Forecasting future student mastery. Issue 3 (3rd July 2019)

• Record Type: Journal Article
• Authors: Slater, Stefan; Baker, Ryan
• Is Part Of: Distance education. Volume 40:Issue 3(2019)
• Page Start: 380
• Page End: 394
• Publication Date: 2019-07-03
• Subjects: Bayesian knowledge tracing -- performance factors analysis -- prediction -- knowledge inference -- forecasting; Distance education -- Periodicals; Distance education -- Australia -- Periodicals; 371.3505
• DOI: 10.1080/01587919.2019.1632169
• Languages: English
• ISSNs: 0158-7919

Abstract

ABSTRACT: Considerable attention has been given to methods for knowledge estimation, a category of methods for automatic assessment of a student's degree of skill mastery or knowledge at a specific time. Knowledge estimation is frequently used to make decisions about when a student has reached mastery and is ready to advance to new material, but there has been little work to forecast how far a student is from mastery or predict how much more practice the student will need before he or she will reach mastery. This article presents a method for predicting the point at which a student will reach skill mastery within an adaptive learning system, based on current approaches to estimating student knowledge. We apply this technique to two popular methods of modeling student learning – Bayesian knowledge tracing and performance factors analysis – and compare prediction correctness. Potential applications and future steps for improving the method are discussed.