Declaration of Conflict of Interest: No conflict of interest is declared by author(s).

Data sharing statement: Data supporting the findings and conclusions are available upon request from the corresponding author(s).

• Adesoji, F. A, & Omilani, N. A. (2012). A Comparison of Secondary Schools Students’ Levels of Conception of Qualitative and Quantitative Inorganic Analysis. American Journal of Scientific and Industrial Research, 3(2), 56-61.
• Adesoji, F. A., Omilani, N. A., & Dada, S. O. (2017). A Comparison of Perceived and Actual; Students’ Learning Difficulties in Physical Chemistry. International Journal of Brain and Cognitive Sciences, 6(1), 1-8.
• Alice, K., et al. (2008). Teori Kognitif Dan Implikasi Dalam Pengajaran Dan Pembelajaran [Cognitive Theory and Implications in Teaching and Learning]. In A. M. Yosof, et al. (Eds.), Pengetahuan Pedagogi Guru (pp. 101-1013). Kuala Lumpur: Universiti Malaysia Sabah.
• Barr, D., Harrison, J., & Conery, L. (2011). Computational Thinking: A Digital Age Skill for Everyone. Learning and Leading with Technology, 38(6), 20-23.
• Barr, V., & Stephenson, C. (2011). Bringing Computational Thinking to K-12: What Is Involved and What Is the Role of the Computer Science Education Community? ACM Inroads, 2(1), 48-54. https://doi.org/10.1145/1929887.1929905
• Basawapatna, A., Repenning, A., Koh, K. H., & Nickerson, H. (2013). The Zones of Proximal Flow: Guiding Students Through a Space of Computational Thinking Skills and Challenges. In ICER '13: Proceedings of the ninth annual international ACM conference on International computing education research (pp. 67–74). https://doi.org/10.1145/2493394.2493404
• Basu, S., Biswas, G., & Kinnebrew, J. S. (2017). Learner Modeling for Adaptive Scaffolding in a Computational Thinking-Based Science Learning Environment. User Modeling and User-Adapted Interaction, 27(1), 5-53.
• Beecher, K. (2017). Computational Thinking: A Beginner’s Guide to Problem Solving and Programming. United Kingdom: BCS Learning & Development Ltd.
• Bers, M. U. (2018). Coding and Computational Thinking in Early Childhood: The Impact of ScratchJr in Europe. European Journal of STEM Education, 3(3), 8.
• Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational Thinking and Tinkering: Exploration of an Early Childhood Robotics Curriculum. Computer & Education, 72, 145–157.
• Bicer, A., et al. (2015). STEM Schools vs. Non-STEM Schools: Comparing Students’ Mathematics Growth Rate on High-Stakes Test Performance. International Journal on New Trends in Education and Their Implications, 6(1), 138-150.
• Brackmann, C. P., et al. (2017). Development of Computational Thinking Skills through Unplugged Activities in Primary School. In WiPSCE '17: Proceedings of the 12th Workshop on Primary and Secondary Computing Education (pp. 65-72).
• Brand, B. R. (2020). Integrating Science and Engineering Practices: Outcomes from a Collaborative Professional Development. International Journal of STEM Education, 7(1), 13. https://doi.org/10.1186/s40594-020-00210-x
• Chang, R. (2010). Chemistry (10th ed.). Boston: McGraw Hill Higher Education.
• Creswell, J. W. (2009). Research Design: Qualitative, Quantitative and Mixed Methods Approaches (3rd Ed.). Los Angeles: Sage Publication, Inc.
• Csizmadia, A., et al. (2015). Computational Thinking: A Guide for Teachers. United Kingdom: Computing At School.
• Curzon, P., McOwan, P. W., Cutts, Q. I., & Bell, T. (2009). Enthusing & Inspiring with Reusable Kinaesthetic Activities. In Proceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE (pp. 94-98).
• Faber, H. H., Wierdsma, M. D. M., Doornbos, R. P., & Van der Ven, J. S. (2017). Teaching Computational Thinking to Primary School Students via Unplugged Programming Lessons. Journal of the European Teacher Education Network, 12, 13-24.
• Farhana, F., & Khan, F. (2020). Hanya 19 Peratus Pilih Aliran Sains [Only 19 Percent Select Science Stream]. Berita Harian.
• Fouh, E., Akbar, M., & Shaffer, C. A. (2012). The Role of Visualization in Computer Science Education. Computer in the school, 29(1-2), 95-117.
• French Academy of Sciences. (2013). Teaching Computer Science in France.Tomorrow Can’t Wait -. Retrieved from http://www.academie-sciences.fr/pdf/rapport/rads_0513gb.pdf
