A Proposed Data Mining Framework for Higher Education System

Khedr, Ayman E.; Seddawy, Ahmed I. El

doi:10.5120/19839-1693

Cited by 18 publications

(16 citation statements)

References 8 publications

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“…Shi et al (2012) apply the EDM technique to help academicians in structuring offered courses, scientifically guiding teaching and learning, and improving teaching management. As for Khedr and El Seddawy (2015), the DM technique is used to create the HEDMS system for HEIs. HEDMS supports academicians in making decisions regarding the improvement of the students' learning processes by providing academicians with useful information on students and Moodle activities.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

Internet use behaviour model for predicting students' performance

Khamis

Ahmad

et al. 2022

Expert Systems

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Internet technology is vital in education as it is an enabler tool used by students for information exchange, communication and creation of knowledge. However, issues related to Internet use behaviour (IUB) and effects on students' performance are still being debated due to inconsistent results. Thus, the main aim of this paper is to propose a specific IUB classification model to discover comprehensive prediction models on students' performance by using educational data mining (EDM). Data related to the students' CGPA and their internet usage activities were collected from 469 undergraduate students. Primarily, in this study several techniques were ensembled to predict students' performance based on IUB. An EDM approach comprising of clustering, classification, correlation, and regression was used. The base classifiers including decision tree (j48) ensembled with EM clustering technique were exploited to develop a high accuracy of prediction results. At first, the usage of clustering technique was to cluster the dataset into smaller variable and applied classification technique to solve the complexity of the variables. Correlation come across to measure the degree of IUB and students' performance and later regression was applied to produce predictive results. Based on 11 IUB categories, the results indicate that online gaming has a negative significant effect on the students' performance. Additionally, the prediction model revealed a high correlation, which generates good predictions of 92%. The prediction model would enable higher learning institutions to detect as early as possible those students who are at risk. This will then help them in taking timely and proactive measures to improve their performance.

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Section: Literature Review and Related Workmentioning

confidence: 99%

Internet use behaviour model for predicting students' performance

Khamis

Ahmad

et al. 2022

Expert Systems

View full text Add to dashboard Cite

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“…Classification algorithms have been previously investigated in different situations such as in [10], [25], [26], [27], [38]. One of the algorithms that are highlighted in this research is Naïve Bayes which depends on a successful application to the Bayesian theorem.…”

Section: Related Workmentioning

confidence: 99%

A Collaborative Evaluation Metrics Approach for Classification Algorithms

Idrees¹,

Alsheref²

2020

Journal of Southwest Jiaotong University

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Evaluating Algorithms is one of the critical steps which should be strongly considered as this is the pillar of most of the decisions. This research proposes a novel method for accurate algorithms’ evaluation according to the metrics’ relationships and weight. The weight of the evaluation metrics is determined according to their invariance level. The proposed method validity is confirmed by applying and evaluating the most famous and well-populated classification techniques. The results have been considered according to the calculated weight of the evaluation measures to reveal the final algorithm evaluation. As a case study, the most suitable classification technique for Tinnitus data are explored. This research considered the Tinnitus data as Tinnitus symptoms are not clearly recognized which highlights the difficulty of the patients to have a direct and fast diagnosis which highlighted the motivation in investigating intelligent methods for fast Tinnitus diagnosing. The research applied the experiment on a real dataset that is gathered in Egypt and the results highlighted that the Support Vector Machine classification algorithm is the most suitable technique for Tinnitus data classification with an accuracy equal to 90.1%.

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“…Different algorithms have been proposed [27] [28] [29], however, Vector space model (VSM) is one of the most popular and successful. VSM contributes to an algebraic model that is able to provide a vector representation to the text document.…”

Section: B Vector Space Modelmentioning

confidence: 99%

A Proposed Model for Detecting Facebook News’ Credibility

Idrees¹,

Alsheref²,

ElSeddawy³

2019

IJACSA

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Social networks are currently one of the main News' sources for most of their users. Moreover, News channels also consider social networks as main channels not only for spreading the news but also for measuring the feedback from their followers. Facebook Followers can comment or react to the news, which represents the follower's feedback about this topic. Therefore, it is a fact that measuring the News' credibility is one of the important tasks that could control the propagation of the fake news as well as the number of News' followers. The proposed model in this research highlights the impact of the News' followers on detecting the News' polarity either it is fake or not. The proposed model focuses on applying an intelligent sentiment analysis using Vector Space Model (VSM) which is one of the most successful techniques on the users' comments and reactions through the emoji. Then the degree of credibility is determined according to the correlation coefficient. An experimental study was applied using Facebook News dataset, which included the News and the followers' feedbacks.

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A Proposed Data Mining Framework for Higher Education System

Cited by 18 publications

References 8 publications

Internet use behaviour model for predicting students' performance

Internet use behaviour model for predicting students' performance

A Collaborative Evaluation Metrics Approach for Classification Algorithms

A Proposed Model for Detecting Facebook News’ Credibility

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