Development of a Google Workspace-Based Learning Style Detection Application to Enhance Student Engagement in Differentiated High School Learning
DOI:
https://doi.org/10.21009/jtp.v27i3.59176Keywords:
Learning Style Detection Application, Student Engagement, Differentiated LearningAbstract
Facts in the field show that low student engagement often stems from teaching strategies that do not align with students' diverse learning styles. This challenge is particularly pronounced in differentiated learning settings, where effectively identifying individual learner preferences (visual, auditory, kinesthetic) is crucial yet difficult to achieve efficiently on a large scale. At SMAN 1 Bantaran, for instance, the lack of a systematic and scalable diagnostic tool has resulted in prolonged periods of low engagement and ineffective personalization. This study addresses this gap by developing a scalable, technology-based solution. The primary aim is to develop and implement a Google Workspace-based learning style detection application to increase student engagement in differentiated learning for high school students. The research method uses Research and Development (R&D) with the ADDIE model. The analysis includes: Needs analysis through observation and interviews to identify problems related to student engagement; Application design using Google Workspace; Questionnaire development, validation by media and content experts, and trial testing on five students; Implementation on Grade XI Package A students; and Evaluation of effectiveness using quantitative analysis (Aiken's V, N-Gain) and qualitative methods (questionnaires, interviews, observations). The collection of pretest, posttest, and effectiveness questionnaire data was then analyzed to assess validity, effectiveness, and increased engagement. The results showed that the application was highly valid (V = 0.95) and effective (87.86%) based on expert assessment and user satisfaction. Student engagement increased by 10.95% with a moderate category (N-Gain = 0.39). The distribution of learning styles showed a balance (4 kinesthetic, 5 auditory, 5 visual). In conclusion, this application has the potential to improve differentiated learning, although further development is needed for optimal results.
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