• Gambari, I. A., Gbodi, B. E., Olakanmi, E. U., & Abalaka, E. N. (2016). 7 Contemporary educational technology Promoting Intrinsic and Extrinsic Motivation among Chemistry Students Using Computer-Assisted Instruction. Retrieved from https://files.eric.ed.gov/fulltext/EJ1105762.pdf
• Gao, X., Li, P., Shen, J., & Sun, H. (2020). Reviewing Assessment of Student Learning in Interdisciplinary STEM Education. International Journal of STEM Education, 7(24), 1-14.
• García Peñalvo, G., et al. (2016). An Overview of the Most Relevant Literature on Coding and Computational Thinking with Emphasis on the Relevant Issues for Teachers. Belgium: TACCLE3 Consortium. Retrieved from https://repositorio.grial.eu/bitstream/grial/688/1/TACCLE3O5Literaturereview-final.pdf
• Garnett, P. J., & Treagust, D. F. (1992a). Conceptual Difficulties Experienced by Senior High School Students of Electrochemistry: Electric Circuits and Oxidation- Reduction Equations. Journal of Research in Science Teaching, 29(2), 121-142. https://doi.org/10.1002/tea.3660290204
• Garnett, P. J., & Treagust, D. F. (1992b). Conceptual Difficulties Experienced by Senior High School Students of Electrochemistry: Electrochemical (Galvanic) and Electrolytic Cells. Journal of Research in Science Teaching, 29(10), 1079-1099.
• Gilbert, J.K. & Treagust, D.F. 2009. Introduction: Macro, submicro and symbolic representations and the relationship between them: Key models in chemical education. In J. K. Gilberth & D. Treagust (eds.). Multiple Representations in Chemical Education (Vol. 4 pp. 1-10). Netherland: Springer. https://doi.org/10.1007/978-1-4020-8872-8_9
• Grover, S., & Pea, R. (2013). Computational Thinking in K–12: A Review of the State of the Field. Educational Researcher, 42(1), 38-43. https://doi.org/10.3102/0013189X12463051
• Gulacar, O., & Bowman, C. R. (2014). Determining What Our Students Need Most: Exploring Student Perceptions and Comparing Difficulty Ratings of Students and Faculty. Chemistry Education Research and Practice, 15, 587-593. https://doi.org/10.1039/C4RP00055B
• Gulacar, O., Milkey, A., & McLane, S. (2019). Exploring the Effect of Prior Knowledge and Gender on Undergraduate Students’ Knowledge Structures in Chemistry. Eurasia Journal of Mathematics, Science and Technology Education, 15(8), em1726. https://doi.org/10.29333/ejmste/106231
• Halimaton, H. (2017). Kongres Kebangsaan STEM. Towards Dignifying National Science And Technology.
• Haseski, H. I., Ilic, U., & Tugtekin, U. (2018). Defining a New 21st Century Skill-Computational Thinking: Concepts and Trends. International Education Studies, 11(4), 29. http://www.ccsenet.org/journal/index.php/ies/article/view/71730
• Hundhausen, C. D., Douglas, S. A., & Stasko, J. T. (2002). A Meta-Study of Algorithm Visualization Effectiveness. Journal of Visual Languages and Computing, (13), 259-290.
• ISTE, & CSTA. 2011. Computational Thinking: Teacher Resources (2nd Ed.). Retrieved from http://www.iste.org/docs/ct-documents/ct-leadershipt-toolkit.pdf?sfvrsn=4
• Johnson, D. W., & Johnson, R. T. (2013). The Impact of Cooperative, Competitive, and Individualistic Learning Environments on Academic Achievement. In J. Hattie, & E. Anderman (Eds.), International handbook of student achievement.
• Johnson, D. W., Johnson, R. T., & Smith, K. A. (2014). Cooperative Learning: Improving University Instruction by Basing Practice on Validated Theory. Journal on Excellence in College Teaching, 25(3&4), 85-118.
• Johnson, D. W., Qin, Z., & Johnson, R. T. (1995). Cooperative versus Competitive Efforts and Problem Solving. American Educational Research Association and SAGE, 65(2), 129-143.
• Jong, O. D., & Treagust, D. F. (2002). The Teaching and Learning of Electtrochemistry. In J. K. Gilbert et al. (Eds.), Chemical Education: Towards Research-Based Practice (pp.317-337). New York: Kluwer Academic Publishers.
• Kalelioğlu, F., & Gülbahar, Y. (2014). The Effects of Teaching Programming via Scratch on Problem Solving Skills: A Discussion from Learners’ Perspective. Informatics in Education, 13(1), 33-50.
• Kalogiannakis, M., & Papadakis, S. (2017). Pre-service kindergarten teachers acceptance of “scratchjr” as a tool for learning and teaching computational thinking and science education. Retrieved from https://keynote.conference-services.net/resources/444/5233/pdf/ESERA2017_0526_paper.pdf
