ReCLif: PROBLEM-BASED LEARNING MODEL TO IMPROVE CRITICAL THINKING FOR BEGINNER LEARNERS
DOI:
https://doi.org/10.59165/educatum.v2i3.89Keywords:
ReCLif, Critical Thinking, 3C3R Design Problems, 4C/ID, Cognitif Load TheoryAbstract
Indonesia's SDGs Roadmap 2030 underlines the importance of improving critical thinking skills to achieve sustainable development goals. Students still experience difficulties in developing critical thinking in biology learning. Conventional models are often considered less effective while complex problem-based learning models can be cognitively burdensome for novice learners. Another factor contributing to this problem is the lack of opportunities for students to apply their knowledge in real situations and solve complex problems. This research aims to develop and test the effectiveness of the ReCLif model as a simple and more effective alternative to problem-based learning for beginner learners. The ReCLif model is designed to provide challenges appropriate to the cognitive level of novice learners. This research uses research and development methods from Plomp and Nieveen as well as an experimental design to compare the ReCLif model with a control group. The research results show that the development of the ReCLif model learning syntax is an integration of complex thinking teaching and learning strategies with 3C3R design problems and 4C/ID instructional design with three learning stages (1) exploring real life; (2) using complex thinking skills; and (3) creating solutions for life situations. The results of the quade rank analysis of covariance test, obtained a Sig value. (0.000) <0.05 (?), then H0 is rejected and the research hypothesis is accepted. This means that there are significant differences in critical thinking skills in students between classes given learning using the ReCLif model, CTL model and TPS model. The results of the Bonferroni Dunn test show that the corrected mean value for the ReCLif model is 80.46, the TPS model is 71.27 and the CTL model is 70.57. It can be concluded that there is a significant difference in the mean value of critical thinking skills between the ReCLif learning model and the CTL and TPS models.
References
J. C. Sanabria and J. Arámburo-Lizárraga, “Enhancing 21st century skills with AR: Using the gradual immersion method to develop collaborative creativity,” Eurasia J. Math. Sci. Technol. Educ., vol. 13, no. 2, pp. 487–501, 2017, doi: 10.12973/eurasia.2017.00627a.
Y. Kusumoto, “Enhancing critical thinking through active learning,” Lang. Learn. High. Educ., vol. 8, no. 1, pp. 45–63, 2018, doi: 10.1515/cercles-2018-0003.
T. Pieterse, H. Lawrence, and H. Friedrich-Nel, “Critical thinking ability of 3rd year radiography students,” Heal. SA Gesondheid, vol. 21, pp. 381–390, 2016, doi: 10.1016/j.hsag.2016.07.002.
H. A. Butler, “Halpern Critical Thinking Assessment predicts real?world outcomes of critical thinking,” Appl. Cogn. Psychol., vol. 26, no. 5, pp. 721–729, 2012.
K. L. Fraser, P. Ayres, and J. Sweller, “Cognitive load theory for the design of medical simulations,” Simul. Healthc., vol. 10, no. 5, pp. 295–307, 2015, doi: 10.1097/SIH.0000000000000097.
S. M. M. Loyens, H. G. Schmidt, T. Van Gog, and F. Paas, “Problem-based learning is compatible with human cognitive architecture: Commentary on Kirschner, Sweller, and Clark (2006),” Educ. Psychol., vol. 42, no. 2, pp. 91–97, 2007, doi: 10.1080/00461520701263350.
C. P. Dwyer, M. J. Hogan, and I. Stewart, “An evaluation of argument mapping as a method of enhancing critical thinking performance in e-learning environments,” Metacognition Learn., vol. 7, no. 3, pp. 219–244, 2012, doi: 10.1007/s11409-012-9092-1.
C. H. Chen, H. T. Hung, and H. C. Yeh, “Virtual reality in problem-based learning contexts: Effects on the problem-solving performance, vocabulary acquisition and motivation of English language learners,” J. Comput. Assist. Learn., vol. 37, no. 3, pp. 851–860, 2021, doi: 10.1111/jcal.12528.
C. Goumopoulos and I. Mavrommati, “A framework for pervasive computing applications based on smart objects and end user development,” J. Syst. Softw., vol. 162, 2020, doi: 10.1016/j.jss.2019.110496.
J. Merritt, M. Y. Lee, P. Rillero, and B. M. Kinach, “Problem-based learning in K-8 mathematics and science education: A literature review,” Interdiscip. J. Probl. Learn., vol. 11, no. 2, pp. 5–17, 2017, doi: 10.7771/1541-5015.1674.
M. Moallem, W. Hung, and N. Dabbagh, Comparative Pedagogical Models of Problem?Based Learning. 2019.
Y. Chung, J. Yoo, S. W. Kim, H. Lee, and D. L. Zeidler, “Enhancing Students’ Communication Skills in the Science Classroom Through Socioscientific Issues,” Int. J. Sci. Math. Educ., vol. 14, no. 1, pp. 1–27, 2014, doi: 10.1007/s10763-014-9557-6.
Y. Yusof, L. M. Fong, and L. C. Sem, “Integrasi konsep dan teori beban kognitif dalam pendidikan kejuruteraan di Malaysia: satu kajian literatur,” Geogr. - Malaysian J. Soc. Sp., vol. 12, no. 3, pp. 46–57, 2016.
J. Sweller, “Instructional Design Consequences of an Analogy between Evolution by Natural Selection and Human Cognitive Architecture,” Instr. Sci., pp. 9–31, 2004.
T. Plomp, “Introduction to Educational Design Research: An Introduction,” An Introd. to Educ. Des. Res. - Part A, pp. 11–50, 2013, [Online]. Available: http://www.eric.ed.gov/ERICWebPortal/recordDetail?accno=EJ815766%0Ahttp://international.slo.nl/edr.
L. Elder and R. Paul, “The Aspiring Thinker’s?: Guido to critical thinking,” Foundadion Crit. Think. Press, pp. 1–50, 2009, [Online]. Available: http://www.criticalthinking.org/files/SAM_Aspiring_Thinkers_GuideOPT.pdf.
A. Wiek, B. Ness, P. Schweizer-Ries, F. S. Brand, and F. Farioli, “From complex systems analysis to transformational change: A comparative appraisal of sustainability science projects,” Sustain. Sci., vol. 7, no. SUPPL. 1, pp. 5–24, 2012, doi: 10.1007/s11625-011-0148-y.
B. Wilson and P. Cole, “A Ciitieal Review of Elaboration Theory,” 1992.
