In search of experimental evidence on Scratch programming and students’ achievements in the first-year college computing class? Consider these datasets

• Published in: Data in brief Vol. 45; p. 108635
• Main Authors: Campbell, Oladele O., Atagana, Harrison I.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01-12-2022; Elsevier
• Subjects: Block-based programming; Coarsened Exact Matching; Constructionism; CS1; Data; Experimental data; Novice programming; Quasi-experiment; CS1; Constructionism; Experimental data; Novice programming; Quasi-experiment; Block-based programming; Coarsened Exact Matching
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2022.108635

This article presents datasets representing the demographics and achievements of computer science students in their first programming courses (CS1). They were collected from a research project comparing the effects of a constructionist Scratch programming and the conventional instructions on the achievements of CS1 students from selected Nigerian public colleges. The project consisted of two consecutive quasi-experiments. In both cases, we adopted a non-equivalent pretest-posttest control group design and multistage sampling. Institutions were selected following purposive sampling, and those selected were randomly assigned to the Scratch programming class (experimental) and the conventional (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 influencing CS1 students' performance, investigate how programming pedagogies or tools affect CS1 students' achievements in higher education, identify important trends using machine learning techniques, and address additional research ideas.