Introduction

• The reason why representer choose this paper…
  • Compared to other review paper, this paper has more higher level overview
• KT
  • one of the fundamental tasks for student behavioral data anaylsis, aiming to monitor students’ evolving knowledge state during their problem-solving process

Overview

A. Problem Definition

• Knowledge Concepts

  • ex) Reading, Listening, Speaking in TOEFL

• Formulating learning sequence like..

  $$ X={([e_{1}, k_{e_{1}}], a_{1}, r_{1}), ([e_{2}, k_{e_{2}}], a_{2}, r_{2}), …(), …} $$

  • $e_{i}$: exercise
  • $k_{e_{i}}$: exercise’s related to KC
  • $a_{t}$: correctness label
  • $r_{t}$: side information

• The general research problem in Knowledge Tracing

  <aside> 💡

  Given sequences of learning interactions in online learning systems, knowledge tracing aims to monitor students’ evolving knowledge states during the learning process and predict their performance on future exercises. The measured knowledge states can be further applied to individualize students’ learning schemes in order to maximize their learning efficiency

  </aside>

• To evaluate the quality of the traced knowledge state by KT model, see how model predicts on student performance is the best option

  • But, KT highlights on more on interpretability and rationality of knowledge state, which is related to the students’ acceptance of the conclusions based on the KT model

Fundamental Knowledge Tracing Models

A. Bayesian Knowledge Tracing

• BKT is one specific form of Hidden Markov Model
  • Two types in HMM, transition / emission

Variants of Knowledge Tracing Models

• To incorporate more learning scenarios, variants of KT models have been emerged

A. Modeling Individualization before Learning

• Attempt 1. learn the individual learning paramter
  • → offers a marginal improvement compared to original BKT