Analyzing Student Performance in Programming Education Using Classification Techniques

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 t...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of emerging technologies in learning Jg. 15; H. 2; S. 127
Hauptverfasser: Sunday, Kissinger, Ocheja, Patrick, Hussain, Sadiq, Oyelere, Solomon Sunday, Samson, Balogun Oluwafemi, Agbo, Friday Joseph
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Vienna International Association of Online Engineering (IAOE) 01.01.2020
Schlagworte:
Academic Achievement
Algorithms
Attendance
Computers
Evaluators
Failure
Mathematics
Programming
ISSN:1863-0383, 1863-0383
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1863-0383
1863-0383
DOI:10.3991/ijet.v15i02.11527