With the rapid development of big data and AI technology, programming is in
high demand and has become an essential skill for students. Meanwhile,
researchers also focus on boosting the online judging system's guidance ability
to reduce students' dropout rates. Previous studies mainly targeted at
enhancing learner engagement on online platforms by providing personalized
recommendations. However, two significant challenges still need to be addressed
in programming: C1) how to recognize complex programming behaviors; C2) how to
capture intrinsic learning patterns that align with the actual learning
process. To fill these gaps, in this paper, we propose a novel model called
Programming Exercise Recommender with Learning Style (PERS), which simulates
learners' intricate programming behaviors. Specifically, since programming is
an iterative and trial-and-error process, we first introduce a positional
encoding and a differentiating module to capture the changes of consecutive
code submissions (which addresses C1). To better profile programming behaviors,
we extend the Felder-Silverman learning style model, a classical pedagogical
theory, to perceive intrinsic programming patterns. Based on this, we align
three latent vectors to record and update programming ability, processing
style, and understanding style, respectively (which addresses C2). We perform
extensive experiments on two real-world datasets to verify the rationality of
modeling programming learning styles and the effectiveness of PERS for
personalized programming guidance.

基于其让学习者更好地掌握编程技能和个性化编程指导的目标，本文提出了一种名为 PERS 的新模型，通过模拟学习者复杂的编程行为，并结合 Felder-Silverman 学习风格模型，实现对编程行为的全面描述和个性化编程指导的有效性验证。