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Showing 1 to 15 of 18 results Save | Export
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Allison S. Theobold; Megan H. Wickstrom; Stacey A. Hancock – Journal of Statistics and Data Science Education, 2024
Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these…
Descriptors: Computer Science Education, Coding, Data Science, Statistics Education
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Macak, Martin; Kruzelova, Daniela; Chren, Stanislav; Buhnova, Barbora – Education and Information Technologies, 2021
Understanding the processes in education, such as the student learning behavior within a specific course, is a key to continuous course improvement. In online learning systems, students' learning can be tracked and examined based on data collected by the systems themselves. However, it is non-trivial to decide how to extract the desired students'…
Descriptors: Student Projects, Learning Analytics, Data Collection, Computer Science Education
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Agbo, Friday Joseph; Sanusi, Ismaila Temitayo; Oyelere, Solomon Sunday; Suhonen, Jarkko – Education Sciences, 2021
This study investigated the role of virtual reality (VR) in computer science (CS) education over the last 10 years by conducting a bibliometric and content analysis of articles related to the use of VR in CS education. A total of 971 articles published in peer-reviewed journals and conferences were collected from Web of Science and Scopus…
Descriptors: Computer Simulation, Computer Science Education, Educational Research, Research Methodology
Zhang, Zhiyong; Zhang, Danyang – Grantee Submission, 2021
Data science has maintained its popularity for about 20 years. This study adopts a bottom-up approach to understand what data science is by analyzing the descriptions of courses offered by the data science programs in the United States. Through topic modeling, 14 topics are identified from the current curricula of 56 data science programs. These…
Descriptors: Statistics Education, Definitions, Course Descriptions, Computer Science Education
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Edwards, John; Hart, Kaden; Shrestha, Raj – Journal of Educational Data Mining, 2023
Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as…
Descriptors: Data Analysis, Computer Science Education, Learning Analytics, Research Methodology
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Mühling, Andreas – Computer Science Education, 2016
Concept maps have a long history in educational settings as a tool for teaching, learning, and assessing. As an assessment tool, they are predominantly used to extract the structural configuration of learners' knowledge. This article presents an investigation of the knowledge structures of a large group of beginning CS students. The investigation…
Descriptors: Concept Mapping, Computer Science Education, Novices, Knowledge Level
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Berland, Matthew; Davis, Don; Smith, Carmen Petrick – International Journal of Computer-Supported Collaborative Learning, 2015
AMOEBA is a unique tool to support teachers' orchestration of collaboration among novice programmers in a non-traditional programming environment. The AMOEBA tool was designed and utilized to facilitate collaboration in a classroom setting in real time among novice middle school and high school programmers utilizing the IPRO programming…
Descriptors: Computer Science Education, Active Learning, Programming, Novices
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Molluzzo, John C.; Lawler, James P. – Information Systems Education Journal, 2015
Big Data is becoming a critical component of the Information Systems curriculum. Educators are enhancing gradually the concentration curriculum for Big Data in schools of computer science and information systems. This paper proposes a creative curriculum design for Big Data Analytics for a program at a major metropolitan university. The design…
Descriptors: Curriculum Design, Data Analysis, Data Collection, Information Systems
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Casey, Kevin – Journal of Learning Analytics, 2017
Learning analytics offers insights into student behaviour and the potential to detect poor performers before they fail exams. If the activity is primarily online (for example computer programming), a wealth of low-level data can be made available that allows unprecedented accuracy in predicting which students will pass or fail. In this paper, we…
Descriptors: Keyboarding (Data Entry), Educational Research, Data Collection, Data Analysis
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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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Kinnunen, Paivi; Simon, Beth – Computer Science Education, 2012
This paper discusses two qualitative research methods, phenomenography and grounded theory. We introduce both methods' data collection and analysis processes and the type or results you may get at the end by using examples from computing education research. We highlight some of the similarities and differences between the aim, data collection and…
Descriptors: Grounded Theory, Qualitative Research, Data Collection, Data Analysis
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Méndez, Gonzalo; Ochoa, Xavier; Chiluiza, Katherine; de Wever, Bram – Journal of Learning Analytics, 2014
Learning analytics has been as used a tool to improve the learning process mainly at the micro-level (courses and activities). However, another of the key promises of learning analytics research is to create tools that could help educational institutions at the meso- and macro-level to gain better insight into the inner workings of their programs…
Descriptors: Data Analysis, Data Collection, Educational Research, Curriculum Design
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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
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Carliner, Saul, Ed.; Ostashewski, Nathaniel, Ed. – Association for the Advancement of Computing in Education, 2015
The Association for the Advancement of Computing in Education (AACE) is an international, non-profit educational organization. The Association's purpose is to advance the knowledge, theory, and quality of teaching and learning at all levels with information technology. "EdMedia 2015: World Conference on Educational Media &…
Descriptors: Educational Technology, Technology Uses in Education, Action Research, Instructional Design
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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
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