Effect of Individual Differences in Predicting Engineering Students' Performance: A Case of Education for Sustainable Development

Nagahi, Morteza; Jaradat, Raed; Nagahisarchoghaei, Mohammad; Ghanbari, Ghodsieh; Poudyal, Sujan; Goerger, Simon R.

doi:10.1109/dasa51403.2020.9317157

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…In EDM, most research is done to predict students' academic performance [7][8][9] and dropout prediction [10][11][12]. Very few works have been done to predict the attention level of students in the classroom [44][45][46][47].…”

Section: Literature Reviewmentioning

confidence: 99%

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Hybrid Feature Extraction Model to Categorize Student Attention Pattern and Its Relationship with Learning

2022

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The increase of instructional technology, e-learning resources, and online courses has created opportunities for data mining and learning analytics in the pedagogical domain. A large amount of data is obtained from this domain that can be analyzed and interpreted so that educators can understand students’ attention. In a classroom where students have their own computers in front of them, it is important for instructors to understand whether students are paying attention. We collected on- and off-task data to analyze the attention behaviors of students. Educational data mining extracts hidden information from educational records, and we are using it to classify student attention patterns. A hybrid method is used to combine various techniques like classifications, regressions, or feature extraction. In our work, we combined two feature extraction techniques: principal component analysis and linear discriminant analysis. Extracted features are used by a linear and kernel support vector machine (SVM) to classify attention patterns. Classification results are compared with linear and kernel SVM. Our hybrid method achieved the best results in terms of accuracy, precision, recall, F1, and kappa. Also, we correlated attention with learning. Here, learning corresponds to tests and a final course grade. For determining the correlation between grades and attention, Pearson’s correlation coefficient and p-value were used.

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Section: Literature Reviewmentioning

confidence: 99%

“…Datasets include information about student academic records and the patterns of student interaction with classroom technology [6]. EDM has been used on student record data to predict academic performance [7][8][9][10][11] and dropout prediction [12][13][14]. EDM has also been used to detect undesirable students' behavior in the classroom.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Extraction Model to Categorize Student Attention Pattern and Its Relationship with Learning

2022

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“…Researchers are interested in student-related data because it may be used to accomplish a variety of tasks, including; prediction of student performance [15][16][17]; dropout prediction [18][19][20]; detection of undesired student behaviors, such as off-task behaviors of students [21][22][23][24][25]; and real time monitoring of a student's psychological status by using sensors and wearable devices [26,27]. The popularity of wearable devices and sensors has increased in classroom settings because they can now be used for a longer time in real-time settings, as research has conducted done to improve the battery lifetime of such devices [28].…”

Section: Introductionmentioning

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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Investigating the Influence of Demographics and Personality Types on Practitioners’ Level of Systems Thinking Skills

Nagahi

Hossain

Amrani

et al. 2022

IEEE Trans. Eng. Manage.

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Effect of Individual Differences in Predicting Engineering Students' Performance: A Case of Education for Sustainable Development

Cited by 4 publications

References 43 publications

Hybrid Feature Extraction Model to Categorize Student Attention Pattern and Its Relationship with Learning

Hybrid Feature Extraction Model to Categorize Student Attention Pattern and Its Relationship with Learning

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

Investigating the Influence of Demographics and Personality Types on Practitioners’ Level of Systems Thinking Skills

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