Learning Analytics Approach to Improve Multiple Representation Skills in Direct-Current Circuits

Arshi Alfianti, Heru Kuswanto

Abstract


A high failure rate in physics subjects is caused by students' inability to understand the material. The physics learning process certainly requires an appropriate learning approach. The learning analytics approach is one of the innovations in learning. It allows students to analyze problems. Therefore, this research aimed to determine the learning analytics approach's effect on improving students' multiple representation skills. This research is experimental. The validation of the lesson plan obtained excellent results based on two material experts and four practitioners. Furthermore, the empirical test was carried out on 504 students. The research design was the pretest-posttest control group design. The control and experimental groups were determined using cluster random sampling. The findings of this research include (1) the normality test results that are higher than 0.05, which means that the research data was normally distributed. (2) The homogeneity test results were higher than 0.05, which indicated that the data was homogeneous. (3) The paired sample T-test obtained a value lower than 0.05, which means that there was an influence of the multiple representation approach. (4) The N-gain value in the experimental class was higher than the control class. Lastly, (5) only 47.2% of students used graphical and mathematical representation skills. Based on these findings, the effect of the learning analytics approach on students' multiple representation skills was fairly good with moderate criteria. For further research, learning products or models can be developed to improve multiple representation skills focused on a combination of graphics and mathematics.

Keywords


Direct-Current; Learning analytics; Multiple representations; Physics learning

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References


Adejo, O., & Connolly, T. (2017). Learning Analytics in Higher Education Development : A Roadmap. Journal of Education and Practice, 8(15), 156–163.

Aiken, L. R. (1985). Three Coefficients For Analyzing The Reliability And Validity Of Ratings. Educational and Psychological Measurement, 45, 131–141. https://journals.sagepub.com/doi/abs/10.1177/0013164485451012

Airey, J., Linder, C., Treagust, D. F., ..., Duit, R., & Fischer, H. E. (2017). Multiple representations in physics education. In … Education. https://link.springer.com/content/pdf/10.1007/978-3-319-58914-5.pdf

Altmeyer, K., Kapp, S., Thees, M., Malone, S., Kuhn, J., & Brünken, R. (2020). The use of augmented reality to foster conceptual knowledge acquisition in STEM laboratory courses—Theoretical background and empirical results. British Journal of Educational Technology, 51(3), 611–628. https://doi.org/10.1111/bjet.12900

Buckingham Shum, S., & Deakin Crick, R. (2016). Learning Analytics for 21st Century Competencies. Journal of Learning Analytics, 3(2), 6–21. https://doi.org/10.18608/jla.2016.32.2

Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683–695. https://doi.org/10.1080/13562517.2013.827653

Darihastining, S., Utomo, E. S., & Chalimah. (2021). The effectiveness of communication and online language disruption during the era of pandemic covid-19 in senior high school students in implementation of learning cycle 7e. Journal of Physics: Conference Series, 1722(1). https://doi.org/10.1088/1742-6596/1722/1/012024

Dawson, S., Joksimovic, S., Poquet, O., & Siemens, G. (2019). Increasing the impact of learning analytics. ACM International Conference Proceeding Series, March, 446–455. https://doi.org/10.1145/3303772.3303784

Elias, T. (2011). Learning Analytics : Definitions , Processes and Potential. 1–23.

Fidan, M., & Tuncel, M. (2019). Integrating augmented reality into problem based learning: The effects on learning achievement and attitude in physics education. In Computers and Education (Vol. 142). Elsevier Ltd. https://doi.org/10.1016/j.compedu.2019.103635

Foung, D., & Chen, J. (2019). A Learning Analytics Approach to the Evaluation of an Online Learning Package in a Hong Kong University. Electronic Journal of E-Learning, 17(1), 11–24.

Hermino, A., & Arifin, I. (2020). Contextual character education for students in the senior high school. European Journal of Educational Research, 9(3), 1009–1023. https://doi.org/10.12973/EU-JER.9.3.1009

Lahope, K. S., Tulandi, D. A., & Mongan, S. W. (2020). Studi Kompetensi Multirepresentasi Mahasiswa pada Topik Interferensi dan Difraksi. Jurnal Pendidikan Fisika, 1(3), 90–94.

