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Download full textEdwards, 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
Direct linkBerland, Matthew; Martin, Taylor; Benton, Tom; Smith, Carmen Petrick; Davis, Don – Journal of the Learning Sciences, 2013
Many have suggested that tinkering plays a critical role in novices learning to program, and recent work in learning analytics (Baker & Yacef, 2009 Blikstein, 2011) allows us to describe new relationships in the process. Using learning analytics, we explore how students progress from exploration, through tinkering, to refinement, a pathway…
Descriptors: Learning Processes, Data Collection, Novices, Females
Pellas, Nikolaos; Peroutseas, Efstratios – Journal of Educational Computing Research, 2016
While pedagogical and technological affordances of three-dimensional (3D) multiuser virtual worlds in various educational disciplines are largely well-known, a study about their effect on high school students' engagement in introductory programming courses is still lacking. This case study presents students' opinions about their participation in a…
Descriptors: High School Students, Educational Games, Computer Simulation, Simulated Environment
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
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
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries
