Exploring group interactions in synchronous mobile computer-supported learning activities

Dlab, Martina Holenko; Botički, Ivica; Hoić-Božić, Nataša; Looi, Chee-Kit

doi:10.1016/j.compedu.2019.103735

“…As previous studies indicated, there are two major challenges that automatic grouping methods need to address, namely the uneven group size problem (Holenko Dlab et al, 2020 ), and the data inaccessibility (“Cold Start”) problem (Lika et al, 2014 ; Pliakos et al, 2019 ). In CSCL, instructors usually configure a group with the size of 3 or 4 members, as prior research results have showed that a large group size would weaken the group performance (Gibbs et al, 2001 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…A widely used approach is using automatic grouping methods to generate groups with heterogeneous attributes within a group, that is to maximize the diversity of students’ characteristics (Lambić et al, 2018 ; Lin et al, 2016 ; Moreno et al, 2012 ). But there are two major challenges the automatic grouping methods need to address, as previous studies indicated, namely the barriers of uneven student numbers within groups (i.e., the uneven group size problem) (Ahmad et al, 2021 ; Holenko Dlab et al, 2020 ), and the inaccessibility of student characteristics at the starting point (i.e., the “Cold Start” problem) (Lika et al, 2014 ; Pliakos et al, 2019 ). To address those two challenges, this research proposed an optimized, genetic algorithm-based grouping method that includes a conceptual model named Feature Categorization Model (FCM) to cope with the “Cold Start” problem and a GA-enabled module named Insert Virtual Members (IVM GA ) to address the group size problem.…”

Section: Introductionmentioning

confidence: 99%

Examining the effect of a genetic algorithm-enabled grouping method on collaborative performances, processes, and perceptions

Li

¹

,

Ouyang

²

,

Chen

³

2022

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Group formation is a critical factor which influences collaborative processes and performances in computer-supported collaborative learning (CSCL). Automatic grouping has been widely used to generate groups with heterogeneous attributes and to maximize the diversity of students’ characteristics within a group. But there are two dominant challenges that automatic grouping methods need to address, namely the barriers of uneven group size problem, and the inaccessibility of student characteristics. This research proposes an optimized, genetic algorithm-based grouping method that includes a conceptual model and an algorithm module to address these challenges. Through a quasi-experiment research, we compare collaborative groups’ performance, processes, and perceptions in China’s higher education. The results indicate that the experimental groups outperform the traditional grouping methods (i.e., random groups and student-formed groups) in terms of final performances, collaborative processes, and student perceptions. Based on the results, we propose implications for implementation of automatic grouping methods, and the use of collaborative analytics methods in CSCL.

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“…3 In SOL, students and instructors can login remotely from any location in the world and concurrently participate in the learning process. 4,5 The advancement of online learning technologies, such as audio, video, and text, has allowed instant feedback and real-time interaction between students, instructors and fellow students. [6][7][8][9][10][11] These features of SOL that resemble physical learning are well accepted by students.…”

Section: Synchronous Online Learning (Sol)mentioning

confidence: 99%

Synchronous online learning during movement control order in higher education institutions: a systematic review

Lee

¹

,

Dorasamy

²

,

Ahmad

³

et al. 2021

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Background: Higher education institutions (HEI) are not spared from the coronavirus disease 2019 (COVID-19) pandemic. The closure of campuses because of the movement control order (MCO) to mitigate the spread of the COVID-19 has forced HEIs to adopt online learning, especially synchronous online learning (SOL). Although teaching and learning can be continued via SOL, retaining students’ interest and sustaining their engagement have not been sufficiently explored. This study presents a systematic review of the research pertaining to SOL associated with students’ interest and engagement in HEIs during the MCO environment. Methods: Five major online databases, i.e., EBSCOhost, Science Direct, Emerald, Scopus and Springer were searched to collect relevant papers published between 1st January 2010 to 15th June 2021 including conference proceedings, peer-reviewed papers and dissertations. Papers written in the English language, based in full-fledged universities, and with these five keywords: (i) synchronous online learning, (ii) engagement, (iii) interest, (iv) MCO/Covid-19 and (v) HEI, were included. Papers focussing on synchronous and asynchronous online learning in schools and colleges were excluded. Each paper was reviewed by two reviewers in order to confirm the eligibility based on the inclusion and exclusion criteria. Results: We found 31 papers of which six papers were related to SOL, engagement and interest in HEIs in the MCO environment. Our review presents three major findings: (i) limited research has been conducted on SOL associated with students’ engagement and interest, (ii) studies related to the context of HEIs in the MCO environment are limited, and (iii) the understanding of the new phenomena through qualitative research is insufficient. We highlight the SOL alignment with students’ engagement, interest, style preference, learner interaction effectiveness, behavior and academic performance. Conclusions: We believe that the findings of this study are timely and require attention from the research community.

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“…3 In SOL, students and instructors can login remotely from any location in the world and concurrently participate in the learning process. 4,5 The advancement of online learning technologies, such as audio, video, and text, has allowed instant feedback and real-time interaction between students, instructors and fellow students. [6][7][8][9][10][11] These features of SOL that resemble physical learning are well accepted by students.…”

Section: Synchronous Online Learning (Sol)mentioning

confidence: 99%

Synchronous online learning during movement control order in higher education institutions: a systematic review

Lee

¹

,

Dorasamy

²

,

Ahmad

³

et al. 2021

F1000Res

1

0

View full text Add to dashboard Cite

Background: Higher education institutions (HEI) are not spared from the coronavirus disease 2019 (COVID-19) pandemic. The closure of campuses because of the movement control order (MCO) to mitigate the spread of the COVID-19 has forced HEIs to adopt online learning, especially synchronous online learning (SOL). Although teaching and learning can be continued via SOL, retaining students’ interest and sustaining their engagement have not been sufficiently explored. This study presents a systematic review of the research pertaining to SOL associated with students’ interest and engagement in HEIs during the MCO environment. Methods: Five major online databases, i.e., EBSCOhost, Science Direct, Emerald, Scopus and Springer were searched to collect relevant papers published between 1st January 2010 to 15th June 2021 including conference proceedings, peer-reviewed papers and dissertations. Papers written in the English language, based in full-fledged universities, and with these five keywords: (i) synchronous online learning, (ii) engagement, (iii) interest, (iv) MCO/Covid-19 and (v) HEI, were included. Papers focussing on synchronous and asynchronous online learning in schools and colleges were excluded. Each paper was reviewed by two reviewers in order to confirm the eligibility based on the inclusion and exclusion criteria. Results: We found 31 papers of which six papers were related to SOL, engagement and interest in HEIs in the MCO environment. Our review presents three major findings: (i) limited research has been conducted on SOL associated with students’ engagement and interest, (ii) studies related to the context of HEIs in the MCO environment are limited, and (iii) the understanding of the new phenomena through qualitative research is insufficient. We highlight the SOL alignment with students’ engagement, interest, style preference, learner interaction effectiveness, behavior and academic performance. Conclusions: We believe that the findings of this study are timely and require attention from the research community.

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Exploring group interactions in synchronous mobile computer-supported learning activities

Cited by 24 publications

References 38 publications

Examining the effect of a genetic algorithm-enabled grouping method on collaborative performances, processes, and perceptions

Examining the effect of a genetic algorithm-enabled grouping method on collaborative performances, processes, and perceptions

Synchronous online learning during movement control order in higher education institutions: a systematic review

Synchronous online learning during movement control order in higher education institutions: a systematic review

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