• Kamisah, O., & Lay, A. N. (2020). MyKimDG Module: An Interactive Platform towards Development of Twenty-First Century Skills and Improvement of Students’ Knowledge in Chemistry. Interactive Learning Environments, 1–14. Published online: 26 Feb 2020. https://doi.org/10.1080/10494820.2020.1729208
• Kamisah, O., & Lee, T. T. (2013). Impact of Interactive Multimedia Module with Pedagogical Agents on Students’ Understanding and Motivation in the Learning of Electrochemistry. International Journal of Science and Mathematics Education, 12(2), 395-421.
• Lambert, L., & Guiffre, H. (2009). Computer Science Outreach in an Elementary School. Journal of Computing Sciences in Colleges, 24(3), 118-124.
• Lay, A. N., & Kamisah, O. (2017). Developing 21st Century Skills through a Constructivist-Constructionist Learning Environment. K-12 STEM Education, 3(2), 205-216.
• Leon, J. M., & Robles, G. (2015). Analyze Your Scratch Projects with Dr. Scratch and Assess Your Computational Thinking Skills. Retrieved from http://jemole.me/replication/2015scratch/InferCT.pdf
• Levy Nahum, T., Hofstein, A., Mamlok-Naaman, R., & Bar-Dov, Z. (2004). Research report (empirical study) can final examinations amplify students’ misconceptions in chemistry? Chemistry education: research and practice, 5(3), 301-325.
• Lye, S. Y., & Koh, J. H. L. (2014). Review on Teaching and Learning of Computational Thinking through Programming: What Is next for K-12? Computers in Human Behavior, 41, 51-61. https://doi.org/10.1016/j.chb.2014.09.012
• Maloney, J., et al. (2010). The Scratch Programming Language and Environment. ACM Transactions on Computing Education, 10(4), 16. https://doi.org/10.1145/1868358.1868363
• Mann, A., & DiPrete, T. A. (2013). Trends in Gender Segregation in the Choice of Science and Engineering Majors. Social Science Research, 42(6), 1519-1541. https://doi.org/10.1016/j.ssresearch.2013.07.002
• Mannila, L., Dagiene, V., Demo, B., & Settle, A. (2014). Computational Thinking in K-9 Education. In ITiCSE-WGR '14: Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference (pp. 1-29). https://doi.org/10.1145/2713609.2713610
• Martin, L. (2002). Defining Inquiry: Exploring the Many Types of Inquiry in the Science Classroom. The Science Teacher, 69, 34-37.
• Mellström, U. (2009). The Intersection of Gender, Race and Cultural Boundaries, or Why Is Computer Science in Malaysia Dominated by Women? Social Studies of Science, 39(6), 885-907.
• Meng, C. C., Idris, N., & Eu, L. K. (2014). Secondary Students' Perceptions of Assessments in Science, Technology, Engineering, and Mathematics (STEM). Eurasia Journal of Mathematics, Science and Technology Education, 10(3), 219-227. https://doi.org/10.12973/eurasia.2014.1070a
• Ministry of Education (MOE). (2012). Integrated Curriculum for Secondary Schools Curriculum: Specifications Chemistry Form 4. Putrajaya: Curriculum Development Centre.
• Ministry of Education (MOE). (2016a). Basics Computer Science: Standard Curriculum and Assessment Document. Putrajaya.
• Ministry of Education (MOE). (2016b). PISA 2015: Programme for International Student Assesment. Putrajaya: Educational Planning and Research Division (EPRD).
• Moreno-León, J., Robles, G., & González, M. R. (2015). Dr. Scratch: Automatic Analysis of Scratch Projects to Assess and Foster Computational Thinking. RED-Revista de Educación a Distancia. Número, 46, 1-23.
• Nelson, T. H., et al. (2015). Supporting Middle School Teachers’ Implementation of STEM Design Challenges. School Science and Mathematics, 116(4), 177-188.
• Novrita Mulya, R. (2012). Pengaruh Sikap Pada Mata Pelajaran Kimia Dan Konsep Diri Terhadap Prestasi Belajar Kimia. Jurnal Formatif, 2(3), 218-226.
• Olabe, J. C., et al. (2014). Solving Math and Science Problems in the Real World with a Computational Mind. New approaches in education research, 3(2), 75-82.
• Orlich, D., et al. (2010). Teaching Trategies: A Guide to Effective Instruction (9th Ed.). Australia: Wadsworth Cengage Learning.
• Papadakis, S. J., Kalogiannakis, M., Zaranis, N., & Papadakis, S. (2016). Developing Fundamental Programming Concepts and Computational Thinking with ScratchJr in Preschool Education: A Case Study. International Journal of Mobile Learning and Organisation, 10(3), 187-202.
