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Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations

Jiahao Chen, Zitao Liu, Shuyan Huang, Qiongqiong Liu, Weiqi Luo

arXiv:2302.06885v3cs.LGcs.AI

TL;DR

Deep KT models face an inaccurate assumption that questions sharing knowledge components have equivalent effects, and their predictions are difficult to interpret. QIKT combines question-centric knowledge-acquisition and problem-solving representations with an IRT-based prediction layer. Across three real-world datasets, it reportedly outperforms other state-of-the-art DLKT approaches in AUC while generating explainable predictions.

  • Problem

    Many KT approaches assume questions sharing knowledge components have equivalent effects, while deep learning KT predictions are difficult to interpret psychologically.

    Source: Introduction · p. 1

  • Method

    QIKT jointly learns question-centric knowledge-acquisition and problem-solving representations and uses an IRT-based prediction layer with explainable personalized parameters.

    Source: Introduction · p. 2

  • Results

    On three real-world datasets, QIKT outperforms other state-of-the-art DLKT approaches in AUC and generates explainable predictions.

    Source: Conclusions · p. 7

  • Takeaways & Limitations

    The reported experiments indicate that prediction performance and explainable predictions can be achieved together in the evaluated KT settings.

    Source: Introduction · p. 2, Conclusions · p. 7

Abstract

from arXiv · show

Knowledge tracing (KT) is a crucial technique to predict students' future performance by observing their historical learning processes. Due to the powerful representation ability of deep neural networks, remarkable progress has been made by using deep learning techniques to solve the KT problem. The majority of existing approaches rely on the \emph{homogeneous question} assumption that questions have equivalent contributions if they share the same set of knowledge components. Unfortunately, this assumption is inaccurate in real-world educational scenarios. Furthermore, it is very challenging to interpret the prediction results from the existing deep learning based KT models. Therefore, in this paper, we present QIKT, a question-centric interpretable KT model to address the above challenges. The proposed QIKT approach explicitly models students' knowledge state variations at a fine-grained level with question-sensitive cognitive representations that are jointly learned from a question-centric knowledge acquisition module and a question-centric problem solving module. Meanwhile, the QIKT utilizes an item response theory based prediction layer to generate interpretable prediction results. The proposed QIKT model is evaluated on three public real-world educational datasets. The results demonstrate that our approach is superior on the KT prediction task, and it outperforms a wide range of deep learning based KT models in terms of prediction accuracy with better model interpretability. To encourage reproducible results, we have provided all the datasets and code at \url{https://pykt.org/}.

Introduction

Knowledge tracing uses students’ interaction histories to model mastery and predict future performance, but deep sequential models face limits in question sensitivity and interpretability. QIKT addresses these gaps with question-centric cognitive modules and an IRT-based prediction layer; experiments report strong performance and interpretability.

Introduction

  • KT models historical learning interactions to track mastery over time and predict students’ future performance.The paper notes that such predictions can support intelligent tutoring systems and MOOC platforms.Source: Introduction · p. 1
  • Most existing approaches treat questions sharing knowledge components as equivalent, although question-specific effects can differ during both learning and problem solving.This assumption implies equal knowledge increments after the same responses and equal responses to different questions sharing components.Source: Introduction · pp. 1–2
  • Deep learning KT models are difficult to interpret psychologically, leaving tutors and students without a transparent decision process for evaluating predictions.The paper identifies this as a barrier to users accepting model-generated results without a convincing diagnosis.Source: Introduction · p. 2
  • QIKT jointly models question-specific knowledge acquisition and problem-solving ability, then uses an IRT-based layer to produce explainable personalized parameters.The acquisition module models knowledge-state changes after responses; the problem-solving module estimates ability for each question.Source: Introduction · p. 2
  • Experiments on three public datasets report superior prediction performance and psychologically meaningful interpretability, with the best AUC on publicly available reproducible KT settings.The paper compares QIKT with a wide range of baselines.Source: Introduction · p. 2

Background and Related Work

Deep sequential KT uses autoregressive models to represent evolving student knowledge, alongside memory-, graph-, and attention-based alternatives. Interpretability work spans local post-hoc explanations, interpretable cognitive structures, and explainable parameters; QIKT combines sequential modeling with an IRT prediction layer and directly optimizes explainable parameter learning.

