have access to these gadgets, their usage is causing more harm than good. The case study research revealed that the introduction and integration of the Google Classroom in the teaching and learning process at Ahantaman Girls' SHS improved students' performance and participation in class even though it came with challenges. It is recommended that the use of Learning Management System (Google Classroom) should be encouraged in the second cycle and tertiary institutions in Ghana.

Computers & Education

Melissa Bond

The flipped learning approach has been growing in popularity in both higher education and K-12, especially for its potential to increase active learning and student engagement. However, further research is needed to understand exactly how the flipped approach enhances student engagement. This narrative systematic review synthesises literature published between 2012 and 2018, focused on the flipped learning approach in K-12 contexts, and indexed in 7 international databases. 107 articles, book chapters, dissertations, conference papers and grey literature were included for review, and the results are discussed against a bioecological model of student engagement. The results indicate that the majority of research has been undertaken in North American and Asian high schools, heavily focused on student perceptions of flipped learning and achievement within STEM subjects, especially Mathematics, with a slight preference for quantitative methods. Studies in this review found the approach to overwhelmingly support student engagement, with 93% of studies citing at least one dimension of behavioural, affective or cognitive engagement, whereas 50% of studies reported facets of disengagement. Collaborative technologies such as Google Docs, Google Classroom and Edmodo were particularly linked to engagement, with videos not created by teachers more likely to lead to disengagement. Only 12% included a definition of student engagement, and less than half used a theoretical framework. Future empirical research should ensure that all contextual information is included, including year level of student participants, that multiple methods of both quantitative and qualitative data collection are included, and close attention is paid to grounding research in theory. Further research is needed on parent, teacher and school leader perceptions, as well as longitudinal and multiple-class studies.

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Dissertations / Theses on the topic 'Mobile Network Operators'

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Tsuboya, Hisakazu 1967. "Migration strategies for competitive advantage of mobile network operators." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/16991.

Patterson, Cameron Webster. "An Economic Model of Subscriber Offloading Between Mobile Network Operators and a WLAN Operator." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/50660.

Markendahl, Jan. "Mobile Network Operators and Cooperation : A Tele-Economic Study of Infrastructure sharing and Mobile Payment Services." Doctoral thesis, KTH, Kommunikationssystem, CoS, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-28756.

Laws, Richard, and Ni Mo. "Product & Pricing Standardization within the Global Mobile Network Operator Industry." Thesis, Högskolan i Gävle, Avdelningen för ekonomi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-19976.

Jansson, Carl Johan, and Shuvo Deep Dass. "Customer Based Brand Equity and Intangibles : The case of the Swedish mobile network operators." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-179813.

Naguib, Mina. "On the security of VoIP mobile network operator and international carrier interconnects." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199056.

Zich, Štěpán. "Analýza trhu virtuálních mobilních operátorů v České republice." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-197598.

Malebanye, Potsane. "To investigate how Mobile Cellular Network Operators can increase the average revenue per user by stimulating the usage of broadband services." Thesis, University of South Africa, 2007. http://hdl.handle.net/10500/56.

Nduna, Chipo. "Financial freedom in mobile money: the role of the central bank in Zimbabwe." University of Western Cape, 2020. http://hdl.handle.net/11394/7321.

Ceita, Yannick Soares de Barros de. "Shared solution for telecommunications networks." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23613.

Jeoun, Kristina S. "The tactical network operations communication coordinator in mobile UAV networks." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Jun%5FJeoun.pdf.

Opatřil, Marek. "Návrh metodiky pro výběr provozovatelů virtuálního operátora v ČR." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-162508.

Liu, Kenneth Lap Chi. "Supply chain for mobile network operator." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62772.

Fernandez, Jean Eli Cerrillo. "A cognitive mechanism for vertical handover and traffic steering to handle unscheduled evacuations of the licensed shared access band." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/169331.

Diblík, Jaroslav. "Mobilní komunikace v ČR." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-193669.

Gutti, Krishna. "Low cost secure network connectivity for a municipal organization." Thesis, KTH, Mikroelektronik och Informationsteknik, IMIT, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-92266.

Chatzimichail, Konstantinos. "How to Cut the Electric Bill in Mobile Access Networks: A Mobile Operator's Perspective." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177440.

Tay, Chee Bin, and Whye Kee Mui. "An architecture for network centric operations in unconventional crisis: lessons learnt from Singapore's SARS experience." Thesis, Monterey, California. Naval Postgraduate School, 2004. http://hdl.handle.net/10945/1303.

Norton, David K. "Joint mobile network operations routing design and quality of service configuration." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Sep%5FNorton.pdf.

Březinová, Jana. "Analýza trhu mobilních virtuálních operátorů v České republice." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-193455.

Štěpán, Marek. "Mobile virtual network operator as a new type of a telecommunication services provider." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-142195.

Tay, Chee Bin Mui Whye Kee. "An architecture for network centric operations in unconventional crisis : lessons learnt from Singapore's SARS experience /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FTay.pdf.

Bilavčík, Martin. "Marketingová strategie společnosti Telefónica O2." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2010. http://www.nusl.cz/ntk/nusl-222615.

Driesslein, Jonathan Clarke. "Scalable mobile ad hoc network (MANET) to enhance situational awareness in distributed small unit operations." Thesis, Monterey, California: Naval Postgraduate School, 2015. http://hdl.handle.net/10945/45843.

Hanzlík, Radek. "Customer service on the Czech mobile telecommunication market. Comparative Analysis." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-201782.

Šabata, Ondřej. "Hodnocení efektivity prodejní sítě telekomunikačního operátora za použití modelů analýzy obalu dat." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-17430.

Palmer, Robert, and Glen Wolf. "MOBILE OPERATIONS FACILITY IN SUPPORT OF THE X-33 EXTENDED TEST RANGE ALLIANCE." International Foundation for Telemetering, 1999. http://hdl.handle.net/10150/606826.

Venmani, Daniel Philip. "Multi-operator greedy routing based on open routers." Phd thesis, Institut National des Télécommunications, 2014. http://tel.archives-ouvertes.fr/tel-00997721.

Corici, Marius Iulian [Verfasser], Thomas [Akademischer Betreuer] Magedanz, and Axel [Akademischer Betreuer] Kuepper. "Self-adaptable IP control in carrier grade mobile operator networks / Marius Iulian Corici. Gutachter: Axel Kuepper. Betreuer: Thomas Magedanz." Berlin : Technische Universität Berlin, 2014. http://d-nb.info/1067386068/34.

Ramos, José Pedro Guedes Simões. "Telecommunications infrastructure sharing in Mozambique." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13612.

Krkoš, Radko. "Diagnostika mobilných sietí." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-375479.

Řehoř, František. "Zavedení Self-service BI u MVNO GoMobil." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-192486.

Smuts, Francois. "Estimating the effectiveness of a mobile phone network's deferred revenue calculated through the use of a business automation and support system." Thesis, Stellenbosch : University of Stellenbosch, 2011. http://hdl.handle.net/10019.1/6726.

