Analysis of a Warranty-Based Quality Management System in the Construction Industry

Nagahisarchoghaei, Mohammad; Dodd, Justin; Nagahi, Morteza; Ghanbari, Ghodsieh; Poudyal, Sujan

doi:10.1109/icdabi51230.2020.9325692

Cited by 2 publications

(2 citation statements)

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“…Machine learning algorithms are widely employed in a variety of fields for classifications and predictions [34][35][36][37]. Student performance prediction is a prominent topic in educational data mining, where machine learning and deep learning are utilized to predict and improve student performance in the classroom.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Student Academic Performance Using a Hybrid 2D CNN Model

2022

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Opportunities to apply data mining techniques to analyze educational data and improve learning are increasing. A multitude of data are being produced by institutional technology, e-learning resources, and online and virtual courses. These data could be used by educators to analyze and understand the learning behaviors of students. The obtained data are raw data that must be analyzed, requiring educational data mining to predict useful information about students, such as academic performance, among other things. Many researchers have used traditional machine learning to predict the academic performance of students, and very little research has been conducted on the architecture of convolutional neural networks (CNNs) in the context of the pedagogical domain. We built a hybrid 2D CNN model by combining two different 2D CNN models to predict academic performance. Our sample comprised 1D data, so we transformed it to 2D image data to test the performance of our hybrid model. We compared the performance of our model with that of different traditional baseline models. Our model outperformed baseline models, such as k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, in terms of accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of Student Academic Performance Using a Hybrid 2D CNN Model

2022

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“…All the mentioned measures and scales, including motivatio n, self -ef ficacy, proactive behavior, as well as the current study variables are part of the comprehensive theoretical model of a bigger study, which will be applied for future data collection and analysis. Other analytical models, such as optimal control and objectoriented modeling, mathematical modeling [46][47][48][49][50][51][52][53][54], can b e used in conjunction with clustering to better interpret the results in later studies. Future studies would also include the type of training needed to enhance students' level of characteristics and skillset to obtain better academic performance.…”

Section: Academic Factorsmentioning

confidence: 99%