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Han, Feifei; Ellis, Robert – Australasian Journal of Educational Technology, 2020
This study combined the methods from student approaches to learning and learning analytics research by using both self-reported and observational measures to examine the student learning experience. It investigated the extent to which reported approaches and perceptions and observed online interactions are related to each other and how they…
Descriptors: Measurement Techniques, Observation, Learning Analytics, Data Collection
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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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Tekin, Ahmet – Eurasian Journal of Educational Research, 2014
Problem Statement: There has recently been interest in educational databases containing a variety of valuable but sometimes hidden data that can be used to help less successful students to improve their academic performance. The extraction of hidden information from these databases often implements aspects of the educational data mining (EDM)…
Descriptors: Foreign Countries, Prediction, Grade Point Average, Undergraduate Students
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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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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
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers