Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance

Table 9

Accuracy results for support vector machine (SVM).

Study	Year	Predictive features	Accuracy (%)
[40]	2016	Attendance, class time, class length, instructor knowledge, instructor appearance, performance, assignments, exams, course materials, communication, motivation, learning outcomes, and grades	91.3
[16]	2018	Specialization, subject, programming skills, analytical skills, personal details, memory, workshops, certifications, and sports	90.3
[27]	2019	Gender, race, grades, and subjects	77
[20]	2019	Gender, nationality, place of birth, relation, StageID, SectionID, GradeID, topic, semester, raised hands, visited resources, announcement view, discussion, parent satisfaction, and attendance	66
[52]	2019	Motivation, personality, learning strategies, socio-economic status, learning approach, and psychosocial influences	90
[28]	2019	Performance, subjects, parental status, family size, location, and address	79.4
[36]	2020	Gender, age, address location, parent job, Travel time, study time, free time, failures, activities, health, and abstance	71.2