Dropout early warning systems for high school students using machine learning

Chung, Jae Young; Lee, Sunbok

doi:10.1016/j.childyouth.2018.11.030

Cited by 150 publications

(78 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Other studies highlight the quality of SL techniques in predicting dropout: NB and SVM were proposed to predict of individual dropouts [12]; and Sequential Forward Selection (SFS), C4.5, RF, KNN and NB, among other classifiers, were proposed to identify students with difficulties in the third week with 97% accuracy [13]. Along these lines, the use of Random Forests (RF) showed excellent performance in predicting school dropout in terms of various performance metrics for binary classification [14]. Finally, ANN, SVM, LR, NB, and DT were analyzed in [15] for similar purposes by using the data recorded by e-learning tools.…”

Section: Supervised Learningmentioning

confidence: 99%

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

2020

View full text Add to dashboard Cite

Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.

show abstract

Section: Supervised Learningmentioning

confidence: 99%

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

2020

View full text Add to dashboard Cite

show abstract

“…The United States has been building an accountability system to keep all students from falling behind through the "No Child Left Behind Act" and "Every Study Succeeds Act" [16]. To support successful learning of all students, in South Korea, it was proposed to operate a three-tier dropout prevention programs [17]. In the first-tier, all students participate in the general prevention program designed to prevent school dropouts.…”

Section: Students' Dropouts In South Koreamentioning

confidence: 99%

The Machine Learning-Based Dropout Early Warning System for Improving the Performance of Dropout Prediction

2019

Self Cite

View full text Add to dashboard Cite

A dropout early warning system enables schools to preemptively identify students who are at risk of dropping out of school, to promptly react to them, and eventually to help potential dropout students to continue their learning for a better future. However, the inherent class imbalance between dropout and non-dropout students could pose difficulty in building accurate predictive modeling for a dropout early warning system. The present study aimed to improve the performance of a dropout early warning system: (a) by addressing the class imbalance issue using the synthetic minority oversampling techniques (SMOTE) and the ensemble methods in machine learning; and (b) by evaluating the trained classifiers with both receiver operating characteristic (ROC) and precision–recall (PR) curves. To that end, we trained random forest, boosted decision tree, random forest with SMOTE, and boosted decision tree with SMOTE using the big data samples of the 165,715 high school students from the National Education Information System (NEIS) in South Korea. According to our ROC and PR curve analysis, boosted decision tree showed the optimal performance.

show abstract

“…In general, early warning systems are perdition models used to prevent expected failure at an early stage. In higher education, these models help teachers to detect drop-outs (Chung and Lee 2019) or possible academic failures (Macfadyen and Dawson 2010). Hence, in this paper, we aimed at developing an early warning system for identifying at-risk students by analyzing students' eBook interaction data.…”

Section: Introductionmentioning

confidence: 99%

Developing an early-warning system for spotting at-risk students by using eBook interaction logs

Akçapınar

Hasnine

Majumdar

et al. 2019

Smart Learn. Environ.

View full text Add to dashboard Cite

Early prediction systems have already been applied successfully in various educational contexts. In this study, we investigated developing an early prediction system in the context of eBook-based teaching-learning and used students' eBook reading data to develop an early warning system for students at-risk of academic failure-students whose academic performance is low. To determine the best performing model and optimum time for possible interventions we created prediction models by using 13 prediction algorithms with the data from different weeks of the course. We also tested effects of data transformation on prediction models. 10-fold cross-validation was used for all prediction models. Accuracy and Kappa metrics were used to compare the performance of the models. Our results revealed that in a sixteen-week long course all models reached their highest performance with the data from the 15th week. On the other hand, starting from the 3rd week, the models classified low and high performing students with an accuracy of over 79%. In terms of algorithms, Random Forest (RF) outperformed other algorithms when raw data were used, however, with the transformed data J48 algorithm performed better. When categorical data were used, Naive Bayes (NB) outperformed other algorithms. Results also indicated that models with transformed data performed lower than the models created using categorical data. However, models with categorical data showed similar performance with models with raw data. The implications of the results presented in this research were also discussed with respect to the field of Learning Analytics.

show abstract

Dropout early warning systems for high school students using machine learning

Cited by 150 publications

References 10 publications

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

The Machine Learning-Based Dropout Early Warning System for Improving the Performance of Dropout Prediction

Developing an early-warning system for spotting at-risk students by using eBook interaction logs

Contact Info

Product

Resources

About