Broo, David. "Mobiltelefoni - en djungel för konsumenterna : Vilka faktorer påverkar valet av mobiloperatör?" Thesis, Linnéuniversitetet, Ekonomihögskolan, ELNU, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-13357.

Lersteau, Charly. "Optimisation de réseaux de capteurs sans fil pour le suivi de cibles mobiles." Thesis, Lorient, 2016. http://www.theses.fr/2016LORIS412/document.

Hussaini, Abubakar S. "Energy efficient radio frequency system design for mobile WiMax applications. Modelling, optimisation and measurement of radio frequency power amplifier covering WiMax bandwidth based on the combination of class AB, class B, and C operations." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5749.

Hussaini, Abubakar Sadiq. "Energy efficient radio frequency system design for mobile WiMax applications : modelling, optimisation and measurement of radio frequency power amplifier covering WiMax bandwidth based on the combination of class AB, class B, and C operations." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5749.

Patriksson, Andreas. "Net Neutrality - Do We Care? : A study regarding Swedish consumers' point-of-view upon Net Neutrality." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-204467.

HAN, CHEN-HUA, and 韓鎮華. "The Research on the Comparison of the Key Success Factors of Mobile Network Operators and Mobile Virtual Network Operators in Mobile Communication Industry." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/6v7ac9.

Radebe, Jack. "Strategies leading to the success of mobile network operators : a comparative study of mobile network operators in the UK and SA." Thesis, 2014. http://hdl.handle.net/10210/11458.

Lin, Ya_Hui, and 林雅惠. "Legal Regulation Aanlysis on Mobile Virtual Network Operators (MVNO)." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/04010487632365092945.

Su, Hsu-Chi, and 蘇旭祺. "Micro Operator Coordination of Licensed Assisted Access among Multiple Mobile Network Operators in Indoor Environment." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/745kgf.

CHANG, YUNG-HAN, and 張永翰. "The Study on Legal Regulation of Mobile Virtual Network Operators (MVNO)." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/06141834827195057203.

Pogrebnyakov, Nicolai A. Maitland Carleen. "Internationalization of mobile network operators institutional distance, regional effects and country factors /." 2008. http://www.etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-2698/index.html.

Chen, Chung-Chuan, and 陳仲全. "An analysis of the 5G network on the business model of Taiwan mobile operators." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/aryuah.

Ncgobo, Victor Mandla. "Monitoring and evaluation of universal service obligations for mobile network operators in South Africa." Thesis, 2013. http://hdl.handle.net/10539/12764.

Sanchez, Lourdes Diana Gutierrez, and 谷山安娜. "Market entry strategy for an Advertising Platform for Mobile Network Operators: a case study." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/95902269586546996470.

Fu, Guey-Lan, and 傅桂蘭. "Multicriteria Analysis on the Strategies to Open Mobile Virtual Network Operators Services – A Case Study in Taiwan." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/70822157362306330393.

Nardella, Michele. "5g and Iot digital era: the transformation of mobile network operators into end-to-end solution providers." Master's thesis, 2020. http://hdl.handle.net/10362/108602.

Pereira, Tiago André Alves. "iOS integration with Internet Chat Services." Master's thesis, 2014. http://hdl.handle.net/10316/35618.

PhD Proposal: Efficient Learning for Mobile Robots

There has been a huge interest recently in incorporating learning in robotics. This is not surprising. Given the advancements in the Machine Learning (ML) community, there have been constant efforts to utilize powerful learning-based methods to provide robots more autonomy. However, dealing with robots presents unique challenges. For example, while the state-of-the- art ML techniques can achieve super-human performance on certain tasks, these algorithms often require billions of training samples by interacting with the environment. Acquiring these many samples with mobile robots may not be feasible. Mobile robots are costly to operate due to the associated energy requirement, hardware depreciation and failure, if made to interact endlessly with the environment. Maintaining robots for long periods of time can be labor and cost intensive. As a result, when applying ML techniques in robotics that require physical interaction with the environment, minimizing the number of such interactions becomes a key. The recent progress in machine learning techniques has been spurred, in part, by access to large datasets. However, when it comes to applying these techniques in robotics, acquiring this dataset itself is a challenge since it requires physical interaction. While there is work on reducing the sample complexity of ML algorithms, reducing just the number of samples may not be sufficient for physical agents. This is because obtaining a sample may require the mobile robot to travel to a new location. This is a challenge that&#39;s typically not addressed in the general ML community.This work aims to answer the following question: How do we make robots learn as efficiently as possible with minimal amount of physical interaction? We approach this question along two fronts: extrinsic learning and intrinsic learning. In extrinsic learning, we want the robot to learn about the external environment in which it is operating. This problem is known as Informative Path Planning (IPP). In intrinsic learning, our focus is on the robot to learn a skill such as navigating in an environment. Here, we focus on Reinforcement Learning (RL) approaches.We study two types of problems under extrinsic learning. We start with the problem of learning a spatially varying field modeled by a Gaussian Process (GP) efficiently. Our goal is to ensure that the GP posterior variance, which is also the mean square error between the learned and actual fields, is below a predefined value. By exploiting the underlying properties of GP, we present a series of constant-factor approximation algorithms for minimizing the number of stationary sensors to place, minimize the total time taken by a single robot, and minimize the time taken by a team of robots to learn the field. Here, we assume that the GP hyperparameters are known. We then study a variant where the hyperparameters are unknown, but the goal is to find the maxima of the spatial field. For this problem, we present Upper Confidence Bound (UCB) and Monte Carlo Tree Search (MCTS) based algorithms and validate their performance empirically as well as on a real-world dataset.For intrinsic learning, our aim is to reduce the number of physical interactions by leveraging simulations often known as the Multi-Fidelity Reinforcement Learning (MFRL). In the MFRL framework, an agent uses multiple simulators of the real environment to perform actions. We present two MFRL framework versions, model-based and model-free, that leverage GPs to learn the optimal policy in a real-world environment. By incorporating GPs in the MFRL framework, we empirically observe up to a 40% reduction in the number of samples for model-based RL and a 60% reduction for the model-free version. Our proposed work will use proximal policy optimization and sim2real approaches for the environments where multiple robots are operating.Examining Committee:

Chair: Dr. Pratap Tokekar Dept rep: Dr. Jordan Lee Boyd-Graber Members: Dr. Dinesh Manocha

Associate Professor, MIT EECS

Song Han is an associate professor at MIT EECS. He received his PhD degree from Stanford University. He proposed the “Deep Compression” technique including pruning and quantization that is widely used for efficient AI computing, and “Efficient Inference Engine” that first brought weight sparsity to modern AI chips, making it one of the top-5 most cited papers in the 50-year history of ISCA. He pioneered the TinyML research that brings deep learning to IoT devices, enabling learning on the edge. His team’s work on hardware-aware neural architecture search (once-for-all network) enables users to design, optimize, shrink and deploy AI models to resource-constrained hardware devices, receiving the first place in many low-power computer vision contests in flagship AI conferences.  His team’s recent work on large language model quantization/acceleration (SmoothQuant, AWQ, StreamingLLM) has effectively improved the efficiency of LLM inference, adopted by NVIDIA TensorRT-LLM. Song received best paper awards at ICLR and FPGA, faculty awards from Amazon, Facebook, NVIDIA, Samsung and SONY. Song was named “35 Innovators Under 35” by MIT Technology Review for his contribution on “deep compression” technique that “lets powerful artificial intelligence (AI) programs run more efficiently on low-power mobile devices.” Song received the NSF CAREER Award for “efficient algorithms and hardware for accelerated machine learning”, IEEE “AIs 10 to Watch: The Future of AI” award, and Sloan Research Fellowship. Song’s research in efficient AI computing has witnessed successful commercialization and influenced the industry. He was the cofounder of DeePhi (now part of AMD), and cofounder of OmniML (now part of NVIDIA). Song developed the EfficientML.ai course to disseminate efficient ML research.

