Matematik Emporium Modeli ve Üniversite Düzeyi Cebir Derslerinde Öğrenmenin Psikososyal Faktörleri

Öz

Bu makalede geleneksel ve matematik Emporium ile yeniden-tasarlanmış üniversite düzeyi cebir derslerinde öğrenmenin psikososyal faktörlerindeki değişimler incelenmiştir. Deney-kontrol grubu öntest-sontest yarı deneysel modeli kullanılan nicel çalışmanın örneklemi 224 öğrenciden oluşmaktadır. Araştırma sonuçları her iki öğretim modelinde de öğrencilerin teknoloji-destekli matematik derslerine karşı tutumlarında, matematikte başarılı olabilmelerine ilişkin inançlarında ve matematiğe karşı genel tutumlarında anlamlı bir değişim olduğunu ortaya koymuştur. Araştırma öğrencilerin matematiğe karşı tutumlarının, matematik öğrenmek için dış motivasyonlarının, matematik derslerindeki öğrenme yaşantılarından kaynaklanan memnuniyetin dönem boyunca sadece yeniden tasarlanan derslerde anlamlı şekilde değiştiğini ortaya çıkarmıştır. Geleneksel yöntemle öğretilen ve yeniden tasarlanmış (Emporium) şekilde öğretilen matematik dersleri karşılaştırıldığında ise her iki eğitim ortamında sadece öğrencilerin matematiğe karşı tutumlarının ve teknoloji destekli matematik eğitimine karşı tutumlarının anlamlı şekilde farklı olduğu belirlenmiştir.  

Anahtar Kelimeler

• Matematik Emporium,Cebir,Derslerin yeniden tasarımı,Öğretim teknolojileri

The Mathematics Emporium Model and Psychosocial Factors of Learning in College Algebra

Öz

In this manuscript, changes in psychosocial factors of learning were examined in two forms of college algebra: traditionally-taught and redesigned using Math Emporium model. Sample of this quasi experimental quantitative study in which experiment-control group pretest-posttest design is used consists of 224 students. Results of the study revealed that attitudes toward technology-supported mathematics, beliefs about being able to do mathematics, and overall attitudes toward mathematics changed significantly in both educational settings. Attitudes toward mathematics, extrinsic motivation to learn mathematics, and satisfaction from mathematics learning experiences, from technology-supported mathematics, and from mathematics instruction changed significantly in redesigned sessions throughout the semester. Attitudes toward mathematics and attitudes toward technology-supported mathematics were significantly different when traditionally-taught and the redesigned college algebra sessions compared. 

Anahtar Kelimeler

• College Algebra,Math Emporium,Course Redesign,Instructional Technologies.

Kaynakça

1. (2011). Computer-aided college algebra: Learning components that students find beneficial. MathAMATYC Educator, 2(2), 12-19.
2. Allen, M., Bourhis, J., Burrell, N., & Mabry, E. (2002). Comparing student satisfaction with distance education to traditional classrooms in higher education: A meta-analysis. The American Journal of Distance Education, 16(2), 83-97.
3. Alt, A. C. (2017). Supporting Math Emporium Students' Learning Through Short Instructional Opportunities (Unpublished doctoral dissertation), Bowling Green State University, Bowling Green, OH.
4. Biner, P., Barone, N., Welsh, K., & Dean, R. (1997). Relative academic performance and its relation to facet and overall satisfaction with interactive telecourses. Distance Education, 18(2), 318-326.
5. Brown, S. (2011), Measure of shape: Skewness and kurtosis. Personal collection of S. Brown, Tompkins Cortland Community College, Dryden, NY: Oak Road Systems.
6. Burn, H. E. (2012). Factors that shape curricular reasoning about college algebra reform. MathAMATYC Educator, 4(1), 23-28.
7. Cardetti, F., & McKenna, P. J. (2011). In their own words: Getting pumped for calculus. PRIMUS, 21(4), 351-363.
8. Cousins-Cooper, K., Staley, K. N., Kim, S., & Luke, N. S. (2017). The effect of the Math Emporium instructional method on students' performance in college algebra. European Journal of Science and Mathematics Education, 5(1), 1-13.