Lawson, C., Beer, C., Rossi, D., Moore, T., & Fleming, J. (2016). Identification of ‘at risk’ students using learning analytics: the ethical dilemmas of intervention strategies in a higher education institution. Educational Technology Research and Development, 64(5), 957–968. https://doi.org/10.1007/s11423-016-9459-0

Liliarti, N., & Kuswanto, H. (2018). Improving the competence of diagrammatic and argumentative representation in physics through android-based mobile learning application. International Journal of Instruction, 11(3), 106–122. https://doi.org/10.12973/iji.2018.1138a

Little, T. D., Chang, R., Gorrall, B. K., Waggenspack, L., Fukuda, E., Allen, P. J., & Noam, G. G. (2020). The retrospective pretest–posttest design redux: On its validity as an alternative to traditional pretest–posttest measurement. International Journal of Behavioral Development, 44(2), 175–183. https://doi.org/10.1177/0165025419877973

Mainali, B. (2021). Representation in teaching and learning mathematics. International Journal of Education in Mathematics, Science and Technology, 9(1), 1–21. https://doi.org/10.46328/ijemst.1111

Masrifah, M., Setiawan, A., Sinaga, P., & Setiawan, W. (2020). An Investigation of Physics Teachers’ Multiple Representation Ability on Newton’s Law Concept. Jurnal Penelitian & Pengembangan Pendidikan Fisika, 6(1), 105–112. https://doi.org/10.21009/1.06112

Nuha, A. A., Kuswanto, H., Apriani, E., & Hapsari, W. P. (2021). Learning Physics with Worksheet Assisted Augmented Reality: The Impacts on Student’s Verbal Representation. Proceedings of the 6th International Seminar on Science Education (ISSE 2020), 541(Isse 2020), 461–469. https://doi.org/10.2991/assehr.k.210326.066

Prahani, B. K., W.W, S., & Yuanita, L. (2017). Pengembangan Perangkat Pembelajaran Fisika Model Inkuiri Terbimbing Untuk Melatihkan Kemampuan Multi Representasi Siswa Sma. JPPS (Jurnal Penelitian Pendidikan Sains), 4(2), 503. https://doi.org/10.26740/jpps.v4n2.p503-517

Ranjeeth, S., Latchoumi, T. P., & Paul, P. V. (2020). A Survey on Predictive Models of Learning Analytics. Procedia Computer Science, 167(2018), 37–46. https://doi.org/10.1016/j.procs.2020.03.180

Sarabi, M. K., & Gafoor, K. A. (2018). Student perception on nature of subjects : Impact on difficulties in learning high school physics , chemistry and biology. Innovations and Researches in Education, 8(1), 42–55.

Sari, D., Sudarti, S., & Bektiarso, S. (2019). Analisis Kesalahan Siswa Mengerjakan Soal Un Materi Rangkaian Arus Listrik Searah Menggunakan Metode Polya. FKIP E-PROCEEDING; Vol 3 No 2 (2018): Prosiding Seminar Nasional Pendidikan Fisika, 3(2), 235–240. https://jurnal.unej.ac.id/index.php/fkip-epro/article/view/9428

Sholihah, N., Wilujeng, I., & Purwanti, S. (2020). Development of android-based learning media on light reflection material to improve the critical thinking skill of high school students. Journal of Physics: Conference Series, 1440(1). https://doi.org/10.1088/1742-6596/1440/1/012034

Sun, F. R., Hu, H. Z., Wan, R. G., Fu, X., & Wu, S. J. (2022). A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom. Interactive Learning Environments, 30(2), 376–392. https://doi.org/10.1080/10494820.2019.1660996

Susdarwati, S., Dimas, A., & Hannum, F. (2021). The development of scientific literacy-based physics learning module on direct current circuit material. Journal of Physics: Conference Series, 1869(1). https://doi.org/10.1088/1742-6596/1869/1/012164

Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027

Yuliati, L., Riantoni, C., & Mufti, N. (2018). Problem solving skills on direct current electricity through inquiry-based learning with PhET simulations. International Journal of Instruction, 11(4), 123–138. https://doi.org/10.12973/iji.2018.1149a




DOI: http://dx.doi.org/10.24042/jipfalbiruni.v13i1.14514

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