Data Structures in Java: Active Learning Techniques to Enhance Learning in STEM
Description
This dataset contains a collection of scholarly articles focused on the implementation of active learning techniques in data structures courses, with a particular emphasis on Java programming and its application in enhancing student learning in STEM (Science, Technology, Engineering, and Mathematics) disciplines. This collection provides a comprehensive view of various teaching strategies that promote deeper and more meaningful learning through active methods. Each included article has been selected for its relevance, accessibility (Open Access), and contribution to educational practice in programming and data structures. Keywords: Active learning, data structures, Java programming, STEM, education, teaching strategies, student engagement. This dataset provides a solid foundation for research and implementation of active learning techniques in data structures and programming courses, benefiting educators and students in the STEM field. Dataset Contents: Learning more about active learning Author: Graeme Stemp-Morlock DOI: 10.1145/1498765.1498771 Publication Date: April 1, 2009 Abstract: Discusses how active learning algorithms can reduce label complexity compared to passive methods. A Compendium of Rationales and Techniques for Active Learning Author: C. Reiness DOI: 10.1187/CBE.20-08-0177 Publication Date: October 1, 2020 Abstract: Provides a collection of strategies for promoting active learning. Defining Active Learning: A Restricted Systemic Review Authors: Peter Doolittle, Krista Wojdak, Amanda Walters DOI: 10.20343/teachlearninqu.11.25 Publication Date: September 22, 2023 Abstract: Defines active learning as a student-centered approach to knowledge construction focusing on higher-order thinking. The Curious Construct of Active Learning Authors: D. Lombardi, T. Shipley DOI: 10.1177/1529100620973974 Publication Date: April 1, 2021 Abstract: Discusses the different interpretations of active learning in STEM domains. Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography Authors: Xuefeng Du, Haohan Wang, Zhenxi Zhu, Xiangrui Zeng, Yi-Wei Chang, Jing Zhang, E. Xing, Min Xu DOI: 10.1093/bioinformatics/btab123 Publication Date: February 23, 2021 Abstract: Proposes a hybrid active learning framework to reduce labeling burden in cryo-ET tasks.