Learning and Social Networks - Similarities, Differences and Impact

Saqr, Mohammed; Montero, Calkin Suero

doi:10.1109/icalt49669.2020.00047

Cited by 5 publications

(41 citation statements)

References 16 publications

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“…The results of the meta-analysis further confirmed these findings and showed that diffusion centralities had higher combined correlation coefficients, narrower confidence intervals, and higher predictive intervals, thus offering solid proof of the robustness and consistency of diffusion centrality measures as indicators of success as well as reflective of the relevance of diffusion-based centrality measures to collaborative context. This can be explained by the fact diffusion centralities capture the role of students in generating reply-worthy promising ideas that are more likely to be taken up as well as they generate longer threads and engagement of others (Chen & Zhang, 2016;Lee & Tan, 2017a;Poquet et al, 2020;Saqr & Montero, 2020;Suthers, 2015;Suthers & Desiato, 2012). Students who can generate such contributions are more likely to be more engaged, more participatory or high achievers (Anderson et al, 2001;de-Marcos et al, 2016;Reychav et al, 2018;Saqr & Montero, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…This can be explained by the fact diffusion centralities capture the role of students in generating reply-worthy promising ideas that are more likely to be taken up as well as they generate longer threads and engagement of others (Chen & Zhang, 2016;Lee & Tan, 2017a;Poquet et al, 2020;Saqr & Montero, 2020;Suthers, 2015;Suthers & Desiato, 2012). Students who can generate such contributions are more likely to be more engaged, more participatory or high achievers (Anderson et al, 2001;de-Marcos et al, 2016;Reychav et al, 2018;Saqr & Montero, 2020). Diffusion-based centralities capture extra information (length of threads, the range of involvement of others and possibility of uptake), as such they are more likely to accurately reflect a realistic view of students' efforts in the discourse.…”

Section: Discussionmentioning

confidence: 99%

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Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study

Saqr

López-Pernas

2021

Intern. J. Comput.-Support. Collab. Learn

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This study empirically investigates diffusion-based centralities as depictions of student role-based behavior in information exchange, uptake and argumentation, and as consistent indicators of student success in computer-supported collaborative learning. The analysis is based on a large dataset of 69 courses (n = 3,277 students) with 97,173 total interactions (of which 8,818 were manually coded). We examined the relationship between students’ diffusion-based centralities and a coded representation of their interactions in order to investigate the extent to which diffusion-based centralities are able to adequately capture information exchange and uptake processes. We performed a meta-analysis to pool the correlation coefficients between centralities and measures of academic achievement across all courses while considering the sample size of each course. Lastly, from a cluster analysis using students’ diffusion-based centralities aimed at discovering student role-taking within interactions, we investigated the validity of the discovered roles using the coded data. There was a statistically significant positive correlation that ranged from moderate to strong between diffusion-based centralities and the frequency of information sharing and argumentation utterances, confirming that diffusion-based centralities capture important aspects of information exchange and uptake. The results of the meta-analysis showed that diffusion-based centralities had the highest and most consistent combined correlation coefficients with academic achievement as well as the highest predictive intervals, thus demonstrating their advantage over traditional centrality measures. Characterizations of student roles based on diffusion centralities were validated using qualitative methods and were found to meaningfully relate to academic performance. Diffusion-based centralities are feasible to calculate, implement and interpret, while offering a viable solution that can be deployed at any scale to monitor students’ productive discussions and academic success.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study

Saqr

López-Pernas

2021

Intern. J. Comput.-Support. Collab. Learn

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“…Network analyses in this review indicated that collaboration in online courses and clinical clerkship occurred primarily among students and less frequently between students and teachers. 42,[44][45][46][47][48][50][51][52][54][55][56][57] SNA also demonstrated which students had the most connections (in the structured courses) and which students were more isolated. [44][45][46][47][48][50][51][52]56 A subset of studies found that social networks established in structured courses and clinical clerkship changed over time.…”

Section: Use Of Sna and Key Findingsmentioning

confidence: 87%

“…42,[44][45][46][47][48][50][51][52][54][55][56][57] SNA also demonstrated which students had the most connections (in the structured courses) and which students were more isolated. [44][45][46][47][48][50][51][52]56 A subset of studies found that social networks established in structured courses and clinical clerkship changed over time. 30,31,37,45,46,48,51 In 4 studies, the role of the instructor became less central, the connections among students increased, and the number of isolated students decreased.…”

