A stacking ensemble machine learning method for early identification of students at risk of dropout

Talamás-Carvajal, Juan Andrés; Ceballos, Héctor G.

doi:10.1007/s10639-023-11682-z

Cited by 6 publications

(8 citation statements)

References 23 publications

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“…No Approach Paper 1 Academic Performance Prediction [11], [12], [17], [18], [23], [24], [35], [37], [38], [41], [44], [45], [51], [56], [57], [64], [71], [74], [75], [78], [80], [81], [84] 2 At-Risk Student Prediction [1], [9], [10], [13], [19], [36], [50], [55], [59], [62], [65], [66], [73], [77], [79], [82] 3 Dropout/Graduation Prediction [14], [15], [20], [21], [47], [48], [54], [60], [61], [63], [72],…”

Section: Table 4 Mapping Of Approachesmentioning

confidence: 99%

“…In addition to dropouts occurring at the course level, students may also choose to withdraw from their academic studies. The term "dropout" in the context of higher education refers to students who discontinue studies prior to the successful completion of their degree program [15], [54]. The student does not engage in the process of transferring to another university or enrolling at the university during the subsequent year [61].…”

Section: • Resourcementioning

confidence: 99%

“…Data Source Paper 1 LMS [1], [9], [11]- [14], [19], [35]- [40], [43]- [45], [54], [58], [63], [65], [66], [68]- [71], [73], [74], [77], [79], [84], [87] 2 SIS [15], [17], [21], [22], [54]- [56], [62], [76], [78], [85], [86] 3 Hybrid/LMS and SIS [10], [18], [20], [23], [24], [34], [46]- [53], [57], [59]- [61], [72], [72], [75], [81], [82] 4 Other [41], [42],…”

Section: Nomentioning

confidence: 99%

“…[20]- [22], [41], [50], [82], [85] 6 Academic, Demography and Behavioral [52], [59], [61], [66], [67], [75], [81], [83], [87] 7 Academic, Pre-university and Admission Test [55] 8 Academic, Demographic, Preuniversity, and Admission Test [15] This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication.…”

Section: Nomentioning

confidence: 99%

“…To boost the prediction performance of students who are likely to drop out, a two-layer ensemble machine learning method is employed [14]. Investigation by [15] employed an ensemble machine learning technique termed stacking to identify early learners who were at risk of dropping out. By combining several prediction models, this technique improves the accuracy as well as reliability of its forecasts.…”

Section: Introductionmentioning

confidence: 99%

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Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

Pelima,

Sukmana,

Rosmansyah

2024

IEEE Access

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Predicting university student graduation is a beneficial tool for both students and institutions. With the help of this predictive capacity, students may make well-informed decisions about their academic and career paths, and institutions can proactively identify students who may not graduate and offer tailored support to ensure their success. The use of machine learning for predicting university student graduation has drawn more attention in recent years. Large datasets of student academic performance data can be used to train machine learning algorithms to identify patterns that are applicable in predicting future outcomes. In accordance with some studies, this approach predicts student graduation with an accuracy rate as high as 90%. Many SLRs have been conducted in this field, but there are still limitations, including not discussing the predictive models and algorithms used, a lack of coverage of the machine learning algorithms applied, small database coverage, keyword selection that does not cover all synonyms relevant to the investigation, and less specific data collection transparency. By delving into the limitations of existing SLRs on this topic, this research not only enhances the understanding of machine learning applications in forecasting student graduation but also fills a crucial gap in the literature. The inclusion of weaknesses in current SLRs provides a foundation for justifying the need for this study, emphasizing the necessity of a more nuanced and comprehensive review to advance the field and guide future research efforts in smart learning environments. This research conducts a thorough systematic review of the existing literature on machine learning-based student graduation prediction models from 70 journal articles from 2018 through 2023 that are pertinent. This review includes the various machine learning algorithms that have been implemented, the various academic performance data that was obtained from students, and the effectiveness of the models that have been developed. It also discusses the difficulties and potential advantages of utilizing machine learning to predict student graduation. The review indicates that the most common approach employed is the prediction of students' academic performance, which relies on data obtained from the Learning Management System and Student Information System. The primary data utilized for prediction purposes consists Student retention and time of academic and behavioral information. Among the various algorithms employed, Support Vector Machine and Random Forest are the most commonly utilized. This study makes a significant contribution to the advancement of learner modules within the smart learning environment.

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Section: Table 4 Mapping Of Approachesmentioning

confidence: 99%

Section: • Resourcementioning

confidence: 99%

Section: Nomentioning

confidence: 99%

Section: Nomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

Pelima,

Sukmana,

Rosmansyah

2024

IEEE Access

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A stacking ensemble machine learning model for evaluating cardiac toxicity of drugs based on in silico biomarkers

Fuadah,

Qauli,

Pramudito

et al. 2024

CPT Pharmacom & Syst Pharma

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This study addresses the critical issue of drug‐induced torsades de pointes (TdP) risk assessment, a vital aspect of new drug development due to its association with arrhythmia and sudden cardiac death. Existing methodologies, particularly those reliant on a single biomarker derived from CiPA O'Hara‐Rudy (CiPAORdv1.0) ventricular cell model without the hERG dynamic as input to the individual machine learning model, have limitations in capturing the complexity inherent in the comprehensive range of factors influencing drug‐induced TdP risk. This study aims to overcome these limitations by proposing a stacking ensemble machine learning approach by integrating multiple in silico biomarkers derived from the CiPAORdv1.0 with hERG dynamic characteristics. The ensemble machine learning model consisted of three artificial neural network (ANN) models as baseline model and support vector machine (SVM), logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) models as meta‐classifier. The highest AUC score of 1.00 (0.90–1.00) for high risk, 0.97 (0.84–1.00) for intermediate risk, and 1.00 (0.87–1.00) for low risk were obtained using seven biomarkers derived from the CiPAORdv1.0 with hERG dynamic characteristics. Furthering our investigation, we explored the model's robustness by incorporating interindividual variability into the generation of in silico biomarkers from a population of human ventricular cell models. This study also enabled an analysis of TdP risk classification under high clinical exposure and therapeutic scenarios for several drugs. Additionally, from a sensitivity analysis, we revealed four important ion channels, namely, CaL, NaL, Na, and Kr channels that affect significantly the important biomarkers for TdP risk prediction.

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Research Plan on the Effects of Interventions on Dropout Predictions for Higher Education Institutions

Talamás-Carvajal

2024

Lecture Notes in Educational Technology

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A stacking ensemble machine learning method for early identification of students at risk of dropout

Cited by 6 publications

References 23 publications

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

A stacking ensemble machine learning model for evaluating cardiac toxicity of drugs based on in silico biomarkers

Research Plan on the Effects of Interventions on Dropout Predictions for Higher Education Institutions

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