Does Interpretability of Knowledge Tracing Models Support Teacher Decision Making?
- URL: http://arxiv.org/abs/2511.02718v1
- Date: Tue, 04 Nov 2025 16:40:24 GMT
- Title: Does Interpretability of Knowledge Tracing Models Support Teacher Decision Making?
- Authors: Adia Khalid, Alina Deriyeva, Benjamin Paassen,
- Abstract summary: We show that decisions based on interpretable KT models achieve mastery faster than decisions based on a non-interpretable model.<n>Teachers rate interpretable KT models higher in terms of usability and trustworthiness.<n>This suggests that the relationship between model interpretability and teacher decisions is not straightforward.
- Score: 1.3775008990177111
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Knowledge tracing (KT) models are a crucial basis for pedagogical decision-making, namely which task to select next for a learner and when to stop teaching a particular skill. Given the high stakes of pedagogical decisions, KT models are typically required to be interpretable, in the sense that they should implement an explicit model of human learning and provide explicit estimates of learners' abilities. However, to our knowledge, no study to date has investigated whether the interpretability of KT models actually helps human teachers to make teaching decisions. We address this gap. First, we perform a simulation study to show that, indeed, decisions based on interpretable KT models achieve mastery faster compared to decisions based on a non-interpretable model. Second, we repeat the study but ask $N=12$ human teachers to make the teaching decisions based on the information provided by KT models. As expected, teachers rate interpretable KT models higher in terms of usability and trustworthiness. However, the number of tasks needed until mastery hardly differs between KT models. This suggests that the relationship between model interpretability and teacher decisions is not straightforward: teachers do not solely rely on KT models to make decisions and further research is needed to investigate how learners and teachers actually understand and use KT models.
Related papers
- Uncertainty-Aware Knowledge Tracing Models [3.8834950760134657]
We show an approach to add new capabilities to Knowledge Tracing models by capturing predictive uncertainty.<n>We show that uncertainty in KT models is informative and that this signal would be pedagogically useful for application in an educational learning platform.
arXiv Detail & Related papers (2025-09-25T20:06:02Z) - Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling [81.00825302340984]
We introduce Speculative Knowledge Distillation (SKD) to generate high-quality training data on-the-fly.<n>In SKD, the student proposes tokens, and the teacher replaces poorly ranked ones based on its own distribution.<n>We evaluate SKD on various text generation tasks, including translation, summarization, math, and instruction following.
arXiv Detail & Related papers (2024-10-15T06:51:25Z) - Exploring and Enhancing the Transfer of Distribution in Knowledge Distillation for Autoregressive Language Models [62.5501109475725]
Knowledge distillation (KD) is a technique that compresses large teacher models by training smaller student models to mimic them.
This paper introduces Online Knowledge Distillation (OKD), where the teacher network integrates small online modules to concurrently train with the student model.
OKD achieves or exceeds the performance of leading methods in various model architectures and sizes, reducing training time by up to fourfold.
arXiv Detail & Related papers (2024-09-19T07:05:26Z) - A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models [26.294808618068146]
Knowledge tracing plays a crucial role in predicting students' future performance.
Deep neural networks (DNNs) have shown great potential in solving the KT problem.
However, there still exist some important challenges when applying deep learning techniques to model the KT process.
arXiv Detail & Related papers (2024-03-12T05:15:42Z) - Comparative Knowledge Distillation [102.35425896967791]
Traditional Knowledge Distillation (KD) assumes readily available access to teacher models for frequent inference.
We propose Comparative Knowledge Distillation (CKD), which encourages student models to understand the nuanced differences in a teacher model's interpretations of samples.
CKD consistently outperforms state of the art data augmentation and KD techniques.
arXiv Detail & Related papers (2023-11-03T21:55:33Z) - Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge
Distillation [70.92135839545314]
We propose the dynamic prior knowledge (DPK), which integrates part of teacher's features as the prior knowledge before the feature distillation.
Our DPK makes the performance of the student model positively correlated with that of the teacher model, which means that we can further boost the accuracy of students by applying larger teachers.
arXiv Detail & Related papers (2022-06-13T11:52:13Z) - Interpretable Knowledge Tracing: Simple and Efficient Student Modeling
with Causal Relations [21.74631969428855]
Interpretable Knowledge Tracing (IKT) is a simple model that relies on three meaningful latent features.
IKT's prediction of future student performance is made using a Tree-Augmented Naive Bayes (TAN)
IKT has great potential for providing adaptive and personalized instructions with causal reasoning in real-world educational systems.
arXiv Detail & Related papers (2021-12-15T19:05:48Z) - Teachers' perspective on fostering computational thinking through
educational robotics [0.6410282200111983]
The Creative Problem Solving Model (CCPS) can be employed to improve the design of educational robotics learning activities.
The objective of the present study is to validate the model with teachers, specifically considering how they may employ the model in their own practices.
Teachers found the CCPS model useful to foster skills but could not recognise the impact of specific intervention methods on CT-related cognitive processes.
arXiv Detail & Related papers (2021-05-11T12:31:44Z) - Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need
in MOOC Forums [58.221459787471254]
Massive Open Online Courses (MOOCs) have become a popular choice for e-learning thanks to their great flexibility.
Due to large numbers of learners and their diverse backgrounds, it is taxing to offer real-time support.
With the large volume of posts and high workloads for MOOC instructors, it is unlikely that the instructors can identify all learners requiring intervention.
This paper explores for the first time Bayesian deep learning on learner-based text posts with two methods: Monte Carlo Dropout and Variational Inference.
arXiv Detail & Related papers (2021-04-26T15:12:13Z) - On the Interpretability of Deep Learning Based Models for Knowledge
Tracing [5.120837730908589]
Knowledge tracing allows Intelligent Tutoring Systems to infer which topics or skills a student has mastered.
Deep Learning based models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have achieved significant improvements.
However, these deep learning based models are not as interpretable as other models because the decision-making process learned by deep neural networks is not wholly understood.
arXiv Detail & Related papers (2021-01-27T11:55:03Z) - Towards Interpretable Deep Learning Models for Knowledge Tracing [62.75876617721375]
We propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models.
Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model.
Experiment results show the feasibility using the LRP method for interpreting the DLKT model's predictions.
arXiv Detail & Related papers (2020-05-13T04:03:21Z)
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.