Affiliation:
1. Department of Computer Science and Engineering, Shanghai Jiao Tong University Shanghai, China
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 that encode heterogeneous behaviors continuously. In this article, 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
Long-Short Term Memory (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.
Funder
National Key AI Program of China
National Science Foundation of China
Shanghai Municipal Science and Technology Commission
Program for Changjiang Young Scholars in University of China, the Program for China Top Young Talents, the Program for Shanghai Top Young Talents, SJTU Global Strategic
Oceanic Interdisciplinary Program of Shanghai Jiao Tong University
Scientific Research Fund of Second Institute of Oceanography
Publisher
Association for Computing Machinery (ACM)
Reference47 articles.
1. Predicting students’ marks from Moodle logs using neural network models;Calvo-Flores M. Delgado;Current Developments in Technology-Assisted Education,2006
Cited by
9 articles.
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