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Showing 1 to 15 of 27 results Save | Export
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O. S. Adewale; O. C. Agbonifo; E. O. Ibam; A. I. Makinde; O. K. Boyinbode; B. A. Ojokoh; O. Olabode; M. S. Omirin; S. O. Olatunji – Interactive Learning Environments, 2024
With the advent of technological advancement in learning, such as context-awareness, ubiquity and personalisation, various innovations in teaching and learning have led to improved learning. This research paper aims to develop a system that supports personalised learning through adaptive content, adaptive learning path and context awareness to…
Descriptors: Cognitive Style, Individualized Instruction, Learning Processes, Preferences
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Jeff Hanson; Blair Taylor; Siddharth Kaza – Information Systems Education Journal, 2025
Cybersecurity content is typically taught and assessed using Bloom's Taxonomy to ensure that students acquire foundational and higher-order knowledge. In this study we show that when students are given the objectives written in the form of a competency-based statements, students have a more clearly defined outcome and are be able to exhibit their…
Descriptors: College Students, Universities, Competency Based Education, Educational Objectives
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Gao, Zhikai; Lynch, Collin; Heckman, Sarah; Barnes, Tiffany – International Educational Data Mining Society, 2021
As Computer Science has increased in popularity so too have class sizes and demands on faculty to provide support. It is therefore more important than ever for us to identify new ways to triage student questions, identify common problems, target students who need the most help, and better manage instructors' time. By analyzing interaction data…
Descriptors: Automation, Classification, Help Seeking, Computer Science Education
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Ragonis, Noa; Shmallo, Ronit – Informatics in Education, 2022
Object-oriented programming distinguishes between instance attributes and methods and class attributes and methods, annotated by the "static" modifier. Novices encounter difficulty understanding the means and implications of "static" attributes and methods. The paper has two outcomes: (a) a detailed classification of aspects of…
Descriptors: Programming, Computer Science Education, Concept Formation, Thinking Skills
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Gitinabard, Niki; Okoilu, Ruth; Xu, Yiqao; Heckman, Sarah; Barnes, Tiffany; Lynch, Collin – International Educational Data Mining Society, 2020
Teamwork, often mediated by version control systems such as Git and Apache Subversion (SVN), is central to professional programming. As a consequence, many colleges are incorporating both collaboration and online development environments into their curricula even in introductory courses. In this research, we collected GitHub logs from two…
Descriptors: Teamwork, Group Activities, Student Projects, Programming
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Jimenez, Fernando; Paoletti, Alessia; Sanchez, Gracia; Sciavicco, Guido – IEEE Transactions on Learning Technologies, 2019
In the European academic systems, the public funding to single universities depends on many factors, which are periodically evaluated. One of such factors is the rate of success, that is, the rate of students that do complete their course of study. At many levels, therefore, there is an increasing interest in being able to predict the risk that a…
Descriptors: Prediction, Risk, Dropouts, College Students
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Alvarez, Niurys Lázaro; Callejas, Zoraida; Griol, David – Journal of Technology and Science Education, 2020
We present an educational data analytics case study aimed at the early detection of potential dropout in Computer Engineering studies in Cuba. We have employed institutional data of 456 students and performed several experiments for predicting their permanency into three (promotion, repetition, and dropout) or two classes (promoting, not…
Descriptors: Foreign Countries, College Students, Computer Science Education, Engineering Education
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Brinda, Torsten; Napierala, Stephan; Tobinski, David; Diethelm, Ira – Education and Information Technologies, 2019
The ability to categorize concepts is an essential capability for human thinking and action. On the one hand, the investigation of such abilities is the purview of psychology; on the other hand, subject-specific educational research is also of interest, as a number of research works in the field of science education show. For computer science…
Descriptors: Information Technology, Computer Science Education, Misconceptions, Student Attitudes
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Bihani, Ankita; Paepcke, Andreas – International Educational Data Mining Society, 2018
We develop a random forest classifier that helps assign academic credit for a student's class forum participation. The classification target are the four classes created by student rank quartiles. Course content experts provided ground truth by ranking a limited number of post pairs. We expand this labeled set via data augmentation. We compute the…
Descriptors: College Credits, Classification, Computer Mediated Communication, Student Participation
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Taniguchi, Tadanari; Maruyama, Yukiko; Kurita, Daisaku; Tanaka, Makoto – International Association for Development of the Information Society, 2018
We propose developing a method to set key educational skills which students need to achieve for each class using a student self-assessment questionnaire in analytical approach. It is difficult to set key academic skills for class since there are little systematic methods to set them. The questionnaire survey with 25 educational skills was…
Descriptors: Student Attitudes, Computer Science Education, Self Evaluation (Individuals), Information Technology
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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
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Maaliw, Renato R. III; Ballera, Melvin A. – International Association for Development of the Information Society, 2017
The usage of data mining has dramatically increased over the past few years and the education sector is leveraging this field in order to analyze and gain intuitive knowledge in terms of the vast accumulated data within its confines. The primary objective of this study is to compare the results of different classification techniques such as Naïve…
Descriptors: Classification, Cognitive Style, Electronic Learning, Decision Making
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Fouh, Eric; Akbar, Monika; Shaffer, Clifford A. – Computers in the Schools, 2012
Computer science core instruction attempts to provide a detailed understanding of dynamic processes such as the working of an algorithm or the flow of information between computing entities. Such dynamic processes are not well explained by static media such as text and images, and are difficult to convey in lecture. The authors survey the history…
Descriptors: Computer Science Education, Educational Assessment, Visualization, Computer Science
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Rafael Krejci; Sean Wolfgand Matsui Siqueira – Interactive Technology and Smart Education, 2014
Purpose: To present YouFlow Microblog and how its functionalities for discourse structuring and message classification allows improving learning. The paper aims to discuss these issues. Design/methodology/approach: The authors developed a survey on microblogs and its functionalities for supporting education, and then the authors developed a new…
Descriptors: Lesson Plans, Electronic Publishing, Case Studies, Classification
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Cheng, Li-Chen; Chu, Hui-Chun; Shiue, Bang-Min – International Journal of Distance Education Technologies, 2015
Identifying learning problems of students has been recognized as an important issue for assisting teachers in improving their instructional skills or learning design strategies. The accumulated assessment data provide an excellent resource for achieving this objective. However, most of conventional testing systems only record students' test…
Descriptors: Teaching Methods, Learning Problems, Innovation, Student Records
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