Self-organized learning environments (SOLEs) pedagogy as a conduit to learners’ metacognitive skills and conceptual understanding of “S” in STEM: The South African study

Hodi Tsamago 1, Anass Bayaga 1 *
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1 Faculty of Education, Nelson Mandela University, Summerstrand, Gqeberha, SOUTH AFRICA
* Corresponding Author
EUR J SCI MATH ED, Volume 11, Issue 3, pp. 533-555.
Published Online: 07 March 2023, Published: 01 July 2023
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The current study examined self-organized learning environments (SOLEs) pedagogy as a conduit to learners’ metacognitive skills and conceptual understanding in physical sciences and science, technology, engineering, and mathematics (STEM) as a whole in Capricorn District of Limpopo Province of South Africa. The aim was based on ongoing debates related to integrating technology and metacognitive skills in STEM education to improve educational outcomes. Anchored upon the aim and through experimental (one urban and one rural) groups and control (one urban and one rural) groups, the study employed a non-equivalent quasi-experimental (control group) design to glean and analyze data from 155 selected participants through a stratified sampling method. Data were collected using physical sciences pre- and post-tests and metacognition self-assessment scale questionnaire. Data analysis employed descriptive (mean [M], standard deviation, and effect size) and inferential (parametric t-test) analysis. The findings indicate that the mean gain score (M=6.37) of the experimental groups (that were taught through SOLEs pedagogy) was higher than that of their counterparts (M=2.60) in the control groups with a p-value (p=0.037) that is less than 0.005.
Similarly, in terms of conceptual understanding, the findings indicate that the experimental groups improved significantly more than the control groups at a significant p-value of 0.00. Finally, the study concludes that SOLEs pedagogy improves learners’ metacognitive skills that, in turn, enhance conceptual understanding of physical sciences content. Furthermore, the current study recommends further longitudinal studies with larger sample sizes to explore SOLEs pedagogy in STEM.


Tsamago, H., & Bayaga, A. (2023). Self-organized learning environments (SOLEs) pedagogy as a conduit to learners’ metacognitive skills and conceptual understanding of “S” in STEM: The South African study. European Journal of Science and Mathematics Education, 11(3), 533-555.


  • Afoan, M. Y., & Corebima, A. D. (2018). The correlation of metacognitive skills and learning results toward students’ retention of Biology learning: Students learning only to pass examinations. Educational Process: International Journal, 7(3), 171-179.
  • Aina, J. K. (2013). Effective teaching and learning in science education through information and communication technology (ICT). IOSR Journal of Research & Method in Education, 2(5), 43-47.
  • Al Zakwani, M., & Walker-Gleaves, C. (2019). The influence of self-organized learning environments (SOLEs) on EFL students in a college in Oman. Journal of Information Technologies and Lifelong Learning, 2(2), 97-106.
  • Al-Mutawah, M. A., Thomas, R., Eid, A., Mahmoud, E. Y., & Fateel, M. J. (2019). Conceptual understanding, procedural knowledge, and problem-solving skills in mathematics: High school graduates work analysis and standpoints. International Journal of Education and Practice, 7(3), 253-273.
  • Anis, M., & Anwar, C. (2020). Self-organized learning environment teaching strategy for ELT in Merdeka Belajar concept for high school students in Indonesia. Journal of English Educators Society, 5(2), 199-204.
  • Anthonysamy, L. (2021). The use of metacognitive strategies for undisrupted online learning: Preparing university students in the age of pandemic. Education and Information Technologies, 26, 6881-6899.
  • Azizah, U., & Mitarlis, H. N. (2019). Metacognitive skills: A solution in chemistry problem solving. Journal of Physics: Conference Series, 1417, 012084.
  • Bahri, A., & Corebima, A. D. (2015). The contribution of learning motivation and metacognitive skill on cognitive learning outcome of students within different learning strategies. Journal of Baltic Science Education, 14(4), 487-500.
  • Ben-David, A., & Orion, N. (2012). Teachers’ voices on integrating metacognition into science education. International Journal of Science Education, 35(18), 3161-3193.
  • Bleeker, A. (2019). Using universal service funds to increase access with disabilities in the Caribbean. United Nations.
  • Cadamuro, A., Bisagno, E., Pecini, C., & Vezzali, L. (2019). Reflecting a ... “bit”. What relationship between metacognition and ICT. Journal of E-Learning and Knowledge Society, 5(3), 183-195.
  • Chilanda, A. (2020). Exploring whether physics teaching methods curriculum content addresses poor performance of pupils in physics: A case of one teacher education university in Kitwe District. International Journal of Scientific and Research Publications, 10(5), 152-160.
  • Dagar, V., & Yadav, A. (2016). Constructivism: A paradigm for teaching and learning. Arts and Social Sciences Journal, 7(4), 1-4.
  • DBE. (2015). National diagnostic report on learner performance 2015. Department of Basic Education.
  • DBE. (2016). Department of Basic Education national diagnostic report on learner performance 2016. Department of Basic Education.
