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Prediction of an Educational Institute Learning Environment Using Machine Learning and Data Mining
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Shoaib, Muhammad; Sayed, Nasir; Amara, Nedra; Latif, Abdul; Azam, Sikandar; Muhammad, Sajjad – Education and Information Technologies, 2022
Technology and data analysis have evolved into a resource-rich tool for collecting, researching and comparing student achievement levels in the classroom. There are sufficient resources to discover student success through data analysis by routinely collecting extensive data on student behaviour and curriculum structure. Educational Data Mining…
Descriptors: Prediction, Artificial Intelligence, Student Behavior, Academic Achievement
Data Journalism and Journalism Education: A Scoping Review
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Michael Yao Wodui Serwornoo; Samuel Danso; Benedine Azanu; Eric Opoku-Mensah – Journalism and Mass Communication Educator, 2024
The digital era has significantly reshaped journalism, emphasizing the pivotal role of data-driven reporting. This review delves into the nexus of data journalism and journalism education, investigating dominant study characteristics, challenges, and gaps for future research. Examining 41 relevant articles through the Arksey and O'Malley…
Descriptors: Journalism Education, Data Analysis, Journalism, Decision Making
Student Assessment in Cybersecurity Training Automated by Pattern Mining and Clustering
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Švábenský, Valdemar; Vykopal, Jan; Celeda, Pavel; Tkácik, Kristián; Popovic, Daniel – Education and Information Technologies, 2022
Hands-on cybersecurity training allows students and professionals to practice various tools and improve their technical skills. The training occurs in an interactive learning environment that enables completing sophisticated tasks in full-fledged operating systems, networks, and applications. During the training, the learning environment allows…
Descriptors: Computer Security, Information Security, Training, Data Collection
Early-Predicting Dropout of University Students: An Application of Innovative Multilevel Machine Learning and Statistical Techniques
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Cannistrà, Marta; Masci, Chiara; Ieva, Francesca; Agasisti, Tommaso; Paganoni, Anna Maria – Studies in Higher Education, 2022
This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…
Descriptors: Dropouts, Potential Dropouts, Dropout Prevention, Dropout Characteristics
Some Pattern Recognitions for a Recommendation Framework for Higher Education Students' Generic Competence Development Using Machine Learning
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So, Joseph Chi-ho; Wong, Adam Ka-lok; Tsang, Kia Ho-yin; Chan, Ada Pui-ling; Wong, Simon Chi-wang; Chan, Henry C. B. – Journal of Technology and Science Education, 2023
The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students' particulars such as their academic background, current study and student activity records, their attended higher education institution's expectations of graduate attributes and self-assessment of their own generic…
Descriptors: Pattern Recognition, Artificial Intelligence, Higher Education, College Students
Digital Methodologies of Education Governance: Pearson plc and the Remediation of Methods
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Williamson, Ben – European Educational Research Journal, 2016
This article analyses the rise of software systems in education governance, focusing on digital methods in the collection, calculation and circulation of educational data. It examines how software-mediated methods intervene in the ways educational institutions and actors are seen, known and acted upon through an analysis of the methodological…
Descriptors: Governance, Educational Administration, Data Analysis, Data Collection