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DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills (1905.06873v1)

Published 14 May 2019 in cs.CY, cs.AI, stat.AP, and stat.ML

Abstract: Spaced repetition is among the most studied learning strategies in the cognitive science literature. It consists in temporally distributing exposure to an information so as to improve long-term memorization. Providing students with an adaptive and personalized distributed practice schedule would benefit more than just a generic scheduler. However, the applicability of such adaptive schedulers seems to be limited to pure memorization, e.g. flashcards or foreign language learning. In this article, we first frame the research problem of optimizing an adaptive and personalized spaced repetition scheduler when memorization concerns the application of underlying multiple skills. To this end, we choose to rely on a student model for inferring knowledge state and memory dynamics on any skill or combination of skills. We argue that no knowledge tracing model takes both memory decay and multiple skill tagging into account for predicting student performance. As a consequence, we propose a new student learning and forgetting model suited to our research problem: DAS3H builds on the additive factor models and includes a representation of the temporal distribution of past practice on the skills involved by an item. In particular, DAS3H allows the learning and forgetting curves to differ from one skill to another. Finally, we provide empirical evidence on three real-world educational datasets that DAS3H outperforms other state-of-the-art EDM models. These results suggest that incorporating both item-skill relationships and forgetting effect improves over student models that consider one or the other.

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Summary

  • The paper introduces DAS3H, a novel model that predicts student performance by capturing both learning and forgetting across interconnected skills.
  • It utilizes additive factor models to derive distinct learning and forgetting curves, achieving better AUC and NLL metrics than existing approaches.
  • Empirical results on multiple datasets highlight DAS3H's potential to enhance adaptive learning systems by optimally scheduling distributed practice.

Overview of DAS3H: Enhancing Student Learning Models with Forgetting Dynamics

The paper "DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills" addresses the crucial challenge of scheduling distributed practice in educational settings where multiple interconnected skills are involved. The traditional models primarily focus on memorization tasks such as flashcard systems, but this paper introduces DAS3H, a model explicitly designed to predict student performance by modeling both learning and forgetting processes on composite skills.

DAS3H Model

The DAS3H model, an extension of the DASH model, leverages additive factor models and incorporates a fine-tuned system for analyzing the impact of distributed practice. It accounts for the intricacies of memory decay and provides distinct learning and forgetting curves for different skills involved in learning tasks. This approach is set against existing educational data mining (EDM) models and showcases significant improvements in predictive accuracy, as evidenced by empirical results across three educational datasets.

Strong Numerical Results

DAS3H's predictive capability is demonstrated through cross-validation methods applied to datasets such as ASSISTments 2012-2013 and the KDD Cup 2010 datasets. The model consistently outperforms alternative approaches like IRT, AFM, and DASH, particularly in AUC and NLL metrics, as revealed by detailed experimental results.

Bold Claims and Comparisons

One of the pivotal claims made is that models like DASH, which do not differentiate the influence of past practice across different skills, do not capture the full spectrum of learning dynamics. DAS3H contends that forgetting curves should vary between skills, and the model supports this claim through quantitative evidence where DAS3H's performance metrics surpass those of DASH. Furthermore, DAS3H's advantage lies in its ability to handle temporal distributions and outcomes of practice on multiple skills simultaneously, thereby offering a robust framework for applications beyond mere vocabulary learning.

Practical and Theoretical Implications

Practically, DAS3H has the potential to redefine adaptive learning environments by offering tailored scheduling systems that adapt to the learning and forgetting patterns of individuals across various domains. Theoretically, the model extends the research frontier in student performance prediction by integrating skill-specific memory dynamics, providing a more nuanced understanding of cognitive processes.

Speculation on Future Developments

Looking forward, DAS3H sets the stage for advancements in AI-driven educational technologies. Its approach could pave the way for more sophisticated models integrating latency measures and contextual features to further enhance prediction capabilities. Additionally, DAS3H could facilitate the development of adaptive intelligent tutoring systems that not only track but optimize learning efficacy over time.

The paper presents DAS3H as a promising step towards optimizing educational practices through refined modeling of student learning and forgetting. This model not only fills significant gaps left by traditional approaches but also offers substantial improvements in predicting student retention and performance on composite skills.

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