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Using Learning Analytics to Understand and Support Collaborative Learning
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0001-5881-3109
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Learning analytics (LA) is a rapidly evolving research discipline that uses insights generated from data analysis to support learners and optimize both the learning process and learning environment. LA is driven by the availability of massive data records regarding learners, the revolutionary development of big data methods, cheaper and faster hardware, and the successful implementation of analytics in other domains. The prime objective of this thesis is to investigate the potential of learning analytics in understanding learning patterns and learners’ behavior in collaborative learning environments with the premise of improving teaching and learning. More specifically, the research questions comprise: How can learning analytics and social network analysis (SNA) reliably predict students’ performance using contextual, theory-based indicators, and how can social network analysis be used to analyze online collaborative learning, guide a data-driven intervention, and evaluate it. The research methods followed a structured process of data collection, preparation, exploration, and analysis. Students’ data were collected from the online learning management system using custom plugins and database queries. Data from different sources were assembled and verified, and corrupted records were eliminated. Descriptive statistics and visualizations were performed to summarize the data, plot variables’ distributions, and detect interesting patterns. Exploratory statistical analysis was conducted to explore trends and potential predictors, and to guide the selection of analysis methods. Using insights from these steps, different statistical and machine learning methods were applied to analyze the data. The results indicate that a reasonable number of underachieving students could be predicted early using self-regulation, engagement, and collaborative learning indicators. Visualizing collaborative learning interactions using SNA offered an easy-to-interpret overview of the status of collaboration, and mapped the roles played by teachers and students. SNA-based monitoring helped improve collaborative learning through a data-driven intervention. The combination of SNA visualization and mathematical analysis of students’ position, connectedness, and role in collaboration was found to help predict students’ performance with reasonable accuracy. The early prediction of performance offers a clear opportunity for the implementation of effective remedial strategies and facilitates improvements in learning. Furthermore, using SNA to monitor and improve collaborative learning could contribute to better learning and teaching.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University , 2018. , p. 143
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 18-011
Keywords [en]
Learning analytics, Social Network Analysis, Collaborative Learning, Medical Education, Interaction Analysis, Machine Learning
National Category
Computer Sciences
Research subject
Information Society
Identifiers
URN: urn:nbn:se:su:diva-159479ISBN: 978-91-7797-440-6 (print)ISBN: 978-91-7797-441-3 (electronic)OAI: oai:DiVA.org:su-159479DiVA, id: diva2:1245435
Public defence
2018-10-22, L70, NOD-huset Borgarfjordsgatan 12, Kista, 09:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Accepted.

Available from: 2018-09-27 Created: 2018-09-05 Last updated: 2018-09-27Bibliographically approved
List of papers
1. How the study of online collaborative learning can guide teachers and predict students' performance in a medical course
Open this publication in new window or tab >>How the study of online collaborative learning can guide teachers and predict students' performance in a medical course
2018 (English)In: BMC Medical Education, ISSN 1472-6920, E-ISSN 1472-6920, Vol. 18, article id 24Article in journal (Refereed) Published
Abstract [en]

Background: Collaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. Although the benefits of collaborative learning have long been recognized, it is still rarely studied by social network analysis (SNA) in medical education, and the relationship of parameters that can be obtained via SNA with students' performance remains largely unknown. The aim of this work was to assess the potential of SNA for studying online collaborative clinical case discussions in a medical course and to find out which activities correlate with better performance and help predict final grade or explain variance in performance. Methods: Interaction data were extracted from the learning management system (LMS) forum module of the Surgery course in Qassim University, College of Medicine. The data were analyzed using social network analysis. The analysis included visual as well as a statistical analysis. Correlation with students' performance was calculated, and automatic linear regression was used to predict students' performance. Results: By using social network analysis, we were able to analyze a large number of interactions in online collaborative discussions and gain an overall insight of the course social structure, track the knowledge flow and the interaction patterns, as well as identify the active participants and the prominent discussion moderators. When augmented with calculated network parameters, SNA offered an accurate view of the course network, each user's position, and level of connectedness. Results from correlation coefficients, linear regression, and logistic regression indicated that a student's position and role in information relay in online case discussions, combined with the strength of that student's network (social capital), can be used as predictors of performance in relevant settings. Conclusion: By using social network analysis, researchers can analyze the social structure of an online course and reveal important information about students' and teachers' interactions that can be valuable in guiding teachers, improve students' engagement, and contribute to learning analytics insights.

