Students' performance evaluation in online education system Vs traditional education system

Jayasinghe, Udeni; Dharmaratne, Anuja; Atukorale, Ajantha S.

doi:10.1109/rev.2015.7087277

Cited by 15 publications

(10 citation statements)

References 16 publications

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“…e classification models discussed in the paper were multilayer perceptron (MLP), J48 classifier, reduced error pruning decision tree, and sequential minimal optimization (SMO). In [7], Jayasinghe et al studied the performance analysis of students based on online and conventional educational patterns. e teachers are directly involved in the evaluation process without considering the learning pattern of the students.…”

Section: Introductionmentioning

confidence: 99%

Analysis of English Performance Rating Based on Machine Learning and Optimized BP Network Technology

Liu

Dong

2021

Security and Communication Networks

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In order to improve the intelligent effect of English performance rating, this paper analyzes the English performance rating process and builds the corresponding algorithm model based on the wisdom of teaching ideas, and it combines BP spatial network technology with spatial sampling and spatial statistical algorithms to construct the system kernel algorithm. Moreover, this paper extracts students’ English answers through text recognition and compares them with the standard library. Among them, objective questions are mainly scored directly through standard answer comparison, and subjective questions compare student scores with standard answers. In addition, this paper uses a needs analysis to construct the framework of the entire English performance rating system and evaluates the practical effect of the system constructed in the paper through experimental research. According to statistical research data, the English performance rating system in this paper is known to have certain effects.

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Section: Introductionmentioning

confidence: 99%

Analysis of English Performance Rating Based on Machine Learning and Optimized BP Network Technology

Liu

Dong

2021

Security and Communication Networks

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“…The Gaussian membership feature was used to assign parameters to the fuzzy scheme, Figure 1. Membership function with a Gaussian distribution can be mathematically represented as equation (5) .…”

Section: Layer Of Membership-functionmentioning

confidence: 99%

“…Feelings, thoughts, behaviours, beliefs, motivations, physical expression, balance, motor, and sensory abilities are frequently overlooked in the Affective and Psychomotor domain. Performance Evaluation of students in TALs should envelop the learning domain such as the cognitive, affective, and psychomotor [5].…”

Section: Introductionmentioning

confidence: 99%

Soft Computing Hybrid System for Student Performance Evaluation

Eguavoen,

2023

JSTIE

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Education Institutions have deployed technology accelerated learning systems and innovations for effective learning outcomes. Evaluating student's performance in these systems must align with the cognitive, affective, and psychomotor learning domains. In this research, a Hybrid soft computing system comprising of the Clustering Algorithm, Machine learning technique, and Optimization algorithm were hybridized and implemented to evaluate student academic performance using academic, social, and economic data of students. The quality of Categorizing information first utilizing Fuzzy C-Means and preparing ANFIS utilizing Particle Swarm Optimization was introduced which formed the Hybrid soft computing system (FCM-PSOANFIS). It demonstrated significantly, a robust predictive capability compared to other hybrid machine learning algorithms such as ANFIS and GANFIS. The results of the proposed Hybrid Soft Computing model (FCM-PSOANFIS) show a higher convergence when compare with ANFIS and GANFIS. The proposed model works better with bigger datasets than with smaller or fewer datasets, and it delivers higher predictive findings under settings that depict student learning capacities while assessing student academic achievement.

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“…The ELE concept is complex and multidimensional [ 1 , 2 , 12 , 21 ]. Despite this, many studies have attempted to examine the antecedents of ELE [ 22 , 23 , 24 ]; they did not introduce a comprehensive model for the antecedents of ELE. However, the results of these studies have not always been consistent [ 25 , 26 ] and, therefore, ELE antecedents remain unidentified.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

E-Learning Success Model in the Context of COVID-19 Pandemic in Higher Educational Institutions

Jaoua

Almurad

Elshaer

et al. 2022

IJERPH

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Nowadays, the extensive use of e-learning in higher educational institutions in many countries leads us to apprehend the reality, precisely the key success/failure factors of the implementation, of e-learning systems in these institutions. This motivation becomes more and more important, inevitable, and urgent, especially for institutions that have heavily adopted e-learning systems under exceptional conditions without any prior planning, such as the COVID-19 pandemic. From this perspective, this research aimed to provide an e-learning success model in the context of the COVID-19 pandemic by assessing e-learning effectiveness and by investigating the key antecedents of e-learning effectiveness. The literature review led to the identification of four main factors influencing e-learning effectiveness: The e-learning system, e-learning readiness, interactivity, and resistance to change. These four variables constituted the antecedents of an effective e-learning system, which was tested in a KSA context. A structured survey, including a sample of 1202 students from Imam Mohammad Ibn Saud Islamic University was used to examine the linkages among our proposed model. The model, with a total of ten direct and six indirect relationships, was tested by using structural equation modeling. The research findings indicate that effective e-learning is supported by the interactions between four factors: the e-learning system, e-learning readiness, interactivity, and resistance to change.

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Students' performance evaluation in online education system Vs traditional education system

Cited by 15 publications

References 16 publications

Analysis of English Performance Rating Based on Machine Learning and Optimized BP Network Technology

Analysis of English Performance Rating Based on Machine Learning and Optimized BP Network Technology

Soft Computing Hybrid System for Student Performance Evaluation

E-Learning Success Model in the Context of COVID-19 Pandemic in Higher Educational Institutions

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