The Development and Validation of the Robotics Learning Self-Efficacy Scale (RLSES)

Meng Jung Tsai*, Ching Yeh Wang, An Hsuan Wu, Chun Ying Hsiao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Robotics education has gradually been emphasized in contemporary school curricula; however, assessment tools for robotics learning are still limited. Based on Bloom’s Taxonomy of educational objectives, this study aimed to develop the Robotics Learning Self-Efficacy Scale (RLSES) with a two-level construct of five dimensions for assessing students’ self-efficacy for learning robotics. A total of 181 elementary, junior high and senior high school students (5th–12th graders) with robotics learning experience were selected as the sample of this study. A questionnaire including 32 candidate items designed for the initial version of the RLSES was administered to the sample. An exploratory factor analysis was conducted and, finally, 16 items were drawn for the final RLSES under five subscales (Comprehension, Practice, Analysis, Application, and Collaboration), with a total explained variance of 85.28%. The Cronbach’s alpha reliability was.97 for the overall scale, ranging from.87 to.95 for the subscales. The inter-correlation analysis showed evidence of discriminant validity. Regression analysis results supported that Practice and Comprehension self-efficacy were significant predictors of Analysis, Application, and Collaboration self-efficacy, confirming the two-level (2 × 3) construct of the RLSES. Significant differences among school levels were found and are discussed.

Original languageEnglish
Pages (from-to)1056-1074
Number of pages19
JournalJournal of Educational Computing Research
Volume59
Issue number6
DOIs
Publication statusPublished - 2021 Oct

Keywords

  • evaluation
  • instrument
  • robotics learning
  • scale validation
  • self-efficacy

ASJC Scopus subject areas

  • Education
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'The Development and Validation of the Robotics Learning Self-Efficacy Scale (RLSES)'. Together they form a unique fingerprint.

Cite this