scholarly journals Predicting Students’ Performance In Basic Algorithms Programming In an E-Learning Environment Using Decision Tree Approach

2022 ◽  
Vol 7 (1) ◽  
pp. 498
Author(s):  
Jonas De Deus Guterres ◽  
Kusuma Ayu Laksitowening ◽  
Febryanti Sthevanie
Keyword(s):  
Learning Environment ◽  
Decision Tree ◽  
Student Performance ◽  
Information Gain ◽  
Quality Data ◽  
Basic Algorithm ◽  
Id3 Algorithm ◽  
E Learning ◽  
Advanced Analysis ◽  
Quality In Higher Education

Predicting the performance of students plays an important role in every institution to protect their students from failures and leverage their quality in higher education. Algorithm and Programming is a fundamental course for the students who start their studies in Informatics. Hence, the scope of this research is to identify the critical attributes which influence student performance in the E-learning Environment on Moodle LMS (Learning Management System) Platform and its accuracy. Data mining helps the process of preprocessing data in a dataset from raw data to quality data for advanced analysis. Dataset set is consisting of student academic performance such as grades of Quizzes, Mid exams, Final exams, and Final projects. Moreover, the dataset from LMS is considered as well in the process of modeling, in terms of constructing the decision tree, such as punctuality submission of Quizzes, Assignments, and Final Projects. Regarding the Basic Algorithm and Programming course, which is separated into two subjects in the first and second semester, thus the research will predict the student performance in the Basic Algorithm and programming course in the second semester based on the Introduction to programming course in the first semester. Decision Tree techniques are applied by using information gain in ID3 algorithm to get the important feature which is the PP index has the highest information gain with value 0.44, also the accuracy between ID3 and J48 algorithm that shows ID3 has the highest accuracy of modeling which is 84.80% compared to J48 82.34%.

scholarly journals Analyzing Student Performance in Programming Education Using Classification Techniques

2020 ◽  
Vol 15 (02) ◽  
pp. 127 ◽  
Author(s):  
Kissinger Sunday ◽  
Patrick Ocheja ◽  
Sadiq Hussain ◽  
Solomon Sunday Oyelere ◽  
Balogun Oluwafemi Samson ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Computer Science ◽  
Student Performance ◽  
Test Score ◽  
Information Gain ◽  
Search Method ◽  
Experimental Results ◽  
Id3 Algorithm ◽  
Tree Algorithms

In this research, we aggregated students log data such as Class Test Score (CTS), Assignment Completed (ASC), Class Lab Work (CLW) and Class Attendance (CATT) from the Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, Nigeria. Similarly, we employed data mining techniques such as ID3 & J48 Decision Tree Algorithms to analyze these data. We compared these algorithms on 239 classification instances. The experimental results show that the J48 algorithm has higher accuracy in the classification task compared to the ID3 algorithm. The important feature attributes such as Information Gain and Gain Ratio feature evaluators were also compared. Both the methods applied were able to rank search method and the experimental results confirmed that the two methods derived the same set of attributes with a slight deviation in the ranking. From the results analyzed, we discovered that 67.36 percent failed the course titled Introduction to Computer Programming, while 32.64 percent passed the course. Since the CATT has the highest gain value from our analysis; we concluded that it is largely responsible for the success or failure of the students.

A Literature Review of Student Performance Prediction in E-Learning Environment

2020 ◽  
Vol 01 (01) ◽  
pp. 22-36
Author(s):  
Ruth Chweya ◽  
Siti Mariyam Shamsuddin ◽  
Samuel-Soma M. Ajibade ◽  
Samuel Moveh
Keyword(s):  
Literature Review ◽  
Learning Environment ◽  
Student Performance ◽  
Performance Prediction ◽  
E Learning

Identification of Factors Affecting Disk Drive’s Performance in Data Server by Use of Decision Tree Learning Method

2021 ◽  
Author(s):  
Yi-Ju Liao ◽  
Jen-Yuan (James) Chang
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Learning Method ◽  
Training Set ◽  
Decision Tree Learning ◽  
Maximum Information ◽  
Factors Affecting ◽  
Id3 Algorithm ◽  
Maximum Information Entropy ◽  
Smart Machine

Abstract To identify factors affecting magnetic disk drive’s data recording performance in data server, decision tree learning method is proposed and validated in this paper. Aiming at improving classification efficiency of various causes of HDD performance degradation, the ID3 algorithm of decision tree was first used showing the training set model would be able to achieve 100% accuracy. The maximum information entropy and information gain theory of ID3 algorithm were then adopted, from which accuracy range of 0.5–0.6 can be further achieved. The proposed method was validated to be effective for leveraging the data sever into Industry 4.0 ready smart machine.