Keywords
Collaborative learning, E-learning, Social network analysis, Computer-supported collaborative learning, Blended learning, Clinical, Case discussions, Learning analytics
National Category
Educational Sciences Computer and Information Sciences
Research subject
Information Society
Identifiers
urn:nbn:se:su:diva-153779 (URN)10.1186/s12909-018-1126-1 (DOI)000424454500001 ()29409481 (PubMedID)
Available from: 2018-03-21 Created: 2018-03-21 Last updated: 2018-09-24Bibliographically approved
2. How learning analytics can early predict under-achieving students in a blended medical education course
Open this publication in new window or tab >>How learning analytics can early predict under-achieving students in a blended medical education course
2017 (English)In: Medical teacher, ISSN 0142-159X, E-ISSN 1466-187X, Vol. 39, no 7, p. 757-767Article in journal (Refereed) Published
Abstract [en]

Aim: Learning analytics (LA) is an emerging discipline that aims at analyzing students' online data in order to improve the learning process and optimize learning environments. It has yet un-explored potential in the field of medical education, which can be particularly helpful in the early prediction and identification of under-achieving students. The aim of this study was to identify quantitative markers collected from students' online activities that may correlate with students' final performance and to investigate the possibility of predicting the potential risk of a student failing or dropping out of a course.Methods: This study included 133 students enrolled in a blended medical course where they were free to use the learning management system at their will. We extracted their online activity data using database queries and Moodle plugins. Data included logins, views, forums, time, formative assessment, and communications at different points of time. Five engagement indicators were also calculated which would reflect self-regulation and engagement. Students who scored below 5% over the passing mark were considered to be potentially at risk of under-achieving.Results: At the end of the course, we were able to predict the final grade with 63.5% accuracy, and identify 53.9% of at-risk students. Using a binary logistic model improved prediction to 80.8%. Using data recorded until the mid-course, prediction accuracy was 42.3%. The most important predictors were factors reflecting engagement of the students and the consistency of using the online resources.Conclusions: The analysis of students' online activities in a blended medical education course by means of LA techniques can help early predict underachieving students, and can be used as an early warning sign for timely intervention.

National Category
Computer and Information Sciences General Practice Educational Sciences
Research subject
Information Society
Identifiers
urn:nbn:se:su:diva-145289 (URN)10.1080/0142159X.2017.1309376 (DOI)000404352900010 ()28421894 (PubMedID)
Available from: 2017-07-25 Created: 2017-07-25 Last updated: 2018-09-24Bibliographically approved
3. How social network analysis can be used to monitor online collaborative learning and guide an informed intervention
Open this publication in new window or tab >>How social network analysis can be used to monitor online collaborative learning and guide an informed intervention
2018 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 13, no 3, article id e0194777Article in journal (Refereed) Published
Abstract [en]

To ensure online collaborative learning meets the intended pedagogical goals (is actually collaborative and stimulates learning), mechanisms are needed for monitoring the efficiency of online collaboration. Various studies have indicated that social network analysis can be particularly effective in studying students' interactions in online collaboration. However, research in education has only focused on the theoretical potential of using SNA, not on the actual benefits they achieved. This study investigated how social network analysis can be used to monitor online collaborative learning, find aspects in need of improvement, guide an informed intervention, and assess the efficacy of intervention using an experimental, observational repeated-measurement design in three courses over a full-term duration. Using a combination of SNA-based visual and quantitative analysis, we monitored three SNA constructs for each participant: the level of interactivity, the role, and position in information exchange, and the role played by each participant in the collaboration. On the group level, we monitored interactivity and group cohesion indicators. Our monitoring uncovered a non collaborative teacher-centered pattern of interactions in the three studied courses as well as very few interactions among students, limited information exchange or negotiation, and very limited student networks dominated by the teacher. An intervention based on SNA-generated insights was designed. The intervention was structured into five actions: increasing awareness, promoting collaboration, improving the content, preparing teachers, and finally practicing with feedback. Evaluation of the intervention revealed that it has significantly enhanced student-student interactions and teacher-student interactions, as well as produced a collaborative pattern of interactions among most students and teachers. Since efficient and communicative activities are essential prerequisites for successful content discussion and for realizing the goals of collaboration, we suggest that our SNA-based approach will positively affect teaching and learning in many educational domains. Our study offers a proof-of-concept of what SNA can add to the current tools for monitoring and supporting teaching and learning in higher education.

