Constructivist Learning Environment: A Perfect Mediator for The Relationship of Students' Multiple Intelligences with Attitudes Towards and Achievement in STEM

Abstract

In STEM disciplines, it is crucial to design research studies clarifying the relationships between academic performance-related constructs and academic performance. This study aims at exploring whether middle school students’ perceptions of constructivist learning environment (P-CLE) mediate the relationship between their perceptions of multiple intelligences (P-MI) and their attitude towards and achievement in STEM disciplines (AA-STEM). Because the relationships among middle school students’ P-CLE, P-MI, and AA-STEM are under investigation, this study is a correlational study. The sample consisted of 579 students from randomly selected 10 middle schools in Kayapınar, a district of Diyarbakır, Turkiye. The students’ GPA scores in STEM-related courses were used to represent their achievement in STEM disciplines. In addition, the Attitude towards STEM Survey (AtSTEM), Multiple Intelligences Inventory (MII), and Constructivist Learning Environment Survey (CLES) were used for the data collection. Lavaan, an R package, was used to conduct structural equation modeling for mediation analysis. The mediation analysis yielded that the P-CLE was a perfect mediator for the relationship between the P-MI and AA-STEM. In consequence, this study emphasizes the importance of providing constructivist learning environments in STEM classes and encouraging students to think of intelligence as something malleable.

Keywords

• Constructivist Learning Environment
• Multiple Intelligences
• STEM

References

1. Ahvan, Y. R., & Pour, H. Z. (2016). The correlation of multiple intelligences for the achievements of secondary students. Educational Research and Reviews, 11(4), 141–145. https://doi.org/10.5897/err2015.2532
2. Aina, J. K. (2018). Physics learning and the application of multiple intelligences. Revista Brasileira de Gestão Ambiental e Sustentabilidade, 5(9), 381–391. https://doi.org/10.21438/rbgas.050926
3. Akdağ, E., & Köksal, M. S. (2022). Investigating the relationship of gifted students’ perceptions regarding science learning environment and motivation for science learning with their intellectual risk taking and science achievement. Science Education International, 33(1), 5–17. https://doi.org/10.33828/sei.v33.i1.1
4. Allen, D., & Fraser, B. J. (2007). Parent and student perceptions of classroom learning environment and its association with student outcomes. Learning Environments Research, 10(1), 67–82. https://doi.org/10.1007/s10984-007-9018-z
5. Aluri, V. L. N., & Fraser, B. J. (2019). Students’ perceptions of mathematics classroom learning environments: Measurement and associations with achievement. Learning Environments Research, 22(3), 409–426. https://doi.org/10.1007/s10984-019-09282-1
6. Armstrong, T. (2000). Multiple intelligences in the classroom (2nd Edition). Association for Supervision and Curriculum Development. http://www.ascd.org
7. Aronson, J., Fried, C. B., & Good, C. (2002). Reducing the effects of stereotype threat on African American college students by shaping theories of intelligence. Journal of Experimental Social Psychology, 38(2), 113–125. https://doi.org/10.1006/jesp.2001.1491
8. Aydin, H. (2019). The effect of multiple intelligence(s) on academic success: A systematic review and meta-analysis. Eurasia Journal of Mathematics, Science and Technology Education, 15. https://doi.org/10.29333/ejmste/xxxxx

