Deep auto encoders to adaptive E-learning recommender system

被引：0
|
作者：
Gomede E. [1 ]
de Barros R.M. [2 ]
Mendes L.D.S. [1 ]
机构：
[1] Electrical Engineering and Computer School, State University of Campinas, Cidade Universitária Zeferino Vaz, Av. Albert Einstein, 400, Distrito Barão Geraldo, Campinas, 13083-852, SP
[2] Computer Science Departments, State University of Londrina, Campus Universitário, Rod. Celso Garcia Cid, Km 380, s/n, Londrina, 86057-970, PR
来源：
Computers and Education: Artificial Intelligence | 2021年 / 2卷
关键词：
Adaptive recommender systems; Artificial neural networks; Deep auto encoder; E-learning; Lifelong learning;
D O I：
10.1016/j.caeai.2021.100009
中图分类号：
学科分类号：
摘要：
Adaptive learning, supported by Information & Communication Technology (TIC), is an important research area for educational systems which aim to improve the outcomes of students. Thus, the investigation of what should be adapted and how much to adapt constitute a foundation to Adaptive E-learning Systems (AES). In this paper, we compared three classes of Deep Auto Encoders and the popularity model to address the problem of learning and predicting the preferences of student on AES: Collaborative Denoising Auto Encoders (CDAE), Deep Auto Encoders for Collaborative Filtering (DAE-CF), and Deep Auto Encoders for Collaborative Filtering using Content Information (DAE-CI). The results point out that the DAE-CF is more effective providing significant adaptability. Furthermore, we present the concept named as signature of preference to represent a more granular class of adaptability. Therefore, this model may be used in e-learning systems to provide adaptability and help to improve the outcomes of students. © 2021 The Author(s)
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