Using Learning Analytics to Illuminate Student Learning Pathways in an Online Fraction Game
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This document summarizes research analyzing student data from an online fraction game to identify different learning strategies. The researchers identified 5 clusters of student strategies - Minimal, Haphazard, Explorer, Strategic Explorer, and Careful. Students with medium or higher prior knowledge benefited most from the Explorer strategy, while those with low prior knowledge did better with the Minimal or Careful strategies. The researchers conclude that a medium level of exploration is most productive, and students with low prior knowledge may need more guidance. Next steps include adapting the game based on individual students' exploration patterns and prior knowledge.
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Using Learning Analytics to Illuminate Student Learning Pathways in an Online Fraction Game
- 1. Using Learning Analytics to Illuminate Student Learning Pathways in an Online Fraction Game Taylor Martin, Nicole Forsgren Velasquez Active Learning Lab, Huntsman School of Business Utah State University
- 3. The Opportunity • The new microscope • Rich and growing streams of digital learning data • Better measures of learning and teaching
- 10. 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
- 11. 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
- 12. 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
- 13. Minimal
- 14. 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
- 15. Haphazard
- 16. 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
- 17. Explorer
- 18. 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
- 20. 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
- 21. Careful
- 22. 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
- 23. 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
- 24. Next Steps • Towards Adaptivity – What degree of fussing? – When? – For whom? • Process Analytics – Identify exploration sequences
- 25. Thank You! • activelearninglab.org • taylor.martin@usu.edu • nicolefv@usu.edu
Editor's Notes
- If the US population demonstrated the level of illiteracy in reading and writing that we have in mathematics (let alone science), the uproar would be deafening. How many times have you been on a plane or at a party explaining that you do math, science, engineering etc education and had a high functioning adult say to you, “oh, I hate math, was never any good at it.”???
- Refraction based on the concept of splitting. So, in this level of refraction, we’ll split the whole laser into halves to power the spaceships. Here the student first tries a 1/3 splitter and you see the ships don’t have enough power. Then the student uses the 1/2 splitter and everyone is happy. So far, the research shows this is the best approach for developing core ideas like a fraction is one number and more and less than w/fractions and preventing misconceptions when we get to more difficult problems (e.g., answer over 1) and expanding the number system (e.g., extending to decimals, percents etc).... But, there’s not a lot of research - that’s what we’re doing. Creating games based these concepts and researching how kids learn in each concept, which are better, maybe even what order they’re best taught in... global structure for comparing proportions - allows students to make general judgments of relative magnitude like deciding whether a container is more than half full. numerical structure of splitting and doubling that produces exact quantified results. Understanding rational number is coordinating the two. Refraction is an online fraction game (& we won the Disney Learning Challenge @ SIGGRAPH last summer!) • Users arrange and split laser beams to achieve their objectives (freeing spaceships). •These tasks are created so that users are solving fraction equivalence, operations, and comparison problems, while being engaged by the environment so that they do not give up on even difficult challenges.
- AKA BOARD STATES