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MOOC Learning in Spontaneous Study Groups: Does Synchronously Watching Videos Make a Difference Nan Li, Himanshu Verma, Afroditi Skevi, Guillaume Zufferey, Pierre Dillenbourg and the right plot expresses that the DSP group was quite asynchronous in the fourth week – group participants were mostly at different video positions or even watching different videos. Figure 1. Sample video navigation patterns of study groups from both courses. Linearity and synchronicity indices The two plots presented before visualised how group students interacted with videos and how synchronous and asynchronous group patterns appeared. To quantify these patterns, we introduced a linearity index and synchronicity index. (1) ‘Linearity index’: this refers to the ratio between the total length of all video content that is watched in a week and the amount of time spent on them. Possible values range between 0 and 1. An index of 1.0 indicates that the full videos were played exactly once without being paused or replayed. This index gives us a rough idea of video interaction intensity. Both pausing and rewinding videos decrease the value, Research Track | 90
MOOC Learning in Spontaneous Study Groups: Does Synchronously Watching Videos Make a Difference Nan Li, Himanshu Verma, Afroditi Skevi, Guillaume Zufferey, Pierre Dillenbourg while searching forward and stopping in advance lead to an increase. Therefore the lower the values are, the more additional time has been spent on the videos and the less linear the video watching behavior is. We are interested in an overall pattern of linearity. When we computed an index value for a certain week, videos in that week were taken into account as a whole. Having said that, there is only one value per group per week. If a student did not finish all the videos, we only take into account the videos that have been watched. In our experiment, NAS students generally watched videos in a more linear way (M=0.832, SD=0.113) than DSP students (M=0.334, SD=0.125), indicating that the DSP course is potentially of a higher difficulty level. 600 (10 min). As we see, the larger the T is, the larger the synchronicity indices are. The index values may converge to one with large Ts. A close-to-zero T would only have a theoretical meaning, because in reality we don’t expect different people to watch the same video frame simultaneously. We finally chose T = 50, where variances among all possible synchronicity indices of different sessions reach maximum (0.088). The largest variance indicates that this T is the value that maximises the differences among all the groups. This value also makes real sense. Within 50 seconds, the teacher usually explains the same topic so students are synchronised on the same ground. Figure 2. Pseudo code for Computing Paired Synchronicity Indices (2) ‘Synchronicity index’: this index is another float number between 0 and 1. It quantifies how synchronously a MOOC study group watched video together. The higher the value is, the more synchronised the group was. We define ‘paired synchronicity index’ as the proportion of time during which one student was doing more or less the same thing as compared to another student. The average of all possible paired synchronicity in a group is the ‘group synchronicity index’. If the average is made on paired synchronicity with respect to the same student, it is called the ‘individual synchronicity index’ for that student. The ‘paired synchronicity index’ is computed by dividing the accumulative synchronous time between the pair by the total length of the study session. Synchronous time actually means that two students are either simultaneously watching the same video content or not watching anything (e.g. they may have a discussion). Perfect synchronisation accurate to a second is not necessary. We introduce threshold value ‘T’ (measured in seconds). For each second of a study session we look at the T seconds both ahead and behind to see if the pair of students was or will be watching the same thing. In other words, we are checking if one student can catch up with the other in T seconds. If the answer is yes, then they are synchronised. The algorithm is described in pseudo code in Figure 2. Different T values result in different synchronicity indices. Figure 3 illustrates how synchronicity indices for all groups in each week change by varying T between 0 and Figure 3. Computing Group Synchronicity Indices by Varying Thresholds Results The group learning patterns While the linearity indices suggest individual video interactivity, the synchronicity indices indicate group dynamics. The synchronicity indices for different groups over a 5-week period are illustrated in Figure 4. The data for NAS group 1 was missing due to technical problems during the study session. In this chart, four distinct time series stand out, each representing a different group. We can see that some groups always stayed synchronised, while others tended to work independently. A clear cut is seen in the middle range of the synchronicity index axis, which separates more synchronised groups from less synchronised groups. The series for each group fluctuates with relatively small ranges, and the data almost does not intersect, indicating a stabilised pattern. We built a mixed-effect linear regression (MELR) model to test each group statistically, with time (in terms of weeks) as the predictor and group synchronicity indices as the response. The group variable introduces a random slope effect. No statistical evidence showed the synchronicity index for each group change over time (p>0.1 for all groups). This suggests that the group learning style, once used by a group, essentially persists throughout the remaining study sessions. Research Track | 91
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Proceedings of the European MOOC St
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Table of Contents Understanding Per
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Understanding Persistence in MOOCs
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Analysing student participation in
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Signals of Success and Self-directe
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Analyzing completion rates in the F
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Challenges for conceptualising EU M
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Page 43 and 44: Scaffolding Self-learning in MOOCs
Page 51 and 52: Towards an Outcome-based Discovery
Page 59 and 60: Dropout Prediction in MOOCs using L
Page 67 and 68: Self-Regulated Learning in MOOCs: D
Page 73 and 74: Extending the MOOCversity A Multi-l
Page 81 and 82: Encouraging Forum Participation in
Page 89: MOOC Learning in Spontaneous Study
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Page 95 and 96: Dropout: MOOC Participants’ Persp
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Page 107 and 108: The SIRET Training Platform: Facing
Page 115 and 116: MOOCs in fragile contexts Barbara M
Page 123 and 124: Cultural Translation in Massive Ope
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Characterizing video use in the cat
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How Students Learn using MOOCs: An
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A Platform that Integrates Quizzes
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Designing Video for Massive Open On
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Proceedings Experience Track Procee
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Opening up higher education through
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Designing your first MOOC from scra
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“A hostage to fortune” - Valida
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and others discussed COER13 issues
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tion process, in addition to the mu
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TORQUEs: Riding the MOOC wave to be
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Engineering MOOCs for Future Engine
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The Discrete Optimization MOOC An E
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Recasting a Traditional Course into
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Evaluation and field trials of MOOC
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Learning Electronics through a Remo
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Learning Electronics through a Remo
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abiertaUGR: modelling online learni
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abiertaUGR: modelling online learni
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University of London International
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The EPFL MOOC Factory Patrick Jerma
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Open Online Courses in the context
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Open Online Courses in the context
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Friendly Handmade Explanation Video
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Friendly Handmade Explanation Video
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Designing for the Unknown Learner H
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Designing for the Unknown Learner H
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MOOC Experience in the University o
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MOOC Experience in the University o
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Mathematics Courses: Fostering indi
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First time MOOC provider: reflectio
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An Academic Online Resource at Téc
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An Academic Online Resource at Téc
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Opening up higher education through
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SPOCs for Remedial Education: Exper
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Experiments with connectivism from
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Towards a quality model for UNED MO
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Project-based MOOCs. A Field Report
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Project-based MOOCs. A Field Report
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Reviewers of Experience Track: Carl
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