Version 1
: Received: 5 April 2024 / Approved: 8 April 2024 / Online: 8 April 2024 (03:59:02 CEST)
How to cite:
Wongvorachan, T.; Srisuttiyakorn, S.; Sriklaub, K. Optimizing Learning: Predicting Research Competency via Statistical Proficiency. Preprints2024, 2024040485. https://doi.org/10.20944/preprints202404.0485.v1
Wongvorachan, T.; Srisuttiyakorn, S.; Sriklaub, K. Optimizing Learning: Predicting Research Competency via Statistical Proficiency. Preprints 2024, 2024040485. https://doi.org/10.20944/preprints202404.0485.v1
Wongvorachan, T.; Srisuttiyakorn, S.; Sriklaub, K. Optimizing Learning: Predicting Research Competency via Statistical Proficiency. Preprints2024, 2024040485. https://doi.org/10.20944/preprints202404.0485.v1
APA Style
Wongvorachan, T., Srisuttiyakorn, S., & Sriklaub, K. (2024). Optimizing Learning: Predicting Research Competency via Statistical Proficiency. Preprints. https://doi.org/10.20944/preprints202404.0485.v1
Chicago/Turabian Style
Wongvorachan, T., Siwachoat Srisuttiyakorn and Kanit Sriklaub. 2024 "Optimizing Learning: Predicting Research Competency via Statistical Proficiency" Preprints. https://doi.org/10.20944/preprints202404.0485.v1
Abstract
In higher education, the cultivation of research competency is pivotal for students’ critical thinking development and their subsequent transition into the professional workforce. While statistics plays a fundamental role in supporting the completion of a research project, it is often perceived as challenging, particularly by students in majors outside mathematics or statistics. The connection between students’ statistical proficiency and their research competency remains unexplored despite its significance. To address this gap, we utilized the supervised machine learning approach to predict students’ research competency, as represented by their performance in a research methods class, with predictors of students’ proficiency in statistical topics. Predictors relating to students’ learning behavior in a statistics course such as assignment completion and academic dishonesty are also included as auxiliary variables. Results indicate that the three primary categories of statistical skills—namely, understanding of statistical concepts, proficiency in selecting appropriate statistical methods, and statistics interpretation skills— can be used to predict students’ research competency, as demonstrated by their final course scores and letter grades. This study advocates for strategic emphasis on the identified influential topics to enhance efficiency in developing students’ research competency. Findings could inform instructors in adopting a strategic approach to teaching the statistical component of research for enhanced efficiency.
Keywords
research competency; statistics; supervised machine learning
Subject
Social Sciences, Education
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
