An Adaptive Neural Fuzzy Inference System for prediction of student performance in Higher Education

Maitra, Sandhya; Madan, Sushila; Mahajan, Prerna

doi:10.1109/icacccn.2018.8748869

Cited by 7 publications

(7 citation statements)

References 10 publications

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“…For instance, a group of data in one Indian public university was applied where the results were very satisfying. However, according to the researcher's analysis, if the number of the data was higher, the results will be more detailed and accurate [20].…”

Section: Resultsmentioning

confidence: 99%

“…Previous studies also showed that the process of predicting academic success and methods of finding remedial measures to improve student performance ( [9], [20], and [23]) are among the most important ways to achieve academic success. Based on this systematic review, we will suggest creating a new system to guide the students to choose appropriate branches of their scientific capability in high school dependent on their data and marks in all subjects in the previous school years.…”

Section: Resultsmentioning

confidence: 99%

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Guiding the Students in High School by Using Machine Learning

Ababneh¹,

Aljarrah²,

Karagozlu³

et al. 2021

TEM Journal

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Machine learning is considered the most significant technique that processes and analyses educational big data. In this research paper, many previous papers related to analysing the educational big data that uses a lot of artificial intelligence techniques were studied. The purpose of the study is to identify weaknesses and gaps in previous researches. The results showed that many researches highlighted early expectations for academic performance. Unfortunately, no one thought of finding an effective way to guide high schooled students to reach their appropriate majors that can be suitable to their abilities. Those students need to be guided to pass this sensitive phase with high efficiency and good results. Thus, this school level is considered as the starting point for students’ academic lives, professional, and future success.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Guiding the Students in High School by Using Machine Learning

Ababneh¹,

Aljarrah²,

Karagozlu³

et al. 2021

TEM Journal

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show abstract

“…In studies where fuzzy logic-based performance evaluation was conducted, performance evaluation was conducted using homework, quizzes, midterms, finals, watching videos, reading books, personal development, communication skills, and participation information [25], [26], [50]. While evaluating student performance, there are studies in which past learning levels are used together with the current situation [51], [52]. In these studies, back propagation fuzzy inference [51] and a combination of two fuzzy inference systems were used [52], [53].…”

Section: Related Workmentioning

confidence: 99%

“…While evaluating student performance, there are studies in which past learning levels are used together with the current situation [51], [52]. In these studies, back propagation fuzzy inference [51] and a combination of two fuzzy inference systems were used [52], [53]. In the study, which uses 4-valued feedback fuzzy logic to evaluate student achievement, each value represents the months of the educational process.…”

Section: Related Workmentioning

confidence: 99%

A Novel Student Performance Evaluation Model Based on Fuzzy Logic for Distance Learning

ÖZSEVEN>¹,

Çağman²

2022

IJMSIT

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Distance learning is an education model in which the educator and the student come together independently of time and place and the learning process is continued. Although it has positive aspects in terms of time and space, it has limitations such as weak interaction and poor functioning of evaluation processes. Assessment systems often include multiple-choice or open-ended questions. In other words, the result is evaluated in solving a given problem, and the student's actions can be ignored until the result is reached. In this study, while evaluating student performance, the student's behavior during the semester and the distractor weight coefficient for multiple-choice exams were added, and a performance evaluation was made on the student's incorrect answers. The proposed model was created based on fuzzy logic, and the uncertainties in the evaluation were attempted to be eliminated.

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“…Based on empirical analysis of three different test cases, the research affirmed superiority of the prediction accuracy as compared to standard ANFIS, OneR, and Random Tree methods. Likewise, [13,14,15] also employed ANFIS systems on the same problem and claimed robust results in comparison with ANN and Multiple Linear Regression (MLR) techniques. However, generally, it is suggested that ANFIS suffers from high computational cost and network complexity due to curse of dimensionality problem.…”

mentioning

confidence: 99%

A Novel Metaheuristic Approach to Optimization of Neuro-Fuzzy System for Students' Performance Prediction

Hussain¹,

Talpur²,

Aftab³

et al. 2020

JSCDM

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Today, technology is aggressively adopted by educational institutes to maximize output of existing resources, to benefit both students and the teachers. The use of technology-based learning makes it possible to collect enormous amount of data pertaining to student and teacher performance. This data is then used for extracting meaningful information that deems useful for administration in making efficient decisions. For this purpose, literature shows significant advantages of data mining and machine learning techniques with regards to educational data mining [1,2,3]. Utilizing behavioral and performance data of students and teachers, researchers have applied these methods to discover knowledge in hidden patterns, to assess the effectiveness of courses, teaching methodologies, curriculum, and overall education system [4]. More specifically, the educational data mining is now being used to predict students' performances in order to identify the vulnerable ones at risk of failing the upcoming examinations so that more focus could be drawn on them to solve any difficulties [5,6].

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An Adaptive Neural Fuzzy Inference System for prediction of student performance in Higher Education

Cited by 7 publications

References 10 publications

Guiding the Students in High School by Using Machine Learning

Guiding the Students in High School by Using Machine Learning

A Novel Student Performance Evaluation Model Based on Fuzzy Logic for Distance Learning

A Novel Metaheuristic Approach to Optimization of Neuro-Fuzzy System for Students' Performance Prediction

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