• Papert, S., & Harel, I. (1991). Situating constructionism. In S. Papert & I. Harel (Eds.), Constructionism. Ablex Publishing Corporation.
• Pellegrino, J. W., & Hilton, M. L. (2012). Education for Life and Work: Developing Transferable Knowledge and Skills in the 21 St Century. Washington, USA: The National Academies of Press.
• Psycharis, S. (2018). Steam in Education: A Literature Review on the Role of Computational Thinking, Engineering Epistemology and Computational Science. Computational STEAM Pedagogy (Csp). S. Psycharis Scientific Culture, 4(2), 51-72.
• Psycharis, S., & Kotzampasaki, E. (2019). The Impact of a STEM Inquiry Game Learning Scenario on Computational Thinking and Computer Self-confidence. Eurasia Journal of Mathematics, Science and Technology Education, 15(4), em1689. https://doi.org/10.29333/ejmste/103071
• Qin, H. (2009). Teaching Computational Thinking through Bioinformatics to Biology Students. In SIGCSE’09 - Proceedings of the 40th ACM Technical Symposium on Computer Science Education (pp. 188-191).
• Reddy, L. (2020). An Evaluation of Undergraduate South African Physics Students’ Epistemological Beliefs When Solving Physics Problems. Eurasia Journal of Mathematics, Science and Technology Education, 16(5), em1844. https://doi.org/10.29333/ejmste/7802
• Reichert, J. T., Couto Barone, D. A., & Kist, M. (2020). Computational Thinking in K-12: An analysis with Mathematics Teachers. Eurasia Journal of Mathematics, Science and Technology Education, 16(6), em1847. https://doi.org/10.29333/ejmste/7832
• Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J., Silverman, B. & Kafai, Y. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 60-67.
• Rodriguez, B., Kennicutt, S., Rader, C., & Camp, T. (2017). Assessing Computational Thinking in CS Unplugged Activities. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education - SIGCSE ’17 (pp. 501-506). New York, New York, USA: ACM Press.
• Rodriguez, B., Rader, C., & Camp, T. (2016). Using Student Performance to Assess CS Unplugged Activities in a Classroom Environment. In ITiCSE '16: Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education (pp. 95-100). https://doi.org/10.1145/2899415.2899465
• Roziah, A. (2005). Pembangunan Dan Keberkesanan Pakej Multimedia Kemahiran Berfikir Bagi Mata Pelajaran Kimia [Development and Effectiveness of Thinking Skills Multimedia Package for Chemistry Subjects] (PhD Thesis), Universiti Kebangsaan Malaysia.
• Rubiah, M. (2016). Implementation of Problem Based Learning Model in Concept Learning Mushroom as a Result of Student Learning Improvement Efforts Guidelines for Teachers. Journal of Education and Practice, 7(22), 26-30.
• Salihuddin, Md S., et al. (2016). Enhancing Student’s Higher Order Thinking Skills (HOTS) through the Socratic Method Approach with Technology. In 1st ICRIL-International Conference on Innovation in Science and Technology, Kuala Lumpur: Universiti Teknologi Malaysia, Kuala.
• Saltan, F., & Kara, M. (2016). ICT Teachers’ Acceptance of “Scratch” as Algorithm Visualization Software. Higher Education Studies, 6(4), 146-155. https://doi.org/10.5539/hes.v6n4p146
• Samri, C., Kamisah, O., & Anuar, N. N. (2020). Level of Computational Thinking Skills among Secondary Science Student: Variation across Gender and Mathematics Achievement. International Council of Association for Science Education, 31(2), 159-163.
• Savery, J. R. (2006). Overview of Problem-Based Learning: Definitions and Distinctions. The Interdisciplinary Journal of Problem-based Learning, 1(1), 9-20.
• Schmidth, H.-J., Marohn, A., & Harrison, A. G. (2007). Factors That Prevent Learning in Electrochemistry. Journal of Research in Science Teaching, 44(2), 258-283.
• Schunk, D. H. (2012). Learning Theories an Educational Perspective (6th ed.). Boston: Pearson Education.
• Shakhman, L., & Barak, M. (2019). The Physics Problem-Solving Taxonomy (PPST): Development and Application for Evaluating Student Learning. Eurasia Journal of Mathematics, Science and Technology Education, 15(11), em1764. https://doi.org/10.29333/ejmste/109266
• Shirey, K. L. (2017). Teacher Productive Resources for Engineering Design Integration in High School Physics Nstruction (Fundamental). In Proceedings of the 2017 ASEE Annual Conference, Columbus.
