Choice-based Assessment: Can Choices Made in Digital Games Predict 6th-Grade Students' Math Test Scores?
Min Chi, Daniel L. Schwartz, Kristen Pilner Blair, Doris B. Chin · Educational Data Mining · 2014
this paper, we mined students' sequential behaviors from an instructional game for color mixing called Lightlet. Students pkaying the game have two broad strategies. They can either test candidate color combinations in an experiment room without risking an incorrect answer. Or they can choose colors from a faux shopping Catalog containing several different mixing charts. While the results shown in the Experiment Room are always correct, only a few of the charts in the Catalog are correct. Thus, if students use the catalog students must apply critical thinking skills to determine what charts to trust. Our primary goal in this work was to identify the crucial choice pattern(s) in students' game play that would contribute to their learning or subsequent performance. Data was collected from 6th graders. The results showed that children who chose to explore the Catalog of different charts during the game performed better in school. More specifically, the types of behavior choices students committed during the game play predicted about 43% of the variation in their subsequent math grades. This project shows that by assessing students' choices during learning, we can discover a great deal about their learning process and can identify and assess choices that are critical for learning but are often missed by most tests.