Applying machine learning approach to predict students’ performance in higher educational institutions

Yakubu, Mohammed Nasiru; Abubakar, A. Mohammed

doi:10.1108/k-12-2020-0865

Cited by 45 publications

(29 citation statements)

References 54 publications

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“…The second highest is IBM SPSS for statistical approaches with 13% ( [13], [66], [70], [82]- [84], [86], [95], [120], [136]). Other most-used tools include Microsoft Excel (17%) ([20], [52], [117], [123], [135], [71], [82]- [84], [86], [89], [112], [115]), RapidMiner Studio (9%) ( [13], [49], [70], [86], [91], [107], [131]), Jupyter Notebook (7%) ( [115], [123], [124], [127], [132]), Orange and R Studio (4%) ( [62], [87], [116], [125], [128], [133] respectively), and Tableau (3%) ( [77], [127]).…”

Section: Prediction Approaches Algorithms Learning Outcome Type and T...mentioning

confidence: 99%

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

et al. 2022

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Student retention is an essential measurement metric in education, indicated by retention rates, which are accumulated as students re-enroll from one academic year to the next. High retention rates can be obtained if institutions aim to provide appropriate support and teaching methods among the various practices to prevent students from deferring their studies. To address this pressing challenge faced by educational institutions, the underlying factors and the methodological aspects of building robust predictive models are reviewed and scrutinized. Educational Data Mining (EDM) and Learning Analytics (LA) have been widely adopted for knowledge discovery from educational data sources, improving the teaching practice, and identifying at-risk students. Various predictive techniques are applied in LA, such as Machine Learning (ML), Statistical Analysis, and Deep Learning (DL). To gain an in-depth review of these techniques, academic publications have been reviewed to highlight their potential to resolve Student Retention issues in education. Additionally, the paper presents a taxonomy of ML approaches and a comprehensive review of the success factors and the features that are not indicative of student performance in three different learning environments: Traditional Learning, Blended Learning, and Online Learning. The survey reveals that supervised ML and DL techniques are broadly applied in Student Retention. However, the application of ensemble and unsupervised learning clustering techniques supporting the heterogenous and homogenous groups of students is generally lacking. Moreover, static and traditional features are commonly used in student performance, ignoring vital factors such as educators-related, cognitive, and personal data. Furthermore, the paper highlights open challenges for future research directions.

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Section: Prediction Approaches Algorithms Learning Outcome Type and T...mentioning

confidence: 99%

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

et al. 2022

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“…Using machine-learning to predict academic success of students at the American University of Nigeria, Yakubu and Abubakar [ 28 ] found that females were 1.2 times more likely to succeed academically than males and that juniors and seniors were more likely to achieve higher cumulative GPA. Kalman et al [ 29 ] studied students’ perspectives of online learning in upper-level and lower-level chemistry courses at a four-year undergraduate university in Georgia during the Spring 2020 transition to online learning.…”

Section: Related Literaturementioning

confidence: 99%

The impact of COVID-19 induced emergency remote instruction on students’ academic performance at an HBCU

et al. 2022

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The outbreak of the COVID-19 pandemic early in 2020 forced universities to shut down their campuses and transition to emergency remote instruction (ERI). Students had to quickly adapt to this new mode of instruction while dealing with all other distractions caused by the pandemic. This study integrates extensive data from students’ institutional records at a large Historically Black College and University (HBCU) institution with data from a students’ survey about the impact of COVID-19 on learning during the Spring 2020 semester to examine the impact of the transition to ERI on students’ performance and identify the main factors explaining variations in students’ performance. The main findings of our analysis are: (a) students’ university experience was positively correlated with performance (continuing students who spent at least one academic year at the university prior to the outbreak had better performance than freshman and new transfer students), (b) students’ perceived change in performance after the transition was positively associated with actual performance (students who perceived a decline in their performance after transition to ERI had significantly worse performance than other students), and (c) students’ prior online learning experiences and students’ emotional experiences with the COVID-19 disease were not significantly associated with performance. These results suggest that the approaches adopted by higher education institutions to support students during times of crisis should pay special attention to certain groups of students.

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“…Machine learning algorithms in [20,21] and data mining techniques in [22,23] are considered as one overview of student performance prediction modeling for further education in both pairs of these papers. Application of machine learning in predicting performance for computer engineering students is subject of the manuscripts [24,25] and data mining techniques are applied in predicting further education for students that study medical curriculum [26].…”

Section: Related Studiesmentioning

confidence: 99%

One Aggregated Approach in Multidisciplinary Based Modeling to Predict Further Students’ Education

Ranđelović¹,

Aleksić

Radovanović

et al. 2022

Mathematics

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In this paper, one multidisciplinary-applicable aggregated model has been proposed and verified. This model uses traditional techniques, on the one hand, and algorithms of machine learning as modern techniques, on the other hand, throughout the determination process of the relevance of model attributes for solving any problems of multicriteria decision. The main goal of this model is to take advantage of both approaches and lead to better results than when the techniques are used alone. In addition, the proposed model uses feature selection methodology to reduce the number of attributes, thus increasing the accuracy of the model. We have used the traditional method of regression analysis combined with the well-known mathematical method Analytic Hierarchy Process (AHP). This approach has been combined with the application of the ReliefF classificatory modern ranking method of machine learning. Last but not least, the decision tree classifier J48 has been used for aggregation purposes. Information on grades of the first-year graduate students at the Criminalistics and Police University, Belgrade, after they chose and finished one of the three possible study modules, was used for the evaluation of the proposed model. To the best knowledge of the authors, this work is the first work when considering mining closed frequent trees in case of the streaming of time-varying data.

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Applying machine learning approach to predict students’ performance in higher educational institutions

Cited by 45 publications

References 54 publications

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

The impact of COVID-19 induced emergency remote instruction on students’ academic performance at an HBCU

One Aggregated Approach in Multidisciplinary Based Modeling to Predict Further Students’ Education

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