Chrome Extension
WeChat Mini Program
Use on ChatGLM

Adapting the Motivated Strategies for Learning Questionnaire to the Japanese Problem-Based Learning Context: A Validation Study.

Children (Basel, Switzerland)(2023)

Cited 0|Views4
No score
Abstract
The COVID-19 pandemic has greatly changed medical education, and medical trainees' self-regulation has become more emphasized. In Japan, the concept of self-regulated learning has not been fully applied in health profession education due to a lack of effective measurement tools. We aimed to validate the translated Japanese version of the Motivated Strategies for Learning Questionnaire in the context of Problem-Based Learning (J-MSLQ-PBL). The questionnaire employs a seven-point Likert-type scale with 81 items and is categorized into two sections: motivation and learning strategies. An exploratory factor analysis (EFA) was conducted by using Promax rotation to examine the factor structure of the scale, using the collected data from 112 Japanese medical students. Factor extraction was based on a scree plot investigation, and an item was accepted when the factor loading was ≥0.40. In the motivation section, the extracted factors from the EFA were well aligned with the subscales of the original MSLQ, including "Self-Efficacy for Learning and Performance", "Task Value", "Self-Efficacy for Learning and Performance", "Test Anxiety", "Extrinsic Goal Orientation", and "Intrinsic Goal Orientation". In the learning strategies, the extracted factors poorly matched the structure of the original subscales. This discrepancy could be explained by insufficient translation, the limited sample size from a single medical school, or cross-cultural differences in learning strategies between Western and Japanese medical students. Only the motivation part of the J-MSLQ-PBL should be implemented to measure the competency elements of self-regulated learning in Japan.
More
Translated text
Key words
measurement,motivated strategies for learning questionnaire,problem-based learning,self-regulated learning,validation
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined