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Zexuan Pan; Maria Cutumisu – AERA Online Paper Repository, 2023
Computational thinking (CT) is a fundamental ability for learners in today's society. Although CT assessments and interventions have been studied widely, little is known about CT predictions. This study predicted students' CT achievement in the ICILS 2018 using five machine learning models. These models were trained on the data from five European…
Descriptors: Computation, Thinking Skills, Artificial Intelligence, Prediction
Gombert, Sebastian; Di Mitri, Daniele; Karademir, Onur; Kubsch, Marcus; Kolbe, Hannah; Tautz, Simon; Grimm, Adrian; Bohm, Isabell; Neumann, Knut; Drachsler, Hendrik – Journal of Computer Assisted Learning, 2023
Background: Formative assessments are needed to enable monitoring how student knowledge develops throughout a unit. Constructed response items which require learners to formulate their own free-text responses are well suited for testing their active knowledge. However, assessing such constructed responses in an automated fashion is a complex task…
Descriptors: Coding, Energy, Scientific Concepts, Formative Evaluation
Bednorz, David; Kleine, Michael – International Electronic Journal of Mathematics Education, 2023
The study examines language dimensions of mathematical word problems and the classification of mathematical word problems according to these dimensions with unsupervised machine learning (ML) techniques. Previous research suggests that the language dimensions are important for mathematical word problems because it has an influence on the…
Descriptors: Word Problems (Mathematics), Classification, Mathematics Instruction, Difficulty Level
Kemper, Lorenz; Vorhoff, Gerrit; Wigger, Berthold U. – European Journal of Higher Education, 2020
We perform two approaches of machine learning, logistic regressions and decision trees, to predict student dropout at the Karlsruhe Institute of Technology (KIT). The models are computed on the basis of examination data, i.e. data available at all universities without the need of specific collection. Therefore, we propose a methodical approach…
Descriptors: Foreign Countries, Predictor Variables, Potential Dropouts, School Holding Power
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
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