pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models

• URL: http://arxiv.org/abs/2206.11460v5
• Date: Mon, 9 Jan 2023 10:17:42 GMT
• Title: pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models
• Authors: Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang, Jiliang Tang, Weiqi Luo
• Abstract summary: Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time. DLKT approaches are still left somewhat unknown and proper measurement and analysis of these approaches remain a challenge. We introduce a comprehensive python based benchmark platform, textscpyKT, to guarantee valid comparisons across DLKT methods.
• Score: 46.05383477261115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been made of using various deep learning techniques to solve the KT problem. However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge. First, data preprocessing procedures in existing works are often private and custom, which limits experimental standardization. Furthermore, existing DLKT studies often differ in terms of the evaluation protocol and are far away real-world educational contexts. To address these problems, we introduce a comprehensive python based benchmark platform, \textsc{pyKT}, to guarantee valid comparisons across DLKT methods via thorough evaluations. The \textsc{pyKT} library consists of a standardized set of integrated data preprocessing procedures on 7 popular datasets across different domains, and 10 frequently compared DLKT model implementations for transparent experiments. Results from our fine-grained and rigorous empirical KT studies yield a set of observations and suggestions for effective DLKT, e.g., wrong evaluation setting may cause label leakage that generally leads to performance inflation; and the improvement of many DLKT approaches is minimal compared to the very first DLKT model proposed by Piech et al. \cite{piech2015deep}. We have open sourced \textsc{pyKT} and our experimental results at https://pykt.org/. We welcome contributions from other research groups and practitioners.

Related papers

• Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing [59.480951050911436]
  We present KCQRL, a framework for automated knowledge concept annotation and question representation learning. We demonstrate the effectiveness of KCQRL across 15 KT algorithms on two large real-world Math learning datasets.
  arXiv  Detail & Related papers  (2024-10-02T16:37:19Z)
• Towards Robust Knowledge Tracing Models via k-Sparse Attention [33.02197868261949]
  textscsparseKT is a simple yet effective framework to improve the robustness and generalization of the attention based DLKT approaches. We show that our textscsparseKT is able to help attentional KT models get rid of irrelevant student interactions.
  arXiv  Detail & Related papers  (2024-07-24T08:49:18Z)
• Downstream-Pretext Domain Knowledge Traceback for Active Learning [138.02530777915362]
  We propose a downstream-pretext domain knowledge traceback (DOKT) method that traces the data interactions of downstream knowledge and pre-training guidance. DOKT consists of a traceback diversity indicator and a domain-based uncertainty estimator. Experiments conducted on ten datasets show that our model outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-20T01:34:13Z)
• Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning [25.566963415155325]
  We propose a low-resource KT framework called LoReKT to address above challenges. Inspired by the prevalent "pre-training and fine-tuning" paradigm, we aim to learn transferable parameters and representations from rich-resource KT datasets. We design an encoding mechanism to incorporate student interactions from multiple KT data sources.
  arXiv  Detail & Related papers  (2024-03-11T13:44:43Z)
• Continual Learning with Pre-Trained Models: A Survey [61.97613090666247]
  Continual Learning aims to overcome the catastrophic forgetting of former knowledge when learning new ones. This paper presents a comprehensive survey of the latest advancements in PTM-based CL.
  arXiv  Detail & Related papers  (2024-01-29T18:27:52Z)
• B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding [51.74479522965712]
  We propose a meta-learner called the B-Learner, which can efficiently learn sharp bounds on the CATE function under limits on hidden confounding. We prove its estimates are valid, sharp, efficient, and have a quasi-oracle property with respect to the constituent estimators under more general conditions than existing methods.
  arXiv  Detail & Related papers  (2023-04-20T18:07:19Z)
• simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing [22.055683237994696]
  We provide a strong but simple baseline method to deal with the KT task named textscsimpleKT. Inspired by the Rasch model in psychometrics, we explicitly model question-specific variations to capture the individual differences among questions. We use the ordinary dot-product attention function to extract the time-aware information embedded in the student learning interactions.
  arXiv  Detail & Related papers  (2023-02-14T08:09:09Z)
• Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks [10.474382290378049]
  We propose Code-based Deep Knowledge Tracing (Code-DKT), a model that uses an attention mechanism to automatically extract and select domain-specific code features to extend DKT. We compared the effectiveness of Code-DKT against Bayesian and Deep Knowledge Tracing (BKT and DKT) on a dataset from a class of 50 students attempting to solve 5 programming assignments.
  arXiv  Detail & Related papers  (2022-06-07T19:29:44Z)
• WRENCH: A Comprehensive Benchmark for Weak Supervision [66.82046201714766]
  benchmark consists of 22 varied real-world datasets for classification and sequence tagging. We use benchmark to conduct extensive comparisons over more than 100 method variants to demonstrate its efficacy as a benchmark platform.
  arXiv  Detail & Related papers  (2021-09-23T13:47:16Z)
• qDKT: Question-centric Deep Knowledge Tracing [29.431121650577396]
  We introduce qDKT, a variant of DKT that models every learner's success probability on individual questions over time. qDKT incorporates graph Laplacian regularization to smooth predictions under each skill. Experiments on several real-world datasets show that qDKT achieves state-of-art performance on predicting learner outcomes.
  arXiv  Detail & Related papers  (2020-05-25T23:43:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.