Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summa...

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Vydáno v:2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS) s. 1 - 10
Hlavní autoři: Yang, Shangshang, Zhen, Cheng, Tian, Ye, Ma, Haiping, Liu, Yuanchao, Zhang, Panpan, Zhang, Xingyi
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 22.09.2023
Témata:
Behavioral sciences
Cognitive diagnosis
Computational modeling
Computer architecture
Education
evolutionary algorithm
Evolutionary computation
Genetics
multi-objective optimization
neural architecture search
Search problems
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Shrnutí:Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures.
DOI:10.1109/DOCS60977.2023.10294588