SANI ABDULLAHI SANI

Mr. SANI ABDULLAHI SANI

Kano University of Science and Technology, Wudil, kano, Nigeria.
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Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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15 January 2021 in Sustainability
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Journal Article
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Nadire CavusNadire Cavus
Nadire Cavus
SANI ABDULLAHI SANISANI ABDULLAHI SANI
SANI ABDULLAHI SANI
YHYusuf Haruna
Yusuf Haruna
Abdulmalik A. LawanAbdulmalik A. Lawan
Abdulmalik A. Lawan
The sudden advent of the COVID-19 pandemic and the associated containment measures require educational institutions of all sizes to adopt eLearning as the only option for sustainable education. Despite the numerous Learning Management Systems, the rapid migration to eLearning posed numerous challenges that negatively affect the effectiveness and sustainability of the educational activities. The current study systematically reviewed recent articles that recognized the value and feasibility of using Social Networking Sites (SNSs) in education. The study highlighted the current eLearning challenges and illustrated effective strategies for the sustainable educational use of SNSs by both institutions, teachers, and students. Thus, solutions to the problems experienced in education during the COVID-19 period were highlighted based on SNS-supported strategies.

6 June 2023
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SANI ABDULLAHI SANISANI ABDULLAHI SANI
SANI ABDULLAHI SANI
Although we are in the post covid era, yet, the study explores a subject of significant interest. However, I have certain reservations regarding the coherence between the topic and the body of the article, as well as the employed research methodology...

7 June 2023 in Qeios Preprints
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SANI ABDULLAHI SANISANI ABDULLAHI SANI
SANI ABDULLAHI SANI
Writing skills are essential for academic and professional success. However, many students struggle to become proficient writers, highlighting the need for effective writing instruction and feedback methods. Automated Writing Evaluation (AWS) systems have emerged as a promising solution to address these challenges. This study proposes a model that utilizes fine-tuned language models to evaluate essay structure, specifically identifying key argumentative and rhetorical elements. The Longformer and Bigbird models were fine-tuned and evaluated for discourse classification. The results demonstrate that the Longformer model outperformed the Bigbird model, achieving an F1 score of 0.634 compared to 0.615. The Longformer model's ability to handle large data inputs without losing vital information contributed to its superior performance. Integrating machine learning models with AWE systems can enhance automated essay evaluation, providing valuable feedback to students. While positional encoding improves discourse classification, future research should focus on expanding data coverage across additional essay categories. This study highlights the significance of leveraging advanced NLP techniques to improve writing skills and lays the foundation for further advancements in automated essay evaluation systems.