A metacognitive knowledge, metacognitive experience, and its effects on learning outcomes for stem and non-stem Malaysian students
List of Authors
  • Poh Leong Tan , S. Chee Choy

Keyword
  • Metacognitive Knowledge, Metacognitive Experience, Quality learning, Learning Outcomes, comparative, STEM and Non-STEM Students

Abstract
  • This study examined the effects of metacognitive knowledge, metacognitive experience, and quality learning on learning outcomes for STEM and non-STEM Malaysian university students. A sample of 958 STEM and 1256 non-STEM students on Diploma and bachelor programmes took part in the study using the adopted and adapted survey questionnaires. The metacognitive theory was used as the framework to build a research model with four constructs which was analysed using SEM. The results showed that all five of the hypotheses were supported for non-STEM students and also the five hypotheses were supported for STEM students. It is estimated that the model for non-STEM students accounted for 39% of the variance for learning outcome and 53% of the variance for quality learning and the model for STEM students accounted for 54% of the variance for learning outcome and 46% of the variance for quality learning. The results indicated a lack of emphasis on using teaching approaches that encouraged critical thinking and reflective thinking skills for STEM students when compared to non-STEM students. It also implies that STEM students are more focused on process and knowledge acquisition than non-STEM students.

Reference
  • 1. Ackerman, P. L., Kanfer, R., & Beier, M. E. (2013). Trait complex, cognitive ability, and domain knowledge predictors of baccalaureate success, STEM persistence, and gender differences. Journal of Educational Psychology, 105(3), 911-927. http://dx.doi.org/10.1037/a0032338.

    2. Adkins, S. J., Rock, R. K., & J., M. J. (2018). Interdisciplinary STEM education reform: Diishing out art in a microbiology laboratory. FEMS Microbiology Letters, 365(1), 1-8, https://doi.org/10.1093/femsle/fnx245365(1).Aikenhead, G. S. (2000). Improving Science Education. In R. Millar, J. Leach, & J. Osborne, Renegotiating the Culture of School Science (pp. 223-239). London: Open University Press.

    3. Ajzen, I. (1991). The theory of planned behaviour. Oranisational Behaviour Human DecisionProcess, 50, 179-211.

    4. Ali, F., Zhou, Y., Hussain, K., Nair, K. P., & Ragavan, A. N. (2016). Does higher education service quality effect student satisfaction, image and loyalty? A study of international students in Malaysian public universities. Quality Assurance in Education, 24 (1), 70-94. http://dx.doi.org/10.1108/QAE-02-2014-0008.

    5. Bendermacher, G., oude Egbrink, M., Wolfhagen, I., & Dolmans, D. (2017). Unravelling quality culture in higher education: a realist review. Higher education, 73(1), 39–60. https://doi.org/10.1007/s10734-015-9979-2.

    6. Berry, S., Jones, T., & Lamb, E. (2017). Health Humanities: The Future of Pre-Health Education is Here. Journal of Medical Humanities, 38(4), 353–360.

    7. Biggs, J., & Tang, C. (2007). Teaching for quality learning at university : What the student does (3rd Edition). Berkshire: Open University Press.

    8. Cohen, J. (1988). Statistical power analysis for the behavioural sciences. New Jersey: Erlbaum.

    9. Crisp, G., Nora, A., & Taggart, A. (2009). Student characteristics, pre-college, college, andenvironmental factors as predictors of majoring in and earning a STEM degree: An analysis of students attending a Hispanic serving institution. American Educational Research Journal, 46 (4), 924–942, DOI: 10.3102/0002831209349460

    10. Diseth, A., Pallesen, S., Brunborg, G. S., & Larsen, S. (2010). Academic achievement among first semester undergraduate psychology students: the role of course experience,effort,motives and learning strategies. Higher Education, 59, 335-352, DOI 10.1007/s10734-009-9251-8.

    11. Echazarra, A., Salinas, D., Méndez, I., Denis, V., & G., R. (2016). How teachers teach andstudents learn: Successful strategies for school. Paris, http://dx.doi.org/10.1787/5jm29kpt0xxx-en: OECD Education Working Papers, No. 130, OECD Publishing.

    12. Efklides, A. (2006). Metacognition and affect: What can metacognitive experiences tell usabout the learning process? Educational Research Review, 1, 3-14.

    13. Ellis, R. A. (2016). Qualitatively different university student experiences of inquiry: Associations among approaches to inquiry, technologies and perceptions of the learning environment. Active Learning in Higher Education, 17(1), 13–23. DOI: https://doi.org/10.1177/1469787415616721.

    14. Fernandez-Duque, D., Baird, J. A., & Posner, M. I. (2000). Executive attention and metacognitive regulation. Consciousness and Cognition ,9, 288–307 .

    15. Flavell, H. J. (1979). Metacognition and cognitive monitoring: A New Area of Cognitive—Developmental Inquiry. American Psychologist, 34 (10), 906-911.

    16. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. journal of marketing research, 48, 39-50.

    17. Graham, M. J., Frederick, J., Byars-Winston, A., Hunter, A., & Handelsman, J. (2013). Persistence of College Students in STEM. Science, 341, 1455-1456, DOI:10.1126/science.1240487.

    18. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective. London: Pearson.

