Bayesian Knowledge Tracing as a model of changing skill mastery during game-based assessment¶

Theory · All theories
Evidence · 1 claim (1 for) · 1 study (1 design), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

BKT models students' changing knowledge states during practice by updating, after each response, the probability that a skill is mastered. It parameterizes initial mastery, transition to mastery after an opportunity, slipping, and guessing, and updates mastery conditional on each correct or incorrect response. The article applies it to Raging Skies by treating each storm-feature measurement as a subskill updated per opportunity, and storm-type identification as evidence for the general interpreting skill. The authors note it "typically models the first attempts of solving a problem".

Design Implications¶

Context¶

Requirements¶

• Tasks must be scoreable as correct or incorrect per step, with retry actions removed so first attempts are modeled

Constraints¶

• Assumes model parameters do not vary with time, a simplification relative to DBN

Target Learners¶

• Grade 5 science students

Target Learning Objectives¶

• Weather observation and interpretation skills from the Weather Watch unit

Claims¶

• Bkt Mastery Profile Weather Skills [+M]

Related Theories¶

• Bayesian Knowledge Tracing (Two-State Hidden Markov Model)
• Bayesian Knowledge Tracing: a four-parameter student-learning model in two forms (HMM and Knowledge Tracing Algorithm)
• BKT+IRT: Bayesian Knowledge Tracing augmented with multidimensional generalizable student abilities and problem effects
• MS-BKT: a multistate knowledge tracing model with data-driven recency weights replacing the learning rate
• Spectral BKT: a BKT variant combining feature compensation (3-gram observations) with model compensation (four latent states)

Examples¶

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Key Sources¶

• 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