9. Baran, M., & Maskan, A. K. (2011). Investigating multiple intelligence fields of 11th grade students with respect to some variables and physics achievement. Necatibey Faculty of Education Electronic Journal of Science and Mathematics Education, 5(2), 156–177.
10. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
11. Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42(5), 815–824. https://doi.org/10.1016/j.paid.2006.09.018
12. Baş, G. (2016). The effect of multiple intelligences theory-based education on academic achievement: A meta-analytic review. Kuram ve Uygulamada Egitim Bilimleri, 16(6), 1833–1864. https://doi.org/10.12738/estp.2016.6.0015
13. Batdı, V. (2017). The effect of multiple intelligences on academic achievement: A meta-analytic and thematic study. Kuram ve Uygulamada Egitim Bilimleri, 17(6), 2057–2092. https://doi.org/10.12738/estp.2017.6.0104
14. Boz, Y., Yerdelen-Damar, S., Aydemir, N., & Aydemir, M. (2016). Investigating the relationships among students’ self-efficacy beliefs, their perceptions of classroom learning environment, gender, and chemistry achievement through structural equation modeling. Research in Science and Technological Education, 34(3), 307–324. https://doi.org/10.1080/02635143.2016.1174931
15. Briggs, S. R., & Cheek, J. M. (1986). The role of factor analysis in the development and evaluation of personality scales. Journal of Personality, 54(1), 106–148.
16. Brophy, S., Klein, S., Portsmore, M., & Rogers, C. (2008). Advancing engineering education in P-12 classrooms. Journal of Engineering Education, July, 369–387.
17. Bybee, R. W. (2010). What is STEM education? Science, 329(5995), 996. https://doi.org/10.1126/science.1194998
18. Chan, D. W. (2006). Perceived multiple intelligences among male and female Chinese gifted students in Hong Kong: The structure of the student multiple intelligences profile. Gifted Child Quarterly, 50(4), 325–338.
19. Chionh, Y. H., & Fraser, B. J. (2009). Classroom environment, achievement, attitudes and self-esteem in geography and athematics in Singapore. International Research in Geographical and Environmental Education, 18(1), 29–44. https://doi.org/10.1080/10382040802591530
20. Corlu, M. S., Capraro, R. M., & Capraro, M. M. (2014). Introducing STEM education: Implications for educating our teachers for the age of innovation. Education and Science, 39(171), 74–85.
21. den Brok, P., Telli, S., Cakiroglu, J., Taconis, R., & Tekkaya, C. (2010). Learning environment profiles of Turkish secondary biology classrooms. Learning Environments Research, 13(3), 187–204. https://doi.org/10.1007/s10984-010-9076-5
22. Douglas, O., Burton, K. S., & Reese-Durham, N. (2008). The Effects of the multiple intelligence teaching strategy on the academic achievement of eighth grade math students. Journal of Instructional Psychology, 35(2), 182–187.
23. Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (eighth edition). The McGraw-Hill Companies.
24. Fraser, B. J. (1998). Classroom environement instruments: Development, validity and applications. Learning Environments Research, 1, 7–33.
25. Fraser, B. J., & Kahle, J. B. (2007). Classroom, home and peer environment influences on student outcomes in science and mathematics: An analysis of systemic reform data. International Journal of Science Education, 29(15), 1891–1909. https://doi.org/10.1080/09500690601167178
26. Friday Institute for Educational Innovation. (2012). Middle and high school STEM-Student survey. https://www-data.fi.ncsu.edu/wp-content/uploads/2020/11/28143332/S- STEM_FridayInstitute_DevAndPsychometricProperties_2015.pdf
27. Goh, S. C., & Fraser, B. J. (1998). Teacher interpersonal behaviour, classroom environment and student outcomes in primary mathematics in Singapore. Learning Environments Research, 1, 199–229.
28. Gurcay, D., & Ferah, H. O. (2017). The effects of multiple intelligences based instruction on students’ physics achievement and attitudes. Journal of Baltic Science Education, 16(5), 666–677.
29. Hafızoglu, A., & Yerdelen, S. (2019). The role of students’ motivation in the relationship between perceived learning environment and achievement in science: A mediation analysis. Science Education International, 30(4), 51–260. https://doi.org/10.33828/sei.v30.i4.2