    19. Huber, C. R., & Kuncel, N. R. (2016). Does college teach critical thinking: A meta-analysis. Review of Educational Research, 86(2), 431-468 .

    20. Huber, M. T., & Hutchings, P. (2004). Intergrative learning: Mapping the terrain. New York: Association of American Colleges and Universities .

    21. Irvine, J., Molyneux, J., & Gillman, M. (2015). Providing a Link with the Real World’:

    22. Learning from the Student Experience of Service User and Carer Involvement in Social Work Education. Social Work Education, 34(2), 138-150, DOI: 10.1080/02615479.2014.957178.

    23. Kelly, T. R., & Knowles, J. G. (2016). A conceptual framework for integrated STEM education. International Journal of STEM Education, 3(11), DOI 10.1186/s40594-016-0046-z3(11).

    24. Kline, R. (2005). Principle and practices of structural equation modelling (2n ed.) New York: Guilford Press

    25. Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and psychological measurement, 30, 607-610.

    26. Kuhn, D. (2000). Metacognitive development. Current Directions in Psychological Science, 9(5), 178-181.

    27. Langstrand, J., Cronemyr, P., & Poksinska, B. (2015). Practise what you preach: quality of education in education on quality. Total Quality Management & Business Excellence, 26 (11-12), 1202-1212, DOI: 10.1080/14783363.2014.925290.

    28. Lai, E. R. (2011). Metacognition: A literature review. Retrieved March 28, 2016, from Pearson: pearsonassessments. com/images/tmrs/Metacognition_Literature_Review_Final.

    29. Larson, J. O. (1995). Fatima rules and other elements of an unintended chemistry curriculum. Annual Meeting of the American Educational Research Association, (p. 16). San Francisco.

    30. Lim, C. B., Bentley, D., Henderson, F., Pan, S. Y., V., B., Balasingam, D., et al. (2016). Equivalent or not?: Beyond measuring teaching and learning standards in a transnational education environment. Quality Assurance in Education, 24(4),528-540, https://doi.org/10.1108/QAE-01-2016-0001.

    31. Malaysian Qualification Agency. (2013). Assessment of students. Putrajaya: MalaysianQualifications Agency

    32. Marcel, V. M., Pascal , W., & Jos , J. (2004). The relation between intellectual and metacognitive skills from a developmental perspective. Learning and Instruction, 14, 89-109.

    33. Ministry of Education Malaysia. (2013). Malaysia education blueprint (2013-2025) higher education. Putrajaya: Ministry of Education Malaysia.

    34. Nordin, N., Halim, N., & Malik, M. (2016). Assessing readiness for self-direxcted learning among college students in the provision of higher learning institution. 7th Asian Conference on Environment-Behaviour Studies (pp. 91-101). Taiwan: National Taiwan University, Taipei.

    35. North, S. (2005). Different values, different skills? A comparison of essay writing by studentsfrom arts and science backgrounds. Studies in Higher Education, 30(5), 517–533. http://dx.doi.org/10.1080/03075070500249153.

    36. Oliver, R. (2001). Assuring the quality of online learning in Australian higher education. In M. Wallace, A. Ellis, & D. Newton, Proceeding of Moving Online II Conference (pp. 222-231). Lismore: Southern Cross university.

    37. Pintrich, P. R. (2002). The role of metacognitive knowledge in learning, teaching, andassessing. Theory Into Practice, 41(4), 219-225, http://dx.doi.org/10.1207/s15430421tip4104_3.

    38. Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 38(2), 353–387. http://dx.doi.org/10.1037/a0026838.

    39. Samuelowicz, K., & Bain, J. (1992). Conceptions of teaching held by academic teachers. Higher Education, 24,93-111. https://doi.org/10.1007/BF00138620.

    40. Srikanthan, G., & Dalrymple, J. F. (2007). A conceptual overview of a holistic model for quality in higher education. International Journal of Educational Management, 21 (3), 173 - 193, DOI: http://dx.doi.org/10.1108/09513540710738647.

    41. Teng, C., J., H., & Baum, T. (2013). Academic perceptions of quality and quality assurance in undergraduate hospitality,tourism and leisure programmes:A comparison of UK and Taiwanese programmes. Journal of Hospitality, Leisure, Sport & Tourism Education, 13, 233–243, http://dx.doi.org/10.1016/j.jhlste.2012.06.002

    42. Wang, X., Su, Y., Cheung, S., Wong, E., & Kwong, T. (2013). An exploration of Biggs’ constructive alignment in course design and its impact on students’ learning approaches. Assessment & Evaluation in Higher Education, 38:4, 477-491, DOI:10.1080/02602938.2012.658018.

    43. West SG, Finch JF, Curran PJ.(1995). Structural equation models with nonnormal variables: problems and remedies. In: Hoyle RH, editor. Structural equation modeling: Concepts, issues and applications. Newbery Park, CA: Sage; 56-75.

    44. Yeager, D., Henderson, M., Paunesku, D., Walton, G., D’Mello, S., Spitzer, B., et al. (2014).

    45. Boring but important: A self-transcendent purpose for learning fosters academic self-regulation. Journal of Personality and Social Psychology, 107 (4), 559-580. http://dx.doi.org/10.1037/a0037637.