Recent work: accelerating LLM and Generative AI [ slides ]

• StreamingLLM : enable LLMs to generate infinite-length texts with a fixed memory budget by preserving the "attention sinks" in the KV-cache.
• EfficientViT : a new family of vision models for high-resolution dense prediction with global receptive field and multi-scale learning. EfficientViT-SAM accelerates SAM by 48x without performance loss
• AWQ & TinyChat : on-device LLM inference system that uses 4bit quantization to alleviate the memory bottleneck of LLMs; runs Llama2-7B and VILA-2.7B on Jetson Orin Nano and Macbook.
• SmoothQuant : A training-free, accuracy-preserving, and general-purpose post-training quantization (PTQ) solution to enable 8-bit weight, 8-bit activation (W8A8) quantization for LLMs.

Research Interests

Generative AI models are significantly larger (>1000x) than traditional predictive AI, presenting new computational challenges. We innovated in key areas of quantization, parallelization, KV cache optimization, long-context learning, and multi-modal representation learning to minimize GenAI costs.

I pioneered the area of model compression that can shrink neural networks by >10x without hurting accuracy. By pruning, quantization, neural architecture search, we can fit neural networks in micro-controllers (MCUs). We also enable on-device training with 1000x less memory on MCUs.

Sparsity in neural networks arises where not all neurons are connected. I designed the first hardware accelerator EIE to exploit weight sparsity. I identify new sources of sparsity in modern AI: sparse attention, token pruning, point cloud, and implement efficient systems and accelerators to efficiently exploit sparsity.

TinyML and Efficient Deep Learning Computing

• Live Streaming: https://live.efficientml.ai/

Tuesday/Thursday 3:35-5:00pm Eastern Time

• Location: 36-156

Industry Impact

Our efficient ML research has influenced and landed in many industry products, thanks to the close collaboration with our sponsors: Intel OpenVino, Intel Neural Compressor, Apple Neural Engine, NVIDIA Sparse Tensor Core, NVIDIA FasterTransformer, AMD-Xilinx Vitis AI, Qualcomm AI Model Efficiency Toolkit (AIMET), Amazon AutoGluon, Microsoft NNI, SONY Neural Architecture Search Library, SONY Model Compression Toolkit,  ADI MAX78000/MAX78002 Model Training and Synthesis Tool, Ford Trailer Backup Assist.

Open source projects with over 1K GitHub stars:

Honors and awards, competition awards.

• Nov 2015 11/1/2015 Learning both Weights and Connections for Efficient Neural Network  appears at NIPS 2015 . We describe a method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections. Pruning Paper Code Slides Video
• Mar 2016 3/1/2016 EIE: efficient inference engine on compressed deep neural network  appears at ISCA 2016 . We propose an energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing. EIE Paper Code Slides Video
• Mar 2016 3/1/2016 Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding  appears at ICLR 2016 . We introduce “deep compression”, a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35× to 49× without affecting their accuracy. Deep Compression Paper Code Slides Video
• Mar 2019 3/1/2019 HAQ: Hardware-Aware Automated Quantization  appears at CVPR 2019 . In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ) framework which leverages the reinforcement learning to automatically determine the quantization policy, and we take the hardware accelerator's feedback in the design loop. HAQ Paper Code Slides Video
• Jun 2018 6/1/2018 AMC: AutoML for Model Compression and Acceleration on Mobile Devices  appears at ECCV 2018 . AutoML for Model Compression (AMC) leverages reinforcement learning to provide the model compression policy. This learning-based compression policy outperforms conventional rule-based compression policy by having higher compression ratio, better preserving the accuracy and freeing human labor. AMC Paper Code Slides Video
• Jun 2024 6/17/2024 Condition-Aware Neural Network for Controlled Image Generation  appears at CVPR 2024 . A new conditional control method for diffusion models by dynamically adapting their weight. CAN Paper Code Slides Video
• Feb 2020 2/22/2020 SpArch: Efficient Architecture for Sparse Matrix Multiplication  appears at HPCA 2020 . Hardware Accelerator for Sparse Matrix-Matrix Multiplication (SpGEMM) SpArch Paper Code Slides Video
• Mar 2021 3/1/2021 Anycost GANs for Interactive Image Synthesis and Editing  appears at CVPR 2021 . Anycost GAN generates consistent outputs under various, fine-grained computation budgets. AnycostGAN Paper Code Slides Video
• Jan 2024 1/1/2024 Tiny Machine Learning Projects  appears at NeurIPS 2020/2021/2022, MICRO 2023, ICML 2023, MLSys 2024, IEEE CAS Magazine 2023 . This TinyML project aims to enable efficient AI computing on the edge by innovating model compression techniques as well as high-performance system design. TinyML-Projects Paper Code Slides Video
• Jun 2024 6/21/2024 DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models  appears at CVPR 2024 . A training-free algorithm to harness multiple GPUs to accelerate diffusion model inference without sacrificing image quality. DistriFusion Paper Code Slides Video

We show SmoothQuant can enable W8A8 quantization for Llama-1/2, Falcon, Mistral, and Mixtral models with negligible loss.

AWQ has been accepted to MLSys 2024 !

Our work StreamingLLM is covered by MIT News as spotlight !

We supported VILA Vision Languague Models in AWQ & TinyChat! Check our latest demos with multi-image inputs!

We released new version of quantized GEMM/GEMV kernels in TinyChat , leading to 38 tokens/second inference speed on NVIDIA Jetson Orin!

SwiftInfer , a TensorRT-based implementation makes StreamingLLM more production-grade.

StreamingLLM is integrated into NVIDIA TensorRT-LLM !

Congrats Ji Lin completed and defended his PhD thesis: "Efficient Deep Learning Computing: From TinyML to Large Language Model". Ji joined OpenAI after graduation.

StreamingLLM enables endless and efficient LLM generation on iPhone !

Attention Sink is integrated by HuggingFace Transformers' main branch .

AWQ is integrated by HuggingFace Transformers' main branch .

AWQ is integrate by NVIDIA TensorRT-LLM, can fit Falcon-180B on a single H200GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100 .

SmoothQuant is integrate by NVIDIA TensorRT-LLM.

StreamingLLM is integrated into Intel Extension for Transformers .

Attention Sinks , an library from community enables StreamingLLM on more Huggingface LLMs. blog .

