Personalized Programming Guidance based on Deep Programming Learning
  Style Capturing
Yingfan Liu Renyu Zhu Ming Gao
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.