Using Learning Analytics to Illuminate Student Learning Pathways in an Online Fraction Game

Taylor Martin, Nicole Forsgren VelasquezActive Learning Lab, Huntsman School of Business

Utah State University

The Opportunity

• The new microscope• Rich and growing streams of digital

learning data• Better measures of learning and teaching

Teaching Fractions

http://games.cs.washington.edu/Refraction/

Visualizing Game States

Learning Gains

• Results: Students improve (pre to post) after playing game

• But… – Visualizations suggest different strategies– What about personalized learning?

• To investigate different strategies, we use cluster analysis

Cluster Analysis

• Variables– Number of unique board states– Total number of board states– Average time per board state– Number of moves until initial 1/3 board state– Success on game level

• Results: 5 clusters (fussing strategies)– Duncan’s Multiple Range Test used to

interpret

Cluster 1: Minimal

• Clustering variables– Number of unique board states: Low– Total number of board states: Low– Average time per board state: Very High– # moves until initial 1/3 board state: Very High– Success on game level: Low

Minimal

Cluster 2: Haphazard

• Clustering variables– Number of unique board states: Medium– Total number of board states: Very High– Average time per board state: Low– # moves until initial 1/3 board state: Very High– Success on game level: Low

Haphazard

Cluster 3: Explorer

• Clustering variables– Number of unique board states: High– Total number of board states: Medium– Average time per board state: High– # moves until initial 1/3 board state: High– Success on game level: Medium

Explorer

Cluster 4: Strategic Explorer

• Contrast to Haphazard• Clustering variables

– Number of unique board states: Very High– Total number of board states: High– Average time per board state: Very Low– # moves until initial 1/3 board state: Medium– Success on game level: High

Strategic Explorer

Cluster 5: Careful

• Contrast to Minimal• Clustering variables

– Number of unique board states: Low– Total number of board states: Very Low– Average time per board state: Medium– # moves until initial 1/3 board state: Low– Success on game level: Very High

Careful

Learning Gains: Transfer

• Posttest transfer score not associated with strategy

• Strategy used is related to learning• If prior knowledge is medium or better:

– Explorer strategy learned the most – All high-fussing strategies (strategic explorers,

explorers, haphazard) were good

• If prior knowledge is low:– Minimal strategy was better than Haphazard– High fussing is counterproductive

Initial Conclusions

• Fussing at a medium level productive• Careful (non fussing) strategies can be

productive, particularly with low prior knowledge

• Students with low prior knowledge may benefit from directed activities or hints

Next Steps• Towards Adaptivity

– What degree of fussing? – When?– For whom?

• Process Analytics– Identify exploration sequences

Thank You!

• activelearninglab.org• taylor.martin@usu.edu• nicolefv@usu.edu