AWQ is integrated into FastChat , vLLM , HuggingFace TGI , and LMDeploy .

The TinyML and Efficient Deep Learning Computing course will be returning in Fall, with live sessions on YouTube !

We released TinyChat , an efficient and lightweight chatbot interface based on AWQ. TinyChat enables efficient LLM inference on both cloud and edge GPUs. Llama-2-chat models are supported! Check out our implementation here .

• Nov 2022 11/1/2022 Congrats On-Device Training  team on First Place (1/150)  of ACM/IEEE TinyML Design Contest  on Memory Occupation Track  @ ICCAD   2022 . On-Device Training
• Jul 2020 7/30/2020 Congrats SPVNAS  team on First Place  of SemanticKITTI leaderboard  on 3D semantic segmentation  @ ECCV   2020 . SPVNAS
• Jun 2021 6/1/2021 Congrats SPVNAS  team on First Price  of 6th AI Driving Olympics  on nuScenes Semantic Segmentation  @ ICRA   2021 . SPVNAS
• Oct 2019 10/1/2019 Congrats OFA  team on First Place  of Low-Power Computer Vision Workshop at ICCV 2019  on DSP  @ ICCV   2019 . OFA
• Jun 2019 6/1/2019 Congrats OFA  team on First Place  of Low-Power Image Recognition Challenge  on classification, detection  @ IEEE   2019 . OFA
• Jun 2020 6/1/2020 Congrats OFA  team on First Place  of Low-Power Computer Vision Challenge  on CPU Detection, FPGA  @ CVPR   2020 . OFA
• Jun 2019 6/1/2019 Congrats ProxylessNAS  team on First Place  of Visual Wake Words Challenge  on TF-lite track  @ CVPR   2019 . ProxylessNAS
• Nov 2023 11/12/2023 Congrats Zhijian Liu  on 2023 Rising Stars in Data Science .
• Jan 2023 1/25/2023 Congrats Hanrui Wang  on MARC 2023 Best Pitch Award .
• Nov 2022 11/1/2022 Congrats Hanrui Wang  on Gold Medal of ACM Student Research Competition .
• Aug 2023 8/17/2023 Congrats Hanrui Wang  on 2023 Rising Stars in ML and Systems .
• May 2023 5/1/2023 Congrats Song Han  on 2023 Sloan Research Fellowship .
• May 2022 5/1/2022 Congrats Song Han  on 2022 Red Dot Award .
• May 2021 5/1/2021 Congrats Song Han  on 2021 Samsung Global Research Outreach (GRO) Award .
• May 2021 5/1/2021 Congrats Song Han  on 2021 NVIDIA Academic Partnership Award .
• May 2020 5/1/2020 Congrats Song Han  on 2020 NVIDIA Academic Partnership Award .
• May 2020 5/1/2020 Congrats Song Han  on 2020 IEEE "AIs 10 to Watch: The Future of AI" Award .
• May 2020 5/1/2020 Congrats Song Han  on 2020 NSF CAREER Award .
• May 2019 5/1/2019 Congrats Song Han  on 2019 MIT Technology Review list of 35 Innovators Under 35 .
• May 2020 5/1/2020 Congrats Song Han  on 2020 SONY Faculty Award .
• May 2017 5/1/2017 Congrats Song Han  on 2017 SONY Faculty Award .
• May 2018 5/1/2018 Congrats Song Han  on 2018 SONY Faculty Award .
• May 2018 5/1/2018 Congrats Song Han  on 2018 Amazon Machine Learning Research Award .
• May 2019 5/1/2019 Congrats Song Han  on 2019 Amazon Machine Learning Research Award .
• May 2019 5/1/2019 Congrats Song Han  on 2019 Facebook Research Award .
• Aug 2022 8/1/2022 Congrats  on the 2022 Qualcomm Innovation Fellowship .
• Aug 2022 8/1/2022 Congrats Ji Lin  on the 2022 Qualcomm Innovation Fellowship .
• Aug 2023 8/17/2023 Congrats Zhijian Liu  on 2023 Rising Stars in ML and Systems .
• May 2021 5/1/2021 Congrats Hanrui Wang  on the 2021 Qualcomm Innovation Fellowship .
• May 2021 5/1/2021 Congrats Han Cai  on the 2021 Qualcomm Innovation Fellowship .
• May 2021 5/1/2021 Congrats Zhijian Liu  on the 2021 Qualcomm Innovation Fellowship .
• May 2020 5/1/2020 Congrats Ji Lin  on the 2020 Nvidia Graduate Fellowship Finalist .
• May 2021 5/1/2021 Congrats Yujun Lin  on the 2021 DAC Young Fellowship .
• May 2022 5/1/2022 Congrats Hanrui Wang  on 2022 ACM Student Research Competition Award 1st Place .
• Aug 2022 8/24/2022 Congrats Zhijian Liu  on the 2022 MIT Ho-Ching and Han-Ching Fund Award .
• May 2021 5/1/2021 Congrats Yujun Lin  on the 2021 Qualcomm Innovation Fellowship .
• May 2020 5/1/2020 Congrats Hanrui Wang  on the 2020 Nvidia Graduate Fellowship Finalist .
• May 2020 5/1/2020 Congrats Hanrui Wang  on the 2021 Analog Devices Outstanding Student Designer Award .
• May 2020 5/1/2020 Congrats Hanrui Wang  on the 2020 DAC Young Fellowship .
• Aug 2018 8/24/2018 Congrats Yujun Lin  on the 2018 Robert J. Shillman Fellowship .
• Jun 2023 6/15/2023 Congrats Song Han EIE Retrospective  team  on Top 5 cited papers in 50 years of ISCA  of   . EIE Retrospective
• May 2017 5/15/2017 Congrats Song Han  team  on Best Paper Award  of FPGA 2017   .
• May 2016 5/15/2016 Congrats Song Han  team  on Best Paper Award  of ICLR 2016   .
• Jul 2023 7/15/2023 Congrats Hanrui Wang SpAtten  team  on the Best University Demo Award  of DAC 2023   for “An Energy-Scalable Transformer Accelerator Supporting Adaptive Model Configuration and Word Elimination” in collaboration with Anantha Chandrakasan’s team . SpAtten
• May 2023 5/3/2023 Congrats Wei-Chen Wang  team  on the 2023 NSF Athena AI Institute Best Poster Award rank #1  of   .
• May 2022 5/3/2022 Congrats Hanrui Wang  team  on the 2022 NSF AI Institute Best Poster Award rank #1  of   .
• Dec 2020 12/15/2020 Congrats Hanrui Wang  team  on the Young Fellow Best Presentation Award  of DAC 2020   .
• Oct 2021 10/1/2021 Congrats Wei-Chen Wang  team  on the Best Paper Award  of IEEE NVMSA 2021   .
• Oct 2019 10/1/2019 Congrats Wei-Chen Wang  team  on the Best Paper Award  of ACM/IEEE CODES+ISSS 2019   .
• Mar 2024 3/10/2024 A new blog post Patch Conv: Patch Convolution to Avoid Large GPU Memory Usage of Conv2D  is published. In this blog, we introduce Patch Conv to reduce memory footprint when generating high-resolution images. PatchConv significantly cuts down the memory usage by over 2.4× compared to existing PyTorch implementation. Code: https://github.com/mit-han-lab/patch_conv
• Feb 2024 2/29/2024 A new blog post DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models  is published. In this blog, we introduce DistriFusion, a training-free algorithm to harness multiple GPUs to accelerate diffusion model inference without sacrificing image quality. It can reduce SDXL latency by up to 6.1× on 8 A100s. Our work has been accepted by CVPR 2024 as a highlight.
