Factors Affecting Students’ Performance in Higher Education: A Systematic Review of Predictive Data Mining Techniques

Saa, Amjed Abu; Al-Emran, Mostafa; Shaalan, Khaled

doi:10.1007/s10758-019-09408-7

Cited by 136 publications

(64 citation statements)

References 41 publications

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“…Data mining technology has been applied to the field of education, such as teaching information management, teachers' teaching evaluation, analysis of students' psychological characteristics, formulation of scientific and reasonable teaching programs, and analysis of students' examination results to find problems and effectively strengthen teaching. Among them, the most commonly used data mining technology for predicting and classifying the factors affecting students' scores is decision-tree, Bayesian classifier and artificial neural network (ANN) [16][17][18]. Therefore, based on theoretical basis and previous experimental summary, association rule is built to mine the value data hidden in students' score data, and the decision-tree model is combined to analyze the factors that affect students' scores.…”

Section: Discussionmentioning

confidence: 99%

Sustainable Development of College and University Education by use of Data Mining Methods

Wang

Chung

2021

Int. J. Emerg. Technol. Learn.

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To improve the education efficiency of the students, the student-centered education plan is explored. First, the Apriori algorithm of association rules is used to mine the potential related patterns in the score data of college students and establish a reasonable teaching method. Second, aided by the decision tree model, the factors affecting students' academic performance are studied, and the potential relationship between different courses is studied. Finally, the Apriori algorithm of association rules combined with decision tree model is used to generate the early warning mechanism of students' achievement, and the course performance of college students is empirically analyzed. The results show that: C language has two sides of dependence on many subjects; higher mathematics → linear algebra → mathematical statistics → computer composition principle → computer network. The teaching scheme of C language → C + + → Java more conforms to the learning mechanism of college students. Through empirical analysis, the early warning mechanism of association rule Apriori algorithm and decision tree model can effectively analyze student's course and give student's achievement. It is found that the method proposed can provide theoretical basis for students, teachers, and university administrators to carry out education reform and education management decision-making, improve students' performance and education quality, and realize the "student-oriented" education concept, so it can be applied to the actual education management.

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Section: Discussionmentioning

confidence: 99%

Sustainable Development of College and University Education by use of Data Mining Methods

Wang

Chung

2021

Int. J. Emerg. Technol. Learn.

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“…Consequently, the trade-off between prediction accuracy and capability in model explanation has become controversial for making decisions in using simple and transparent models like multiple linear regression or potentially more accurate but complicated black-box machine learning models. Recently Abu Saa et al (2019) have highlighted the frequent use of machine learning techniques for educational data mining including Decision Trees, Naïve Bayes, artificial neural networks, support vector machine, and logistic regression [31]. Therefore, the use of machine learning algorithms in solving educational research problems such as student satisfaction can be a future exploratory direction.…”

Section: The Use Of Machine Learningmentioning

confidence: 99%

“…However, numerous previous studies showed the potential superior predictive performance using MLPR as an artificial neural network over the statistical multiple linear regression in wide-ranging applications such as food quality [82], climate prediction processes [52], behavior and deformation of dams [83], and epidemiological data [53]. Furthermore, Abu Saa et al (2019) highlighted many emerging educational data mining studies using machine learning [31]. When comparing the MAE, RMSE, and R 2 values of the testing set, both ENet and MLPR outperformed conventional and stepwise multiple linear regression models.…”

Section: The Selection Of Predictive Modelsmentioning

confidence: 99%

Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques

2021

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Despite the wide adoption of emergency remote learning (ERL) in higher education during the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting student satisfaction for this novel learning environment in crisis. The present study investigated important predictors in determining the satisfaction of undergraduate students (N = 425) from multiple departments in using ERL at a self-funded university in Hong Kong while Moodle and Microsoft Team are the key learning tools. By comparing the predictive accuracy between multiple regression and machine learning models before and after the use of random forest recursive feature elimination, all multiple regression, and machine learning models showed improved accuracy while the most accurate model was the elastic net regression with 65.2% explained variance. The results show only neutral (4.11 on a 7-point Likert scale) regarding the overall satisfaction score on ERL. Even majority of students are competent in technology and have no obvious issue in accessing learning devices or Wi-Fi, face-to-face learning is more preferable compared to ERL and this is found to be the most important predictor. Besides, the level of efforts made by instructors, the agreement on the appropriateness of the adjusted assessment methods, and the perception of online learning being well delivered are shown to be highly important in determining the satisfaction scores. The results suggest that the need of reviewing the quality and quantity of modified assessment accommodated for ERL and structured class delivery with the suitable amount of interactive learning according to the learning culture and program nature.

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“…A trend that has arisen in Education research during the past few years is to identify the variables influencing students' performance in Higher Education, especially by using data mining methods. This field is usually identified as educational data mining (EDM) (Abu Saa et al, 2019). The motivation behind this is to identify difficulties in students' learning performance, which aligns with the EHEA goals of providing high-quality education.…”

Section: Assessment In Higher Education: Background and Research Questionsmentioning

confidence: 99%

Solving the mystery about the factors conditioning higher education students' assessment: Finland versus Spain

Miñano

Campo

Grande

et al. 2020

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PurposeThe purpose of this paper is twofold: first, to compare the assessment in two subjects of the Business Administration Degree between Finland and Spain and, second, to test whether there are factors such as gender, age, subject, students’ motivation, or preferences that may have an impact on the assessment.Design/methodology/approachA survey was designed for students enrolled in Statistics and Financial Accounting subjects in the two universities, and multivariate statistical analysis were run.FindingsFirst, coursework marks are higher than the final examination marks. In both universities and subjects, learning is enhanced by student involvement in coursework activities that are directly related to the learning outcomes. Second, there are differences in assessment by culture, gender, and type of subject. Finnish students are more used to work in teams and apply varied teaching resources than Spanish students.Research limitations/implicationsThe sample size and the analyses are from two subjects in two universities. More similar studies are needed to generalize the findings.Practical implicationsThere are several implications for Higher Education. First, university policymakers should design training courses on the good implementation of new assessment processes and criteria in order to align learning objectives and assessment criteria. Second, teachers from different countries should openly discuss their manner of assessment and promote creativity and innovation in their methodologies to assess learning outcomes. Third, students should engage with deeper learning and competence development in subjects. This will contribute to their future employability.Originality/valueOur findings not only question the concept of assessment validity and the compulsory relationship between assessment and learning but also provide suggestions to improve assessment criteria.

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Factors Affecting Students’ Performance in Higher Education: A Systematic Review of Predictive Data Mining Techniques

Cited by 136 publications

References 41 publications

Sustainable Development of College and University Education by use of Data Mining Methods

Sustainable Development of College and University Education by use of Data Mining Methods

Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques

Solving the mystery about the factors conditioning higher education students' assessment: Finland versus Spain

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