Analysis of Students’ Academic Performance by Using Machine Learning Tools

Gafarov, Fail; Rudneva, Ya. B.; Sharifov, U. Yu.; Trofimova, Anna; Bormotov, P.M.

doi:10.2991/assehr.k.200509.104

Cited by 7 publications

(2 citation statements)

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“…They have done a massive amount of experiments with the help of the Weka tool and claimed more than 80.00% accuracy in their self-generated dataset. Similarly, Gafarov, F. M., et al [73] applied the data analysis on the records of students from Kazan Federal University. The data was collected with the collaboration of the institution ranging from 2012 to 2019.…”

Section: Students Dropout Prediction Using MLmentioning

confidence: 99%

A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques

2021

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Educational Data Mining plays a critical role in advancing the learning environment by contributing state-of-the-art methods, techniques, and applications. The recent development provides valuable tools for understanding the student learning environment by exploring and utilizing educational data using machine learning and data mining techniques. Modern academic institutions operate in a highly competitive and complex environment. Analyzing performance, providing high-quality education, strategies for evaluating the students’ performance, and future actions are among the prevailing challenges universities face. Student intervention plans must be implemented in these universities to overcome problems experienced by the students during their studies. In this systematic review, the relevant EDM literature related to identifying student dropouts and students at risk from 2009 to 2021 is reviewed. The review results indicated that various Machine Learning (ML) techniques are used to understand and overcome the underlying challenges; predicting students at risk and students drop out prediction. Moreover, most studies use two types of datasets: data from student colleges/university databases and online learning platforms. ML methods were confirmed to play essential roles in predicting students at risk and dropout rates, thus improving the students’ performance.

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Section: Students Dropout Prediction Using MLmentioning

confidence: 99%

A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques

2021

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“…They used 12 features to predict the student dropout status. At the same time, Gafarov et al [24] experimented with the use of various machine learning methods, including neural networks, to predict the academic performance of the students. They compared the performance of the accuracy of the models taking into account the inclusion and exclusion of the Unified State Exam (USE) results.…”

Section: Literature Reviewmentioning

confidence: 99%

Customized Rule-Based Model to Identify At-Risk Students and Propose Rational Remedial Actions

Albreiki

Habuza

Shuqfa

et al. 2021

BDCC

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Detecting at-risk students provides advanced benefits for reducing student retention rates, effective enrollment management, alumni engagement, targeted marketing improvement, and institutional effectiveness advancement. One of the success factors of educational institutes is based on accurate and timely identification and prioritization of the students requiring assistance. The main objective of this paper is to detect at-risk students as early as possible in order to take appropriate correction measures taking into consideration the most important and influential attributes in students’ data. This paper emphasizes the use of a customized rule-based system (RBS) to identify and visualize at-risk students in early stages throughout the course delivery using the Risk Flag (RF). Moreover, it can serve as a warning tool for instructors to identify those students that may struggle to grasp learning outcomes. The module allows the instructor to have a dashboard that graphically depicts the students’ performance in different coursework components. The at-risk student will be distinguished (flagged), and remedial actions will be communicated to the student, instructor, and stakeholders. The system suggests remedial actions based on the severity of the case and the time the student is flagged. It is expected to improve students’ achievement and success, and it could also have positive impacts on under-performing students, educators, and academic institutions in general.

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Clustering-based knowledge graphs and entity-relation representation improves the detection of at risk students

Albreiki,

Habuza,

Palakkal

et al. 2023

Educ Inf Technol

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Analysis of Students’ Academic Performance by Using Machine Learning Tools

Cited by 7 publications

References 0 publications

A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques

A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques

Customized Rule-Based Model to Identify At-Risk Students and Propose Rational Remedial Actions

Clustering-based knowledge graphs and entity-relation representation improves the detection of at risk students

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