Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Abstract

Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students' achievement, university's performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students' academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students' graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naï ve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students' graduation time.

Predicting baccalaureate student result to prevent failure: a hybrid model approach

“…Sedova et al [21], according to their paper, there is a substantial correlation between a student's achievement and the amount of conversation time they had in class, the number of times they used reasoning, and other factors. Suhaimi et al [22], in their paper, compared interests among those who were interested in studying the performance of students by gender; they discovered that the methodology used in studies involving men and women differs. Imran et al [23] examines the identification of students at risk based on socioeconomic and cultural factors.…”

Profiles of University Students Who Graduate on Time: A Cohort Study from the Chilean Context

“…Moreover, on-time graduation has been related to other student sociodemographic characteristics such as place of residence [32,[37][38][39], gender, although the research is inconclusive [33,37,40], age and marital status [40], and family characteristics associated with the support provided and education level [30,36,40,41]. In addition, academic factors prior to university entry have been identified, such as high school grades [30,32,40,42,43] and admission test results, particularly in mathematics and language [30,38,44]. Nevertheless, some studies such as Al-Nassar et al [45] do not agree with this assertion, maintaining that academic information prior to university is not relevant to their research.…”

“…On the other hand, program and university-related factors have an impact on on-time graduation. These include in particular academic support [41,44] and financial support programs [30,38], approved credits [18,32,37,41], especially in the first year [38,41,44], and the first-semester grade average [37,38,40]. The approval of key subjects for each program is also relevant since this could mark the student's trajectory in terms of timely graduation [45].…”

Systematic Literature Review on Battery Management Systems and predicting Solar Big Data

“…Manusia dipaksa untuk mengembangkan sumber daya baru yang tidak berpolusi dan hemat energi. [1] Karena energi listrik memiliki kelebihan yang luar biasa, pengembangan transportasi listrik dan penelitian telah menjadi arus utama pengembangan transportasi hijau dibandingkan dengan teknologi motor dan kontrol motor dimana sistem manajemen baterai belum terlalu matang. Itu dapat meningkatkan keamanan dan stabilitas seluruh sistem baterai yang Online mendeteksi setiap tahap tegangan sel baterai, arus, suhu baterai secara real-time.…”