  • DBE. (2017). National senior certificate examinations 2017 diagnostic report: Part 1. Department of Basic Education.
  • DBE. (2018). National senior certificate examinations 2018 diagnostic report: Part 1. Department of Basic Education.
  • DBE. (2019). National senior certificate 2018 diagnostic report part 1. Department of Basic Education.
  • DBE. (2020). Report on the 2019 National senior certificate diagnostics report part 1. Department of Basic Education.
  • DBE. (2021). Report on the 2019 National senior certificate diagnostics report part 1. Department of Basic Education.
  • Dolan, P., Leat, D., Smith, L. M., Mitra, S., Todd, L., & Wall, K. (2013). Self-organized learning environments (SOLEs) in an English school: An example of transformative pedagogy? The Online Educational Research Journal, 3(11), 1-19.
  • Du Toit, S., & Kotze, G. (2009). Metacognitive strategies in the teaching and learning of mathematics. Pythagoras, 70, 57-67.
  • Flavell, J. H. (1979). Metacognition and cognitive monitoring. American Psychologist, 34, 906-911.
  • Geduld, B. (2019). A snapshot of teachers’ knowledge and teaching behavior with regard to developing self-regulated learning. Journal of Education, 77, 60-78.
  • Ghavifekr, S., & Rosdy, W. A. (2015). Teaching and learning with technology: Effectiveness of ICT integration in schools. International Journal of Research in Education and Science, 1(2), 175-191.
  • Gillwald, A., Mothobi, O., & Rademan, B. (2018). The state of ICT in South Africa. International Development Research Center.
  • Gravetter, F., & Forzano, L. (2018). Research methods for behavioral sciences. Cengage.
  • Hadwin, A.F., Järvelä, S., & Miller, M. (2011). Self-regulated, co-regulated, and socially shared regulation of learning. In B. J. Zimmerman, & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 65-84). Routledge
  • Havenga, M. (2015). The role of metacognitive skills in solving object-oriented programming problems: a case study. The Journal for Transdisciplinary Research in Southern Africa, 11(1), 133-147.
  • Heslup, S. (2018). Fostering independent learning amongst English for Academic Purposes students through exploration of digital tools. HBKU Press/Qatar Foundation Annual Research Conference.
  • Hinton, C., Fischer, K. W., & Glennon, C. (2012). Mind, brain, and education. Jobs for the Future.
  • Human Sciences Research Council. (2020). Department of Basic Education and HSRC release TIMSS 2019 grade 5 study. HSRC.
  • Jazuli, A., Setyosari, P., Sulthon, & Kuswandi, D. (2017). Improving conceptual understanding and problem-solving in mathematics through a contextual learning strategy. Global Journal of Engineering Education, 19(1), 49-53.
  • John, M. (2019). Physical sciences teaching and learning in Eastern Cape rural schools: Reflections of pre-service teachers. South African Journal of Education, 39(1), S1-S12.
  • John, M., Molepo, J., & Chirwa, M. (2015). Exploring grade 11 learners’ conceptual understanding of refraction: A South African case study. International Journal of Education Sciences, 10(3), 391-398.
  • Kane, S., Lear, M., & Dube, C. M. (2014). Reflections on the role of metacognition in student reading and learning at higher education level. African Education Review, 11(4), 512-525.
  • Khandan, R., & Shannon, L. (2021). The effect of teaching-learning environments on student’s engagement with lean mindset. Education Sciences, 11, 466.
  • Kibirige, I., & Tsamago, H. E. (2019). Grade 10 learners’ science conceptual development using computer simulations. EURASIA Journal of Mathematics, Science and Technology Education, 15(7), em1717.
  • Kibirige, I., Maake, R., & Mavhunga, F. (2014). Effect of practical work on grade 10 learners’ performance in Science in Mankweng Circuit, South Africa. Mediterranean Journal of Social Sciences, 5(23), 1568-1577.
  • Kim, T. K. (2015). T test as a parametric statistic. Korean Journal of Anesthesiology, 68(6) 540-546.
  • Konyango, O. B., N., O., Otieno, M., & Orodho, J. A. (2018). Influence of resources on students’ academic performance in physics at secondary schools in Ugenya Sub-County, Siaya County, Kenya. Greener Journal of Educational Research, 5(8), 111-118.
  • Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers of Psychology, 4, 863.
  • Mitra, S. (2003). Minimally invasive education: A progress report on the ‘hole-in-the-wall’ experiments. The British Journal of Educational Technology, 34(33), 367-371.
  • Mitra, S., & Crawley, E. (2014). Effectiveness of self-organized learning by children: Gateshead experiments. Journal of Education and Human Development, 3(3), 79-88.
  • Mitra, S., & Dangwal, R. (2010). Limits to self-organized learning: The Kalikuppam experiment. British Journal of Educational Technology, 41(5), 672-688.
  • Mitra, S., & Dangwal, R. (2017). Acquisition of computer literacy skills through self-organizing systems of learning among children in Bhutan and India. Springer.
