2019
DOI: 10.1007/978-981-13-8618-3_15
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A Study of Factors to Predict At-Risk Students Based on Machine Learning Techniques

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Cited by 12 publications
(9 citation statements)
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“…However, we can see from the Table that only twenty-one (21) studies addressed this. Lastly, only fourteen (14) of the studies acknowledged that the obtained results were verified using a second dataset, on unseen data other than the training and testing sets ([16], [27], [121], [129], [130], [135], [46], [50], [61], [63], [104], [106], [117], [120]), which may imply the generalizability of results.…”
Section: Quality Assessment Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…However, we can see from the Table that only twenty-one (21) studies addressed this. Lastly, only fourteen (14) of the studies acknowledged that the obtained results were verified using a second dataset, on unseen data other than the training and testing sets ([16], [27], [121], [129], [130], [135], [46], [50], [61], [63], [104], [106], [117], [120]), which may imply the generalizability of results.…”
Section: Quality Assessment Resultsmentioning
confidence: 99%
“…Figure 7 presents a bar of the frequency of studies based on the sample size. It is observed that only twenty-six (26) studies used a dataset with more than or equal to 1000 instances to train the predictive models ([13], [16], [58], [65], [68], [69], [80], [89], [90], [99], [107], [112], [27], [119]- [121], [123], [127], [129], [45], [46], [50], [51], [53], [55], [56]). However, it is known that most Machine Learning algorithms require a massive amount of data to perform well and produce accurate prediction results.…”
Section: B Dataset Sample Population Size and Collection Methodsmentioning
confidence: 99%
“…Predictors Outcome [36] 116 features for the production phase (product data) and 84 for the learning phase Team performance [12] 23 factors including academic demographic, social, and behavioral factors with prior semester performance. Student at risk [37] 11 variables include socio-economic background, university entrance examination results, and CGPA.…”
Section: Refmentioning
confidence: 99%
“…In comparison to artificial intelligence computing methods, traditional approaches failed to consistently show the capacity to reach accurate predictions or classifications [9]. As a result, a third approach to predicting student achievement and academic performance indicated in recent literature uses machine learning techniques, such as Artificial Neural Network methods (ANN) and some other techniques including Decision Tree [10], Support Vector Machines [2], Bayesian algorithms [11], and Ensemble Learning [12]. This method has been successfully applied in various fields, including business, engineering, meteorology, and economics, without significant differences in the obtained results or the level of accuracy.…”
Section: Introductionmentioning
confidence: 99%
“…To perform such a prediction, several educational parameters can be employed to evaluate the performance of students, such as exams grades, Grade Point Average (GPA), lecture absenteeism, number of attempts to pass a course or an exam. Moreover, other demographic features such as gender, family relationship, parent profession, marital status, and personal habits [1,2]. Predicting students' performance for educational organizations has been conducted by many scientific communities.…”
Section: Introductionmentioning
confidence: 99%