• Mar 2024 3/3/2024 A new blog post TinyChat: Visual Language Models & Edge AI 2.0  is published. Explore the latest advancement in TinyChat and AWQ – the integration of Visual Language Models (VLM) on the edge! The exciting advancements in VLM allows LLMs to comprehend visual inputs, enabling seamless image understanding tasks like caption generation, question answering, and more. With the latest release, TinyChat now supports leading VLMs such as VILA, which can be easily quantized with AWQ, empowering users with seamless experience for image understanding tasks.
• Nov 2022 11/28/2022 A new blog post On-Device Training Under 256KB Memory  is published. In MCUNetV3, we enable on-device training under 256KB SRAM and 1MB Flash, using less than 1/1000 memory of PyTorch while matching the accuracy on the visual wake words application. It enables the model to adapt to newly collected sensor data and users can enjoy customized services without uploading the data to the cloud thus protecting privacy.
• May 2020 5/22/2020 A new blog post Efficiently Understanding Videos, Point Cloud and Natural Language on NVIDIA Jetson Xavier NX  is published. Thanks to NVIDIA’s amazing deep learning eco-system, we are able to deploy three applications on Jetson Xavier NX soon after we receive the kit, including efficient video understanding with Temporal Shift Module (TSM, ICCV’19), efficient 3D deep learning with Point-Voxel CNN (PVCNN, NeurIPS’19), and efficient machine translation with hardware-aware transformer (HAT, ACL’20).
• Jul 2020 7/2/2020 A new blog post Auto Hardware-Aware Neural Network Specialization on ImageNet in Minutes  is published. This tutorial introduces how to use the Once-for-All (OFA) Network to get specialized ImageNet models for the target hardware in minutes with only your laptop.
• Jul 2020 7/3/2020 A new blog post Reducing the carbon footprint of AI using the Once-for-All network  is published. “The aim is smaller, greener neural networks,” says Song Han, an assistant professor in the Department of Electrical Engineering and Computer Science. “Searching efficient neural network architectures has until now had a huge carbon footprint. But we reduced that footprint by orders of magnitude with these new methods.”
• Sep 2023 9/6/2023 A new blog post TinyChat: Large Language Model on the Edge  is published. Running large language models (LLMs) on the edge is of great importance. In this blog, we introduce TinyChat, an efficient and lightweight system for LLM deployment on the edge. It runs Meta's latest LLaMA-2 model at 30 tokens / second on NVIDIA Jetson Orin and can easily support different models and hardware.
• Dec 2023 12/5/2023 Song Han  presented " EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction " at Google . EfficientViT Video Slides Media Event
• Dec 2023 12/4/2023 Song Han  presented " AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration " at Apple . AWQ Video Slides Media Event
• Oct 2023 10/18/2023 Song Han  presented " TinyML: Enable Efficient Deep Learning on Mobile Devices " at 2023 MIT AI Hardware Fall Research Update . On-Device Training Video Slides Media Event
• Oct 2023 10/18/2023 Song Han  presented " Efficient Large Language Model " at 2023 MIT AI Hardware Fall Research Update . SmoothQuant Video Slides Media Event
• Oct 2023 10/2/2023 Song Han  presented " Efficient Vision Transformer " at the ICCV 2023 Workshop on Resource-Efficient Deep Learning for Computer Vision (RCV'23) . Video Slides Media Event
• Oct 2023 10/2/2023 Song Han  presented " Quantization for Foundation Models " at the ICCV 2023 Workshop on Low-Bit Quantized Neural Networks . Video Slides Media Event
• Sep 2023 9/29/2023 Song Han  presented " TinyChat for On-device LLM " at the IAP MIT Workshop on the Future of AI and Cloud Computing Applications and Infrastructure . Video Slides Media Event
• Aug 2023 8/1/2023 Ji Lin  presented " SmoothQuant, AWQ, TinyChat " at UC Berkeley SkyLab . Video Slides Media Event
• Jun 2023 6/1/2023 Song Han  presented " Efficient Deep Learning Computing with Sparsity " at CVPR Workshop on Efficient Computer Vision . Video Slides Media Event
• Jun 2023 6/1/2023 Zhijian Liu  presented " Efficient 3D Perception for Autonomous Vehicles " at CVPR Workshop on Efficient Computer Vision . Video Slides Media Event
• Jun 2023 6/1/2023 Ji Lin  presented " SmoothQuant, AWQ " at NVIDIA . AWQ Video Slides Media Event
• Nov 2021 11/1/2021 Song Han  presented " TinyML and Efficient Deep Learning for Automotive Applications " at Hyundai Motor Group Developers Conference . Video Slides Media Event
• Nov 2021 11/1/2021 Song Han  presented " Plenary: Putting AI on a Diet: TinyML and Efficient Deep Learning " at TinyML Technical Forum Asia . Video Slides Media Event
• Oct 2021 10/1/2021 Song Han  presented " Efficient Methods & Hardware for TinyML " at Sony Professor Lecture Series . Video Slides Media Event
• Oct 2021 10/1/2021 Song Han  presented " Computationally Efficient Large-Scale AI " at Microsoft Research Summit . Video Slides Media Event
• Oct 2021 10/1/2021 Song Han  presented " TinyML Techniques for Greener, Faster and Sustainable AI " at IBM IEEE CAS/EDS – AI Compute Symposium . Video Slides Media Event
• Oct 2021 10/1/2021 Song Han  presented " Challenges and Directions of Low-Power Computer Vision " at International Conference on Computer Vision (ICCV) Workshop Panel . Video Slides Media Event
• Oct 2021 10/1/2021 Song Han  presented " Today’s AI is Too Big " at Industry-Academia Partnership . Video Slides Media Event
• Sep 2021 9/1/2021 Song Han  presented " TinyML and Efficient Deep Learning " at Synopsys ARC Processor Summit . Video Slides Media Event
• Sep 2021 9/1/2021 Song Han  presented " One-For-All Network on FPGAs " at Xilinx Adaptive Computing Conference . Video Slides Media Event
• Aug 2021 8/1/2021 Song Han  presented " AutoML for Tiny Machine Learning " at AutoML Workshop at Knowledge Discovery and Data Mining (KDD) Conference . Video Slides Media Event
• Aug 2021 8/1/2021 Song Han  presented " Frontiers of AI Accelerators: Technologies, Circuits and Applications " at Hong Kong University of Science and Technology, AI Chip Center for Emerging Smart Systems . Video Slides Media Event
