Student Performance Prediction Using Dynamic Neural Models

• URL: http://arxiv.org/abs/2106.00524v1
• Date: Tue, 1 Jun 2021 14:40:28 GMT
• Title: Student Performance Prediction Using Dynamic Neural Models
• Authors: Marina Delianidi, Konstantinos Diamantaras, George Chrysogonidis, Vasileios Nikiforidis
• Abstract summary: We address the problem of predicting the correctness of the student's response on the next exam question based on their previous interactions. We compare the two major classes of dynamic neural architectures for its solution, namely the finite-memory Time Delay Neural Networks (TDNN) and the potentially infinite-memory Recurrent Neural Networks (RNN) Our experiments show that the performance of the RNN approach is better compared to the TDNN approach in all datasets that we have used.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the problem of predicting the correctness of the student's response on the next exam question based on their previous interactions in the course of their learning and evaluation process. We model the student performance as a dynamic problem and compare the two major classes of dynamic neural architectures for its solution, namely the finite-memory Time Delay Neural Networks (TDNN) and the potentially infinite-memory Recurrent Neural Networks (RNN). Since the next response is a function of the knowledge state of the student and this, in turn, is a function of their previous responses and the skills associated with the previous questions, we propose a two-part network architecture. The first part employs a dynamic neural network (either TDNN or RNN) to trace the student knowledge state. The second part applies on top of the dynamic part and it is a multi-layer feed-forward network which completes the classification task of predicting the student response based on our estimate of the student knowledge state. Both input skills and previous responses are encoded using different embeddings. Regarding the skill embeddings we tried two different initialization schemes using (a) random vectors and (b) pretrained vectors matching the textual descriptions of the skills. Our experiments show that the performance of the RNN approach is better compared to the TDNN approach in all datasets that we have used. Also, we show that our RNN architecture outperforms the state-of-the-art models in four out of five datasets. It is worth noting that the TDNN approach also outperforms the state of the art models in four out of five datasets, although it is slightly worse than our proposed RNN approach. Finally, contrary to our expectations, we find that the initialization of skill embeddings using pretrained vectors offers practically no advantage over random initialization.

Related papers

• Investigating Sparsity in Recurrent Neural Networks [0.0]
  This thesis focuses on investigating the effects of pruning and Sparse Recurrent Neural Networks on the performance of RNNs. We first describe the pruning of RNNs, its impact on the performance of RNNs, and the number of training epochs required to regain accuracy after the pruning is performed. Next, we continue with the creation and training of Sparse Recurrent Neural Networks and identify the relation between the performance and the graph properties of its underlying arbitrary structure.
  arXiv  Detail & Related papers  (2024-07-30T07:24:58Z)
• Time Elastic Neural Networks [2.1756081703276]
  We introduce and detail an atypical neural network architecture, called time elastic neural network (teNN) The novelty compared to classical neural network architecture is that it explicitly incorporates time warping ability. We demonstrate that, during the training process, the teNN succeeds in reducing the number of neurons required within each cell.
  arXiv  Detail & Related papers  (2024-05-27T09:01:30Z)
• Learning Useful Representations of Recurrent Neural Network Weight Matrices [30.583752432727326]
  Recurrent Neural Networks (RNNs) are general-purpose parallel-sequential computers. How to learn useful representations of RNN weights that facilitate RNN analysis as well as downstream tasks? We consider several mechanistic approaches for RNN weights and adapt the permutation equivariant Deep Weight Space layer for RNNs. Our two novel functionalist approaches extract information from RNN weights by 'interrogating' the RNN through probing inputs.
  arXiv  Detail & Related papers  (2024-03-18T17:32:23Z)
• Bayesian Neural Networks with Domain Knowledge Priors [52.80929437592308]
  We propose a framework for integrating general forms of domain knowledge into a BNN prior. We show that BNNs using our proposed domain knowledge priors outperform those with standard priors.
  arXiv  Detail & Related papers  (2024-02-20T22:34:53Z)
• Dynamic Analysis and an Eigen Initializer for Recurrent Neural Networks [0.0]
  We study the dynamics of the hidden state in recurrent neural networks. We propose a new perspective to analyze the hidden state space based on an eigen decomposition of the weight matrix. We provide an explanation for long-term dependency based on the eigen analysis.
  arXiv  Detail & Related papers  (2023-07-28T17:14:58Z)
• Knowledge Enhanced Neural Networks for relational domains [83.9217787335878]
  We focus on a specific method, KENN, a Neural-Symbolic architecture that injects prior logical knowledge into a neural network. In this paper, we propose an extension of KENN for relational data.
  arXiv  Detail & Related papers  (2022-05-31T13:00:34Z)
• Rethinking Nearest Neighbors for Visual Classification [56.00783095670361]
  k-NN is a lazy learning method that aggregates the distance between the test image and top-k neighbors in a training set. We adopt k-NN with pre-trained visual representations produced by either supervised or self-supervised methods in two steps. Via extensive experiments on a wide range of classification tasks, our study reveals the generality and flexibility of k-NN integration.
  arXiv  Detail & Related papers  (2021-12-15T20:15:01Z)
• Scene Understanding for Autonomous Driving [0.0]
  We study the behaviour of different configurations of RetinaNet, Faster R-CNN and Mask R-CNN presented in Detectron2. We observe a significant improvement in performance after fine-tuning these models on the datasets of interest. We run inference in unusual situations using out of context datasets, and present interesting results.
  arXiv  Detail & Related papers  (2021-05-11T09:50:05Z)
• Binary Graph Neural Networks [69.51765073772226]
  Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. In this paper, we present and evaluate different strategies for the binarization of graph neural networks. We show that through careful design of the models, and control of the training process, binary graph neural networks can be trained at only a moderate cost in accuracy on challenging benchmarks.
  arXiv  Detail & Related papers  (2020-12-31T18:48:58Z)
• Modeling Token-level Uncertainty to Learn Unknown Concepts in SLU via Calibrated Dirichlet Prior RNN [98.4713940310056]
  One major task of spoken language understanding (SLU) in modern personal assistants is to extract semantic concepts from an utterance. Recent research collected question and answer annotated data to learn what is unknown and should be asked. We incorporate softmax-based slot filling neural architectures to model the sequence uncertainty without question supervision.
  arXiv  Detail & Related papers  (2020-10-16T02:12:30Z)
• Neural Networks Enhancement with Logical Knowledge [83.9217787335878]
  We propose an extension of KENN for relational data. The results show that KENN is capable of increasing the performances of the underlying neural network even in the presence relational data.
  arXiv  Detail & Related papers  (2020-09-13T21:12:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.