Bayesian Knowledge Tracing (BKT) models were in active use in the Intelligent Tutoring Systems (ITS) field for over 20 years. They have been intensively studied, and a number of useful extensions to them were proposed and experimentally tested. Among the most widely researched extensions to BKT models are various types of individualization. Individualization, broadly defined, is a way to account for variability in students that are working with the ITS that uses BKT model to represent and track student learning. One of the approaches to individualizing BKT is to split its parameters into per-skill and per-student components. In this work, we are proposing an approach to individualizing BKT that is based on Hierarchical Bayesian Models (HBM) and, in addition to capturing student-level variability in the data, weighs the contribution of per-student and per-skill effects to the overall variance in the data.

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Individualizing Bayesian Knowledge Tracing. Are Skill Parameters More Important Than Student Parameters?

This contract was minted on an Ethereum Testnet Network for testing purposes.
Contract address: 0xf935Dd110068D05869a421270EA9D5a4D64Ad7Df
Edition of 5 (number of copies)
Minted on 25 October 2022.

Individualizing Bayesian Knowledge Tracing. Are Skill Parameters More Important Than Student Parameters?

This contract was minted on an Ethereum Testnet Network for testing purposes.Contract address: 0xf935Dd110068D05869a421270EA9D5a4D64Ad7DfEdition of 5 (number of copies)Minted on 25 October 2022.

This contract was minted on an Ethereum Testnet Network for testing purposes.
Contract address: 0xf935Dd110068D05869a421270EA9D5a4D64Ad7Df
Edition of 5 (number of copies)
Minted on 25 October 2022.