Early prediction of undergraduate Student's academic performance in completely online learning: A five-year study

Bravo‐Agapito, Javier; Romero, Sonia J.; Pamplona, Sonia

doi:10.1016/j.chb.2020.106595

Cited by 62 publications

(43 citation statements)

References 17 publications

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“…Academic self-efficiency is an important variable of success in attending online teaching (Bravo-Agapito et al, 2021;Delnoij et al, 2020;Hong et al, 2021;Littenberg-Tobias & Reich, 2020). On the other hand, the possibility (accessibility) of attending such classes at home can affect student self-regulation in online learning.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

Students’ experiences and acceptance of emergency online learning due to COVID-19

Mаrković

Pavlović

Mamutović

2021

AJET

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At the outbreak of the COVID-19 pandemic, higher education institutions around the world were compelled to bring forth suitable alternatives to secure continuity in the process of education. To gather data that would indicate the quality and efficiency of online teaching in higher education in south and south-eastern Serbia initiated at the outbreak of the pandemic and the state of emergency declared in March 2020, we conducted a research study to provide the necessary information from students attending undergraduate academic studies. The research was conducted in two phases. The first phase consisted of quantitative research conducted on a sample of 211 students. The data collected were processed by resorting to factor analysis to determine specific factors as a starting point for designing questions for the second research phase. The second phase was implemented by conducting semi-structured interviews on a sample of 61 students. The main intention was to determine key characteristics of the teaching process realised in the context of an emergency transition to the form of teaching which had not been prepared or resorted to beforehand. We identified certain specifics in this regard, as a starting point for improving existing practice. Implications for practice or policy: It is necessary to secure a suitable level of institutional support, to maintain the quality of higher education in unforeseen social circumstances. It is necessary to adequately prepare for and support teachers and students in online teaching at both institutional and national level. To avoid adverse effects on individual students in the circumstances of online teaching, it is necessary to meet students´ educational, social and emotional needs.

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Section: Discussion and Limitationsmentioning

confidence: 99%

Students’ experiences and acceptance of emergency online learning due to COVID-19

Mаrković

Pavlović

Mamutović

2021

AJET

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“…Age was strikingly distinguished as a negative indicator. Furthermore, cluster examination suggested some connections with Moodle that are firmly identified [16].…”

Section: Financial Warning Systemsmentioning

confidence: 91%

“…e following matrix relation could be obtained by the quadratic subunit in (16) for each row of M data.…”

Section: Gmdh Deep Neural Networkmentioning

confidence: 99%

Financial Early Warning System Model Combining Hybrid Semantic Hierarchy with Group Method of Data Handling Neural Network for Detection of Banks’ Risks

2021

Discrete Dynamics in Nature and Society

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Banks, financial, and credit institutions encountering the weakening financial system and increased risk factors cause high inflation and great losses for an economy. Detecting financial risks in advance could help financial institutions avoid losses, and the financial system could be eventually affected less. Early warning systems for banks could be helpful to identify financial risks and take measures to deal with hazardous situations. Various approaches have already been put forward. However, inaccuracy issues in risk detection are one of the main issues. Combining semantic hierarchy with the GMDH neural network to predict financial risks is proposed. A semantic hierarchy approach based on converting risk-related values and picking influential variables could be practical in risk detection. Besides, the GMDH algorithm utilizing neural networks based on available data has the capability of predicting possible risks that could occur in the future. The outcomes of the proposed method when compared to non-data mining methods suggest that it improves accuracy by almost 20%.

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“…Bravo-Agapito et al [13] explained their study based on the prediction of 802 undergraduate student's academic performance in completely online learning. They used exploratory factor analysis, multiple linear regressions, and cluster analysis.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance

et al. 2021

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In recent years, the world's population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible future. In this sense, machine learning is a way to analyze huge datasets to make strong predictions or decisions. The main objective of this research work is to build a predictive model for evaluating students’ performance. Hence, the contributions are threefold. The first is to apply several supervised machine learning algorithms (i.e. ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression) on our education dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important factors that influence the success or failure of the students. The experimental results showed that the Log-linear Regression provides a better prediction as well as the behavioral factors that influence students’ performance.

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Early prediction of undergraduate Student's academic performance in completely online learning: A five-year study

Cited by 62 publications

References 17 publications

Students’ experiences and acceptance of emergency online learning due to COVID-19

Students’ experiences and acceptance of emergency online learning due to COVID-19

Financial Early Warning System Model Combining Hybrid Semantic Hierarchy with Group Method of Data Handling Neural Network for Detection of Banks’ Risks

Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance

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