Development and Implementation of Android-Based Coleega Application with Representation Learning Cycle Model: An Effort to Improve Representation Competence in Colligative Properties of Solution
DOI:
https://doi.org/10.26740/jcer.v10n1.p21-30Keywords:
conceptual understanding, chemical representations, representational competence, representation learning cycle, android-based applicationsAbstract
This study investigates the effectiveness of Coleega, an Android-based application integrated with the Representation Learning Cycle (RLC) model, in enhancing students’ representational competence on colligative properties of solutions. The RLC model, adapted from Problem-Based Science, structures learning into six phases: orientation, questioning, planning, execution, analysis, and presentation. Conducted at a senior high school in East Java with 68 students selected via cluster random sampling, the study employed pretests and posttests to measure conceptual understanding and representational skills. Validation confirmed feasibility (Aiken’s V = 0.74) and readability (92%). Effectiveness was demonstrated by independent t-test (p < 0.001), showing significantly higher posttest scores in the experimental group (M = 87.94) compared to the control (M = 80.94). Findings highlight the novelty of embedding RLC into mobile technology, demonstrating that Coleega is both feasible and effective in strengthening representational competence in chemistry learning.
References
[1] Johnstone, A. H. (1991). Why Is Science Difficult to Learn? Things are Seldom What They Seem. Journal of Computer Assisted Learning, 7(2), 75-83.
[2] Kozma, R. B., & Russell, J. (2005). Students Becoming Chemists: Developing Representational Competence. In J. K. Gilbert (Ed.), Visualization in Science Education, 121–145. Dordrecht: Springer.
[3] Talanquer, V. (2011). Macro, Submicro, And Symbolic: The Many Faces of The Chemistry “Triplet”. International Journal of Science Education, 33(2), 179-195.
[4] Johnson, T., Shaffer, T., Holland, L., Veltri, L., Lucas, J., Elshamy, Y, & Rutto, P. (2022). A Low-Cost and Simple Demonstration of Freezing Point Depression and Colligative Properties with Common Salts and Ice Cream. Journal of Chemical Education, 99(10), 3590-3594. https://doi.org/10.1021/acs.jchemed.2c00626
[5] Analita, R., Bakti, I., Nugraheni, P., & Noviyanti, E. (2023). The learners’ conceptual understanding: Literature review of vapor-pressure lowering and boiling-point elevation. Journal of Education and Learning (Edulearn), 17(4), 641-651. https://doi.org/10.11591/edulearn.v17i4.20805
[6] Agung, D., Fauziah, C., and Salsabila, H. (2022). Identifikasi Miskonsepsi Penurunan Titik Beku dan Kenaikan Titik Didih pada Mahasiswa Pendidikan Kimia. Jurnal Riset Pendidikan Kimia, 12(2), 135-143. https://doi.org/10.21009/JRPK.122.08
[7] Mieke. D. C. (2012). Representation Use and Strategy Choice in Physics Problem Solving. American Physical Society, 8(2). https://doi.org/10.1103/PhysRevSTPER.8.020117
[8] Sim, J. H. & Daniel, E. G. S. (2014). Representational Competence in Chemistry: A Comparison between Students with Different Levels of Understanding of Basic Chemical Concepts and Chemical Representations. Cogent Education, 1(1). https://doi.org/10.1080/2331186X.2014.991180
[9] Herunata. (2016). Model Pembelajaran Daur Belajar Representasi (RLC) Dalam Pembelajaran Kimia. Seminar Nasional Kimia dan Pembelajarannya (SNKP) 2016.
[10] Sitorus, I. D. S. and Yusnelti. (2018). Pengembangan Media Pembelajaran Menggunakan Lectora Inspire Untuk Materi Sifat Koligatif Larutan Kelas XII MIA SMA Adyaksa 1 Kota Jambi. Jambi: Perpustakaan Universitas Jambi.
[11] Lee, W. W., & Owens, D. L. (2004). Multimedia-Based Instructional Design: Computer-Based Training, Web-Based Training, Distance Broadcast Training, Performance-Based Solutions. San Francisco: John Wiley & Sons.
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