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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Rob Abbott
University of North Carolina at Charlotte
Computer Science & IT Engineering

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Predicting students’ academic performance using e-learning logs Malak Abdullah; Mahmoud Al-Ayyoub; Farah Shatnawi; Saif Rawashdeh; Rob Abbott
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp831-839

Abstract

The outbreak of coronavirus disease 2019 (COVID-19) drives most higher education systems in many countries to stop face-to-face learning. Accordingly, many universities, including Jordan University of Science and Technology (JUST), changed the teaching method from face-to-face education to electronic learning from a distance. This research paper investigated the impact of the e-learning experience on the students during the spring semester of 2020 at JUST. It also explored how to predict students’ academic performances using e-learning data. Consequently, we collected students’ datasets from two resources: the center for e-learning and open educational resources and the admission and registration unit at the university. Five courses in the spring semester of 2020 were targeted. In addition, four regression machine learning algorithms had been used in this study to generate the predictions: random forest (RF), Bayesian ridge (BR), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The results showed that the ensemble model for RF and XGBoost yielded the best performance. Finally, it is worth mentioning that among all the e-learning components and events, quiz events had a significant impact on predicting the student’s academic performance. Moreover, the paper shows that the activities between weeks 9 and 12 influenced students’ performances during the semester.
Combating propaganda texts using transfer learning Malak Abdullah; Dia Abujaber; Ahmed Al-Qarqaz; Rob Abbott; Mirsad Hadzikadic
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp956-965

Abstract

Recently, it has been observed that people are shifting away from traditional news media sources towards trusting social networks to gather news information. Social networks have become the primary news source, although the validity and reliability of the information provided are uncertain. Memes are crucial content types that are very popular among young people and play a vital role in social media. It spreads quickly and continues to spread rapidly among people in a peer-to-peer manner rather than a prescriptive. Unfortunately, promoters and propagandists have adopted memes to indirectly manipulate public opinion and influence their attitudes using psychological and rhetorical techniques. This type of content could lead to unpleasant consequences in communities. This paper introduces an ensemble model system that resolves one of the most recent natural language processing research topics; propaganda techniques detection in texts extracted from memes. The paper also explores state-of-the-art pretrained language models. The proposed model also uses different optimization techniques, such as data augmentation and model ensemble. It has been evaluated using a reference dataset from SemEval-2021 task 6. Our system outperforms the baseline and state-of-the-art results by achieving an F1-micro score of 0.604% on the test set.
Co-Authors Ahmed Al-Qarqaz Dia Abujaber Farah Shatnawi Mahmoud Al-Ayyoub Malak Abdullah Mirsad Hadzikadic Saif Rawashdeh