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Walainart Meepan; Suwithida Charungkaittikul; Rabkwan Poosakaew; Waree Suebsamut – Journal of Education and Learning, 2024
The 3Ds Career Planning Program is a sequential learning activity consisting of three stages: Diagnosing (8 activities), Designing (9 activities), and Doing (2 activities). These stages help students determine their future careers by exploring themselves and the world of careers, creating paths to desired jobs, and putting their plans into action.…
Descriptors: Program Effectiveness, Nonformal Education, Career Planning, Secondary School Students
Muñoz-Campos, Verónica; Franco-Mariscal, Antonio-Joaquín; Blanco-López, Ángel – International Journal of Science Education, 2020
This study concerns a framework for designing Teaching-Learning Sequences that aims to integrate the implementation of scientific practices in the context of daily problems. Said framework consists of three stages (formulation of the design principles, instructional design and design of the learning activities). It is based on four design…
Descriptors: Instructional Design, Teaching Methods, Guidelines, Sequential Learning
van Ginkel, Gisbert; Oolbekkink, Helma; Meijer, Paulien C.; Verloop, Nico – Teachers and Teaching: Theory and Practice, 2016
Being adaptive to the individual novice teacher is considered a condition for effective teacher mentoring. The aims of this study are therefore to explore (1) mentoring activities through which mentors intend to adapt to the individual novice teacher and (2) characteristics of adaptive mentors. Information was collected through on-site,…
Descriptors: Mentors, Individual Differences, Novices, Individualized Instruction
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