Enhancing Computer Students’ Academic Performance through Predictive Modelling - A Proactive Approach

Mutanu, Leah; Machoka, Philip

doi:10.1109/iccse.2019.8845452

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Alternatively, many researchers [21][22][23] have used the multi-layer perceptron (MLP) model for automated risk prediction. Zeineddine et al [21] compared the performance between the MLP model and conventional machine learning approaches, such as k-means, k-nearest neighbors, naive Bayes, SVMs, DTs and logistic regression (LR).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The accuracy of their results fell from 56% to 83%. The method proposed by Mutanu et al [22] reached about 83% accuracy using parameters such as grade point average (GPA) before enrollment. Lee et al [23] used the online learning behaviors of students before different exams to predict their scores on those exams.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, if the provided learning data are accumulated during different learning periods, the performance of the model is drastically reduced even with such operations. To avoid filling in zeros, which may alter the distribution of data, previous studies [21,22,25,26] used the same length of time as the input when training the model. For example, if we want to predict the outcomes of students in the ninth week, we only provide the model with the cumulative nine weeks of data.…”

Section: Literature Reviewmentioning

confidence: 99%

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Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks

2021

Sustainability

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While the use of deep neural networks is popular for predicting students’ learning outcomes, convolutional neural network (CNN)-based methods are used more often. Such methods require numerous features, training data, or multiple models to achieve week-by-week predictions. However, many current learning management systems (LMSs) operated by colleges cannot provide adequate information. To make the system more feasible, this article proposes a recurrent neural network (RNN)-based framework to identify at-risk students who might fail the course using only a few common learning features. RNN-based methods can be more effective than CNN-based methods in identifying at-risk students due to their ability to memorize time-series features. The data used in this study were collected from an online course that teaches artificial intelligence (AI) at a university in northern Taiwan. Common features, such as the number of logins, number of posts and number of homework assignments submitted, are considered to train the model. This study compares the prediction results of the RNN model with the following conventional machine learning models: logistic regression, support vector machines, decision trees and random forests. This work also compares the performance of the RNN model with two neural network-based models: the multi-layer perceptron (MLP) and a CNN-based model. The experimental results demonstrate that the RNN model used in this study is better than conventional machine learning models and the MLP in terms of F-score, while achieving similar performance to the CNN-based model with fewer parameters. Our study shows that the designed RNN model can identify at-risk students once one-third of the semester has passed. Some future directions are also discussed.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%