Augmenting Convolution Neural Networks by Utilizing Attention Mechanism for Knowledge Tracing
12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022
; 643 IFIP:80-86, 2022.
Article
in English
| Scopus | ID: covidwho-1898990
ABSTRACT
The devastating, ongoing Covid-19 epidemic has led to many students resorting to online education. In order to better guarantee the quality, online education faces severe challenges. There is an important part of online education referred to as Knowledge Tracing (KT). The objective of KT is to estimate students’ learning performance using a series of questions. It has garnered widespread attention ever since it was proposed. Recently, an increasing number of research efforts have concentrated on deep learning (DL)-based KT attributing to the huge success over traditional Bayesian-based KT methods. Most existing DL-based KT methods utilize Recurrent Neural Network and its variants, i.e. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) etc. Recurrent neural networks are good at modeling local features, but underperforms at long sequence modeling, so the attention mechanism is introduced to make up for this shortcoming. In this paper, we introduce a DL-based KT model referred to as Convolutional Attention Knowledge Tracing (CAKT) utilizing attention mechanism to augment Convolutional Neural Network (CNN) in order to enhance the ability of modeling longer range dependencies. © 2022, IFIP International Federation for Information Processing.
Attention mechanism (AM); Convolutional Neural Network (CNN); Knowledge Tracing (KT); Student performance prediction (SPP); Convolutional neural networks; E-learning; Education computing; Long short-term memory; Students; Attention mechanism; Attention mechanisms; Convolutional neural network; Knowledge tracing; Knowledge tracings; On-line education; Performance prediction; Student performance; Student performance prediction; Convolution
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022
Year:
2022
Document Type:
Article
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