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Predicting Students' Engagement in Online Courses Using Machine Learning
Alsirhani, Jawaher,Alsalem, Khalaf International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.9
No one denies the importance of online courses, which provide a very important alternative, especially for students who have jobs that prevent them from attending face-to-face in traditional classes; Engagement is one of the most important fundamental variables that indicate the course's success in achieving its objectives. Therefore, the current study aims to build a model using machine learning to predict student engagement in online courses. An online questionnaire was prepared and applied to the students of Jouf University in the Kingdom of Saudi Arabia, and data was obtained from the input variables in the questionnaire, which are: specialization, gender, academic year, skills, emotional aspects, participation, performance, and engagement in the online course as a dependent variable. Multiple regression was used to analyze the data using SPSS. Kegel was used to build the model as a machine learning technique. The results indicated that there is a positive correlation between the four variables (skills, emotional aspects, participation, and performance) and engagement in online courses. The model accuracy was very high 99.99%, This shows the model's ability to predict engagement in the light of the input variables.
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Alhayek, Samar,Alsalem, Mohammed,Alotaibi, Yazeed,Omair, Aamir Korean Association of Maxillofacial Plastic and Re 2019 Maxillofacial Plastic Reconstructive Surgery Vol.41 No.-
Background: The present study aimed to determine whether laypeople and professionals rate the facial appearance of individuals with repaired complete unilateral or bilateral cleft lip and palate (UCLP, BCLP) similarly based on viewing full facial images. Methods: The study followed a cross-sectional analytical design where five young patients aged 10 to 14 years, who had completed all stages of their unilateral or bilateral cleft lip and palate treatment (bilateral: three, unilateral: two), were evaluated by two groups. The assessment was done by laypeople and 97 qualified professionals (33 orthodontists, 32 plastic surgeons, and 32 oral and maxillofacial surgeons). Professionals were not involved in any stage of the patients' treatment. Results: The facial appearance assessment of the professional groups on different facial aesthetics was significantly lower than that of laypeople, and they had higher perceived need for further treatment. On the other hand, laypeople had higher aesthetic ratings and lower perceived need for further treatment. Differences were also observed between the assessments of the professional groups. Participants who had lower aesthetic assessments of the repair tended to report a higher influence of cleft lip and palate on social activities and professional life. Conclusion: Differences in perception exist between healthcare professionals and laypeople. The discrepancies between the professional groups could be attributed to different treatment modalities and protocols.
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Spatial Decision Support System for Residential Solar Energy Adoption
Ahmed O. Alzahrani,Hind Bitar,Abdulrahman Alzahrani,Khalaf O. Alsalem International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.6
Renewable energy is not a new terminology. One of the fastest growing renewable energies is solar energy. The implementation of solar energy provides several advantages including the reduction of some of the environmental risks of fossil fuel consumption. This research elaborated the importance of the adaption of solar energy by developing a spatial decision support system (SDSS), while the Residential Solar Energy Adoption (RSEA) is an instantiation artifact in the form of an SDSS. As a GIS web-based application, RSEA allows stakeholders (e.g., utility companies, policymakers, service providers homeowners, and researchers) to navigate through locations on a map interactively. The maps highlight locations with high and low solar energy adoption potential that enables decision-makers (e.g., policymakers, solar firms, utility companies, and nonprofit organizations) to make decisions. A combined qualitative and quantitative methodological approach was used to evaluate the application's usability and user experience, and results affirmed the ability of the factors of utility, usefulness, and a positive user experience of the residential solar energy adoption of spatial decision support system (RSEA-SDSS). RSEA-SDSS in improving the decision-making process for potential various stakeholders, in utility, solar installations, policy making, and non-profit renewable energy domains.
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