Predicting Student Academic Performance using Machine Learning and Time Management Skill Data

Rimadana, Meizar Raka; Kusumawardani, Sri Suning; Santosa, Paulus Insap; Erwianda, Maximillian Sheldy Ferdinand

doi:10.1109/isriti48646.2019.9034585

Cited by 12 publications

(14 citation statements)

References 18 publications

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“…Most methods or algorithms for predicting academic performance are ML-based classification algorithms because they provide higher accuracy against other proposals (Rimadana et al, 2019;Gamao and Gerardo, 2019;Xu et al, 2019;Vora and Kamatchi, 2019;Tsiakmaki et al, 2020;Jayaprakash et al, 2020). These classification algorithms are mainly: Support Vector Machine (SVM), Naive Bayes (NB), Decision Trees (DT), Random Forests (RF), and Artificial Neural Networks (ANN).…”

Section: Methods and Algorithms That Predict Academic Performancementioning

confidence: 99%

“…Within this technique, we found algorithms and methods that were massively used in the literature. Among them, we highlight the SVM, RF the random forests, and NNs (Rimadana et al, 2019;Shanthini et al, 2018;Vora and Kamatchi, 2019;Jayaprakash et al, 2020;Pandey and Taruna, 2018;Imran et al, 2019). In the study (Popescu and Leon, 2018), the RF achieved a close approximation to the performance of undergraduate students that occurs in the classroom and improves as more data is fed into it.…”

Section: Supervised Machine Learningmentioning

confidence: 96%

“…This factor uses time management skills as the preponderant factor, such as the time spent on a subject, time to study, time to take breaks, and travel time, among others. The study by Rimadana et al (2019) proposes a system for predicting academic performance using time management characteristics. The process to solve the problem starts with obtaining the data through TSQ questionnaires.…”

Section: Timementioning

confidence: 99%

“…Techniques that applied methods and algorithms for the prediction of academic performance Rimadana et al (2019)Shanthini et al (2018);Gamao and Gerardo (2019);Xu et al (2019);Vora and Kamatchi (2019);;…”

mentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Review Regarding the Prediction of Academic Performance

De-La-Cruz¹,

Rojas-Coaquira²,

Huerta³

et al. 2022

Journal of Computer Science

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The prediction of students' academic performance is an area of great concern for universities and educational institutions since academic performance is one of the most important aspects of the learning process. To analyze this behavior, this study makes a critical analysis of the topic of interest and aims to review, analyze and summarize the latest research advances related to the prediction of academic performance. The systematic literature review method is applied to answer three questions: (1) What factors are determinants in predicting students' academic performance? ( 2) what methods are used to predict students' academic performance? and(3) what are the objectives and interests in predicting students' academic performance? After conducting the study of 50 outstanding articles, as results, we found that academic factor is the guideline for predicting academic performance; supervised machine learning is the most used technique, highlighting support vector machine, random forests, and neural networks; the most outstanding objectives for the application of prediction were: Student performance with 53%, risk of failure with 14%, search for student knowledge with 12%, avoid dropout with 12%, and decision making with 10%.

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Section: Methods and Algorithms That Predict Academic Performancementioning

confidence: 99%

Section: Supervised Machine Learningmentioning

confidence: 96%

Section: Timementioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review Regarding the Prediction of Academic Performance

De-La-Cruz¹,

Rojas-Coaquira²,

Huerta³

et al. 2022

Journal of Computer Science

View full text Add to dashboard Cite

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“…Друга публикация с автори Rimadana и др. [5] представя създадени модели за прогнозиране на учебната ефективност на студентите чрез данни от анкети относно техните умения за разпределяне на времето за изпълнение на учебни дейности. Резултатите показват, че прогнозният модел, създаден чрез алгоритъма Support Vector Machine с 80% точност може да прогнозира учебната ефективност.…”

Section: прилагане на машинно обучение при прогнозиране на учебната е...unclassified

Learning Performance Improvement Through Participation in Online Seminar: Machine Learning Analysis

Ivanova¹

2022

STEM

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Learning performance is related to students’ learning activities during a learning process. Their learning behavior could lead to successful course accomplishment or not, to better or worse final marks. Seminar practices have their impact on development of some students’ competences like: topics analysis, discussion and presentation and the planned tasks concern learning performance. In online environment, the seminars could be organized in the form of different learning scenarios and it depends on the functional and technical features of the organized educational environment as well as on the course goal. In this paper an investigation and analysis of students’ participation in online seminars is conducted with aim to understand the dependence between their learning performance, online tasks realization and final results. eLearning informatics gives possibilities for usage contemporary methods for research and learning analytics as one of them is machine learning. Machine learning algorithms are utilized to group students according to their learning behavior and final outcome. The created analytical models could be in support of educators and students to improve their educational activities. The accuracy of machine learning algorithms is evaluated to find the best model according to collected data during one semester.

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