30. IBM SPSS Statistics (No. 22). (2016). IBM Corp.
31. Kelley, T. R., & Knowles, J. G. (2016). A conceptual framework for integrated STEM education. International Journal of STEM Education, 3(1), 11. https://doi.org/10.1186/s40594-016-0046-z
32. Kertil, M., & Gurel, C. (2016). Mathematical modeling: A bridge to STEM education. International Journal of Education in Mathematics, Science and Technology, 4(1), 44. https://doi.org/10.18404/ijemst.95761
33. Kline, R. B. (2005). Principles and practice of structural equation modeling. Guilford Press.
34. Küçüközer, H., Kırtak Ad, V. N., Ayverdi, L., & Eğdir, S. (2012). Turkish adaptation of constructivist learning environment survey. Elementary Education Online, 11(3), 671–688. http://ilkogretim-online.org.tr
35. Lillbacka, R. G. V. (2013). Realism, constructivism, and intelligence analysis. International Journal of Intelligence and CounterIntelligence, 26(2), 304–331. https://doi.org/10.1080/08850607.2013.732450
36. Luo, W., Wei, H. R., Ritzhaupt, A. D., Huggins-Manley, A. C., & Gardner-McCune, C. (2019). Using the S-STEM survey to evaluate a middle school robotics learning environment: Validity evidence in a different context. Journal of Science Education and Technology, 28(4), 429–443. https://doi.org/10.1007/s10956-019-09773-z
37. Marchand, G. C., & Taasoobshirazi, G. (2013). Stereotype threat and women’s performance in physics. International Journal of Science Education, 35(18), 3050–3061. https://doi.org/10.1080/09500693.2012.683461
38. Martín-Páez, T., Aguilera, D., Perales-Palacios, F. J., & Vílchez-González, J. M. (2019). What are we talking about when we talk about STEM education? A review of literature. Science Education, 103(4), 799–822. https://doi.org/10.1002/sce.21522
39. Nasri, N., Rahimi, N. M., Nasri, N. M., & Talib, M. A. A. (2021). A comparison study between universal design for learning-multiple intelligence (Udl-mi) oriented stem program and traditional stem program for inclusive education. Sustainability (Switzerland), 13(2), 1–12. https://doi.org/10.3390/su13020554
40. Ogbuehi, P. I., & Fraser, B. J. (2007). Learning environment, attitudes and conceptual development associated with innovative strategies in middle-school mathematics. Learning Environments Research, 10(2), 101–114. https://doi.org/10.1007/s10984-007-9026-z
41. Okur, M., & Kural, E. (2021). The effect of multiple intelligence theory-based science teaching on academic success in Turkey: A Meta-Analysis study. Eğitim Bilim ve Araştırma Dergisi, 2(2), 140–156. https://dergipark.org.tr/tr/pub/ebad
42. Oral, B. (2001). An investigation of university students’ intelligences categories according to their fields of study. Education and Science, 26(122), 19–31.
43. Özcan, H., & Koca, E. (2019). Turkish adaptation of the attitude towards STEM scale: A validity and reliability study. Hacettepe Egitim Dergisi, 34(2), 387–401. https://doi.org/10.16986/HUJE.2018045061
44. Pallant, J. (2005). SPSS Survival Manual 2nd Edition (Second). Allen & Unwin. www.allenandunwin.com/spss.htm
45. Pallant, J. (2007). SPSS Survival Manual: A Step by Step Guide to Data Analysis using SPSS for Windows (Third). Open University Press.
46. Pallrand, G. J., & Seeber, F. (1984). Spatial ability and achievement in introductory physics. Journal of Research in Science Teaching, 21(5), 507–516.
47. Pamuk, S., Sungur, S., & Oztekin, C. (2017). A multilevel analysis of students’ science achievements in relation to their self-regulation, epistemological beliefs, learning environment perceptions, and teachers’ personal characteristics. International Journal of Science and Mathematics Education, 15(8), 1423–1440. https://doi.org/10.1007/s10763-016-9761-7
48. Partin, M. L., & Haney, J. J. (2012). The CLEM model: Path analysis of the mediating effects of attitudes and motivational beliefs on the relationship between perceived learning environment and course performance in an undergraduate non-major biology course. Learning Environments Research, 15(1), 103–123. https://doi.org/10.1007/s10984-012-9102-x
49. Pratiwi, W. N. W., Rochintaniawati, D., & Agustin, R. R. (2018). The effect of multiple intelligence-based learning towards students’ concept mastery and interest in matter. Journal of Science Learning, 1(2), 49–52.