• Shute, V. J., Sun, C., & Clarke, J. A. (2017). Demystifying Computational Thinking. Educational Research Review, 22, 142-158. https://doi.org/10.1016/j.edurev.2017.09.003
• Smit, K., De Brabander, C. J., & Martens, R. L. (2016). Scandinavian Journal of Educational Research Student-Centred and Teacher-Centred Learning Environment in Pre-Vocational Secondary Education: Psychological Needs, and Motivation. Scandinavian Journal of Educational Research, 58(6), 695-712.
• Smith, S., & Burrow, L. E. (2016). Programming Multimedia Stories in Scratch to Integrate Computational Thinking and Writing with Elementary Students. Journal of Mathematics Education, 9(2), 119-131.
• Sridaran, R., & Shailaja, J. (2015). Computational Thinking, the Intellectual Thinking for the 21st Century. International Journal of Advanced Networking Applications (IJANA), 39-46.
• Stevens, R. J., & Slavin, R. E. (1995). The Cooperative Elementary School: Effects on Students’ Achievement, Attitudes, and Social Relations. American Educational Research Journal, 32(2), 321-351. https://doi.org/10.3102/00028312032002321
• Su, A. Y. S., et al. (2014). Investigating the Role of Computer-Supported Annotation in Problem-Solving-Based Teaching: An Empirical Study of a Scratch Programming Pedagogy. British Journal of Educational Technology, 45(4), 647-665.
• Sustekova, E., Kubiatko, M., & Usak, M. (2019). Validation of Critical Thinking Test on Slovak Conditions. Eurasia Journal of Mathematics, Science and Technology Education, 15(12), em1798. https://doi.org/10.29333/ejmste/112295
• Swaid, S. I. (2015). Bringing Computational Thinking to STEM Education. Procedia Manufacturing, 3, 3657-3662. https://doi.org/10.1016/j.promfg.2015.07.761
• Tanujaya, B., Mumu, J., & Margono, G. (2017). The Relationship between Higher Order Thinking Skills and Academic Performance of Student in Mathematics Instruction. International Education Studies, 10(11), 78.
• Telegina, N. V., Drovosekov, S. E., Vasbieva, D. G., & Zakharova, V. L. (2019). The Use of Project Activity in Teaching Mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 15(8), em1738. https://doi.org/10.29333/ejmste/108439
• Thies, R., & Vahrenhold, J. (2013). On Plugging ‘Unplugged’ into CS Classes. In SIGCSE 2013 - Proceedings of the 44th ACM Technical Symposium on Computer Science Education (pp. 365-370).
• Tsarava, K., et al. (2017). Training Computational Thinking: Game-Based Unplugged and Plugged-in Activities in Primary School. In Proceedings of 11th European Conference on Game-Based Learning ECGBL 2017 (pp. 687-695).
• Vitores, A., & Gil-juárez, A. (2016). The Trouble with ‘Women in Computing’: A Critical Examination of the Deployment of Research on the Gender Gap in Computer Science. Journal of Gender Studies, 25(6), 666-680. https://doi.org/10.1080/09589236.2015.1087309
• Voogt, J., Erstad, O., Dede, C., & Mishra, P. (2013). Challenges to Learning and Schooling in the Digital Networked World of the 21st Century. Journal of Computer Assisted Learning, 29, 403-413.
• Voogt, J., et al. (2015). Computational Thinking in Compulsory Education: Towards an Agenda for Research and Practice. Education and Information Technologies, 20, 715-728.
• Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes (M. Cole, V. J. Steiner, & S. Ellen, Eds.). Harvard University Press
• Wing, J. M. (2006). Computational Thinking. Communications of the ACM, 49(3), 33-35.
• Yadav, A., Hong, H., & Stephenson, C. (2016). Computational Thinking for All: Pedagogical Approaches to Embedding 21st Century Problem Solving in K-12 Classrooms. TechTrends, 60, 565-568.
• Yasar, O., Veronesi, P., Maliekal, J., Little, L. J., Vattana, S. E., & Yeter, I. H. (2016). Computational Pedagogy: Fostering a New Method of Teaching. Computer in Education Journal, 16(3), 51-72.
• Yochum, S. M., & Luoma, J. R. (1995). Augmenting a Classical Electrochemical Demonstration. Journal of Chemistry Education, 72(1), 55-56. https://doi.org/10.1021/ed072p55
• You, S. (2013). Gender and Ethnic Differences in Precollege Mathematics Coursework Related to Science, Technology, Engineering, and Mathematics (STEM) Pathways. School Effectiveness and School Improvement, 24(1), 64-86.

