Advanced Learning Analytics in Academic Education

Fayoumi, Ayman G.; Hajjar, Abd El Salam Al

doi:10.4018/ijswis.2020070105

Cited by 28 publications

(9 citation statements)

References 35 publications

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“…erefore, it is crucial to study the factors affecting the success of college students' entrepreneurship, and the ability of college students is the key that directly determines the success of their entrepreneurship, so it is crucial to study the ability of college students' entrepreneurship. As the future force of social development, the improvement of the entrepreneurial ability of college students is related to the implementation of China's innovative country construction plan and the speed of implementation [1]. erefore, it is an effective way to solve the difficulties of entrepreneurship, the low success rate of entrepreneurship, and the pressure of social employment for college students by studying the factors influencing the entrepreneurial ability of college students and exploring the strategies to improve the it in three levels [2].…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Enhancement Mechanism of University Students’ Employability and Entrepreneurship Based on Deep Learning in the Context of the Digital Era

Meng

Ren

Huang

2021

Scientific Programming

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This paper adopts a deep learning approach to analyze and study the mechanism of quantitative enhancement of college students’ employment and entrepreneurial abilities in the context of the digital era. The deep learning connotation is predetermined as five abilities, which are metacognitive ability, active communication and cooperation ability, deep processing ability, creative practice ability, and learning empathy experience, and, based on this, the deep learning questionnaire is designed, and it is reclassified by exploratory factor analysis to reduce the dimensionality, and the specific indicators and scientific connotation dimensions of the deep learning questionnaire are determined; and, through the deep learning of each dimension, the problems of deep learning of college students are examined and in-depth analysis is conducted, and the inner relationship and correlation among the dimensions of deep learning of college students are derived through correlation analysis. The success of innovation and entrepreneurship depends on the innovation and entrepreneurial ability of college students, and the formation of the ability influenced various factors. Therefore, not only is studying the influencing factors of college students’ innovation and entrepreneurship ability in line with the requirements of the times and social development, but also it can solve real problems. This thesis adopts a combination of two methods, qualitative research and quantitative research, to study the influencing factors of college students’ innovation and entrepreneurship ability and tries to ensure the scientificity, accuracy, and comprehensiveness of the conclusion. In this paper, we analyzed the requirements of the employment prediction system for graduating secondary school students, carried out the software framework and database design of the employment analysis and prediction system for secondary school students, and designed the system modularly based on the analysis results. By applying the proposed deep feedforward neural network prediction model to the prediction system, a software system applicable to the employment prediction and guidance of secondary school students is implemented.

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Section: Introductionmentioning

confidence: 99%

A Quantitative Enhancement Mechanism of University Students’ Employability and Entrepreneurship Based on Deep Learning in the Context of the Digital Era

Meng

Ren

Huang

2021

Scientific Programming

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“…These projects aim to enhance operations and the value of universities with the end objective of attaining sustainable development and worldwide effectiveness, increasing the digital economy's contribution to GDP, and improving the overall quality of the education system [36]. According to (Fayoumi and Hajjar, [33], incorporating new data mining and decision-making approaches in higher education is a bold step toward improved performance. Predictive and descriptive analytics provide intriguing insights into essential elements of education.…”

Section: Classification 41 Higher Educationmentioning

confidence: 99%

Prospers and Obstacles in Using Artificial Intelligence in Saudi Arabia Higher Education Institutions—The Potential of AI-Based Learning Outcomes

Alotaibi

Alshehri

2023

Sustainability

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Within the framework of the ongoing implementation of the 2030 Vision for Comprehensive Development of Higher Education in Saudi Arabia, the integration of artificial intelligence (AI) has emerged as a pivotal objective for the country’s numerous higher education institutions. This study aims to examine the opportunities and challenges that arise from the adoption of AI-based learning outcomes in Saudi Arabia’s higher education institutes. Moreover, the research also investigates the contribution of major higher education institutes in Saudi Arabia to the field of AI-based learning outcomes. To gather relevant literature, the Scopus and Web of Science databases were utilised, resulting in the selection of fifty-five studies for final analysis. The study employed the PRISMA statement 2020 for records filtration and utilised VOS viewer software to classify the literature on AI-based learning outcomes in Saudi Arabian universities. Through detailed analysis, three significant data streams were identified and examined. The findings indicate that AI is in a nascent stage within the realm of learning, and it has become an undeniable reality for higher education institutions. Embracing this transformative technology is crucial for meeting future learning challenges, and it is imperative that all students acquire the necessary technical skills to interact with and create artificial intelligence in the future. According to the findings, AI has the potential to address significant educational challenges, revolutionise teaching and learning methodologies, and accelerate progress toward the Saudi 2030 objectives. However, the study also highlights certain challenges associated with the implementation of AI-based learning in the higher education context of Saudi Arabia, emphasising the need for teachers to acquire new technological skills to effectively utilise AI pedagogically.

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“…AI can solve complex tasks through supervised, unsupervised, and reinforcement learning (Dhanaraj, Rajkumar & Hariharan, 2020;Sarker, 2021;Singh, Singh & Deka, 2021). Artificial neural network (ANN) has also been used in decision support system (DSS) in higher education system for advanced decision making and largely uses big data (Fayoumi & Hajjar, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Can Artificial Intelligence (AI) Manage Behavioural Biases Among Financial Planners?

Hasan

Vaz

Athota

et al. 2023

Journal of Global Information Management

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The main novelty of this paper is proposing artificial intelligence (AI) to manage behavioural biases in the financial decision-making process. An empirical study by Kahneman and Tversky identifies the evidence of behavioural biases in the investment decision-making process: a reversal of an established tenet in traditional finance. Financial planners are vulnerable to behavioural biases and are therefore unable to provide optimal investment solutions for their clients. Identifying the limitations of current practice, this research attempts to address how AI can help financial planners in subduing their behavioural biases and proposes the adoption of AI in financial planning services to circumvent behavioural biases. In recent years, AI has attained significant efficacy and has proven to be efficacious through supervised and unsupervised learning. Applying these AI techniques in mitigating behavioural biases, this study confirms that the backpropagation within the neural network and deep reinforcement learning can help overcome confirmation and hindsight biases.

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Advanced Learning Analytics in Academic Education

Cited by 28 publications

References 35 publications

A Quantitative Enhancement Mechanism of University Students’ Employability and Entrepreneurship Based on Deep Learning in the Context of the Digital Era

A Quantitative Enhancement Mechanism of University Students’ Employability and Entrepreneurship Based on Deep Learning in the Context of the Digital Era

Prospers and Obstacles in Using Artificial Intelligence in Saudi Arabia Higher Education Institutions—The Potential of AI-Based Learning Outcomes

Can Artificial Intelligence (AI) Manage Behavioural Biases Among Financial Planners?

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