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The World Congress on Digital Education emphasized the importance of digital literacy (DL) in education. To gain a deeper understanding of the intrinsic connection between DL and employment motivation and offer guidance to college students in developing a reasonable outlook on employment, this study conducted an empirical analysis using structural equation modelling (SEM). Proportional sampling was used to collect a valid sample of 1,053 college students. The results showed a subtle relationship between the DL and Employment Motivation Slackness (EMS). In addition, Career Self-Efficacy (CSE) and Academic Achievement (ACH) played two different mediating roles: significant and non-significant, respectively. Therefore, this study suggests that the relationship between DL and EMS should be considered with caution. Overemphasis on DL and CSE in career counselling may lead to EMS among university students due to overconfidence and information overload, thereby affecting the quality of employment outcomes.
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This dataset contains simulated records for 3,000 students, generated for the purpose of evaluating fairness in predicted grading models. The dataset includes decile rankings based on historical performance, predicted grades, and demographic attributes such as socioeconomic status, school type, gender, and ethnicity. The data was created using controlled randomization techniques and includes noise to reflect real-world prediction uncertainty. While entirely synthetic, the dataset is designed to mimic key structural patterns relevant to algorithmic fairness and educational inequality.

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This dataset contains simulated records for 3,000 students, generated for the purpose of evaluating fairness in predicted grading models. The dataset includes decile rankings based on historical performance, predicted grades, and demographic attributes such as socioeconomic status, school type, gender, and ethnicity. The data was created using controlled randomization techniques and includes noise to reflect real-world prediction uncertainty. While entirely synthetic, the dataset is designed to mimic key structural patterns relevant to algorithmic fairness and educational inequality.

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¢This study delves into the connections between green ELT, DEIB, virtual reality, mediation, life skills, and task-based teaching, learning, and assessment in the context of sustainable and inclusive education. The study emphasizes the significance of incorporating ecological concepts into language instruction, advocating for diversity, fairness, and inclusivity in learning environments, and using virtual reality technology to augment language acquisition.

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In primary education in China, mathematics, science, and Chinese are commonly considered as the core subjects. This emphasis is primarily due to their significance in providing a strong foundation for students' overall academic development in their whole life. Mathematics cultivates logical thinking, problem-solving skills, and numerical proficiency, which are essential in various disciplines. Science education fosters scientific literacy, critical thinking, and an understanding of the natural world.

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The dataset file contains all the relevant data for this paper, including original text data, labels, and statistical information, which is utilized for training, testing, and validation of the proposed models or arguments. Additionally, there is a question bank file that comprises all test questions, filtered test data, and annotated result data after testing. This data is used to evaluate the performance of the models or methods proposed in the paper.

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The dataset file contains all the relevant data for this paper, including original text data, labels, and statistical information, which is utilized for training, testing, and validation of the proposed models or arguments. Additionally, there is a question bank file that comprises all test questions, filtered test data, and annotated result data after testing. This data is used to evaluate the performance of the models or methods proposed in the paper.

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There is a growing trend towards combining mathematics and computer science education. Although there are surely synergies between the two disciplines, has sufficient thought been given to the benefit of fostering independently the unique skillsets they offer in order to best harness these synergies?

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The primary article search was carried out by using selected keywords. These keywords were jointly selected by the authors, with criteria that were based on the frequently used authors' keywords found in most of the related articles. For example, the list of keywords such as “blockchain” combined with “education”, or “lifelong learning” or “life-long learning” or “digital certificate” or “academic record” or “e-learning” was used to conduct the search. 

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Any work using this dataset should cite the following paper:

Nirmalya Thakur, Saumick Pradhan, and Chia Y. Han, “Investigating the impact of COVID-19 on Online Learning-based Web Behavior”, Proceedings of the 7th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications (IHIET-AI 2022), Lausanne, Switzerland, April 21-23, 2022, DOI: http://dx.doi.org/10.54941/ahfe100850

Abstract

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