Increasing Students’ Learning Outcomes through Cooperative Learning Model Mind Mapping Type
The purpose of this study is to find out how to apply the cooperative learning model, mind mapping type to improve the learning outcomes of elementary school students. This type of research is classroom action research which consists of two cycles which each cycle is carried out 3 times. The research procedure includes planning, implementing actions, observing and reflecting. The subjects in this study were 26 fourth grade students at SD Inpres Batanghari Jambi. The research result showed that in the first cycle, from 26 students, only 14 students completed individually, with an average score of 67.30. This has met the minimum completeness criteria or is in the moderate category, but this result does not meet the classical completeness criteria because only 53.85% of students have studied thoroughly, while the classical completeness that must be achieved is 85% of the total number of students. In cycle II, from 26 students, there were 24 students (92.31%) who met the minimum completeness criteria. Classically, it has also been fulfilled, namely the average value obtained is 80.19 or is in the high category. Based on the results of the analysis, it was concluded that the learning outcomes of fourth grade elementary school students through the application of the Mind Mapping cooperative learning model had increased.
Aghajani, M., & Adloo, M. (2018). The Effect of Online Cooperative Learning on Students' Writing Skills and Attitudes through Telegram Application. International Journal of Instruction, 11(3), 433-448.
Buchmann, R. A., Ghiran, A.-M., Osman, C.-C., & Karagiannis, D. (2018). Streamlining semantics from requirements to implementation through agile mind mapping methods. Paper presented at the International Working Conference on Requirements Engineering: Foundation for Software Quality.
Casey, A., & Fernandez-Rio, J. (2019). Cooperative learning and the affective domain. Journal of Physical Education, Recreation & Dance, 90(3), 12-17.
Fox, J., Pittaway, L., & Uzuegbunam, I. (2018). Simulations in entrepreneurship education: Serious games and learning through play. Entrepreneurship Education and Pedagogy, 1(1), 61-89.
Goh, C., Leong, C., Kasmin, K., Hii, P., & Tan, O. (2017). Students’ experiences, learning outcomes and satisfaction in e-learning. Journal of E-learning and Knowledge Society, 13(2).
Hariyadi, S., Corebima, A. D., & Zubaidah, S. (2018). Contribution of Mind Mapping, Summarizing, and Questioning in the RQALearning Model to Genetic Learning Outcomes. Journal of Turkish Science Education, 15(1), 80-88.
Huang, R., Ritzhaupt, A. D., Sommer, M., Zhu, J., Stephen, A., Valle, N., . . . Li, J. (2020). The impact of gamification in educational settings on student learning outcomes: A meta-analysis. Educational Technology Research and Development, 68(4), 1875-1901.
Khodabandeh, F. (2021). The Comparison of Mind Mapping‐Based Flipped Learning Approach on Introvert and Extrovert EFL Learners’ Speaking Skill. Iranian Journal of English for Academic Purposes, 10(1), 35-53.
Koul, R., Lerdpornkulrat, T., & Poondej, C. (2018). Learning environments and student motivation and engagement: A review of studies from Thailand. Asian Education Miracles, 241-255.
Kövecses-Gősi, V. (2018). Cooperative learning in VR environment. Acta Polytechnica Hungarica, 15(3), 205-224.
Krishnaraj, N., Elhoseny, M., Thenmozhi, M., Selim, M. M., & Shankar, K. (2020). Deep learning model for real-time image compression in Internet of Underwater Things (IoUT). Journal of Real-Time Image Processing, 17(6), 2097-2111.
Lin, M.-H., & Chen, H.-g. (2017). A study of the effects of digital learning on learning motivation and learning outcome. Eurasia Journal of Mathematics, Science and Technology Education, 13(7), 3553-3564.
Loewen, S., Crowther, D., Isbell, D. R., Kim, K. M., Maloney, J., Miller, Z. F., & Rawal, H. (2019). Mobile-assisted language learning: A Duolingo case study. ReCALL, 31(3), 293-311.
Susanto, R., Rachmadtullah, R., & Rachbini, W. (2020). Technological and pedagogical models: Analysis of factors and measurement of learning outcomes in education. Journal of Ethnic and Cultural Studies, 7(2), 1-14.
Thabtah, F., & Peebles, D. (2020). A new machine learning model based on induction of rules for autism detection. Health informatics journal, 26(1), 264-286.
Toğaçar, M., Ergen, B., Cömert, Z., & Özyurt, F. (2020). A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. Irbm, 41(4), 212-222.
Yasmin, M., Sohail, A., Sarkar, M., & Hafeez, R. (2017). Creative methods in transforming education using human resources. Creativity Studies, 10(2), 145-158.
Zappone, A., Di Renzo, M., & Debbah, M. (2019). Wireless networks design in the era of deep learning: Model-based, AI-based, or both? IEEE Transactions on Communications, 67(10), 7331-7376.
Zhou, Y., & Wei, M. (2018). Strategies in technology-enhanced language learning. Studies in Second Language Learning and Teaching, 8(2), 471-495.
Zilka, G. C., Rahimi, I. D., & Cohen, R. (2019). Sense of challenge, threat, self-efficacy, and motivation of students learning in virtual and blended courses. American Journal of Distance Education, 33(1), 2-15.