ABSTRACT
There has been significant efforts in studying collaborative and social learning using aggregate networks. Such efforts have demonstrated the worth of the approach by providing insights about the interactions, student and teacher roles, and predictability of performance. However, using an aggregated network discounts the fine resolution of temporal interactions. By doing so, we might overlook the regularities/irregularities of students' interactions, the process of learning regulation, and how and when different actors influence each other. Thus, compressing a complex temporal process such as learning may be oversimplifying and reductionist. Through a temporal network analysis of 54 students interactions (in total 3134 interactions) in an online medical education course, this study contributes with a methodological approach to building, visualizing and quantitatively analyzing temporal networks, that could help educational practitioners understand important temporal aspects of collaborative learning that might need attention and action. Furthermore, the analysis conducted emphasize the importance of considering the time characteristics of the data that should be used when attempting to, for instance, implement early predictions of performance and early detection of students and groups that need support and attention.
- Skye Bender-deMoll. 2018. ndtv: Network Dynamic Temporal Visualizations. Retrieved from https://cran.r-project.org/package=ndtvGoogle Scholar
- Per Block. 2015. Reciprocity, Transitivity, and the Mysterious. Social Networks 40, October: 163--173.Google ScholarCross Ref
- Carter T Butts. 2010. sna: Tools for Social Network Analysis. R package version 2.2-0.Google Scholar
- Bodong Chen, Alyssa F Wise, Simon Knight, and Britte Haugan Cheng. 2016. Putting temporal analytics into practice: the 5th international workshop on temporality in learning data. In Proceedings of the sixth international conference on learning analytics & knowledge, 488--489.Google ScholarDigital Library
- Marielle Dado and Daniel Bodemer. 2017. A review of methodological applications of social network analysis in computer-supported collaborative learning. Educational Research Review 22: 159--180. Google Scholar
- Dragan Ga, Shane Dawson, Tim Rogers, Danijela Gasevic, Dragan Gasevic, Shane Dawson, Tim Rogers, Danijela Gasevic, Dragan Ga, Shane Dawson, Tim Rogers, and Danijela Gasevic. 2016. Internet and Higher Education Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. Internet and Higher Education 28: 68--84. Google ScholarCross Ref
- Iassen Halatchliyski, Tobias Hecking, Tilman Göhnert, and H. Ulrich Hoppe. 2013. Analyzing the Flow of Ideas and Profiles of Contributors in an Open Learning Community. Proceedings of the Third International Conference on Learning Analytics and Knowledge - LAK '13 1, 2: 66--74. Google ScholarDigital Library
- Petter Holme and Jari Saramäki. 2012. Temporal networks. Physics Reports 519, 3: 97--125. Google ScholarCross Ref
- Rob Hyndman, Yanfei Kang, Pablo Montero-Manso, Thiyanga Talagala, Earo Wang, Yangzhuoran Yang, and Mitchell O'Hara-Wild. 2019. tsfeatures: Time Series Feature Extraction. Retrieved from https://cran.r-project.org/package=tsfeaturesGoogle Scholar
- Amy M Johnson, Roger Azevedo, and Sidney K D'Mello. 2011. The Temporal and Dynamic Nature of Self-Regulatory Processes During Independent and Externally Assisted Hypermedia Learning. Cognition and Instruction 29, 4: 471--504. Google ScholarCross Ref
- Srećko Joksimović. 2017. An analytics-based approach to the study of learning networks in digital education settings. Retrieved from https://www.era.lib.ed.ac.uk/bitstream/handle/1842/25819/Joksimovic2017.pdf?sequence=1Google Scholar
- Srećko Joksimović, Areti Manataki, Dragan Gašević, Shane Dawson, Vitomir Kovanović, and Inés Friss de Kereki. 2016. Translating network position into performance. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK '16, 314--323. Google ScholarDigital Library
- Manu Kapur, John Voiklis, and Charles K. Kinzer. 2008. Sensitivities to early exchange in synchronous computer-supported collaborative learning (CSCL) groups. Computers & Education 51, 1: 54--66. Google ScholarDigital Library
