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.
Use these controls to play and interact with the animation
N=5044
51 online and blended courses
Nine terms - 2015 to 2018
Psychology, Spanish, College Algebra, various Computing courses, and Nursing.
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
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
3244 (64.3%)
915
(18.1%)
293
(5.8%)
340
(6.7%)
352 (5.0%)
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