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Malmi, Lauri; Sheard, Judy; Kinnunen, Päivi; Simon; Sinclair, Jane – ACM Transactions on Computing Education, 2023
Use of theory within a field of research provides the foundation for designing effective research programs and establishing a deeper understanding of the results obtained. This, together with the emergence of domain-specific theory, is often taken as an indicator of the maturity of any research area. This article explores the development and…
Descriptors: Learning Theories, Computer Science Education, Learning Processes, Models
Saba, Janan; Hel-Or, Hagit; Levy, Sharona T. – Instructional Science: An International Journal of the Learning Sciences, 2023
This article concerns the synergy between science learning, understanding complexity, and computational thinking (CT), and their impact on near and far learning transfer. The potential relationship between computer-based model construction and knowledge transfer has yet to be explored. We studied middle school students who modeled systemic…
Descriptors: Transfer of Training, Science Instruction, Learning Management Systems, Learning Processes
Shi, Yang; Chi, Min; Barnes, Tiffany; Price, Thomas W. – International Educational Data Mining Society, 2022
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts. Though many innovations have been made in KT, most models including the state-of-the-art Deep KT (DKT) mainly leverage each student's response either as correct or incorrect, ignoring its content. In…
Descriptors: Programming, Knowledge Level, Prediction, Instructional Innovation
Ait-Adda, Samia; Bousbia, Nabila; Balla, Amar – E-Learning and Digital Media, 2023
Our aim in this paper is to improve the efficiency of a learning process by using learners' traces to detect particular needs. The analysis of the semantic path of a learner or group of learners during the learning process can allow detecting those students who are in needs of help as well as identify the insufficiently mastered concepts. We…
Descriptors: Semantics, Learning Processes, Learning Analytics, Models
Payne, Lisa – Journal of Further and Higher Education, 2019
Student engagement is a complex phenomenon, with diverse interpretations, even within further and higher education. This article presents three potential, interconnected, novel models which provide a measure of unification of the range of interpretations and hence may improve understanding of the broad phenomenon. The models emerged from a study…
Descriptors: Learner Engagement, College Students, Models, Learning Processes
Aguilar, J.; Buendia, O.; Pinto, A.; Gutiérrez, J. – Interactive Learning Environments, 2022
Social Learning Analytics (SLA) seeks to obtain hidden information in large amounts of data, usually of an educational nature. SLA focuses mainly on the analysis of social networks (Social Network Analysis, SNA) and the Web, to discover patterns of interaction and behavior of educational social actors. This paper incorporates the SLA in a smart…
Descriptors: Learning Analytics, Cognitive Style, Socialization, Social Networks
Mao, Ye; Shi, Yang; Marwan, Samiha; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2021
As students learn how to program, both their programming code and their understanding of it evolves over time. In this work, we present a general data-driven approach, named "Temporal-ASTNN" for modeling student learning progression in open-ended programming domains. Temporal-ASTNN combines a novel neural network model based on abstract…
Descriptors: Programming, Computer Science Education, Learning Processes, Learning Analytics
Molina, Ana I.; Arroyo, Yoel; Lacave, Carmen; Redondo, Miguel A. – British Journal of Educational Technology, 2018
The incorporation of advanced information and communication technologies into the field of education has made the design and deployment of courses and instructional units more and more complicated. In order to support such complex task, methods and techniques have been proposed in the last years for the standardization, formalization and modelling…
Descriptors: Programming Languages, Models, Cooperative Learning, Learning Processes
Maria-Dorinela Dascalu; Stefan Ruseti; Mihai Dascalu; Danielle S. McNamara; Stefan Trausan-Matu – Grantee Submission, 2022
The use of technology as a facilitator in learning environments has become increasingly prevalent with the global pandemic caused by COVID-19. As such, computer-supported collaborative learning (CSCL) gains a wider adoption in contrast to traditional learning methods. At the same time, the need for automated tools capable of assessing and…
Descriptors: Computational Linguistics, Longitudinal Studies, Technology Uses in Education, Teaching Methods
Vojinovic, Oliver; Simic, Vladimir; Milentijevic, Ivan; Ciric, Vladimir – IEEE Transactions on Education, 2020
Contribution: A model of tiered in-lab, compulsory lab programming sessions suitable for use in flipped learning environments. This article also addresses the scarcity of research that explores the effects of in-class activity organization. Background: When facing a subject they perceive as dry or difficult, some students may feel discouraged from…
Descriptors: Assignments, Teaching Methods, Flipped Classroom, Class Activities
Maruyama, Ryoga; Ogata, Shinpei; Kayama, Mizue; Tachi, Nobuyuki; Nagai, Takashi; Taguchi, Naomi – International Association for Development of the Information Society, 2022
This study aims to explore an educational learning environment that supports students to learn conceptual modelling with the unified modelling language (UML). In this study, we call the describing models "UML programming." In this paper, we show an educational UML programming environment for science, technology, engineering, art, and…
Descriptors: Case Studies, Programming Languages, Learning Processes, Models
Magdin, Martin; Turcáni, Milan – Turkish Online Journal of Educational Technology - TOJET, 2015
Individualization of learning through ICT [Information and Communication Technology] allows to students not only the possibility choose the time and place to study, but especially pace adoption of new knowledge on the basis of preferred learning styles. Analysis of learning processes should give the answer to difficult questions from pedagogical…
Descriptors: Management Systems, Information Technology, Electronic Learning, Cognitive Style
Downey, James P.; Kher, Hemant V. – Journal of Information Technology Education: Research, 2015
Technology training in the classroom is critical in preparing students for upper level classes as well as professional careers, especially in fields such as technology. One of the key enablers to this process is computer self-efficacy (CSE), which has an extensive stream of empirical research. Despite this, one of the missing pieces is how CSE…
Descriptors: Longitudinal Studies, Computer Literacy, Self Efficacy, Technology Education
Jumaat, Nurul Farhana; Tasir, Zaidatun – Journal of Educational Computing Research, 2016
Scaffolding refers to a guidance that helps students during their learning sessions whereby it makes learning easier for them. This study aims to develop a framework of metacognitive scaffolding (MS) to guide students in learning Authoring System through Facebook. Thirty-seven master degree students who were enrolled in Authoring System course…
Descriptors: Metacognition, Scaffolding (Teaching Technique), Programming, Computer Science Education
Knauf, Rainer; Sakurai, Yoshitaka; Tsuruta, Setsuo; Jantke, Klaus P. – Journal of Educational Computing Research, 2010
University education often suffers from a lack of an explicit and adaptable didactic design. Students complain about the insufficient adaptability to the learners' needs. Learning content and services need to reach their audience according to their different prerequisites, needs, and different learning styles and conditions. A way to overcome such…
Descriptors: Prerequisites, College Instruction, Educational Experiments, Cognitive Style
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