- A V Y Lee and S C Tan. 2017. Temporal analytics with discourse analysis: Tracing ideas and impact on communal discourse. In ACM International Conference Proceeding Series, 120--127. Google ScholarDigital Library
- Hao Liao, Manuel Sebastian Mariani, Matúš Medo, Yi Cheng Zhang, and Ming Yang Zhou. 2017. Ranking in evolving complex networks. Physics Reports 689: 1--54. Google ScholarCross Ref
- Jonna Malmberg, Sanna Järvelä, and Hanna Järvenoja. 2017. Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning. Contemporary Educational Psychology 49: 160--174. Google ScholarCross Ref
- Inge Molenaar and Ming Ming Chiu. 2014. Dissecting sequences of regulation and cognition: statistical discourse analysis of primary school children's collaborative learning. Metacognition and Learning 9, 2: 137--160. Google ScholarCross Ref
- Vincenzo Nicosia, John Tang, Cecilia Mascolo, Mirco Musolesi, Giovanni Russo, and Vito Latora. 2013. Graph metrics for temporal networks. In Temporal networks. Springer, 15--40.Google Scholar
- Zacharoula Papamitsiou and Anastasios A Economides. 2015. Temporal learning analytics visualizations for increasing awareness during assessment. International Journal of Educational Technology in Higher Education 12, 3: 129--147.Google Scholar
- R Core Team. 2018. R: A Language and Environment for Statistical Computing. Retrieved from https://www.r-project.orgGoogle Scholar
- Mohammed Saqr and Ahmad Alamro. 2019. The role of social network analysis as a learning analytics tool in online problem based learning. BMC Medical Education 19, 1: 1--11. Google ScholarCross Ref
- Mohammed Saqr, Uno Fors, and Jalal Nouri. 2018. Using social network analysis to understand online Problem-Based Learning and predict performance. PloS one 13, 9: e0203590. Google ScholarCross Ref
- Mohammed Saqr, Jalal Nouri, Uno Fors, Jalal Nouri, and Uno Fors. 2019. Time to focus on the temporal dimension of learning: A learning analytics study of the temporal patterns of students' interactions and self-regulation. International Journal of Technology Enhanced Learning 11, 4: 398. Google ScholarCross Ref
- George Siemens. 2013. Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist 57, 10: 1380--1400. Google ScholarCross Ref
- Bodong Chen. Simon Knight, Alyssa Friend Wise. 2017. Time for change: Why Learning analytics needs temporal analysis. Journal of Learning Analytics 4, 3: 7--17. https://doi.org/ Google ScholarCross Ref
- Olga Viberg, Mathias Hatakka, Olof Bälter, and Anna Mavroudi. 2018. The current landscape of learning analytics in higher education. Computers in Human Behavior 89, October 2017: 98--110. Google ScholarCross Ref
- Duy Vu, Philippa Pattison, and Garry Robins. 2015. Relational event models for social learning in MOOCs. Social Networks 43: 121--135. Google ScholarCross Ref
Index Terms
- High resolution temporal network analysis to understand and improve collaborative learning
Recommendations
A Learning Analytics Study of the Effect of Group Size on Social Dynamics and Performance in Online Collaborative Learning
Transforming Learning with Meaningful TechnologiesAbstractEffective collaborative learning is rarely a spontaneous phenomenon. In fact, it requires that a set of conditions are met. Among these central conditions are group formation, size and interaction dynamics. While previous research has demonstrated ...
Can forming a virtual learning community enhance learning on a face-to-face learning programme?
A Virtual Learning Environment (VLE) was set up in support of the curriculum at a new medical school, which delivers a five-year undergraduate face-to-face programme. We set out to evaluate the effectiveness of the VLE without any preconceptions, ...
Time to focus on the temporal dimension of learning: a learning analytics study of the temporal patterns of students' interactions and self-regulation
In this learning analytics study, we attempt to understand the role of temporality measures for the prediction of academic performance. The study included four online courses over a full-year duration. Temporality was studied on daily, weekly, course-wise ...
Comments