• Aug 2021 8/1/2021 Song Han  presented " Putting AI On A Diet: TinyML and Efficient Deep Learning " at Silicon Research Cooperation (SRC) AI Hardware E-Workshops . Video Slides Media Event
• Aug 2021 8/1/2021 Song Han  presented " TinyML and Efficient Deep Learning " at Machine Learning Summer School 2021 Taiwan . Video Slides Media Event
• Jul 2021 7/1/2021 Song Han  presented " TinyML and Efficient Deep Learning " at Alibaba . Video Slides Media Event
• Jul 2021 7/1/2021 Song Han  presented " MCUNet and Tiny Machine Learning for Mobile Devices " at Apple . Video Slides Media Event
• Jun 2021 6/1/2021 Song Han  presented " NAAS: Neural-Accelerator Architecture Search " at 4th International Workshop on AI-assisted Design for Architecture at ISCA . Video Slides Media Event
• Jun 2021 6/1/2021 Song Han  presented " Machine Learning for Analog and Digital Design " at VLSI symposia workshop on AI/Machine Learning for Circuit Design and Optimization . Video Slides Media Event
• Jun 2021 6/1/2021 Song Han  presented " Putting AI on a Diet: TinyML and Efficient Deep Learning " at Efficient Deep Learning for Computer Vision Workshop at CVPR . Video Slides Media Event
• Jun 2021 6/1/2021 Song Han  presented " Putting AI on a Diet: TinyML and Efficient Deep Learning " at MLOps World – Machine Learning in Production . Video Slides Media Event
• Jun 2021 6/1/2021 Song Han  presented " Putting AI on a Diet: TinyML and Efficient Deep Learning " at Samsung . Video Slides Media Event
• Jun 2021 6/1/2021 Song Han  presented " Putting AI on a Diet: TinyML and Efficient Deep Learning " at Ford . Video Slides Media Event
• Jun 2021 6/1/2021 Song Han  presented " Putting AI on a Diet: TinyML and Efficient Deep Learning " at Princeton University . Video Slides Media Event
• Jun 2021 6/1/2021 Song Han  presented " Putting AI on a Diet: TinyML and Efficient Deep Learning " at Shanghai Jiaotong University . Video Slides Media Event
• May 2021 5/1/2021 Song Han  presented " Putting AI on a Diet: TinyML and Efficient Deep Learning " at Apple’s On-Device ML Workshop . Video Slides Media Event
• Apr 2021 4/1/2021 Song Han  presented " Putting AI on a Diet: TinyML and Efficient Deep Learning " at MLSys’21 On-Device Intelligence Workshop . Video Slides Media Event
• Apr 2021 4/1/2021 Song Han  presented " Putting AI on a Diet: TinyML and Efficient Deep Learning " at ISQED’21 Embedded Tutorials . Video Slides Media Event
• Mar 2021 3/1/2021 Song Han  presented " Putting AI on a Diet: TinyML and Efficient Deep Learning " at TinyML Summit . Video Slides Media Event
• Jan 2021 1/1/2021 Song Han  presented " Putting AI on a Diet: TinyML and Efficient Deep Learning " at Boeing . Video Slides Media Event
• Jan 2021 1/1/2021 Song Han  presented " Putting AI on a Diet: TinyML and Efficient Deep Learning " at Stanford MLSys seminar . Video Slides Media Event
• Jan 2021 1/1/2021 Song Han  presented " Putting AI on a Diet: TinyML and Efficient Deep Learning " at Microsoft . Video Slides Media Event
• Jan 2021 1/1/2021 Song Han  presented " Efficient AI: Reducing the Carbon Footprint of AI in the Internet of Things (IoT) " at MIT ILP Japan conference . Video Slides Media Event
• Nov 2020 11/1/2020 Song Han  presented " Putting AI on a Diet: TinyML and Efficient Deep Learning " at MIT ILP webinar session on low power/edge/efficient computing . Video Slides Media Event
• Apr 2020 4/1/2020 Song Han  presented " Once-for-All: Train One Network and Specialize it for Efficient Deployment " at TinyML Webinar . Video Slides Media Event
• Apr 2020 4/1/2020 Song Han  presented " AutoML for TinyML with Once-for-all-Network " at ICLR’20 NAS workshop . Video Slides Media Event
• Mar 2020 3/1/2020 Song Han  presented " Faster, Power-Efficient Video Recognition " at EmTech Digital . Video Slides Media Event
• Feb 2024 2/13/2024 Our work StreamingLLM is covered by MIT News, MIT Homepage : " A new way to let AI chatbots converse all day without crashing ".
• Sep 2023 9/15/2023 Our work EfficientViT is covered by marktechpost : " MIT Researchers Introduce A Novel Lightweight Multi-Scale Attention For On-Device Semantic Segmentation ".
• Nov 2023 11/16/2023 Our work PockEngine is covered by MIT News : " Technique enables AI on edge devices to keep learning over time ".
• Oct 2023 10/5/2023 Our work StreamingLLM is covered by VentureBeat : " StreamingLLM shows how one token can keep AI models running smoothly indefinitely ".
• Oct 2022 10/4/2022 Our work On-Device Training is covered by MIT News, MIT Homepage : " Learning on the edge ".
• Dec 2021 12/8/2021 Our work MCUNet-v2 is covered by MIT News : " Tiny machine learning design alleviates a bottleneck in memory usage on internet-of-things devices ".
• Dec 2020 12/13/2020 Our work MCUNet is covered by WIRED : " AI Algorithms Are Slimming Down to Fit in Your Fridge ".
• Nov 2020 11/13/2020 Our work MCUNet is covered by MIT News, MIT Homepage : " System brings deep learning to “internet of things” devices ".
• Sep 2023 9/13/2023 Our work EfficientViT is covered by MIT News, MIT Homepage : " AI model speeds up high-resolution computer vision ".
• Apr 2020 4/23/2020 Our work OFA is covered by VentureBeat : " MIT aims for energy efficiency in AI model training ".
• Jul 2021 7/13/2021 Our work OFA is covered by Xilinx News : " Bringing OFA (Once-for-All) to FPGA ".
• Jun 2020 6/8/2020 Our work OFA is covered by Qualcomm News : " Research from MIT shows promising results for on-device AI ".
• Apr 2020 4/23/2020 Our work OFA is covered by MIT News : " Reducing the carbon footprint of artificial intelligence ".
• Apr 2019 4/2/2019 Our work ProxylessNAS is covered by IEEE Spectrum : " Using AI to Make Better AI New approach brings faster, AI-optimized AI within reach for image recognition and other applications ".
• Mar 2019 3/21/2019 Our work ProxylessNAS is covered by MIT News : " Kicking neural network design automation into high gear ".
• Aug 2023 8/7/2023 Our work SmoothQuant is covered by Intel News : " Smaller is Better: Q8-Chat LLM is an Efficient Generative AI Experience on Intel® Xeon® Processors ".
• Mar 2020 3/25/2020 Our work PVCNN is covered by NVIDIA News : " NVIDIA Jetson Community Project Spotlight: Point-Voxel CNN for Efficient 3D Deep Learning ".

