Prediction of Instructor Performance using Machine and Deep Learning Techniques

Abunasser, Basem S.; AL-Hiealy, Mohammed Rasheed J.; Barhoom, Alaa M.; Almasri, Abdelbaset; Abu-Naser, Samy S.

doi:10.14569/ijacsa.2022.0130711

Cited by 12 publications

(6 citation statements)

References 14 publications

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“…Xia and Li (2022) share their findings on the use of AI in the area of HE Teacher Development, and from their conclusions, findings are seemingly in concordance with those of the Gupta and Yadav's (2022) study into HE educator digital literacy which highlight the need to widen training opportunities for further development in this area. Abunasser et al (2022)'s study also adds to this by sharing useful insights into AI-facilitated HE instructor performance prediction.…”

Section: Implications Of Genai For He Instructionmentioning

confidence: 92%

A Systematic Review of Generative AI and (English Medium Instruction) Higher Education

Bannister,

Santamaría-Urbieta,

Alcalde-Peñalver

2023

AULA_ABIERTA

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This systematic review investigates the current state of research on Generative Artificial Intelligence (GenAI) and its implications for (EMI) Higher Education. The study employs a methodology based on an evidence-informed and theoretically credible framework to answer two research questions: (1) What studies of relevance to (EMI) Higher Education have been published thus far, considering the most recent developments of GenAI? and (2) Which key areas are currently lacking in extant literature and in need of further scholarly exploration in this regard in (EMI) Higher Education research? The results of the study reveal a limited number of pertinent publications, indicating a sparse scholarly landscape with a dearth of work on the implications of Generative AI in EMI Higher Education. Based on these findings, preliminary recommendations have been made to guide future research in this area. This study contributes to the literature by highlighting the need for further research on the potential of GenAI to enhance the teaching and learning experience in (EMI) Higher Education and provides a theoretical framework to guide future research. These findings may inform researchers and educators interested in exploring how GenAI may be leveraged from different educational perspectives.

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Section: Implications Of Genai For He Instructionmentioning

confidence: 92%

A Systematic Review of Generative AI and (English Medium Instruction) Higher Education

Bannister,

Santamaría-Urbieta,

Alcalde-Peñalver

2023

AULA_ABIERTA

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“…The major evolutionary process of AI covers initial AI, machine learning, and the recent Deep Learning. One of the major challenges of educational institutions is the accumulated amount of data and how it can be utilized to boost the academic programs' quality (Abunasser & AL-Hiealy, 2022). The most common AI data mining techniques; C4.5 algorithms, k-mean algorithms, support vector machines, a priori algorithms, and expectation-maximization algorithms, are commonly used.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Emerging Trends in Artificial Intelligence in Education in Nigeria

Bali

2024

Preprint

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In the domain of education, the integration of Artificial Intelligence (AI) has ushered in a paradigm shift towards a more technologically-driven landscape, demonstrating its efficacy as an emergency strategy. The pervasive influence of computer technology has catalyzed a surge in online learning within the country, yielding positive educational outcomes. Despite these advancements, a considerable number of educational institutions in Nigeria have yet to leverage AI technologies. Recognizing the expanding significance of AI in education, this study seeks to align with this trajectory by aggregating instances of AI implementation in education from developed countries. The methodology employed involves a comprehensive review of current advancements in AI applications within the Nigerian educational context. The review process, spanning papers retrieved from four digital libraries published between 2008 and 2022, culminated in the inclusion of 73 papers. These selected papers demonstrated the utilization of AI software tools and technologies, adhering to predefined exclusion and inclusion criteria. The findings of the study reveal a prevalent use of AI technologies in education in Nigeria, encompassing evolutionary software modelling, student performance prediction, multimedia e-learning platforms and frameworks, and the incorporation of Moodle learning. This discernible trend indicates a growing demand for the application of AI technology in the educational landscape of Nigeria. However, the study also highlights a discrepancy wherein more sophisticated AI techniques, such as intelligent tutoring systems, learnable robots or agents, web-based educational systems, and learning management systems explored extensively in other nations were infrequently applied in the Nigerian context. In light of these observations, the study proposes that educational institutions in Nigeria should consider adopting AI practices from more advanced nations. This strategic alignment is posited as a means to augment student learning opportunities and bridge the existing gap between the current state of AI integration in Nigerian education and the more advanced applications witnessed globally.

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“…2 shows the schematic of a license plate recognition system. Recently, deep learning-based approaches [16], particularly those employing You Only Look Once (YOLO) architecture [17], have gained attention due to their superior performance in object detection tasks [18], [19,30]. These methods leverage large-scale datasets and powerful deep neural network architectures to achieve high accuracy as well as realtime processing capabilities [20].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-based Framework for Vehicle License Plate Detection

Yang,

Yang

2024

IJACSA

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In the contemporary landscape of smart transportation systems, the imperative role of intelligent traffic monitoring in bolstering efficiency, safety, and sustainability cannot be overstated. Leveraging recent strides in computer vision, machine learning, and data analytics, this study addresses the pressing need for advancements in car license plate recognition within these systems. Employing an innovative approach based on the YOLOv5 architecture in deep learning, the study focuses on refining the accuracy of license plate recognition. A bespoke dataset is meticulously curated to facilitate a comprehensive evaluation of the proposed methodology, with extensive experiments conducted and metrics such as precision, recall, and F1-score employed for assessment. The outcomes underscore the efficacy of the approach in significantly enhancing the precision and accuracy of license plate recognition using performance evaluation of the proposed method. This tailored dataset ensures a rigorous evaluation, affirming the practical viability of the proposed approach in realworld scenarios. The study not only showcases the successful application of deep learning and YOLOv5 in achieving accurate license plate detection and recognition but also contributes to the broader discourse on advancing intelligent traffic monitoring for more robust and efficient smart transportation systems.

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Prediction of Instructor Performance using Machine and Deep Learning Techniques

Cited by 12 publications

References 14 publications

A Systematic Review of Generative AI and (English Medium Instruction) Higher Education

A Systematic Review of Generative AI and (English Medium Instruction) Higher Education

Analysis of Emerging Trends in Artificial Intelligence in Education in Nigeria

A Deep Learning-based Framework for Vehicle License Plate Detection

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