9. Csikszentmihalyi, M., & Wong, M. M. H. (2014). Motivation and academic achievement: The effects of personality traits and the quality of experience. In M. Csikszentmihalyi (Ed.) Applications of Flow in Human Development and Education: he Collected Works of Mihaly Csikszentmihalyi. (pp. 437-465). Dordrecht: Springer Netherlands.
10. Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1(1), 16-29.
11. Demiroz, E. (2016). The Mathematics Emporium: Infusion of instructional technology into college level mathematics and psychosocial factors of learning (Unpublished doctoral dissertation). University of Missouri – Kansas City, Kansas City, MO.
12. Ernest, P. (1991). The philosophy of mathematics education. London: Falmer Press. Gordon, S. P. (2008). What's wrong with college algebra? Primus, 18(6), 516-541.
13. Gunawardena, C. N., Linder-VanBerschot, J. A., LaPointe, D. K., & Rao, L. (2010). Predictors of learner satisfaction and transfer of learning in a corporate online education program. The American Journal of Distance Education, 24(4), 207-226.
14. Haladyna, T., Shaughnessy, J., & Shaughnessy, J. M. (1983). A causal analysis of attitude toward mathematics. Journal for Research in Mathematics Education, 14(1), 19-29. http://doi.org/10.2307/748794
15. Haver, W., Small, D., Ellington, A., Edwards, B., Kays, V. M., Haddock, J., & Kimball, R. (2007). College algebra. In V. J. Katz (Ed.). (2007). Algebra: Gateway to a technological future, (pp. 33-40). Washington, DC: Mathematical Association of America.
16. Heafner, T. (2004). Using technology to motivate students to learn social studies. Contemporary Issues in Technology and Teacher Education, 4(1), 42-53.
17. Hegeman, J. S. (2015). Using instructor-generated video lectures in online mathematics courses improves student learning. Online Learning, 19(3), 70-87.
18. House, J. D., & Telese, J. A. (2008). Relationships between student and instructional factors and algebra achievement of students in the United States and Japan: An analysis of TIMSS 2003 data. Educational Research and Evaluation, 14(1), 101-112.
19. Kearns, L. E., Shoaf, J. R., & Summey, M. B. (2004). Performance and satisfaction of second-degree BSN students in web-based and traditional course delivery environments. Journal of Nursing Education, 43(6), 280.
20. Klein, H. J., Noe, R. A., & Wang, C. (2006). Motivation to learn and course outcomes: The impact of delivery mode, learning goal orientation, and perceived barriers and enablers. Personnel Psychology, 59(3), 665-702.
21. Knowles, E., & Kerkman, D. (2007). An investigation of students’ attitude and motivation toward online learning. Insight: A Collection of Faculty Scholarship, 2, 70-80.
22. Larson, D. K., & Sung, C. H. (2009). Comparing student performance: online versus blended versus face-to-face. Journal of Asynchronous Learning Networks, 13(1), 31-42.
23. Leedy, P. D., & Ormrod, J. E. (2010). Practical research: Planning and design (9th ed.). Upper Saddle River, NJ: Prentice Hall.
24. Levy, Y., & Ellis, T. J. (2011). A guide for novice researchers on experimental and quasiexperimental studies in information systems research. Interdisciplinary Journal of information, knowledge, and management, 6(1), 151-161.
25. McLeod, D. B. (1992). Research on affect in mathematics education: A reconceptualization. In D.A. Grouws (Ed.), Handbook of research on mathematics teaching and learning (pp. 575-596). New York: Macmillan.
26. Middleton, J. A., & Spanias, P. A. (1999). Motivation for achievement in mathematics: Findings, generalizations, and criticisms of the research. Journal for Research in Mathematics Education, 30(1), 65-88.
27. Missouri Statewide Course Redesign Initiative-MSCRI (2011). Final project plan. Retrieved from http://info.umkc.edu/courseredesign/umkc-course-redesign/ on 03 June 2016.
28. Nayak, S. (2017). Transforming college algebra. Retrieved from https://impact.oregonstate.edu/2017/12/transforming-math-111/ on February 18, 2020.
29. NCAT (2015). Six models of course redesign. Retrieved from http://www.thencat.org/PlanRes/R2R_ModCrsRed.htm on 03 June 2016.
