Detecting Behaviors in
Student Progress Trajectories
Colm Howlin
Realizeit
@cp_h
colm.howlin@realizeitlearning.com
Charles Dziuban
University of Central Florida
@papuga
charles.dziuban@ucf.edu
Presented at Educational Data Mining 2019. July 2 - 5, 2019, Montréal, Canada
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Moved from a broadcast model to on-demand availability.
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Instructors want to know what their students are doing and what they should be doing.
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Are there student putting in lots of effort but getting nowhere? Are there students who aren’t really trying?
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Impact on student learning – attainment, retention of knowledge, grades.
Motivation
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Progress Trajectories
N=5044
51 online and blended courses
Nine terms - 2015 to 2018
Psychology, Spanish, College Algebra, various Computing courses, and Nursing.
Summarizing a Trajectory
Repeated Fuzzy Clustering
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Initial attempts using a single application of crisp or fuzzy clustering did not produce coherent groupings - Outlier behaviors were being missed
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He et al. [2016, 2017] used clustering to search for hidden communities in social networks.
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First used clustering to discover the most apparent communities.
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Then decreased the weights on the edges in the social network that represented hidden communities
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Repeating the clustering uncovers previously hidden communities.
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Capture how well a cluster represented a student trajectory
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Fuzzy clustering naturally lends itself to this
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Repeated Fuzzy Clustering
Finding Outliers
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Students should be well described by at most two clusters – interpretable by instructors
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Radviz visualizes a clustering solution - points close to the center are evenly distributed among all clusters
Clustered Behaviors
3244 (64.3%)
915
(18.1%)
293
(5.8%)
340
(6.7%)
352 (5.0%)
Next Steps
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So what?
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Stage 1: Instructor feedback → Stage x: Automated Interventions
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Needs to be actionable
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What is driving behaviours?
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How consistent are behaviours?
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What are the long term impacts?
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Algorithm
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Include effort / engagement in the clustering
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How robust is the algorithm to the choice of the number of clusters on each repetition?
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The impact of the method to choose outliers
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Colm Howlin
Realizeit
@cp_h
colm.howlin@realizeitlearning.com
Charles Dziuban
University of Central Florida
@papuga
charles.dziuban@ucf.edu
Detecting Behaviors in Student Progress Trajectories - Presented at Educational Data Mining 2019. July 2 - 5, 2019, Montréal, Canada