Deep Sequential Modeling for Knowledge Tracing

  • Deep sequential KT models use autoregressive architectures to capture dependencies among chronologically ordered interactions and represent student knowledge states.Examples include recurrent architectures such as LSTM and GRU; prior work addressed reconstruction, prerequisite relations, text, forgetting, and generalization.Source: Deep Sequential Modeling for Knowledge… · p. 2
  • Other neural KT approaches include memory-augmented models for latent KC relations, graph models for interaction relations, and attention models for dependencies between interactions.These approaches use external memory, graph neural networks, and attention mechanisms, respectively.Source: Deep Sequential Modeling for Knowledge… · p. 2

Interpreting Deep Learning Based Knowledge Tracing Models

  • Interpretability methods include post-hoc local explanations that inspect individual predictions, including relevance propagation from output back to input.Lu et al. (2022) applied layerwise relevance propagation to a deep sequential KT model.Source: Interpreting Deep Learning Based… · pp. 2–3
  • Global interpretability with explainable structures embeds cognitive modules into DLKT architectures to expose knowledge-state modeling.Examples use multidimensional IRT factors or temporal cognitive modeling.Source: Interpreting Deep Learning Based… · p. 3
  • Global interpretability with explainable parameters estimates correct-response probabilities using cognitively interpretable parameters derived from DLKT outputs.Examples transform DKT hidden states into IRT-like parameters or learn student abilities and KC difficulties with a memory network.Source: Interpreting Deep Learning Based… · p. 3

Deep Sequential Modeling for Knowledge Tracing

  • QIKT uses an IRT final layer and directly incorporates explainable parameter learning into optimization, distinguishing it from output-focused IRT approaches and memory-network Deep-IRT.The paper describes its sequential architecture as more applicable and as having better prediction accuracy than Deep-IRT.Source: Interpreting Deep Learning Based… · p. 3

Problem Statement

QIKT predicts a student’s performance on an arbitrary future question from historical interactions, representing question- and KC-level information through distinct cognitive modules. Its prediction layer combines acquisition, mastery, and application scores using an IRT function.

Problem Statement

  • QIKT estimates the probability that a student will answer an arbitrary future question correctly from chronologically ordered historical interactions.Each interaction records a question, its associated knowledge components, a binary response, and a timestamp.Source: Problem Statement · p. 3
  • The interaction encoder combines question, response, and associated-KC information for question-centric acquisition, while a separate encoding supports question-agnostic knowledge-state modeling.For question-centric interactions, correct and incorrect responses are represented by concatenating the question and KC embeddings with a zero vector in opposite directions.Source: Interaction Encoder · p. 4
  • A question-centric knowledge acquisition module updates a question-level knowledge state with an LSTM and projects it nonlinearly into question-specific acquisition scores.The acquisition network extracts knowledge states with a fully connected layer before the nonlinear projection; sum pooling is used.Source: Question-centric Knowledge Acquisition… · p. 4
  • A question-agnostic state module models general knowledge changes to complement question-sensitive acquisition, which may be overly sensitive to guessing or slipping.The module updates a general state with another LSTM and projects mastery into KC space to compute mastery scores.Source: Question-agnostic Knowledge State Module · p. 4
  • The problem-solving module projects student mastery together with information about the next question to estimate question-specific knowledge application ability.Its input includes the student’s knowledge mastery and available information about the target question.Source: Question-centric Problem Solving Module · p. 5
  • The IRT prediction layer combines acquisition, mastery, and application scores, while training uses binary cross-entropy and adds objectives for the three internal scores.The prediction function has no learnable parameters; the additional score objectives are included to improve their discriminative ability.Source: Interpretable Prediction Layer · p. 5, Optimization of QIKT · p. 5

Experiment

Experiments evaluate QIKT on three educational datasets against deep learning knowledge-tracing baselines. They assess prediction performance, question-centric behavior, and the effects of removing model components.

Experiment Settings

Overall Performance

  • 3.30%, 2.60%, and 3.60% AUC gains over DKT occur on ASSIST2009, Algebra2005, and NeurIPS34, respectively; QIKT beats 13 baselines except for two IEKT losses.Source: Table 2 · p. 6

Qualitative Question-centric Effects

  • QIKT’s question-centric predictions vary across interactions, whereas question-agnostic KS outputs are smoother, including for homogeneous questions.Source: Results · p. 7, Figure 5 · p. 7

Ablation Study

  • QIKT has the highest AUC except against QIKT w/o KS on NeurIPS34; versus QIKT without IRT, its AUC changes by −0.09% on ASSIST2009, +0.81% on Algebra2005, and +0.07% on NeurIPS34.Source: Results · p. 7, Table 3 · p. 7

Conclusions

QIKT uses question-centric cognitive representations and an IRT-based prediction layer. Experiments on three real-world datasets report higher AUC than state-of-the-art deep learning KT approaches and explainable predictions.

  • QIKT estimates knowledge acquisition and measures student problem-solving ability for each specific question.Source: Conclusions · p. 7
  • QIKT’s IRT-based prediction layer is designed to make its predictions more explainable.Source: Conclusions · p. 7
  • On three real-world datasets, experiments show QIKT outperforms state-of-the-art DLKT approaches in AUC and generates explainable predictions.Source: Conclusions · p. 7

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