  • Mitra, S., Kulkarni, S., & Stanfield, J. (2016). The Palgrave international handbook of alternative education. Palgrave Macmillan.
  • Mullis, I. V., Martin, M. O., Foy, P., Kelly, D. L., & Fishbein, B. (2020). TIMSS 2019: International results in Mathematics and science. TIMSS.
  • Nahdi, D. S., & Jatisunda, M. G. (2019). Conceptual understanding and procedural knowledge: A case study on learning mathematics of fractional material in elementary school. Journal of Physics: Conference Series, 1477, 1-5.
  • National Planning Commission. (2011). National development plan 2030: Our future–Make it work. The Presidency Republic of South Africa.
  • Ozturk, N. (2020). The instrument of teaching metacognition in reading classrooms: The ITMR. International Journal of Assessment Tools in Education, 7(3), 305-322.
  • Panchu, P., Bahuleyan, B., Seethalakshmi, K., & TomThomas. (2016). Metacognitive knowledge: A tool for academic success. International Journal of Medical Research Professionals, 2(5), 131-134.
  • Pedone, R., Semerari, A., Riccardi, I., Procacci, M., Nicolò, G., & Carcione, A. (2017). Developing a self-report measure of metacognition self-assessment scale instrumet description and factor structure. Clinical Neuropsychiatry, 14(3), 185-194.
  • Phage, I. (2018). Undergraduate physics students’ conceptual understanding in the learning of kinematics using a blended approach. In Proceedings of the SOCIOINT 2018 5th International Conference on Education, Social Sciences and Humanities (pp. 654-659).
  • Queirós, A., Faria, D., & Almeida, F. (2017). Strengths and limitations of qualitative and quantitative research methods. European Journal of Education Studies, 3(9), 369-387.
  • Rahman, S. (2017). The advantages and disadvantages of using qualitative and quantitative approaches and methods in language “testing and assessment” research: A literature review. Journal of Education and Learning, 6(1), 102-112.
  • Reddy, L., Nair, P., & Ramaila, S. (2014). A critical assessment of first year entering university science students’ conceptual understanding. In Proceedings of the SAIP 2014 (pp. 461-465). SA Institute of Physics.
  • Rumahlatu, D., & Sangur, K. (2019). The influence of project-based learning strategies on the metacognitive skills, concept understanding and retention of senior high school students. Journal of Education and Learning, 13(1), 104-110.
  • Salmi, H., & Thuneberg, H. (2019). The role of self‑determination in informal and formal science learning contexts. Learning Environmental Research, 22, 43-63.
  • Schraw, G., Kauffman, D. F., & Lehman, S. (2006). Self-regulated learning. In L. Nadel (Ed.), Encyclopedia of cognitive science. John Wiley & Sons.
  • Siyaya, M. C., Omotosho, A. O., Uleanya, C., & Gamede, B. T. (2022). Information literacy and metacognitive abilities of teachers: Case of a South African rural school. International Journal of Education & Literacy Studies, 10(1), 173-178.
  • Surif, J., Ibrahim, N. H., & Mokhtar, M. (2012). Conceptual and procedural knowledge in problem solving. Procedia-Social and Behavioral Sciences, 56, 416-425.
  • Tachie, S. A. (2019a). Foundation phase students’ metacognitive abilities in mathematics classes: Reflective classroom discourse using an open approach. Problems of Education in the 21st Century, 77(4), 528-544.
  • Tachie, S. A. (2019b). Meta-cognitive skills and strategies application: How this helps learners in mathematics problem-solving. EURASIA Journal of Mathematics, Science and Technology Education, 15(5), em1702.
  • Torrijo, F. J., Garzón-Roca, J., Cobos, G., & Eguibar, M. Á. (2021). Combining project based learning and cooperative learning strategies in a geotechnical engineering course. Education Sciences, 11, 467.
  • Vega, N., Stanfield, J., & Mitra, S. (2020). Investigating the impact of computer supported collaborative learning (CSCL) to help improve reading comprehension in low performing urban elementary schools. Education and Information Technologies, 25, 1571-1584.
  • Vygotsky, L. S. (1968). Thought and language (newly revised, translated, and edited by Alex Kozulin). MIT Press.
  • White, H., & Sabarwal, S. (2014). Quasi-experimental design and methods. United Nations Children’s Fund.
  • Wood, R. (2019). Students’ motivation to engage with science learning activities through the lens of self-determination theory: Results from a single-case school-based study. EURASIA Journal of Mathematics, Science and Technology Education, 15(7), 1-22.
  • Yildiz, Y., & Yucedal, H. M. (2020). Learner autonomy: A central theme in language learning. International Journal of Social Sciences & Educational Studies, 7(3), 208-212.
  • Zenda, R. (2016). Factors affecting academic achievement of learners in physical science in selected Limpopo secondary schools [DEd thesis, University of South Africa].
  • Zimmerman, B. J., & Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 299-315). Routledge.