Section: Use Of Sna and Key Findingsmentioning

confidence: 87%

“…Of those 25, 23 presented whole network graphs (i.e., illustrations of the network structure). 3,30,32,[34][35][36][37][38][39]41,[43][44][45][46][47][50][51][52][54][55][56][57][58] Two studies presented individual ego network maps. 31,42 To illustrate the value of SNA visualization, we include a whole network map 45 and an ego network map 31 in Figure 2.…”

Section: Study Focusmentioning

confidence: 99%

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Social Network Analysis in Undergraduate and Postgraduate Medical Education: A Scoping Review

Steinert,

Fontes,

Mortaz-Hejri

et al. 2023

Acad Med

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Purpose Social network analysis (SNA) is a theoretical framework and analytical approach used to study relationships among individuals and groups. While SNA has been employed by many disciplines to understand social structures and dynamics of interpersonal relationships, little is known about its use in medical education. Mapping and synthesizing the scope of SNA in undergraduate and postgraduate medical education can inform educational practice and research. Method This scoping review was based on searches conducted in Medline, Embase, Scopus, and ERIC in December 2020 and updated in March 2022. After removal of duplicates, the search strategy yielded 5,284 records, of which 153 met initial inclusion criteria. Team members conducted full-text reviews, extracted relevant data, and conducted descriptive and thematic analyses to determine how SNA has been used as a theoretical and analytical approach in undergraduate and postgraduate medical education. Results Thirty studies, from 11 countries, were retained. Most studies focused on undergraduate medical students, primarily in online environments, and explored students’ friendships, information sharing, and advice seeking through SNA. Few studies included residents and attending staff. Findings suggested that SNA can be a helpful tool for monitoring students’ interactions in online courses and clinical clerkships. SNA can also be used to examine the impact of social networks on achievement, the influence of social support and informal learning outside the classroom, and the role of homophily in learning. In clinical settings, SNA can help explore team dynamics and knowledge exchange among medical trainees. Conclusions While SNA has been underutilized in undergraduate and postgraduate medical education, findings indicate that SNA can help uncover the structure and impact of social networks in the classroom and the clinical setting. SNA can also be used to help design educational experiences, monitor learning, and evaluate pedagogical interventions. Future directions for SNA research in medical education are described.

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Empowering Health Care Education Through Learning Analytics: In-depth Scoping Review

Bojić¹,

Mammadova²,

Ang³

et al. 2023

J Med Internet Res

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Background Digital education has expanded since the COVID-19 pandemic began. A substantial amount of recent data on how students learn has become available for learning analytics (LA). LA denotes the “measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” Objective This scoping review aimed to examine the use of LA in health care professions education and propose a framework for the LA life cycle. Methods We performed a comprehensive literature search of 10 databases: MEDLINE, Embase, Web of Science, ERIC, Cochrane Library, PsycINFO, CINAHL, ICTP, Scopus, and IEEE Explore. In total, 6 reviewers worked in pairs and performed title, abstract, and full-text screening. We resolved disagreements on study selection by consensus and discussion with other reviewers. We included papers if they met the following criteria: papers on health care professions education, papers on digital education, and papers that collected LA data from any type of digital education platform. Results We retrieved 1238 papers, of which 65 met the inclusion criteria. From those papers, we extracted some typical characteristics of the LA process and proposed a framework for the LA life cycle, including digital education content creation, data collection, data analytics, and the purposes of LA. Assignment materials were the most popular type of digital education content (47/65, 72%), whereas the most commonly collected data types were the number of connections to the learning materials (53/65, 82%). Descriptive statistics was mostly used in data analytics in 89% (58/65) of studies. Finally, among the purposes for LA, understanding learners’ interactions with the digital education platform was cited most often in 86% (56/65) of papers and understanding the relationship between interactions and student performance was cited in 63% (41/65) of papers. Far less common were the purposes of optimizing learning: the provision of at-risk intervention, feedback, and adaptive learning was found in 11, 5, and 3 papers, respectively. Conclusions We identified gaps for each of the 4 components of the LA life cycle, with the lack of an iterative approach while designing courses for health care professions being the most prevalent. We identified only 1 instance in which the authors used knowledge from a previous course to improve the next course. Only 2 studies reported that LA was used to detect at-risk students during the course’s run, compared with the overwhelming majority of other studies in which data analysis was performed only after the course was completed.

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Learning and Social Networks - Similarities, Differences and Impact

Cited by 5 publications

References 16 publications

Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study

Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study

Social Network Analysis in Undergraduate and Postgraduate Medical Education: A Scoping Review

Empowering Health Care Education Through Learning Analytics: In-depth Scoping Review

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