BEST: An Instructional Design Model to Empower Graduate Student Self-Efficacy in Research
Traditional research methodology education faces challenges in developing student self-efficacy and integrating modern technology, necessitating innovative instructional approaches for graduate students.
This study introduces the BEST model (begin with learner analysis, establish clear learning objectives, select engaging learning activities, and tailor instruction using technology) to address these challenges through systematic TPACK integration.
A qualitative investigation was conducted with 10 graduate students through focus groups and in-depth interviews over one month, analyzed using narrative and content analysis approaches.
Content analysis revealed improvements in self-efficacy (from four to eight participants), peer learning (from three to six), and critical thinking (from three to six) while maintaining active learning engagement throughout implementation.
Students showed significant improvements in self-efficacy (four to eight participants), peer learning (three to six), and critical thinking (three to six) while maintaining active learning engagement.
Institutions should implement robust technical support systems and flexible learning pathways while ensuring adequate infrastructure before adoption. A phased implementation approach with peer mentoring is recommended to address technology integration challenges.
Future research should examine the BEST model’s effectiveness across different cultural contexts and disciplines through comparative and longitudinal studies while exploring the integration of emerging educational technologies.
The BEST model enhances the quality of graduate research education by developing students’ confidence and competence, potentially leading to more capable researchers and improved research outcomes.
Longitudinal studies should track the impact on research productivity and quality, while mixed-methods approaches incorporating standardized measures would strengthen the empirical foundation for the model.
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