National Category
Computer and Information Sciences Educational Sciences
Research subject
Information Society
Identifiers
urn:nbn:se:su:diva-156090 (URN)10.1371/journal.pone.0194777 (DOI)000428093900105 ()29566058 (PubMedID)
Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-09-24Bibliographically approved
4. Time to focus on the temporal dimension of learning: A learning analytics study of the temporal patterns of students’ interactions and self-regulation
Open this publication in new window or tab >>Time to focus on the temporal dimension of learning: A learning analytics study of the temporal patterns of students’ interactions and self-regulation
(English)In: International Journal of Technology Enhanced Learning, ISSN 1753-5255, E-ISSN 1753-5263Article in journal (Refereed) Accepted
Abstract [en]

Time dynamics is an important element of the self-regulated learning theory. Researchers have consistently reported that students who use time and learning strategies efficiently perform better than their counterparts who don’t. Likewise, there is a sufficient volume of evidence that supports the claim that delay in performing the learning tasks (procrastination) is a consistent negative predictor of academic achievement. Although temporality is an interesting aspect of learning processes, it is yet poorly studied. Therefore, in this learning analytics study, we attempt to better understand the role of temporality measures for the prediction of academic performance by using statistical modelling and applying machine learning methods.  

The study included four online courses over a full year duration. Students were classified as low- and high achievers. Temporality was studied on daily, weekly, course-wise and year wise. The patterns of each performance group in each period were visually plotted and compared. Correlation with the performance was done. Visualizing the activities have highlighted a certain pattern. On the week level, early participation was a consistent predictor of high achievement. This finding was consistent from course to course and during most periods of the year. On an individual course level, high achievers were also likely to participate early and consistently. With a focus on temporal measures, we were able to predict high achievers with reasonable accuracy in each course.

The study of temporality and how certain temporal patterns are more consistent have contributed to the production of a reasonably accurate and reproducible predictive models. These findings highlight the idea that temporality dimension is a significant source of information about learning patterns and has the potential to inform educators about students’ activities and to improve the accuracy and reproducibility of predicting students’ performance.

Keywords
Learning analytics, Temporality, Time, Problem based learning, Collaborative learning, Social network analysis, Self-regulation
National Category
Computer Sciences Educational Sciences
Research subject
Information Society
Identifiers
urn:nbn:se:su:diva-159753 (URN)
Available from: 2018-09-05 Created: 2018-09-05 Last updated: 2018-09-24
5. Using social network analysis to understand online Problem-Based Learning and predict performance
Open this publication in new window or tab >>Using social network analysis to understand online Problem-Based Learning and predict performance
2018 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 13, no 9, article id e0203590Article in journal (Refereed) Published
Abstract [en]

Social network analysis (SNA) may be of significant value in studying online collaborative learning. SNA can enhance our understanding of the collaborative process, predict the under-achievers by means of learning analytics and uncover the role dynamics of learners and teachers alike. As such, it constitutes an obvious opportunity to improve learning, inform teachers and stakeholders.  Besides, it can facilitate data-driven support services for students.

This study included four courses in Qassim University. Online interaction data were collected and processed following a standard data mining technique. The SNA parameters relevant to knowledge sharing and construction were calculated on the individual and the group level. The analysis included quantitative network analysis and visualizatization, correlation tests as well as predictive and explanatory regression models.

Our results showed a consistent moderate to strong positive correlation between performance, interaction parameters and students’ centrality measures across all the studied courses, regardless of the subject matter. In each of the studied courses, students with stronger ties to prominent peers (better social capital) in small interactive and cohesive groups tended to do better. The results of correlation tests were confirmed using regression tests, which were validated using a next year dataset. Using SNA indicators, we were able to classify students according to achievement with a high accuracy (93.3%). This demonstrates the possibility of using interaction data to predict underachievers with a reasonable reliability, which is an obvious opportunity for intervention and support.

Keywords
Social network analysis, Problem based learning, Learning analytics, Medical education, Interaction analysis
National Category
Computer Sciences Educational Sciences
Research subject
Information Society
Identifiers
urn:nbn:se:su:diva-159751 (URN)10.1371/journal.pone.0203590 (DOI)000445626400028 ()
Available from: 2018-09-05 Created: 2018-09-05 Last updated: 2018-10-15Bibliographically approved

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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