Forecasting and Avoiding Student Dropout Using the K-Nearest Neighbor Approach

Abstract

number of students in these schools in India, and the problem is continuing studies at school or college for many students is difficult due to reasons such as financial problem, domestic problems at home, being unable to cope up with studies, language or medium of education and many other problems like this. The dropout ratio in 2018-2019 for secondary level classes IX and X is at 17% as compared to 4% in primary classes I to V, and to minimize the dropout ratio, the technique of prediction is proposed; in the field of machine learning and data mining, the most widely used prediction method is K-nearest neighbors. This method is more versatile and simple and can handle different types of data in the process of prediction. The prediction of students is classified into dropout or no-dropout category and hence enables the teacher to counsel the students who are at risk of dropping out.

Why do Junior High School Students from Poor Families in Southwest China Maintain a High Dropout Rate under Multiple Policies Support?

“…During our home visits to dropouts, we found the main reasons for students' unwillingness to go back to school were as follows: (1) The hopelessness of further studies lead to the weariness of studies, and finally choose to drop out. (2) The consequences that some college students are unable to find employment or some college students have low incomes after employment bring about negative impacts, resulting in some people form the concept that studying is useless. (3) Some parents and their children have developed the psychological dependence that poor families will always get social assistance.…”

“…The high dropout rate of junior high school students exists in many countries and regions. In recent years, India [1][2], Bangladesh [3], Singapore [4], Israel [5] and China [6][7][8][9][10] have reported high dropout rate of junior high school students. In some regions, the cumulative dropout rate of junior high school students in three years was more than 50% [6][7].…”

A Machine Learning Approach to Detect Student Dropout at University

“…The various features of the students are defined and categorized into two as student welfare feature and student performance feature Student welfare feature defines parent involvement, medium of instructions, qualified parent, earning members in a family, annual income of parents, time spent with friends, and playing, working in the family shop, doing a part-time job, liberty is given and financial assistance. Student performance feature defines previous examination performance, everyday reading and writing activity, academic pressure, need for extra classes, and re-examination performance [11].…”

Drop-Out Prediction in Higher Education Among B40 Students

“…They used Weka tool kit to utilize the classifier algorithm on the selected dataset and produced a comparative result of each algorithm performance in terms of recall, precision and accuracy. Meanwhile, [19] only concentrated on k-NN to perform extensive evaluation and predict student drop-out at an early stage of study. The technique is versatile, simple and can handle different type of data.…”

“…Mardolkar & Kumaran (2020) [19] Evaluate and propose k-NN method to predict students' drop-out 552 | P a g e www.ijacsa.thesai.org Comparative studies of two or more prediction models had been the core for 13 studies while the remaining used single classifier. In comparing prediction models performance, DT was the main choice among the researchers which was used in 11 studies followed by NB/ k-NN (six studies), RF (five studies) and ANN/ SVM (four studies).…”