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

  • Moved from a broadcast model to on-demand availability.

  • Instructors want to know what their students are doing and what they should be doing.

  • Are there student putting in lots of effort but getting nowhere? Are there students who aren’t really trying?

  • 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

  • Initial attempts using a single application of crisp or fuzzy clustering did not produce coherent groupings - Outlier behaviors were being missed

  • He et al. [2016, 2017] used clustering to search for hidden communities in social networks.

    • First used clustering to discover the most apparent communities.

    • Then decreased the weights on the edges in the social network that represented hidden communities

    • Repeating the clustering uncovers previously hidden communities.

  • Capture how well a cluster represented a student trajectory

    • Fuzzy clustering naturally lends itself to this

Repeated Fuzzy Clustering

Finding Outliers

  • Students should be well described by at most two clusters – interpretable by instructors

  • 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

  • So what?

    • Stage 1: Instructor feedback → Stage x: Automated Interventions

    • Needs to be actionable

      • What is driving behaviours?

      • How consistent are behaviours?

      • What are the long term impacts?

  • Algorithm

    • Include effort / engagement in the clustering

    • How robust is the algorithm to the choice of the number of clusters on each repetition?

    • The impact of the method to choose outliers

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

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