Forecasting Student’s Preference in E-learning Systems

Nováis, Paulo; Gonçalves, Filipe; Durães, Dalila

doi:10.1007/978-3-030-04191-5_18

Cited by 1 publication

(2 citation statements)

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“…The updates of the fireflies will be iterated until reaching the tolerance from the optimum value, or the maximum number of iterations is conducted. Mathematically, the firefly's new position i about a brighter firefly j is given by Equation (1) [20,38]:…”

Section: Firefly Algorithmmentioning

confidence: 99%

“…They used prediction rules whose interpretation will detect the educational process's weaknesses and evaluate the study material's quality. For improving learning systems in an e-learning environment, Novais et al [20] proposed an intelligent learning system to monitor the patterns of students' behavior during e-assessments, to support the teaching procedure within school environments.…”

Section: Introductionmentioning

confidence: 99%

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Using Artificial Intelligence to Predict Students’ Academic Performance in Blended Learning

et al. 2022

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University electronic learning (e-learning) has witnessed phenomenal growth, especially in 2020, due to the COVID-19 pandemic. This type of education is significant because it ensures that all students receive the required learning. The statistical evaluations are limited in providing good predictions of the university’s e-learning quality. That is forcing many universities to go to online and blended learning environments. This paper presents an approach of statistical analysis to identify the most common factors that affect the students’ performance and then use artificial neural networks (ANNs) to predict students’ performance within the blended learning environment of Saudi Electronic University (SEU). Accordingly, this dissertation generated a dataset from SEU’s Blackboard learning management system. The student’s performance can be tested using a set of factors: the studying (face-to-face or virtual), percentage of attending live lectures, midterm exam scores, and percentage of solved assessments. The results showed that the four factors are responsible for academic performance. After that, we proposed a new ANN model to predict the students’ performance depending on the four factors. Firefly Algorithm (FFA) was used for training the ANNs. The proposed model’s performance will be evaluated through different statistical tests, such as error functions, statistical hypothesis tests, and ANOVA tests.

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Section: Firefly Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%