"Mastery Learning Heuristics and Their Hidden Models"

Assistant Professor Shayan Doroudi contributed an article on mastery learning heuristics in the publication Artificial Intelligence in Education.

The article’s title is “Mastery Learning Heuristics and Their Hidden Models.”  

Doroudi’s research interests include learning analytics, learning sciences, educational technology, and educational data science. He is particularly interested in studying the prospects and limitations of data-driven algorithms in learning technologies, including lessons that can be drawn from the rich history of educational technology. Doroudi earned his B.S. in Computer Science from the California Institute of Technology, and his M.S. and Ph.D. in Computer Science from Carnegie Mellon.

Abstract

​Mastery learning algorithms are used in many adaptive learning technologies to assess when a student has learned a particular concept or skill. To assess mastery, some technologies utilize data-driven models while others use simple heuristics. Prior work has suggested that heuristics may often perform comparably to model-based algorithms. But is there any reason we should expect these heuristics to be reasonable? In this chapter, we show that two prominent mastery learning heuristics can be reinterpreted as model-based algorithms. In particular, we show that the N-Consecutive Correct in a Row heuristic and a simplified version of ALEKS’ mastery learning heuristic are both optimal policies for variants of the Bayesian knowledge tracing model. By putting mastery learning heuristics on the same playing field as model-based algorithms, we can gain insights on their hidden assumptions about learning and why they might perform well in practice.