APA

Chongo, S., Osman, K., & Nayan, N. A. (2021). Impact of the Plugged-in and Unplugged Chemistry Computational Thinking Modules on Achievement in Chemistry. Eurasia Journal of Mathematics, Science and Technology Education, 17(4), em1953. https://doi.org/10.29333/ejmste/10789

Vancouver

Chongo S, Osman K, Nayan NA. Impact of the Plugged-in and Unplugged Chemistry Computational Thinking Modules on Achievement in Chemistry. EURASIA J Math Sci Tech Ed. 2021;17(4):em1953. https://doi.org/10.29333/ejmste/10789

AMA

Chongo S, Osman K, Nayan NA. Impact of the Plugged-in and Unplugged Chemistry Computational Thinking Modules on Achievement in Chemistry. EURASIA J Math Sci Tech Ed. 2021;17(4), em1953. https://doi.org/10.29333/ejmste/10789

Chicago

Chongo, Samri, Kamisah Osman, and Nazrul Anuar Nayan. "Impact of the Plugged-in and Unplugged Chemistry Computational Thinking Modules on Achievement in Chemistry". Eurasia Journal of Mathematics, Science and Technology Education 2021 17 no. 4 (2021): em1953. https://doi.org/10.29333/ejmste/10789

Harvard

Chongo, S., Osman, K., and Nayan, N. A. (2021). Impact of the Plugged-in and Unplugged Chemistry Computational Thinking Modules on Achievement in Chemistry. Eurasia Journal of Mathematics, Science and Technology Education, 17(4), em1953. https://doi.org/10.29333/ejmste/10789

MLA

Chongo, Samri et al. "Impact of the Plugged-in and Unplugged Chemistry Computational Thinking Modules on Achievement in Chemistry". Eurasia Journal of Mathematics, Science and Technology Education, vol. 17, no. 4, 2021, em1953. https://doi.org/10.29333/ejmste/10789