Using machine learning methods to identify significant variables for the prediction of first-year Informatics Engineering students dropout

Bello, Felipe; Kohler, Jacqueline; Hinrechsen, Karen; Araya, Verónica Gallegos; Hidalgo, Luciano; Jara, José Luis

doi:10.1109/sccc51225.2020.9281280

Cited by 11 publications

(11 citation statements)

References 12 publications

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“…[26] used the [5, 6, 16, 17, 21, 25, 27, 28, 30, 31, 33, 35, 37, 40, 41, 43, 44, 46, 52-57, 59, 60] 26 (60%) Family information Father's qualification, mother's qualification, father's occupation, and mother's occupation. [16,23,28,30,41,46,52,53,55] 9 (21%)…”

Section: Further Discussionmentioning

confidence: 99%

“…Refs. [21][22][23][24][25][26][27][28] predicted the students' performance and whether or not the student will drop out. Researchers in Ref.…”

Section: Predicting Student Dropout Authors Inmentioning

confidence: 99%

“…Similarly, the random forest (RF) has been applied in Ref. [23] to identify the relevant features that influence student drop out. Authors in Ref.…”

Section: Predicting Student Dropout Authors Inmentioning

confidence: 99%

“…Authors in Ref. [23] calculated the importance of each feature using mean decrease accuracy (MDA) and mean decrease Gini (MDG) measures. e proposed dataset includes 206 first-year informatics engineering students' records and 40 features, including students' academic achievement in the first semester, university test results, and demographics features.…”

Section: Predicting Student Dropout Authors Inmentioning

confidence: 99%

“…Recall (63.4%) [59] Improved ID3 Accuracy (74%) (CGPA) and assessment grades are university features that are frequently used as indicators to predict student performance. A group of studies [21,23,24] found that CGPA is the most influential attribute to identify at-risk students who may drop out. Furthermore, assessment grades are considered in several studies (i.e., [29,32,34,42] and [44]) as influential factors to predict student achievement at the end of the course.…”

Section: E Influential Factors For Predicting Student's Performancementioning

confidence: 99%

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Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Alwarthan

Aslam

Khan

2022

Applied Computational Intelligence and Soft Computing

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Recently, educational institutions faced many challenges. One of these challenges is the huge amount of educational data that can be used to discover new insights that have a significant contribution to students, teachers, and administrators. Nowadays, researchers from numerous domains are very interested in increasing the quality of learning in educational institutions in order to improve student success and learning outcomes. Several studies have been made to predict student achievement at various levels. Most of the previous studies were focused on predicting student performance at graduation time or at the level of a specific course. The main objective of this paper is to highlight the recently published studies for predicting student academic performance in higher education. Moreover, this study aims to identify the most commonly used techniques for predicting the student's academic level. In addition, this study summarized the highest influential features used for predicting the student academic performance where identifying the most influential factors on student’s performance level will help the student as well as the policymakers and will give detailed insights into the problem. Finally, the results showed that the RF and ensemble model were the most accurate models as they outperformed other models in many previous studies. In addition, researchers in previous studies did not agree on whether the admission requirements have a strong relationship with students' achievement or not, indicating the need to address this issue. Moreover, it has been noticed that there are few studies which predict the student academic performance using students’ data in arts and humanities major.

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Section: Further Discussionmentioning

confidence: 99%

“…Refs. [21][22][23][24][25][26][27][28] predicted the students' performance and whether or not the student will drop out. Researchers in Ref.…”

Section: Predicting Student Dropout Authors Inmentioning

confidence: 99%

“…Similarly, the random forest (RF) has been applied in Ref. [23] to identify the relevant features that influence student drop out. Authors in Ref.…”

Section: Predicting Student Dropout Authors Inmentioning

confidence: 99%

Section: Predicting Student Dropout Authors Inmentioning

confidence: 99%

Section: E Influential Factors For Predicting Student's Performancementioning

confidence: 99%

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Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Alwarthan

Aslam

Khan

2022

Applied Computational Intelligence and Soft Computing

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Learning recommendations from educational event data in higher education

Park,

Liss,

van der Aalst

2024

J Intell Inf Syst

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This paper presents a novel approach for generating actionable recommendations from educational event data collected by Campus Management Systems (CMS) to enhance study planning in higher education. The approach unfolds in three phases: feature identification tailored to the educational context, predictive modeling employing the RuleFit algorithm, and extracting actionable recommendations. We utilize diverse features, encompassing academic histories and course sequences, to capture the multi-dimensional nature of student academic behaviors. The effectiveness of our approach is empirically validated using data from the computer science bachelor’s program at RWTH Aachen University, with the goal of predicting overall GPA and formulating recommendations to enhance academic performance. Our contributions lie in the novel adaptation of behavioral features for the educational domain and the strategic use of the RuleFit algorithm for both predictive modeling and the generation of practical recommendations, offering a data-driven foundation for informed study planning and academic decision-making.

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Second-Year University Student Retirement Prediction Model Using Machine Learning

Saelim,

Chochiang,

Vasupongayya

2024

2024 16th International Conference on Knowledge and Smart Technology (KST)

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Using machine learning methods to identify significant variables for the prediction of first-year Informatics Engineering students dropout

Cited by 11 publications

References 12 publications

Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Learning recommendations from educational event data in higher education

Second-Year University Student Retirement Prediction Model Using Machine Learning

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