Type of Active Learning Implementation in Science Education: A Systematic Review

Maulida Ridani; Retno Arianingrum

doi:10.23887/ijerr.v7i2.65623

Authors

Maulida Ridani Universitas Negeri Yogyakarta, Yogyakarta, Indonesia
Retno Arianingrum Universitas Negeri Yogyakarta, Yogyakarta, Indonesia

DOI:

https://doi.org/10.23887/ijerr.v7i2.65623

Keywords:

Active Learning, Systematic Revie, Qualitative Study

Abstract

Involving students in active learning is associated with positive learning outcomes. Despite the evidence supporting the positive learning outcomes of active learning, the adoption of active learning has been slow. Numerous education researchers have documented their application of active learning. However, there is no research yet that systematically analyzes these studies regarding the type of active learning implementation in science education. This systematic review analysis study was conducted to review the type of active learning implementation in science education. The main criteria for the selection of articles is the implementation of active learning in the field of science education with a period ranging from 2012 to 2022 from the well-known international database. There were 20 articles were obtained that fit into the criteria using the Active Learning keyword. The review was focused on the course, type of active learning, aims, method, samples, and data collection tools. The results show that the most studied course is interdisciplinary chemistry. Active learning types coded Not applicable, Others, Hybrid learning, Group work, Online-based learning, Hands-on Learning, and Inquiry. Articles aim to investigate active learning effects using quantitative methods. Undergraduate student samples enriched these studies, with data collected through surveys and descriptions. These findings can inspire science educators to adopt suitable active learning strategies, impacting students' achievements in science education.

References

Abu Bakar, M. A., & Ismail, N. (2020). Mathematical instructional: A conceptual of redesign of active learning with metacognitive regulation strategy. International Journal of Instruction, 13(3), 633–648. https://doi.org/10.29333/iji.2020.13343a.

Al Mamun, M. A., Lawrie, G., & Wright, T. (2022). Exploration of learner-content interactions and learning approaches: The role of guided inquiry in the self-directed online environments. Computers & Education, 178, 104398. https://doi.org/10.1016/j.compedu.2021.104398.

Amin, I., & Sukestiyarno, Y. L. (2015). Analysis of metacognitive skills on learning mathematics in high school. Int Journal of Education and Research, 3(3), 213–222. https://doi.org/https://www.ijern.com/journal/2015/March-2015/18.pdf.

Anakin, M., & McDowell, A. (2021). Enhancing Students’ Experimental Knowledge with Active Learning in a Pharmaceutical Science Laboratory. Pharmacy Education, 21, 29–38. https://doi.org/10.46542/pe.2021.211.2938.

Blömeke, S., Hoth, J., Döhrmann, M., Busse, A., Kaiser, G., & König, J. (2015). Teacher change during induction: development of beginning primary teachers’ knowledge, beliefs, and performance. International Journal of Science and Mathematics Education, 13, 287–308. https://doi.org/10.1007/s10763-015-9619-4.

Celik, H. C. (2018). The effects of activity based learning on sixth grade students’ achievement and attitudes towards mathematics activities. EURASIA Journal of Mathematics, Science and Technology Education, 14(5), 1963–1977. https://doi.org/10.29333/ejmste/85807.

Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Taylor & Francis Online, 219–243. https://doi.org/10.1080/00461520.2014.965823.

Cho, H. J., Melloch, M. R., & Levesque-Bristol, C. (2021). Enhanced student perceptions of learning and performance using concept-point-recovery teaching sessions: a mixed-method approach. International Journal of STEM Education, 8(1). https://doi.org/10.1186/s40594-021-00276-1.

Damşa, C., Langford, M., Uehara, D., & Scherer, R. (2021). Teachers’ agency and online education in times of crisis. Computers in Human Behavior, 121(November 2020). https://doi.org/10.1016/j.chb.2021.106793.

