An Enhanced CNN Model on Temporal Educational Data for Program-Level Student Classification

Vo, Chau; Nguyen, Hua Phung

doi:10.1007/978-3-030-41964-6_38

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…For deep learning methods, CNN (Convolutional Neural Network) is designed for image-related tasks. An innovative study used an enhanced 2D-CNN (Two-dimensional Convolutional Neural Network) model on a set of temporal educational data by transforming the data into a colour-image-like structure [54]. This current research adopted 1D-CNN (One-dimensional Convolutional Neural Network) models, using hyperparameters kernel and stride to control the feature-extracting window size and the window slice step, to examine its capacity on temporal clickstream data.…”

Section: Methodsmentioning

confidence: 99%

Predicting Student Performance Using Clickstream Data and Machine Learning

Liu

Fan

et al. 2022

Education Sciences

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Student performance predictive analysis has played a vital role in education in recent years. It allows for the understanding students’ learning behaviours, the identification of at-risk students, and the development of insights into teaching and learning improvement. Recently, many researchers have used data collected from Learning Management Systems to predict student performance. This study investigates the potential of clickstream data for this purpose. A total of 5341 sample students and their click behaviour data from the OULAD (Open University Learning Analytics Dataset) are used. The raw clickstream data are transformed, integrating the time and activity dimensions of students’ click actions. Two feature sets are extracted, indicating the number of clicks on 12 learning sites based on weekly and monthly time intervals. For both feature sets, the experiments are performed to compare deep learning algorithms (including LSTM and 1D-CNN) with traditional machine learning approaches. It is found that the LSTM algorithm outperformed other approaches on a range of evaluation metrics, with up to 90.25% accuracy. Four out of twelve learning sites (content, subpage, homepage, quiz) are identified as critical in influencing student performance in the course. The insights from these critical learning sites can inform the design of future courses and teaching interventions to support at-risk students.

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Section: Methodsmentioning

confidence: 99%

Predicting Student Performance Using Clickstream Data and Machine Learning

Liu

Fan

et al. 2022

Education Sciences

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“…Depeursinge dataset [65] 843 images Relevant but only 400 cases Naydenova [73] 1093 images Relevant but only 777 cases Self-generated dataset [44] Average accuracy = 92.16% The accuracy of pneumonia detection was only 88.33%, no other performance metrics were evaluated. Gu et al [125] FCN [92] and DCNN MC [40], JSRT [42], and Chest X-rays 14…”

Section: Comparative Analysis and Discussionmentioning

confidence: 99%

“…To minimize MSFE, the errors associated with the majority and minority classes are simultaneously minimized, resulting in improved unbiased classification accuracy [39] [40]. A better classification performance can be obtained based on the MSFE when compared with that obtained based on the MSE [41]; however, the improvement is only minor [35] [40]. Data sampling can also help in data balancing problems.…”

Section: A Data Balancing Augmentation and Enhancement Using Tradimentioning

confidence: 99%

Intelligent Pneumonia Identification From Chest X-Rays: A Systematic Literature Review

2021

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Chest radiography is a significant diagnostic tool used to detect diseases afflicting the chest. The automatic detection techniques associated with computer vision are being adopted in medical imaging research. Over the last decade, several remarkable advancements have been made in the field of medical diagnostics with the application of deep learning techniques. Various automated systems have been proposed for the rapid detection of pneumonia from chest X-rays. Although several algorithms are currently available for pneumonia detection, a detailed review summarizing the literature and offering guidelines for medical practitioners is lacking. This study will help practitioners to select the most effective and efficient methods from a real-time perspective, review the available datasets, and understand the results obtained in this domain. It will also present an overview of the literature on intelligent pneumonia identification from chest X-rays. The usability, goodness factors, and computational complexities of the algorithms employed for intelligent pneumonia identification are analyzed. Additionally, this study discusses the quality, usability, and size of the available chest X-ray datasets and techniques for coping with unbalanced datasets. A detailed comparison of the available studies reveals that the majority of the applied datasets are highly unbalanced and limited, providing unreliable results and rendering methods that are unsuitable for large-scale use. Large-scale balanced datasets can be obtained via smart techniques, such as generative adversarial networks. Current literature has indicated that deep learning-based algorithms achieve the best results for pneumonia classification with an accuracy of 98.7%, a sensitivity of 0.99, and a specificity of 0.98. The higher accuracy offered by deep-learning algorithms in addition to their appropriate class balancing techniques serves as a good reference for further research.

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“… As an extended version of Ref. 16, our work is also the first one that defines a deep learning-based solution to the task which handles temporal data by means of image processing techniques, does image augmentation for more training data, and constructs convolutional neural networks for both binary and multiclass classification.  Our work analyses the gap between the challenges of the task and the abilities of the existing techniques, by discussing the sharp performance changes from binary classification to multiclass classification and identifying the instances of interest which belong to the minority class in the task.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…In our previous work, Ref. 16, only binary classification was supported. In this work, we reconsider CNN models to provide multiclass classification in three different design schemes which generate multiclass CNN models, one-vs-one CNN models, and one-vsall CNN models.…”

Section: From Binary Classification To Multiclass Classificationmentioning

confidence: 99%

Enhanced CNN Models for Binary and Multiclass Student Classification on Temporal Educational Data at the Program Level

Chau

Phụng

2020

Vietnam J. Comp. Sci.

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In educational data mining, student classification is an important and popular task by predicting final study status of each student. In the existing works, this task has been considered in many various contexts at both course and program levels with different learning approaches.However, its realworld characteristics such as temporal aspects, data imbalance, data overlapping, and data shortage with sparseness have not yet been fully investigated. Making the most of deep learning, our work is the first one addressing those challengesfor the program-level student classification task. In a simple but effective manner, convolutional neural networks (CNNs) are proposed to exploit their wellknown advantages on images for temporal educational data. As a result, the task is resolved by our enhanced CNN models with more effectiveness and practicability on real datasets. Our CNN models outperform other traditional models and their various variants on a consistent basis for program-level student classification.

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An Enhanced CNN Model on Temporal Educational Data for Program-Level Student Classification

Cited by 4 publications

References 8 publications

Predicting Student Performance Using Clickstream Data and Machine Learning

Predicting Student Performance Using Clickstream Data and Machine Learning

Intelligent Pneumonia Identification From Chest X-Rays: A Systematic Literature Review

Enhanced CNN Models for Binary and Multiclass Student Classification on Temporal Educational Data at the Program Level

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