Integration of Mobile Learning in Rifdarmon Model to Improve Student Learning Outcomes

Mobile learning utilizes mobile technology, such as smartphones, tablets, and other mobile devices, to facilitate a flexible and collaborative learning process. This research aims to implement the integration of mobile learning into the Rifdarmon model in the learning process and evaluate the effectiveness of mobile learning integration into the Rifdarmon model in improving student learning outcomes in the Sensors and Transducers course. This research uses a quantitative approach with an experimental method. The subjects in this study are active students in the Automotive Engineering Department of Padang State University taking the Sensors and Transducers course. The sampling technique used is cluster random sampling, where the researcher randomly selects two classes, the control and experimental classes. Then all students in the selected clusters become the sample. The data in this study were collected based on the pre-test and post-test scores obtained by students in the Sensors and Transducers course. Based on the results and discussion, the implementation of mobile learning integration into the Rifdarmon model in the learning process was successfully carried out in this research. There was a significant increase in learning outcomes for students after following the learning process, both with the conventional learning model (control class) and mobile learning integration into the Rifdarmon model (experimental class).

Authors

Institutions

Publication Details

Journal
International Journal of Interactive Mobile Technologies (iJIM)
Published
2024-12-03
DOI
https://doi.org/10.3991/ijim.v18i23.51217
Citations
4
Primary Topic
Educational Methods and Impacts
Type
article
Field-Weighted Citation Impact
8.38
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integration of Mobile Learning in Rifdarmon Model to Improve Student Learning Outcomes

Hasan Maksum, Remon Lapisa, Ganefri, Rifdarmon et al.
4 citations
International Journal of Interactive Mobile Technologies (iJIM)
Educational Methods and Impacts
8.38
article

Integration of Mobile Learning in Rifdarmon Model to Improve Student Learning Outcomes

Hasan Maksum, Remon Lapisa, Ganefri, Rifdarmon, Muhammad Giatman, Nizwardi Jalinus
article en
4 citations

Abstract

Mobile learning utilizes mobile technology, such as smartphones, tablets, and other mobile devices, to facilitate a flexible and collaborative learning process. This research aims to implement the integration of mobile learning into the Rifdarmon model in the learning process and evaluate the effectiveness of mobile learning integration into the Rifdarmon model in improving student learning outcomes in the Sensors and Transducers course. This research uses a quantitative approach with an experimental method. The subjects in this study are active students in the Automotive Engineering Department of Padang State University taking the Sensors and Transducers course. The sampling technique used is cluster random sampling, where the researcher randomly selects two classes, the control and experimental classes. Then all students in the selected clusters become the sample. The data in this study were collected based on the pre-test and post-test scores obtained by students in the Sensors and Transducers course. Based on the results and discussion, the implementation of mobile learning integration into the Rifdarmon model in the learning process was successfully carried out in this research. There was a significant increase in learning outcomes for students after following the learning process, both with the conventional learning model (control class) and mobile learning integration into the Rifdarmon model (experimental class).

International Journal of Interactive Mobile Technologies (iJIM)Vol. 18(23)
State University of Padang (ID)
Openalex Percentile: Top 2%
Educational Methods and Impacts
8.38
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.