Dancy, M., Henderson, C., & Turpen, C. (2016). How faculty learn about and implement research-based instructional strategies: the case of peer instruction. Physical Review-PER, 12. https://doi.org/10.1103/PhysRevPhysEducRes.12.010110.

Deslauriers, L., Logan, S. M., Kelly, M., Kristina, C., & Greg, K. (2019). Measuring Actual Learning versus Feeling of Learning in Response to Being Actively Engaged in the Classroom. Proceedings of the National Academy of Sciences of the United States of America, 116(39), 19251–19257. https://doi.org/10.1073/pnas.1821936116.

Dewi, C. A., Khery, Y., & Erna, M. (2019). An ethnoscience study in chemistry learning to develop scientific literacy. Jurnal Pendidikan IPA Indonesia, 8(2), 279–287. https://doi.org/10.15294/jpii.v8i2.19261.

Doolittle, P., Wojdak, K., & Walters, A. (2023). Defining Active Learning: A Restricted Systematic Review. Teaching and Learning Inquiry, 11. https://doi.org/10.20343/teachlearninqu.11.25.

Driessen, E. P., Knight, J. K., Smith, M. K., & Ballen, C. J. (2020). Demystifying the Meaning of Active Learning in Postsecondary Biology Education.

Eddy, S., & Hogan, K. (2014). Getting under the hood: how and for whom does increasing course structure work? CBE-Life Sciences Education, 13(3), 453–468. https://doi.org/10.1187/cbe.14-03-0050.

Fioletov, V., McLinden, C. A., Griffin, D., Krotkov, N., Liu, F., & Eskes, H. (2022). Quantifying urban, industrial, and background changes in NO 2 during the COVID-19 lockdown period based on TROPOMI satellite observations. Atmospheric Chemistry and Physics, 22(6). https://doi.org/10.5194/acp-22-4201-2022.

Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 8410–8415. https://doi.org/10.1073/pnas.1319030111.

Froyd, J. E., Borrego, M., Cutler, S., Henderson, C., & Prince, M. J. (2013). Estimates of use of research-based instructional strategies in core electrical or computer engineering courses. IEEE Transactions on Education, 56(4), 393–399. https://doi.org/10.1109/TE.2013.2244602.

Fuad, M., Efendi, A., & Muhammad, U. A. (2020). The use of pepaccur local wisdom for Indonesian literary teaching materials. JPI (Jurnal Pendidikan Indonesia), 9(2), 213–223. https://doi.org/10.23887/jpi-undiksha.v9i2.22779.

Geoffrey, L., Herman, I., B., & Mena. (2015). Tracking the spread of research-based instructional strategies (pp. 1–5). https://doi.org/10.1109/FIE.2015.7344218.

Goeltz, J. C., & Cuevas, L. A. (2021). Guided inquiry activity for teaching titration through total titratable Acidity in a general chemistry laboratory course. Journal of Chemical Education, 98(3), 882–887. https://doi.org/10.1021/acs.jchemed.0c01198.

Hora, M. T., & Ferrare, J. J. (2013). Instructional systems of practice: A multidimensional analysis of math and science undergraduate course planning and classroom teaching. Journal of the Learning Sciences, 22(2), 212–257. https://doi.org/10.1080/10508406.2012.729767.

Houseknecht, J. B., Bachinski, G. J., Miller, M. H., White, S. A., & Andrews, D. M. (2020). Effectiveness of the active learning in organic chemistry faculty development workshops. Chemistry Education Research and Practice, 21(1), 387–398. https://doi.org/10.1039/c9rp00137a.

Knight, J., Wise, S., & Southard, K. (2013). Understanding clicker discussions: student reasoning and the impact of instructional cues. CBE-Life Sciences Education, 12(4), 645–654. https://doi.org/10.1187/cbe.13-05-0090.

Lombardi, D., & Shipley, T. F. (2021). Astronomy Team, Biology Team, Chemistry Team, Engineering Team, Geography Team, & et al. Psychological Science in the Public Interest, 22(1), 8–43. https://doi.org/10.1177/152910062097397.

