Developing Mobile Learning of Physics (MOBLEP) with android-based problem-based learning approach to improve students’ learning independence

Sukemi, Sukemi and Fathurohman, Apit (2023) Developing Mobile Learning of Physics (MOBLEP) with android-based problem-based learning approach to improve students’ learning independence. Momentum: Physic Education Journal, 7 (1). ISSN 2548-9127

Text (Jurnal Momentum: Physics education jurnal) Revisi+Momentum_Development+of+Mobile+Learning+of+Physics+(MOBLEP)+with+an+Android-Based+Problem-Based+Learning+Approach+to+Improve+Students’+Independence+of+Learning.docx - Accepted Version Download (514kB)

Abstract

This development research aims to produce Mobile Learning of Physics (Moblep) by applying Android-based Problem Based Learning Approach to Increase Student Learning Independence which is valid and practical. The development model used is the Rowntree model modified with Tessmer's formative evaluation method. The tessmer's formative evaluation stages in this study include self-evaluation, expert review, one-to-one, and small group. At the expert review stage, data were collected through interviews, expert tests, and questionnaires using nine material experts, nine design experts, and eleven language experts. The one-to-one stage and the small group stage were carried out at SMA Negeri 1 Suak Tapeh. The results showed that the Mobile Learning of Physics (Moblep) with the Android-based Problem-Based Learning Approach that was developed, based on the results of the expert review, obtained a total percentage score of 94.73% from the validator and was included in the "very valid" category. Based on the results of the student response questionnaire at the one-to-one evaluation stage, the average percentage was 83.5%, and at the small group stage, the average percentage was 95.2%, so this Moblep was included in the "very practical" category.The implication of this research is that the results of this study can be applied as reference material and considered as additional references for further research.

Item Type:	Article
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Dr. Sukemi Sukemi
Date Deposited:	12 Apr 2023 01:06
Last Modified:	17 Apr 2023 02:18
URI:	http://repository.unsri.ac.id/id/eprint/95906

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