Search for: All records

The knowledge tracing (KT) task consists of predicting students’ future performance on instructional activities given their past performance. Recently, deep learning models used to solve this task yielded relative excellent prediction results relative to prior approaches. Despite this success, the majority of these models ignore relevant information that can be used to enhance the knowledge tracing performance. To overcome these limitations, we propose a generic framework that also accounts for the engagement level of students, the difficulty level of the instructional activities, and the natural language processing embeddings of the text of each concept. Furthermore, to capture the fact that students’ knowledge states evolve over time we employ a LSTM-based model. Then, we pass such sequences of knowledge states to a Temporal Convolutional Network to predict future performance. Several empirical experiments have been conducted to evaluate the effectiveness of our proposed framework for KT using Cognitive Tutor datasets. Experimental results showed the superior performance of our proposed model over many existing deep KT models. And AUC of 96.57% has been achieved on the Algebra 2006-2007 dataset. 

Assessing the correctness of student answers in a dialog-based intelligent tutoring system (ITS) is a well-defined Natural Language Processing (NLP) task that has attracted the attention of many researchers in the field. Inspired by Vaswani’s transformer, we propose in this paper an attention-based transformer neural network with a multi-head attention mechanism for the task of student answer assessment. Results show the competitiveness of our proposed model. A highest accuracy of 71.5% was achieved when using ELMo embeddings, 10 heads of attention, and 2 layers. This is very competitive and rivals the highest accuracy achieved by a previously proposed BI-GRU-Capsnet deep network (72.5%) on the same dataset. The main advantages of using transformers over BI-GRU-Capsnet is reducing the training time and giving more space for parallelization. 

Graph Convolutional Networks have achieved impressive results in multiple NLP tasks such as text classification. However, this approach has not been explored yet for the student answer assessment task. In this work, we propose to use Graph Convolutional Networks to automatically assess freely generated student answers within the context of dialogue-based intelligent tutoring systems. We convert this task to a node classification task. First, we build a DTGrade graph where each node represents the concatenation of the student answer and its corresponding reference answer whereas the edges represent the relatedness between nodes. Second, the DTGrade graph is fed to two layers of Graph Convolutional Networks. Finally, the output of the second layer is fed to a softmax layer. The empirical results showed that our model reached the state-of-the-art results by obtaining an accuracy of 73%. 

Motivated by the good results of capsule networks in text classification and other Natural Language Processing tasks, we present in this paper a Bi-GRU Capsule Networks model to automatically assess freely-generated student answers assessment within the context of dialogue-based intelligent tutoring systems. Our proposed model is composed of several important components: an embedding layer, a Bi-GRU layer, a capsule layer and a SoftMax layer. We have conducted a number of experiments considering a binary classification task: correct or incorrect answers. Our model has reached a highest accuracy of 72.50 when using an Elmo word embedding as detailed in the body of the paper.