Magen Nagar, N. (2016). The effects of learning strategies on mathematical literacy: A comparison between lower and higher achieving countries. IJRES, 2(2), 306–321. https://files.eric.ed.gov/fulltext/EJ1105113.pdf.

Markant, D. B., Ruggeri, A., Gureckis, T. M., & Xu, F. (2016). Enhanced Memory as a Common Effect of Active Learning. Mind, Brain, and Education, 10(3), 142–152. https://doi.org/10.1111/mbe.12117.

McLaughlin, J. E., Roth, M. T., Glatt, D. M., Gharkholonarehe, N., Davidson, C. A., Griffin, L. M., & Mumper, R. J. (2014). The flipped classroom: a course redesign to foster learning and engagement in a health professions school. Academic Medicine, 89(2), 236–243. https://doi.org/10.1097/ACM.0000000000000086.

Owens, D. C., Barlow, A. T., & Smith-Walters, C. (2020). Student Motivation and Resistance in Active Learning Classrooms. Active Learning in College Science: The Case for Evidence-Based Practice, 50(4), 927–942. https://doi.org/10.1007/978-3-030-33600-4_57.

Partanen, L. (2018). Student-centred active learning approaches to teaching quantum chemistry and spectroscopy: Quantitative results from a two-year action research study. Chemistry Education Research and Practice, 19(3), 885–904. https://doi.org/10.1039/c8rp00074c.

Patrick, L. E., Howell, L. A., & Wischusen, W. (2016). Perceptions of active learning between faculty and undergraduates: Differing views among departments. The Journal of STEM Education: Innovations and Research. https://doi.org/https://www.jstem.org/jstem/index.php/JSTEM/article/download/2121/1776.

Stains, M., Harshman, J., Barker, M., Chasteen, S., Cole, R., DeChenne-Peters, S., & Young, A. M. (2018). Anatomy of STEM teaching in north American universities Science. Science, 359(6383), 1468–1470. https://doi.org/10.1126/science.aap8892.

Stains, M., & Vickrey, T. (2017). Fidelity of implementation: an overlooked yet critical construct to establish effectiveness of evidence-based instructional practices. CBE-Life Sciences Education, 16, 1–11. https://doi.org/10.1187/cbe.16-03-0113.

Suharko, S., & Kusumadewi, C. D. M. (2019). Organisasi Masyarakat Sipil Dan Restorasi Sungai: Studi pada Gerakan Memungut Sehelai Sampah di Sungai Karang Mumus di Kota Samarinda. Jurnal Sosiologi Reflektif, 14(1), 81. https://doi.org/10.14421/jsr.v14i1.1677.

Tang, M., & Liao, H. (2021). From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey. Omega (United Kingdom), 100(2), 102141.1-18. https://doi.org/10.1016/j.omega.2019.102141.

Theobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Arroyo, E. N., Behling, S., & Freeman, S. (2020). Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. Proceedings of the National Academy of Sciences, 6476–6483. https://doi.org/10.1073/pnas.1916903117.

Torres-Gastelú, C. A., & Kiss, G. (2016). Perceptions of students towards ICT competencies at the University. Informatics in Education, 15(2), 319–338. https://doi.org/10.15388/infedu.2016.16.

Wahyuni, I., Mukrimah, M., Hanifah, F. N., Ariesca, J. D., & Windayani, W. (2022). Question Card Game to Improve Senior High School Students’ Higher-Order Thinking Skills in Biology Learning. International Journal of Biology Education Towards Sustainable Development, 2(2), 63–70. https://doi.org/10.53889/ijbetsd.v2i2.147.

Wardah, A. C., & Wiyarsi, A. (2020). A systematic review: How are mental model of chemistry concepts? Universal Journal of Educational Research, 8(2), 332–345. https://doi.org/10.13189/ujer.2020.080202.