Predictive Modeling of Students Performance Through the Enhanced Decision Tree
Prognostic of student performance is one of the major issues in many institutions. The student’s performance is based on many factors such as internal examinations, grade obtained in university examination, Academic, Extra Curricular and Co-Curricular act
- PDF / 309,429 Bytes
- 16 Pages / 439.37 x 666.142 pts Page_size
- 7 Downloads / 181 Views
Abstract Prognostic of student performance is one of the major issues in many institutions. The student’s performance is based on many factors such as internal examinations, grade obtained in university examination, Academic, Extra Curricular and Co-Curricular activities and also concern with their activities in learning environment. Student performance prediction is used to model the students into any one of the four categories as excellent, good, average, and poor performance student. The category selection was determined using supervised classifiers. Academic institution can easily able to identify any academic problems and the corresponding solutions for their students through this predictive modeling approach. We have collected real world dataset related to student’s academic performance from leading academic institution in India which consists of details about the students such as CGPA, Lab performance, History of arrears and so on. We have applied various supervised classifiers such as DT, SVM, KNN, NB, NN and Improved DT on student’s academic performance dataset. Besides, the conventional decision tree is further improved by the use of normalized factor and Association Function. By comparing the accuracy of these two methods, the best result is exposed for Improved Decision Tree than all other supervised classifiers in the literature. Keywords Data mining Improved decision trees
Educational data mining Supervised classifiers
S. Sivakumar (&) Faculty of Computing, Botho University, Gaborone, Botswana e-mail: [email protected] S. Sivakumar R. Selvaraj (&) Department of Information Systems, BIUST, Gaborone, Botswana e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 A. Kalam et al. (eds.), Advances in Electronics, Communication and Computing, Lecture Notes in Electrical Engineering 443, https://doi.org/10.1007/978-981-10-4765-7_3
21
22
S. Sivakumar and R. Selvaraj
1 Introduction Data Mining (DM) indicates to the nontrivial removal of indirect, earlier doubtful, possibly valuable data in databases. Both infer either filtering through a lot of material or clever investigating the material to precisely pinpoint where the qualities dwell. Data mining uses the wide availability of sizeable amounts of data, where those can be used as beneficial data for research. This provides a way toward finding canny, fascinating and novel examples, and enlightening reasonable, and prescient models from extensive volumes of information. Educational Data Mining (EDM) is a separate research field started to mature a few years ago and to analysis the related student data from the dataset. EDM will provide an opportunity to collect, analysis, predict and forecast the student’s performance from the student’s academic performance dataset. It is used to derive new discoveries and hypotheses about how students learn. EDM is an inderdispilanry research field that uses the concept from Data Mining, Machine Learning and Statistics. Student’s Performance has been measured from their activities s
Data Loading...
