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This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined 1) how different ML algorithms influenced the precision of middle-school students’ (N = 359) performance (i.e. posttest math knowledge scores) prediction and 2) what types of in-game features (i.e. student in-game behaviors, math anxiety, mathematical strategies) were associated with student math knowledge scores. The results indicated that the Random Forest algorithm showed the best performance (i.e. the accuracy of models, error measures) in predicting posttest math knowledge scores among the seven algorithms employed. Out of 37 features included in the model, the validity of the students’ first mathematical transformation was the most predictive of their posttest math knowledge scores. Implications for game learning analytics and supporting students’ algebraic learning are discussed based on the findings.
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The Contour to Classification Game
The Contour to Classification game is a browser-based game that teaches middle school students basic concepts in supervised learning. The game is an online variant of the Neural Network game that was presented at AAAI Fall Symposium Teaching AI in K-12 track in 2019. We share preliminary findings from implementing the online version of the original Neural Network game in a pilot research study and describe the game’s evolution to the Contour to Classification game. The new game uses a simulation of a neural network to engage students, through digital drawing and selection interactions, in the classification of images. The players act as nodes in a multi-step process of compositing salient smaller features to form larger features and ultimately a partial contour of an object that is used to make a prediction. After evaluating the prediction, information is sent back through the network in processes mimicking back propagation and gradient descent. Additional rounds of the game can be played to witness how the network evolves and gets “better” at classifying images from contours. Through this game, we aimed for students to learn the structure, components, and functioning of a neural network, and the processes involved in supervised learning. The Contour to Classification game supports online student learning by providing the image classification experience using purely visual inputs to each layer. We will conclude with a discussion of if and how the evolving design addresses classroom needs and scaling considerations.
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- Award ID(s):
- 2022502
- PAR ID:
- 10252929
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 35
- Issue:
- 17
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 15583-15590
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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