Bibliographic Details
| Title: |
Deep auto encoders to adaptive E-learning recommender system |
| Authors: |
Everton Gomede, PhD, Rodolfo Miranda de Barros, PhD, Leonardo de Souza Mendes, PhD |
| Source: |
Computers and Education: Artificial Intelligence, Vol 2, Iss , Pp 100009- (2021) |
| Publisher Information: |
Elsevier, 2021. |
| Publication Year: |
2021 |
| Collection: |
LCC:Electronic computers. Computer science |
| Subject Terms: |
Adaptive recommender systems, Deep auto encoder, Artificial neural networks, E-learning, Lifelong learning, Electronic computers. Computer science, QA75.5-76.95 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2666-920X |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S2666920X21000035; https://doaj.org/toc/2666-920X |
| DOI: |
10.1016/j.caeai.2021.100009 |
| Access URL: |
https://doaj.org/article/ca68644ce84c44a3bc3c9383795944d4 |
| Accession Number: |
edsdoj.68644ce84c44a3bc3c9383795944d4 |
| Database: |
Directory of Open Access Journals |
