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Denis Zhidkikh; Ville Heilala; Charlotte Van Petegem; Peter Dawyndt; Miitta Jarvinen; Sami Viitanen; Bram De Wever; Bart Mesuere; Vesa Lappalainen; Lauri Kettunen; Raija Hämäläinen – Journal of Learning Analytics, 2024
Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first…
Descriptors: Learning Analytics, Prediction, School Holding Power, Academic Achievement
Feklistova, Lidia; Lepp, Marina; Luik, Piret – Education Sciences, 2021
In every course, there are learners who successfully pass assessments and complete the course. However, there are also those who fail the course for various reasons. One of such reasons may be related to success in assessment. Although performance in assessments has been studied before, there is a lack of knowledge on the degree of variance…
Descriptors: Online Courses, Educational Technology, Programming, Learner Engagement
Canedo, Edna Dias; Santos, Giovanni Almeida; Leite, Leticia Lopes – Informatics in Education, 2018
The teaching-learning methodology adopted in the Introduction to Computer Science classes may be a process that makes it difficult to understand the principles of programming language for undergraduate students in Computer Science and related areas, generating high failure and course drop out rates. This paper presents an analysis of the results…
Descriptors: Teaching Methods, Introductory Courses, Programming, Programming Languages
Varga, Erika B.; Sátán, Ádám – Hungarian Educational Research Journal, 2021
The purpose of this paper is to investigate the pre-enrollment attributes of first-year students at Computer Science BSc programs of the University of Miskolc, Hungary in order to find those that mostly contribute to failure on the Programming Basics first-semester course and, consequently to dropout. Our aim is to detect at-risk students early,…
Descriptors: Identification, At Risk Students, Computer Science Education, Undergraduate Students
van Kessel, Cathryn – Educational Studies: Journal of the American Educational Studies Association, 2016
The HBO series, "The Leftovers," provides a thought-provoking platform for discussing Baudrillard's conceptualization of evil and the implications for contemporary pedagogical discourse about student (dis)engagement. The dystopic scenario of 2% of the world's population suddenly disappearing might help us rethink our own society,…
Descriptors: Television, Programming (Broadcast), Learner Engagement, Teaching Methods
Mao, Ye; Zhi, Rui; Khoshnevisan, Farzaneh; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2019
Early prediction of student difficulty during long-duration learning activities allows a tutoring system to intervene by providing needed support, such as a hint, or by alerting an instructor. To be effective, these predictions must come early and be highly accurate, but such predictions are difficult for open-ended programming problems. In this…
Descriptors: Difficulty Level, Learning Activities, Prediction, Programming
Atapattu, Thushari; Falkner, Katrina – Journal of Learning Analytics, 2018
Lecture videos are amongst the most widely used instructional methods within present Massive Open Online Courses (MOOCs) and other digital educational platforms. As the main form of instruction, student engagement behaviour, including interaction with videos, directly impacts the student success or failure and accordingly, in-video dropouts…
Descriptors: Lecture Method, Video Technology, Online Courses, Mass Instruction
Carvalho, Elizabeth Simão – Interdisciplinary Journal of e-Skills and Lifelong Learning, 2015
Teaching object-oriented programming to students in an in-classroom environment demands well-thought didactic and pedagogical strategies in order to guarantee a good level of apprenticeship. To teach it on a completely distance learning environment (e-learning) imposes possibly other strategies, besides those that the e-learning model of Open…
Descriptors: Foreign Countries, Distance Education, Programming, Computer Science Education
Balter, Olle; Cleveland-Innes, Martha; Pettersson, Kerstin; Scheja, Max; Svedin, Maria – Canadian Journal of Higher Education, 2013
This study investigates the relationship between approaches to studying and course completion in two online preparatory university courses in mathematics and computer programming. The students participating in the two courses are alike in age, gender, and approaches to learning. Four hundred and ninety-three students participating in these courses…
Descriptors: Foreign Countries, Higher Education, Online Courses, Computer Science Education
Benda, Klara; Bruckman, Amy; Guzdial, Mark – ACM Transactions on Computing Education, 2012
We present the results of an interview study investigating student experiences in two online introductory computer science courses. Our theoretical approach is situated at the intersection of two research traditions: "distance and adult education research," which tends to be sociologically oriented, and "computer science education…
Descriptors: Computer Science Education, Programming, Distance Education, Online Courses
Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing
Boyer, Kristy Elizabeth, Ed.; Yudelson, Michael, Ed. – International Educational Data Mining Society, 2018
The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…
Descriptors: Data Collection, Data Analysis, Computer Science Education, Program Proposals
Ashby, Nicole, Ed. – US Department of Education, 2007
"The Achiever" is a monthly publication for parents and community leaders from the Office of Communications and Outreach, U.S. Department of Education. This issue contains the following articles: (1) Spellings, Education Community Discuss President's Agenda; (2) Arts Integration at Oklahoma School Provides Multiple Paths for Learning;…
Descriptors: Dropouts, Summer Programs, Community Leaders, Charter Schools
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
Simonson, Michael, Ed. – Association for Educational Communications and Technology, 2012
For the thirty-fifth year, the Research and Theory Division of the Association for Educational Communications and Technology (AECT) is sponsoring the publication of these Proceedings. Papers published in this volume were presented at the national AECT Convention in Louisville, Kentucky. The Proceedings of AECT's Convention are published in two…
Descriptors: Educational Technology, Handheld Devices, Workplace Learning, Electronic Learning
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