Learning curves show error rates decreased for wind direction, air movement, and cloud type but stayed high for wind speed and precipitation skills¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2019 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Under BKT, error rates for wind direction, air movement, and cloud type decreased with more learning opportunities, while wind speed and precipitation type and amount remained high. → Ying Cui 2019

Evidence¶

Ying Cui 2019¶

Ying Cui, Man-Wai Chu, Fu Chen. (2019). Analyzing Student Process Data in Game-Based Assessments with Bayesian Knowledge Tracing and Dynamic Bayesian Networks. Journal of Educational Data Mining, Volume 11, No 1. https://jedm.educationaldatamining.org/index.php/JEDM/article/view/397

q2 · i? · design · r2

BKT learning curves (Figure 7) plot mean error rate against learning opportunities; model-predicted error rates overlapped observed error rates. The article reports the contrasting skill patterns quoted above; no effect size is printed.

"The error rates of wind direction, air movement, and cloud type decreased along with more learning opportunities. However, the error rates of wind speed and precipitation type and amount remained high regardless of the number of learning opportunities."

Discussion¶

Related Claims¶

• BKT estimates show most students mastered wind direction and air movement but few mastered wind speed, precipitation type, or amount — related
• DBN analysis shows wind direction and air movement reached the highest posterior mastery probabilities while other skills stayed flat or declined — related
• Both BKT and DBN show high classification accuracy but low consistency for wind direction, cloud type, and air movement — related