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Zi Xiang Poh; Ean Teng Khor – International Journal on E-Learning, 2024
Machine learning and data mining techniques have been widely used in educational settings to identify the important features that tend to influence students' learning performance and predict their future performance. However, there is little to no research done in the context of Singapore's education. Hence, this study aims to fill the gap by…
Descriptors: Learning Analytics, Goodness of Fit, Academic Achievement, Online Courses
Lezhnina, Olga; Kismihók, Gábor – International Journal of Research & Method in Education, 2022
In our age of big data and growing computational power, versatility in data analysis is important. This study presents a flexible way to combine statistics and machine learning for data analysis of a large-scale educational survey. The authors used statistical and machine learning methods to explore German students' attitudes towards information…
Descriptors: Student Attitudes, Scientific Literacy, Numeracy, Foreign Countries
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
Silva-Maceda, Gabriela; Arjona-Villicaña, P. David; Castillo-Barrera, F. Edgar – IEEE Transactions on Education, 2016
Learning to program is a complex task, and the impact of different pedagogical approaches to teach this skill has been hard to measure. This study examined the performance data of seven cohorts of students (N = 1168) learning programming under three different pedagogical approaches. These pedagogical approaches varied either in the length of the…
Descriptors: Programming, Teaching Methods, Intermode Differences, Cohort Analysis
Doukakis, Spyros; Giannakos, Michail N.; Koilias, Christos; Vlamos, Panayiotis – Informatics in Education, 2013
This paper presents results of a questionnaire focused on investigating students' confidence and behavioral intention in the area of programming, particularly that of structures, problem solving, and programming commands (Conditional--Loop). Responses from 116 1st year students regarding informatics were used. The results indicate that the…
Descriptors: Foreign Countries, Programming, Computer Science Education, Problem Solving
Robins, Anthony – Computer Science Education, 2010
Compared to other subjects, the typical introductory programming (CS1) course has higher than usual rates of both failing and high grades, creating a characteristic bimodal grade distribution. In this article, I explore two possible explanations. The conventional explanation has been that learners naturally fall into populations of programmers and…
Descriptors: Programming, Learning Processes, Grading, Simulation
Fitzgerald, Sue; Lewandowski, Gary; McCauley, Renee; Murphy, Laurie; Simon, Beth; Thomas, Lynda; Zander, Carol – Computer Science Education, 2008
Debugging is often difficult and frustrating for novices. Yet because students typically debug outside the classroom and often in isolation, instructors rarely have the opportunity to closely observe students while they debug. This paper describes the details of an exploratory study of the debugging skills and behaviors of contemporary novice Java…
Descriptors: Troubleshooting, Teaching Methods, Computer Science Education, Programming