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Mariano Andrés Castellaro; Nadia Soledad Peralta; Juan Manuel Curcio – European Journal of Psychology of Education, 2024
A structural/cross-sectional micro-analytic perspective prevails in the Socio-constructivist research of peer knowledge construction. This paper proposes an alternative micro-sequential approach that focuses on the transitions between events during the activity. From this micro-sequential perspective, the study aimed to (a) explore the…
Descriptors: Cooperation, Competence, Grade 7, Grade 6
Changtong, Nidawan; Maneejak, Nantakarn; Yasri, Pratchayapong – International Journal of Educational Methodology, 2020
STEM education is a pedagogical philosophy which aims to draw the interrelationship between science, technology, engineering and mathematics to solve complex problems in real life situations. In order to use STEM education to foster students' learning, the researchers designed STEM activities for middle school students related to the concept of…
Descriptors: Foreign Countries, STEM Education, Middle School Students, Learning Activities
Doroudi, Shayan; Holstein, Kenneth; Aleven, Vincent; Brunskill, Emma – International Educational Data Mining Society, 2016
How should a wide variety of educational activities be sequenced to maximize student learning? Although some experimental studies have addressed this question, educational data mining methods may be able to evaluate a wider range of possibilities and better handle many simultaneous sequencing constraints. We introduce Sequencing Constraint…
Descriptors: Intelligent Tutoring Systems, Sequential Approach, Problem Solving, Learning Processes
Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam – International Educational Data Mining Society, 2015
This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…
Descriptors: Learning Activities, Learning Processes, Data Collection, Student Behavior