Student classification for academic performance prediction using neuro fuzzy in a conventional classroom

Hidayah, Indriana; Permanasari, Adhistya Erna; Ratwastuti, Ning

doi:10.1109/iciteed.2013.6676242

Cited by 36 publications

(19 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There are also several researchers in another study who have used psychometric factor to predict students performance [35,36,32,37]. A psychometric factor is identified as student interest, study behavior, engage time, and family support.…”

Section: The Important Attributes Used In Predicting Student's Performentioning

confidence: 99%

A Review on Predicting Student's Performance Using Data Mining Techniques

Shahiri

Husain

Rashid

2015

Procedia Computer Science

598

349

View full text Add to dashboard Cite

Predicting students performance becomes more challenging due to the large volume of data in educational databases. Currently in Malaysia, the lack of existing system to analyze and monitor the student progress and performance is not being addressed. There are two main reasons of why this is happening. First, the study on existing prediction methods is still insufficient to identify the most suitable methods for predicting the performance of students in Malaysian institutions. Second is due to the lack of investigations on the factors affecting students achievements in particular courses within Malaysian context. Therefore, a systematical literature review on predicting student performance by using data mining techniques is proposed to improve students achievements. The main objective of this paper is to provide an overview on the data mining techniques that have been used to predict students performance. This paper also focuses on how the prediction algorithm can be used to identify the most important attributes in a students data. We could actually improve students achievement and success more effectively in an efficient way using educational data mining techniques. It could bring the benefits and impacts to students, educators and academic institutions.

show abstract

Section: The Important Attributes Used In Predicting Student's Performentioning

confidence: 99%

A Review on Predicting Student's Performance Using Data Mining Techniques

Shahiri

Husain

Rashid

2015

Procedia Computer Science

598

349

View full text Add to dashboard Cite

show abstract

“…Many systems and numerous algorithm exists which produce to constructs classification model for student prediction such as Hidayah et.al [1] where researcher used ANFIS techniques with Fuzzy logic toolbox for classify the students on the basis of their academic performance. They attempted and mapped the conditions according to created rules for removing difficulty for classifies the students.…”

Section: Literature Surveymentioning

confidence: 99%

Student Prediction System for Placement Training Using Fuzzy Inference System

Rathore

Jayanthi

2017

IJSC

View full text Add to dashboard Cite

Proposed student prediction system is most vital approach which may be used to differentiate the student data/information on the basis of the student performance. Managing placement and training records in any larger organization is quite difficult as the student number are high; in such condition differentiation and classification on different categories becomes tedious. Proposed fuzzy inference system will classify the student data with ease and will be helpful to many educational organizations. There are lots of classification algorithms and statistical base technique which may be taken as good assets for classify the student data set in the education field. In this paper, Fuzzy Inference system has been applied to predict student performance which will help to identify performance of the students and also provides an opportunity to improve to performance. For instance, here we will classify the student's data set for placement and non-placement classes.

show abstract

“…[11] Directed an assessment survey; take into consideration the development of the teaching and analysis of exams at the start and finish of the classes. In 2015, [12] described the consumption of the "Neuro-Fuzzy" ranking of student in Electrical Engineering of Malaysian Public University. The analysis presented that the production of the arrangement might conclude the pupil's likelihood of attaining outstanding grade even if the pupil got impotence in some course or subject.…”

Section: Related Workmentioning

confidence: 99%

Advanced Scholastic Analytics by Implementing Data Mining Techniques

2019

IJITEE

View full text Add to dashboard Cite

Over the past few years, the adaption of Edification management System in the sector of education has increased. Mining, clustering of essential data and finding out unique patterns from the field of education to research student’s behaviors and performance is widely recognized as Educational Data Mining (EDM) and it is a progressing profession involved with the production of new techniques for discovering distinctive and progressively large-scale information from educational environments and employing those techniques for deeper comprehend learning. It also provides an inherent understanding of teaching and learning processes for the efficient scheduling of education. This paper recommends the use of two information mining techniques in educational data. First, in admittance information, the association rule was implemented to discover some knowledge to support admission schedules. Second, a decision tree was implemented in grades and graduate student job information to estimate work type after graduation. The findings of this research provide an excellent understanding of admittance scheduling and work prediction.

show abstract

Student classification for academic performance prediction using neuro fuzzy in a conventional classroom

Cited by 36 publications

References 1 publication

A Review on Predicting Student's Performance Using Data Mining Techniques

A Review on Predicting Student's Performance Using Data Mining Techniques

Student Prediction System for Placement Training Using Fuzzy Inference System

Advanced Scholastic Analytics by Implementing Data Mining Techniques

Contact Info

Product

Resources

About