Predicting Postgraduate Students’ Performance Using Machine Learning Techniques

Koutina, Maria; Kermanidis, Katia Lida

doi:10.1007/978-3-642-23960-1_20

Cited by 49 publications

(23 citation statements)

References 10 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The latest work concerning data mining predicting students' performance was undertaken by Koutina and Kermanidis [22]. They employed six well-known data mining techniques, which are the most efficient machine learning algorithms, to predict postgraduate students' performance.…”

Section: Related Workmentioning

confidence: 99%

Learning to Prevent Inactive Student of Indonesia Open University

Tama¹

2015

J Inf Process Syst

View full text Add to dashboard Cite

The inactive student rate is becoming a major problem in most open universities worldwide. In Indonesia, roughly 36% of students were found to be inactive, in 2005. Data mining had been successfully employed to solve problems in many domains, such as for educational purposes. We are proposing a method for preventing inactive students by mining knowledge from student record systems with several state of the art ensemble methods, such as Bagging, AdaBoost, Random Subspace, Random Forest, and Rotation Forest. The most influential attributes, as well as demographic attributes (marital status and employment), were successfully obtained which were affecting student of being inactive. The complexity and accuracy of classification techniques were also compared and the experimental results show that Rotation Forest, with decision tree as the base-classifier, denotes the best performance compared to other classifiers.

show abstract

Section: Related Workmentioning

confidence: 99%

Learning to Prevent Inactive Student of Indonesia Open University

Tama¹

2015

J Inf Process Syst

View full text Add to dashboard Cite

show abstract

“…Table-2 gave a brief finding of different research papers with their author's name, main attributes helpful for prediction accuracy with different data mining algorithm used. [24]. They represented their result in Table 6 under "Total accuracy (%) of re-sample data and feature selection".…”

Section: Different Data Mining Techniques Used For Predicting Studentmentioning

confidence: 99%

“…The most important personal attributes of the student like gender, age, interested in the study, admission type, Study Behaviour are taken into consideration [7,8,9,11,12,13,18,19,24]. The family attributes like parent's qualification, parent's occupation, family income, family status, Family Support for study are also taken as important for the academics prediction [7,9,15,19,24].…”

Section: Important Factors Of Students Used For Predicting Student's mentioning

confidence: 99%

“…Maria Koutina and Katia Lida Kermanidis (2011) they tried to find out the best techniques for predicting the final grade of the postgraduate students of Ionian University Informatics, Greece. On the basis of reviewed literature, they considered Gender, Age, Marital Status, Number of children, Occupation, Job associated with computers, Bachelor, Another master, Computer literacy, Bachelor in informatics [24].…”

Section: Important Factors Of Students Used For Predicting Student's mentioning

confidence: 99%

“…The family attributes like parent's qualification, parent's occupation, family income, family status, Family Support for study are also taken as important for the academics prediction [7,9,15,19,24]. Whereas for academic attributes like high school grade, students' previous semester marks, class test grade, seminar performance, assignment performance, attendance in class and lab work, previous schools marks are taken into consideration [5,6,7,8,9,10,15,16,18,19,24] and for institutional attributes most the researcher are taken medium of teaching, accommodation type, infrastructure, water and toilet facilities, teaching methodology, transportation facilities into consideration [4,7,9,12,16,18,24].…”

Section: Important Factors Of Students Used For Predicting Student's mentioning

confidence: 99%

See 2 more Smart Citations

Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques

Kumar¹,

Singh²,

Handa³

2017

IJEME

View full text Add to dashboard Cite

One of the most challenging tasks in the education sector in India is to predict student's academic performance due to a huge volume of student data. In the Indian context, we don't have any existing system by which analyzing and monitoring can be done to check the progress and performance of the student mostly in Higher education system. Every institution has their own criteria for analyzing the performance of the students. The reason for this happing is due to the lack of study on existing prediction techniques and hence to find the best prediction methodology for predicting the student academics progress and performance. Another important reason is the lack in investigating the suitable factors which affect the academic performance and achievement of the student in particular course. So to deeply understand the problem, a detail literature survey on predicting student's performance using data mining techniques is proposed. The main objective of this article is to provide a great knowledge and understanding of different data mining techniques which have been used to predict the student progress and performance and hence how these prediction techniques help to find the most important student attribute for prediction. Actually, we want to improve the performance of the student in academic by using best data mining techniques. At last, it could also provide some benefits for faculties, students, educators and management of the institution.

show abstract