Comprehensive Test and Evaluation Path of College Teachers' Professional Development Based on a Cloud Education Big Data Platform

Wang, Jinyu

doi:10.3991/ijet.v18i05.38497

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Other machine learning algorithms, such as artificial neural networks, have not been tested, which have reported good results in prediction tasks, even in different application domains [38]. Besides, the best analytical model generated in this study is not deployed in a cloud education Big Data platform [39].…”

Section: 3mentioning

confidence: 98%

Analysis of Student Performance Applying Data Mining Techniques in a Virtual Learning Environment

Aguagallo,

Salazar-Fierro,

García-Santillán

et al. 2023

Int. J. Emerg. Technol. Learn.

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Students' academic performance is a key factor for educational institutions and society, which is an important indicator of the quality of the teaching-learning process and the appropriation of knowledge. Its analysis allows an understanding of the behavior of students and teachers, generating valuable knowledge for making timely academic decisions. In this study, the following phases were carried out: (i) identification of factors that influence the academic performance of engineering university students, (ii) early prediction of academic success, and (iii) identification of use patterns in a virtual learning environment (VLE). The Knowledge Discovery in Databases (KDD) methodology was applied based on predictive and descriptive data mining techniques, using academic and socioeconomic data and interactions (resources and activities) with the VLE. The tools and programming languages used were Pentaho Data Integration for data integration and processing; Jupyter Notebook, Python, and Scikit-Learn for correlation analysis and prediction modeling; and R Studio for the clustering task. The results show that VLE resources such as files, links, and activities such as participation in forums are factors that lead to good academic performance. On the other hand, it was possible to make predictions of academic success (pass or not) with an accuracy greater than 95% and to identify the main patterns of use of the VLE. The group with excellent academic performance (grades 9 to 10) is recognized for using file-type resources and high participation in class and forum activities.

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Section: 3mentioning

confidence: 98%

Analysis of Student Performance Applying Data Mining Techniques in a Virtual Learning Environment

Aguagallo,

Salazar-Fierro,

García-Santillán

et al. 2023

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

“…There has been some progress in incorporating quantitative measures into the evaluation process, such as assessing the duration and outcomes of activities; however, more comprehensive and multifaceted approaches to evaluation are still needed (Brunila & Nehring, 2023). Besides, no research has been conducted on the objectivity of the quality of ideological and political education in colleges and universities and the construction of relevant models (Wang, 2023).…”

Section: Related Workmentioning

confidence: 99%

Exploration on Innovation Path of Precise Ideological and Political Work in Colleges and Universities Under the Background of Big Data Driving

Luo

2024

International Journal of Web-Based Learning and Teaching Technologies

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However, although existing models for evaluating the effectiveness of universities provide a large number of modeling solutions, it is difficult to objectively evaluate dynamic coefficients based on the differences in precision ideological and political work systems of different types of universities in the evaluation process of innovative paths for precision ideological and political work. In this context, this article proposes an innovative path for the precision of ideological and political work in universities under the background of big data driving, and constructs a complex system discrete dynamic modeling technology. This model quantitatively describes the consistency between different precise ideological and political work models, the similarity of research strategies for precise ideological and political work, and expected evaluation indicators using multiple transformation dynamic coefficients. This study provides new ideas and methods for innovative research in the field of ideological and political work in universities.

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“…

With advancements in deep learning technologies, big data, and artificial intelligence (AI) technologies are now widespread in various domains, such as smart grids [1], telecommunications [2], and so on. Educational big data also profoundly influences and reshapes academic research and industrial applications in education [3,4]. Massive Open Online Courses (MOOCs) [5], generating a large amount of diversified learning behavior data, has been a research hotspot in educational big data and AI + Education [6,7].

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confidence: 99%

Enhancing academic performance prediction with temporal graph networks for massive open online courses

Huang,

Chen

2024

J Big Data

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Educational big data significantly impacts education, and Massive Open Online Courses (MOOCs), a crucial learning approach, have evolved to be more intelligent with these technologies. Deep neural networks have significantly advanced the crucial task within MOOCs, predicting student academic performance. However, most deep learning-based methods usually ignore the temporal information and interaction behaviors during the learning activities, which can effectively enhance the model’s predictive accuracy. To tackle this, we formulate the learning processes of e-learning students as dynamic temporal graphs to encode the temporal information and interaction behaviors during their studying. We propose a novel academic performance prediction model (APP-TGN) based on temporal graph neural networks. Specifically, in APP-TGN, a dynamic graph is constructed from online learning activity logs. A temporal graph network with low-high filters learns potential academic performance variations encoded in dynamic graphs. Furthermore, a global sampling module is developed to mitigate the problem of false correlations in deep learning-based models. Finally, multi-head attention is utilized for predicting academic outcomes. Extensive experiments are conducted on a well-known public dataset. The experimental results indicate that APP-TGN significantly surpasses existing methods and demonstrates excellent potential in automated feedback and personalized learning.

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Comprehensive Test and Evaluation Path of College Teachers' Professional Development Based on a Cloud Education Big Data Platform

Cited by 3 publications

References 18 publications

Analysis of Student Performance Applying Data Mining Techniques in a Virtual Learning Environment

Analysis of Student Performance Applying Data Mining Techniques in a Virtual Learning Environment

Exploration on Innovation Path of Precise Ideological and Political Work in Colleges and Universities Under the Background of Big Data Driving

Enhancing academic performance prediction with temporal graph networks for massive open online courses

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