block-based programming Environments (BBPE) are an emerging way for teaching programming in individuals with minimum programming skills. In such environments, blocks are used to represent instructions and learners con...
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ISBN:
(纸本)9781509054688
block-based programming Environments (BBPE) are an emerging way for teaching programming in individuals with minimum programming skills. In such environments, blocks are used to represent instructions and learners construct programs by snapping these blocks together. However, there are not any satisfactory research data in the literature related to how learners perceive the utility of this approach and the evaluation of BBPE in middle school classrooms. Moreover, there exists a great interest in Physical Computing using blockly@rduino which is a widely used visual programming editor. This paper aims at applying the blocks for @ rduino in the Students' Educational (B@SE) Process as a school project that will last up to three years starting in the current school year with the first class middle-school and continue with the same students for the next two years.
This article presents datasets representing the demograph-ics and achievements of computer science students in their first programming courses (CS1). They were collected from a research project comparing the effects o...
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This article presents datasets representing the demograph-ics and achievements of computer science students in their first programming courses (CS1). They were collected from a research project comparing the effects of a construction-ist Scratch programming and the conventional instructions on the achievements of CS1 students from selected Nige-rian public colleges. The project consisted of two consecu-tive quasi-experiments. In both cases, we adopted a non-equivalent pretest-posttest control group design and multi-stage sampling. Institutions were selected following purpo-sive sampling, and those selected were randomly assigned to the Scratch programming class (experimental) and the con-ventional (comparison) class. A questionnaire and pre-and post-introductory programming achievement tests were used to collect data. To strengthen the research design, we used the Coarsened Exact Matching (CEM) algorithm to create matched samples from the unmatched data obtained from both experiments. Future studies can use these data to identify the factors influ-encing CS1 students' performance, investigate how program-ming pedagogies or tools affect CS1 students' achievements in higher education, identify important trends using machine learning techniques, and address additional research ideas.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://***/licenses/by/4.0/)
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