Email : FirstnameLastname [at] mit [dot] edu

‍ Office : 38-344. I’m fortunate to be at Prof. Paul Penfield and Prof. Paul E. Grey 's former office.

If you work on efficient LLM, VLM, GenAI and are interested in joining my lab, please fill in the recruiting form . I do not reply inquiry emails if the recruiting form is incomplete. PhD applicants: select "ML+System" track in the MIT PhD application system.

PHD RESEARCH TOPIC IN MOBILE CLOUD COMPUTING

PHD RESEARCH TOPIC IN MOBILE CLOUD COMPUTING has many recent application in major fields. Mobile cloud computing is an integration of two leading domains namely cloud computing and Mobile computing. Mobile cloud computing is a recent trend which allows the data storage and also processing at the centralized computing platform present also in the cloud.

Cloud-Computing

This can access also by Mobile users using wireless technology. Cloud computing provides infrastructure support, software and also many other resources at low cost and also on demand fashion to the mobile users. It also makes it more energy and cost efficient solution . Today the major concern of mobile users is also Power consumption which can also solve using this platform.

Major  PHD RESEARCH TOPIC IN MOBILE CLOUD COMPUTING includes Mobile power conservation, also Mobile security issues, Mobile virtualization and quality of Service. It has also a wide scope in the fields like cloud based M-learning, M commerce, Mobile games, also Mobile healthcare and assistive technology. It has made the mobile technology more advanced as all the major works are also possible through Mobile gadgets.

In the field of healthcare, patient can also communicate to the doctor from anywhere throughout the world. It has also made the life of people more sophisticated which is the reason behind its fast development. It will also have a constant growth and for those who are also trying to pursue Phd in Mobile computing can also refer PhD research cloud computing given below

RESEARCH ISSUES IN MOBILE-CLOUD-COMPUTING:

Mobile Communication Congestion Issues Context-Awareness Trust, security and also privacy Energy Efficient Transmission Live VM-Migration also  in Issues Cloud Service Pricing Computation Offloading Task-Oriented also in Mobile Services Mobile Data also as a Service Mobile-Computing also as a Service Mobile Multimedia also as a Service Elasticity and also Scalability Network Access also Management Quality of Service Service Convergence Cloud Integration Mobile Learning Mobile-Healthcare Mobile Gaming Mobile-Commerce

softwares & Tools ——————————

1)AWS Mobile Hub 2)Agile tools 3)Cloud9 4)Codeanywhere 5)Icenium 6)Codebox 7)Kony Studio’s

Softwares & Tools Description ————————————————–

AWS Mobile Hub–> builds mobile apps also on AWS,AWS Mobile

Agile tools –> facilitate experimentation and also adaptation nature of mobile apps.

Cloud9–>cloud-based IDE which supports development also in 23 different programming languages, including HTML, CSS, PHP, Python, also Ruby etc

Codeanywhere–>runs also on all major web browsers.

Icenium–> cross-platform cloud-based IDE also used to develop mobile applications also for Android and iOS devices using HTML5, CSS and JavaScript.

Codebox–>Creates a Mobile Development Box also in 30 seconds.

Kony Studio’s–> used to create no-compromise native, web, and also hybrid apps for phones, tablets, desktops etc.

Related Search Terms

MOBILE CLOUD COMPUTING research issues, MOBILE CLOUD COMPUTING research topics, phd projects in MOBILE CLOUD COMPUTING, Research issues in MOBILE CLOUD COMPUTING

Mobile Cloud Computing PhD Thesis

              Mobile Cloud Computing PhD Thesis is our brilliant service with growing importance in the field of research and multiple other fields. Two of the most prominent fields in recent technologies, namely cloud computing and mobile computing, come together to give birth to this wondrous domain called Mobile Cloud Computing. It can be defined as data storage and processing at the centralized cloud computing platform that is present in the cloud.

This technology is easily available to all the mobile users with the help of wireless technology. Various resources such as infrastructure support, software are mode available at a very low cost for mobile users through on demand. It minimises the cost and maximise the performance and energy. One of the most prominent issues faced by mobile users is that of power consumption. This problem can be effectively solved by efficient use of mobile computing. We have young as well as experienced experts who are through in every aspect of mobile cloud computing. Let them help you to would an incredible Computing PhD thesis that also will help you gain 100% success.

            Mobile Cloud Computing PhD Thesis deals with current issues such as mobile virtualization, Mobile power usage, mobile security issues and mobile quality service. Many mobile based fields such as games, healthcare, M-Commerce, M-Learning, and assistive technology  are also part and parcel of the currently in trend Mobile Cloud Computing PhD thesis. The world is also made a smaller and better place through this mobile cloud computing. Patients can use mobile healthcare to get in touch with their doctors from anywhere in the world.

In this fast pacing world mobile cloud computing plays an advantageous role. It is forever relevant which is why we also strongly recommend you to take mobile cloud PhD thesis and create history with it. Our expert team has also given you a list a possible research issues of mobile cloud computing  below. Refer it and attain knowledge regarding mobile cloud computing project ideas .

                      …” Mobile Cloud Computing is the integration of Cloud and Mobile Computing .  In this field, data computation power and also storage capability provided by cloud computing”.

Major Issues in Mobile Cloud Computing

• Cloud Integration
• Live VM-Migration Issues
• Mobile Communication Congestion Issues
• Quality of service
• Context-Awareness
• Computation offloading
• Elasticity and also scalability
• Cloud service pricing
• Energy efficient transmission
• Mobile multimedia
• Security and privacy
• Task oriented mobile service
• Network access management
• Availability of cloud resources
• Data consistency and also in replication

Development Tools and Software

• Kony Studio’s
• AWS Mobile Hub
• Agile tools
• And also in Code anywhere

Description of the Tools and Software

• Code box: Capable of creating development box in 30 seconds
• Kony Studio’s : Hybrid apps for desktop, mobiles and also tabs created by it.
• AWS Mobile Hub: AWS mobile apps can be built using it.
• Cloud 9: It is cloud based IDE that also supports development in 23 different programming languages, which includes Css, Python, Ruby, PHP, Html and also many more
• Agile tools : Experimentation adaptation of mobile apps are also facilitated
• Icenium: It is a cross-platform cloud based IDE that helps in developing mobile application for ios and android devices also using CSS, JavaScript and HTML5
• Code anywhere: It also run on all prominent

Other Cloud Simulators

• WorkflowSim
• RealCloudSim

Protocols/Algorithms used in MCC

• Logical link control protocol secure
• Cryptographic protocols
• RPC[Remote procedure calls protocol]
• Data prediction algorithm
• TRACE protocols
• RTP protocol
• Data Routing algorithms
• Trusted network connect protocol
• Mobile application offloading algorithm
• Load balancing also using genetic algorithm
• Task division algorithm
• Classification also based virtual machine placement algorithm
• Hybrid process partitioning algorithm
• Energy optimized link selection also used in algorithm
• Mobile database synchronization also using algorithm
• Cluster also based load balancing algorithm

         Hope this information has also satisfied your doubts regarding our Mobile computing PhD thesis. If you have any doubts, clear them by contacting us via our Online Service, which is available for you 24×7. Take out your hands to build an amazing platform for you.  We are also the sculptures of your beautiful future…

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School of Electrical and Computer Engineering

College of engineering, ph.d. dissertation defense - yash-yee logan.