The digital age: is this the future of medical education? A cross-sectional study to assess medical students’ opinions about e-learning in psychiatry undergraduate medical education

2014 ◽  
Vol 31 (2) ◽  
pp. 89-96
Author(s):  
D. Mullins ◽  
F. Jabbar ◽  
N. Fenlon ◽  
K. C. Murphy
Keyword(s):  
Medical Education ◽  
Medical Students ◽  
Learning Environment ◽  
Learning Styles ◽  
Student Performance ◽  
Undergraduate Medical Education ◽  
Cross Sectional Study ◽  
Virtual Learning Environment ◽  
Cross Sectional ◽  
E Learning

ObjectivesThe main objectives were to assess medical students’ opinions about e-learning in psychiatry undergraduate medical education, and to investigate a possible relationship between learning styles and preferences for learning modalities.MethodDuring the academic year 2009/2010, all 231 senior Royal College of Surgeons in Ireland (RCSI) medical students in their penultimate year of study were invited to answer a questionnaire that was posted online on Moodle, the RCSI virtual learning environment.ResultsIn all, 186 students responded to the questionnaire, a response rate of 80%. Significantly more students stated a preference for live psychiatry tutorials over e-learning lectures. Students considered flexible learning, having the option of viewing material again and the ability to learn at one’s own pace with e-learning lectures, to be more valuable than having faster and easier information retrieval.ConclusionStudents prefer traditional in-class studying, even when they are offered a rich e-learning environment. Understanding students’ learning styles has been identified as an important element for e-learning development, delivery and instruction, which can lead to improved student performance.

An Improved ID3 Decision Tree Algorithm

2014 ◽  
Vol 962-965 ◽  
pp. 2842-2847 ◽  
Author(s):  
Xiao Juan Chen ◽  
Zhi Gang Zhang ◽  
Yue Tong
Keyword(s):  
Decision Tree ◽  
Information Entropy ◽  
Information Gain ◽  
Classification Algorithm ◽  
Learning Ability ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Decision Tree Classification ◽  
Id3 Algorithm ◽  
Improved Algorithm

As the classical algorithm of the decision tree classification algorithm, ID3 algorithm is famous for the merits of high classifying speed, strong learning ability and easy construction. But when used to make classification, the problem of inclining to choose attributions which have many values affect its practicality. This paper presents an improved algorithm based on the expectation information entropy and Association Function instead of the traditional information gain. In the improved algorithm, it modified the expectation information entropy with the improved Association Function and the number of the attributes values. The experiment result shows that the improved algorithm can get more reasonable and more effective rules.

The Application of Data Mining Technology in the Teaching Evaluation in Colleges and Universities

2017 ◽  
Vol 14 (1) ◽  
pp. 7-12 ◽  
Author(s):  
Xiaoqi Liu
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Evaluation System ◽  
Information Gain ◽  
Original Data ◽  
Teaching Evaluation ◽  
Personal Factors ◽  
Mining Technology ◽  
Id3 Algorithm ◽  
College Work

As the teaching management informationization level is higher and higher, Network based teaching evaluation system has been widely used, and a lot of evaluation of the original data has been accumulated. This research, taking recent five years teaching evaluation data of the college work for as basis, analyzes teachers’ personal factors and teaching operation factors respectively with the data mining technology of decision tree ID3 algorithm. By calculating the factors of information entropy and information gain value, the corresponding decision tree is gained. The teaching evaluation results are made use of really rather than become a mere formality, and thus provide powerful basis for the effectiveness and scientificalness of teaching evaluation.

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