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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
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
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
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
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
Valsamidis, Stavros; Kontogiannis, Sotirios; Kazanidis, Ioannis; Theodosiou, Theodosios; Karakos, Alexandros – Educational Technology & Society, 2012
Learning Management Systems (LMS) collect large amounts of data. Data mining techniques can be applied to analyse their web data log files. The instructors may use this data for assessing and measuring their courses. In this respect, we have proposed a methodology for analysing LMS courses and students' activity. This methodology uses a Markov…
Descriptors: Foreign Countries, Electronic Learning, College Mathematics, Integrated Learning Systems
Doolan, Stephen M. – Written Communication, 2013
Recently, scholars have suggested that "second-language writers" are made up of two distinct groups: Generation 1.5 (long-term U.S.-resident language learners) and more traditional L2 students (e.g., international or recently arrived immigrants). To investigate that claim, this study compares the first-year composition writing of…
Descriptors: Writing (Composition), Freshman Composition, College Freshmen, English (Second Language)
Meade, Adam W.; Craig, S. Bartholomew – Psychological Methods, 2012
When data are collected via anonymous Internet surveys, particularly under conditions of obligatory participation (such as with student samples), data quality can be a concern. However, little guidance exists in the published literature regarding techniques for detecting careless responses. Previously several potential approaches have been…
Descriptors: Online Surveys, Data Collection, Research Problems, Identification
Blikstein, Paulo; Worsley, Marcelo; Piech, Chris; Sahami, Mehran; Cooper, Steven; Koller, Daphne – Journal of the Learning Sciences, 2014
New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and…
Descriptors: Programming, Computer Science Education, Learning Processes, Introductory Courses
Niemi, David; Gitin, Elena – International Association for Development of the Information Society, 2012
An underlying theme of this paper is that it can be easier and more efficient to conduct valid and effective research studies in online environments than in traditional classrooms. Taking advantage of the "big data" available in an online university, we conducted a study in which a massive online database was used to predict student…
Descriptors: Higher Education, Online Courses, Academic Persistence, Identification
Whitmarsh, Lona; Wentworth, Diane Keyser – Career Development Quarterly, 2012
Career development research has often explored gender differences in and development of career patterns (Gottfredson, 2006). Hyde's (2005) meta-analysis indicated that men and women shared more similarities than differences. Applying Hyde's gender similarities hypothesis to careers, the authors conducted a 2-stage study. Stage 1 was an analysis of…
Descriptors: Career Development, Research, Gender Differences, Pattern Recognition
Adbo, Karina; Taber, Keith S. – International Journal of Science Education, 2009
The results presented here derive from a longitudinal study of Swedish upper secondary science students' (16-19 years of age) developing understanding of key chemical concepts. The informants were 18 students from two different schools. The aim of the present study was to investigate the mental models of matter at the particulate level that…
Descriptors: Foreign Countries, Secondary School Science, Chemistry, High School Students