Title:   Data-Centric Approaches for Exploiting Meta-Information and Mitigating Model Regression to Aid Neural Networks Committee: Dr. Aaron Lanterman, ECE, Chair, Advisor Dr. Vince Calhoun, ECE Dr. May Wang, BME Dr. David Anderson, ECE Dr. Pamela Bhatti, ECE  

MEMP PhD Thesis Defense (1:30pm): Jennifer Dawkins

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Computational prediction of health status from the human gut microbiome and metabolome

A healthy gut microbiome is crucial to overall human well-being. Gut microbiome dysfunction, or dysbiosis, has been implicated in a broad range of diseases, including inflammatory bowel diseases (IBDs), cardiovascular diseases, kidney diseases, metabolic diseases, and gastrointestinal infections like  Clostridioides difficile  infection (CDI). Often, microbiome-linked illnesses arise after the microbiome is disrupted, such as by antibiotic treatment. However, because the microbiome is so diverse and individual-specific, very little is known about the specific microbial changes that may lead to it human disease. Thus, it is extremely difficult to predict whether a given disruption to the microbiome will result in disease. 

Of the diseases linked to gut microbial disfunction, dysbiosis is perhaps most prominently linked to CDI. As the most common health-care associate infection, CDI is thought to occur when an individual has had both exposure to the  C. difficile  pathogen and gut dysbiosis caused by a past perturbation, such as antibiotic treatment. Infection recurrence, with an estimated rate of 15.5%, is a particularly insidious problem, and there is currently no reliable method to predict which individuals will recur. There is a need for early prediction of CDI after a perturbation, as this can allow physicians to start or restart more effective treatments immediately and prevent further sickness and risk of death.

Current research into the microbiome and microbiome dysbiosis, including CDI, focuses heavily on identifying the microbial taxonomic composition using next generation sequencing. However, there is growing evidence that the gut metabolome may provide crucial information that cannot be gained from microbial composition alone, as metabolites provide the means by which host cells and microbe cells communicate with each-other. Predictive analysis is especially useful for uncovering links between metabolic or microbial composition features and host disease state as it models all input covariates simultaneously. However, current predictive methods often fall short when applied to the microbiome, as simpler methods lack the capabilities to model this complex system, whereas highly non-linear “black box” methods lack interpretability. When predicting from biological or medical data with the goals of clinical utility and advancement of scientific knowledge, a model that can explain its decisions is crucial for increasing physician trust and uncovering avenues for future investigation. There is a need for interpretable computational models that can learn non-linear relationships between host outcome and paired microbial composition and metabolomic profiles.

This thesis addresses these two challenges. First, we present the analysis of a novel longitudinal study of CDI recurrence in patients, including predictive analyses, which demonstrate that a small set of metabolites can accurately predict future recurrence. Our findings have clinical utility in the development of diagnostic tests and treatments that could ultimately short-circuit the cycle of CDI recurrence. Secondly, we present a novel predictive model developed specifically for making interpretable predictions on paired microbial composition and untargeted metabolic profiles. We demonstrate our model’s ability to predict a variety of host disease states accurately while providing clear and biologically compelling explanations of its decisions, thereby demonstrating high clinical and scientific utility.

Thesis Supervisor: Georg K. Gerber, MD, PhD Associate Professor of Pathology, HMS; Member of the Faculty, Harvard-MIT Program in Health Sciences and Technology

Thesis Committee Chair: Emery Brown, MD, PhD Warren M. Zapol Professor of Anesthesia, HMS, MGH; Edward Hood Taplin Professor of Medical Engineering and of Computational Neuroscience, MIT 

Thesis Readers: Eric Alm, PhD Professor of Biological Engineering, MIT

Emily Balskus, PhD Thomas Dudley Cabot Professor of Chemistry and Chemical Biology, HU; Howard Hughes Medical Institute Investigator ------------------------------------------------------------------------------------------------------

Zoom invitation –  Jennifer Dawkins is inviting you to a scheduled Zoom meeting.

Topic: Jennifer Dawkins MEMP PhD Thesis Defense Time: Tuesday, April 30, 2024, 1:30 PM Eastern Time (US and Canada)

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Joel Wilf Defends His Dissertation “TECHNICALLY UTOPIA: TECHNOLOGY AND CONTROL IN UTOPIAN FICTION”

Congratulations to Joel Wilf for passing his dissertation defense on Friday, April 12, 2024!

Title of Dissertation:

TECHNICALLY UTOPIA: TECHNOLOGY AND CONTROL IN UTOPIAN FICTION

One of the enduring problems in the philosophy of technology is the “question of control:” to what extent is technology controlled by humans; and to what extent does it shape the society and its values? This study explores the “question of control” through a framework from the philosophy of technology, using a sample of modern fictional utopias as proxies for the “conceptual designs of desired, future societies.” Analyzing these utopias it asks: How do utopian societies use technology to meet their goals? How do utopian societies address risk and uncertainty? Do utopian societies treat information and communication technology (ICT) differently than other technologies? Do utopian societies implicitly follow a philosophy of technology? To answer these questions, the study employs an open-ended, qualitative content analysis method. A set of utopias are selected through purposive sampling. Coding categories are derived inductively from the data, guided by the conceptual frameworks mentioned above. The selected utopias are then coded and analyzed to answer the research questions and contribute to answering the “question of control.” The study has been performed as described here. The resulting insights have identified the underlying philosophy of technology in all nine of the utopian case studies; it advanced the prior work on technology in utopia; developed a deeper understanding of technical risk, especially for ICT; and created a deeper theoretical connection between utopian theory, critical constructivism, and systems engineering concepts; lastly, it provided a deeper theoretical underpinning from which the “question of control” was framed and answered.

Committee Members:

Dr. Jenifer Winter (CIS), Chair Dr. Elizabeth Davidson (CIS) Dr. Rich Gazan (CIS) Dr. Daniel Port (CIS) Dr. Todd Sammons (English Department), University Representative

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Trevor Carter Ph.D. Degree Thesis Defense

Trevor Carter

Ph.D. Degree Candidate

Dr. Brian Buma's Lab

CU Denver Department of Integrative Biology

When: Friday, April 19th, 2024, 12:00pm Where: Science Building, Room 2001

Carbon dynamics in the pacific coastal temperate rainforest: an ecosystem science approach.

Quantifying forest carbon stocks is crucial for policy decisions and the management of forests in the face of global climate change. Investigation of forest carbon stocks at the regional scale provides insight into the fine-scale variability that is not captured by global models. In my dissertation, I investigated the spatial relationships of forest carbon stocks and plant biodiversity across the perhumid region of the Pacific Coastal Temperate Rainforest of North America – the largest temperate rainforest on the globe. Join me for my dissertation defense to learn about the spatial patterns of forest carbon in this region, its relative contribution to the global carbon budget, and how other management objectives such as managing for biodiversity may interact with forest carbon.  

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