50. Rita, R. D., & Martin-Dunlop, C. S. (2011). Perceptions of the learning environment and associations with cognitive achievement among gifted biology students. Learning Environments Research, 14(1), 25–38. https://doi.org/10.1007/s10984-011-9080-4
51. Roberts, A. (2012). A Justification for STEM education. TECHNOLOGY AND ENGINEERING TEACHERe, May/June(June), 1–5. https://doi.org/10.1126/science.1201783
52. Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. http://www.jstatsoft.org/
53. Sánchez-Martín, J., Álvarez-Gragera, G. J., Dávila-Acedo, M. A., & Mellado, V. (2017). What do K-12 students feel when dealing with technology and engineering issues? Gardner’s multiple intelligence theory implications in technology lessons for motivating engineering vocations at Spanish Secondary School. European Journal of Engineering Education, 42(6), 1330–1343. https://doi.org/10.1080/03043797.2017.1292216
54. Schijndel, T. J. P. van, Jansen, B. R. J., & Raijmakers, M. E. J. (2018). Do individual differences in children’s scientific curiosity relate to their inquiry-based learning? International Journal of Science Education, 40(9), 996–1015. https://doi.org/10.1080/09599693.2018.1460772
55. Schreiber, J. B., Stage, F. K., King, J., Nora, A., & Barlow, E. A. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323–338. http://heldref-publications.metapress.com/index/C256M70841UU1114.pdf
56. Shekhar, P., Borrego, M., Demonbrun, M., Finelli, C., Crockett, C., & Nguyen, K. (2020). Negative student response to active learning in STEM classrooms: A systematic review of underlying reasons 49(6).
57. Smith, K. A., Douglas, T. C., & Cox, M. F. (2009). Supportive teaching and learning strategies in STEM education. In New Directions for Teaching and Learning (Issue 117, pp. 19–32). Wiley Periodicals, Inc. https://doi.org/10.1002/tl
58. Snyder, R. F. (1999). The relationship between learning styles/multiple Intelligences and academic achievement of high school students. In Source: The High School Journal (Vol. 83, Issue 2). https://about.jstor.org/terms
59. Stohlmann, M., Moore, T. J., & Roehrig, G. H. (2012). Considerations for Teaching Integrated STEM Education. Journal of Pre-College Engineering Education Research, 2(1), 28–34. https://doi.org/10.5703/1288284314653
60. Struyf, A., De Loof, H., Boeve-de Pauw, J., & Van Petegem, P. (2019). Students’ engagement in different STEM learning environments: integrated STEM education as promising practice? International Journal of Science Education, 41(10), 1387–1407. https://doi.org/10.1080/09500693.2019.1607983
61. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Pearson Education, Inc. www.ablongman.com.
62. Talanquer, V. (2014). DBER and STEM education reform: Are we up to the challenge? Journal of Research in Science Teaching, 51(6), 809–819. https://doi.org/10.1002/tea.21162
63. Taylor, P. C., Fraser, B. J., & Fisher, D. L. (1997a). Monitoring constructivist classroom learning environments. International Journal of Educational Research, 27(4), 293–302. https://doi.org/10.1016/S0883-0355(97)90011-2
64. Theobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Arroyo, E. N., Behling, S., Chambwe, N., Cintron, D. L., Cooper, J. D., Dunster, G., Grummer, J. A., Hennessey, K., Hsiao, J., Iranon, N., Jones II, L., Jordt, H., & Keller, M. (2020). Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. PNAS Latest Articles, 1–8. https://doi.org/10.1073/pnas.1916903117/-/DCSupplemental
65. Träff, U., Olsson, L., Skagerlund, K., Skagenholt, M., & Östergren, R. (2019). Logical reasoning, spatial processing, and verbal working memory: Longitudinal predictors of physics achievement at age 12–13 years. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.01929
66. Wolf, S. J., & Fraser, B. J. (2008). Learning environment, attitudes and achievement among middle-school science students using Inquiry-based laboratory activities. Research in Science Education, 38(3), 321–341. https://doi.org/10.1007/s11165-007-9052-y
67. Yang, X. (2015). Rural junior secondary school students’ perceptions of classroom learning environments and their attitude and achievement in mathematics in West China. Learning Environments Research, 18(2), 249–266. https://doi.org/10.1007/s10984-015-9184-3