Jointly Modeling Heterogeneous Student Behaviors and Interactions Among Multiple Prediction Tasks

TL;DR

This paper introduces a novel multi-task learning framework that models heterogeneous student behaviors using a specialized LSTM and attention mechanisms, improving multiple prediction tasks related to student success in a smart campus setting.

Contribution

It proposes a new LSTM variant with soft-attention for modeling heterogeneous behaviors and a co-attention mechanism for explicit task interaction, addressing data sparsity and relatedness among tasks.

Findings

The model effectively captures diverse student behaviors.

It improves prediction accuracy across multiple student-related tasks.

Experimental results demonstrate the model's superiority over baselines.

Abstract

Prediction tasks about students have practical significance for both student and college. Making multiple predictions about students is an important part of a smart campus. For instance, predicting whether a student will fail to graduate can alert the student affairs office to take predictive measures to help the student improve his/her academic performance. With the development of information technology in colleges, we can collect digital footprints which encode heterogeneous behaviors continuously. In this paper, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem. To this end, we propose a variant of LSTM and a soft-attention mechanism. The proposed LSTM is able to learn the student profile-aware representation from heterogeneous behavior sequences. The proposed soft-attention mechanism can dynamically learn different importance degrees of different days for every student. In this way, heterogeneous behaviors can be well modeled. In order to model interactions among multiple prediction tasks, we propose a co-attention mechanism based unit. With the help of the stacked units, we can explicitly control the knowledge transfer among multiple tasks. We design three motivating behavior prediction tasks based on a real-world dataset collected from a college. Qualitative and quantitative experiments on the three prediction tasks have demonstrated the effectiveness of our model.