30. NCAT (2015a). Improving the quality of student learning: University of Massachusetts-Amherst, Retrieved from http://www.thencat.org/PCR/R2/UMA/UMA_PR1.htm on 03 June 2016.
31. NCAT (2015b). Improving the quality of student learning: Tallahassee Community College, http://www.thencat.org/PCR/R3/TCC/TCC_PR1.htm/ on 03 June 2016.
32. NCAT (2015c). Summaries of other course redesign efforts, Retrieved from http://www.thencat.org/CommGrd/OCRP.htm on 03 June 2016.
33. NCAT (2015d). Impact on students: University of Central Florida, Retrieved from http://www.thencat.org/PCR/R1/UCF/UCF_PR1.htm on 03 June 2016.
34. NCAT (2015e). Impact on students: University of Alabama, Retrieved from http://www.thencat.org/PCR/R2/UA/UA_FR1.htm on 03 June 2016.
35. Papanastasiou, C. (2000). Effects of attitudes and beliefs on mathematics achievement. Studies in Educational Evaluation, 26(1), 27-42.
36. Parsons, S. J. (2004). Overcoming poor failure rates in mathematics for engineering students: A support perspective. Newport: Harper Adams University College. Retrieved from http://www.hull.ac.uk/engprogress/Prog3Papers/Progress3%20Sarah%20Parsons.pdf on 02 June 2016.
37. Pierce, R., Stacey, K., & Barkatsas, A. (2007). A scale for monitoring students’ attitudes to learning mathematics with technology. Computers & Education, 48(2), 285-300.
38. Roach, V., & Lemasters, L. (2006). Satisfaction with online learning: A comparative descriptive study. Journal of Interactive Online Learning, 5(3), 317-332. Rochowicz Jr, J. A. (1996). The impact of using computers and calculators on calculus instruction: Various perceptions. Journal of Computers in Mathematics and Science Teaching, 15(4), 423-435.
39. Rugutt, J., & Chemosit, C. C. (2009). What motivates students to learn? Contribution of student-to-student relations, student-faculty interaction and critical thinking skills. Educational Research Quarterly, 32(3), 16-28.
40. Small, D. (2006). College algebra: A course in crisis. MAA Notes, 69, 83-89.
41. Sundre, D., Barry, C., Gynnild, V., & Ostgard, E. T. (2012). Motivation for achievement and attitudes toward mathematics instruction in a required calculus course at the Norwegian University of Science and Technology. Numeracy, 5(1), 1-18.
42. Thompson, C. J., & McCann, P. (2010). Redesigning College Algebra for Student Retention: Results of a Quasi-Experimental Research Study. MathAMATYC Educator, 2(1), 34-38.
43. Tocci, C. M., & Engelhard, G. Jr. (1991). Achievement, parental support, and gender differences in attitudes toward mathematics. Journal of Educational Research, 84(5), 280-286.
44. Thompson, C. J., & McCann, P. (2010). Redesigning college algebra for student retention: Results of a quasi-experimental research study. MathAMATYC Educator, 2(1), 34-38.
45. Twigg, C. A. (2003). Improving quality and reducing cost: designs for effective learning. Change, 35(4), 22-29.
46. Twigg, C. A., & National Center for Public Policy and Higher Education. (2005). Course redesign improves learning and reduces cost. Policy alert. San Jose, CA: National Center for Public Policy and Higher Education. Retrieved from http://files.eric.ed.gov/fulltext/ED518668.pdf / on 03 May 2016.
47. Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F. (1992). The academic motivation scale: A measure of intrinsic, extrinsic, and amotivation in education. Educational and Psychological Measurement, 52(4), 1003-1017.
48. Warner, R. M. (2014). Applied statistics: From bivariate through multivariate techniques. Thousand Oaks, CA: SAGE Publications.
49. Waugh, R. F. (2002). Creating a scale to measure motivation to achieve academically: Linking attitudes and behaviours using Rasch measurement. British Journal of Educational Psychology, 72(1), 65-86.
50. Webel, C., Krupa, E., & McManus, J. (2015). Benny goes to college: is the “math emporium” reinventing individually prescribed instruction. The MathAMATYC Educator, 6(3), 4-13.
51. Webel, C., Krupa, E. E., & McManus, J. (2017). The Math Emporium: Effective for whom, and for what?. International Journal of Research in Undergraduate Mathematics Education, 3(2), 355-380.
52. Wilder, S., & Berry, L. (2016). Emporium Model: The key to content retention in secondary math courses. Journal of Educators Online, 13(2), 53-75.