Bu araştırmanın amacı, yeniden tasarlanan üniversite düzeyi cebir derslerinde tutumlar, motivasyon ve memnuniyet gibi matematik öğrenmenin psikososyal faktörleri ile akademik başarı arasındaki yordamsal ilişkinin incelenmesidir. Yeniden tasarlanan derslere ilişkin hazırlanan değerlendirme raporları, üniversite düzeyinde matematik giriş dersleri de dahil olmak üzere geleneksel olarak öğretilen derslerle eşdeğer ve / veya daha iyi düzeyde akademik başarı elde edildiğini göstermektedir. Ancak, elde edilen eşit düzeyde ya da daha yüksek bir akademik başarının nedenleri literatürde tam olarak belgelenmemiştir. Bu bağlamda, Emporium modeli kullanılarak tasarlanan üniversite düzeyi cebir dersinin akademik başarısı bu araştırma çalışmasının odak noktası olarak seçilmiştir. Bu araştırma makalesi kapsamında, matematik öğreniminin psikososyal faktörlerine ek olarak üniversite cebiri bağlamında matematik başarısı da incelenmiştir. Psikososyal değişkenlere ilişkin veriler araştırmacı tarafından geliştirilen likert tipi ölçek ile akademik başarı verileri ise cebir dersi final sınavı notlarından elde edilmiştir. Verilerin analizinde hiyerarşik çoklu regresyon analizinden yararlanılmıştır. Araştırmanın sonuçları, sadece matematik öğretiminden memnuniyetin üniversite cebirinde matematik başarısının anlamlı bir yordayıcısı olduğunu göstermiştir; öğrenen motivasyonu ve memnuniyeti matematiğe yönelik tutumun önemli yordayıcıları olarak belirlenmiş ve matematiğe yönelik tutum, giriş düzeyinde yeniden tasarlanan üniversite cebir derslerinde öğrenci memnuniyeti ve motivasyon arasındaki ilişkide aracı değişken rolünü üstlenmiştir.
The aim of this study is to examine the procedural relationship between the psychosocial factors of mathematics learning such as attitudes, motivation and satisfaction and academic achievement in redesigned college-level algebra course sections. Evaluation reports on the redesigned courses show that they have achieved a level of academic achievement equivalent to and / or better than traditionally taught courses, including university-level mathematics introductory courses. However, the reasons for equal or higher academic achievement are not fully documented in the literature. In this context, the academic success of the university-level algebra course designed using the Emporium model was chosen as the focus of this research study. In this manuscript, in addition to the psychosocial factors of mathematics learning, mathematics achievement in the context of university algebra was also examined. The data related to the psychosocial variables were obtained from a likert scale developed by the researcher, and academic achievement data from the final exam grades of the algebra course. Hierarchical multiple regression analysis was used to analyze the collected data. The results of the study indicaed that satisfaction from mathematics teaching was the only significant predictor of mathematics achievement in college-level algebra; learner motivation and satisfaction were determined as important predictors of attitude towards mathematics, and attitude towards mathematics played the role of mediating variable in the relationship between student satisfaction and motivation in introductory level redesigned university algebra courses.
___
Al Khatib, S. A. (2010). Meta-cognitive self-regulated learning and motivational beliefs as predictors of college students’ performance. International Journal for Research in Education, 27(8), 57-72.
Aldridge, J. M., Fraser, B. J., Taylor, P. C., & Chen, C. C. (2000). Constructivist learning environments in a cross-national study in Taiwan and Australia. International Journal of Science Education, 22(1), 37-55. DOI:10.1080/095006900289994.
Baker, E.L., Gearhart, M., & Herman, J.L. (1994). Evaluating the apple classrooms of tomorrow. In E.L. Baker, & H.F. O'Neil, Jr. (Eds.). Technology assessment in education and training (pp. 173-198). Lawrence Erlbaum.
Becker, K., & Maunsaiyat, S. (2004). A comparison of students' achievement and attitudes between constructivist and traditional classroom environments in Thailand vocational electronics programs. Journal of Vocational Education Research, 29(2), 133-153.
Bennett, G., & Green, F. P. (2001). Student learning in the online environment: No significant difference?. Quest, 53(1), 1-13. DOI:10.1080/00336297.2001.10491727.
Boaler, J. (1997a). Experiencing school mathematics: Teaching styles, sex, and setting. Buckingham: Open University Press.
Boaler, J. (1997b). Reclaiming school mathematics: The girls fight back. Gender and Education, 9(3), 285-305. DOI: 10.1080/09540259721268.
Bolliger, D. U. (2004). Key factors for determining student satisfaction in online courses. International Journal on E-learning, 3(1), 61-67.
Cetin-Dindar, A. (2016). Student motivation in constructivist learning environment. Eurasia Journal of Mathematics, Science & Technology Education, 12(2), 233-247. DOI: 10.12973/eurasia.2016.1399a.
Chiu, M. M., & Xihua, Z. (2008). Family and motivation effects on mathematics achievement: Analyses of students in 41 countries. Learning and Instruction, 18(4), 321-336. DOI: 10.1016/j.learninstruc.2007.06.003.
Chung, J. (1991). Collaborative learning strategies: The design of instructional environments for the emerging new school. Educational Technology, 31(12), 15-22. https://www.jstor.org/stable/44427555
Covington, M. V. (2000). Goal theory, motivation, and school achievement: An integrative review. Annual Review of Psychology, 51(1), 171-200. DOI: 10.1146/annurev.psych.51.1.171.
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 (pp. 437-465). Springer Netherlands. DOI: 10.1111/j.1467-6494.1991.tb00259.x.
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. https://hdl.handle.net/10355/50121
Dethlefs, T. M. (2002). Relationship of constructivist learning environment to student attitudes and achievement in high school mathematics and science. (Unpublished doctoral dissertation). University of Nebraska – Lincoln. https://www.elibrary.ru/item.asp?id=6710574
Douglas, J., Douglas, A., & Barnes, B. (2006). Measuring student satisfaction at a UK university. Quality Assurance in Education, 14(3), 251-267. DOI: 10.1108/09684880610678568
Driver, R. (1989a). Students’ conceptions and the learning of science. International Journal of Science Education, 11(5), 481-490. DOI: 10.1080/0950069890110501
Driver, R. (1989b). Changing conceptions, In P. Adey (Ed.) Adolescent development and school science (pp. 79-99), Falmer Press. http://www.cdbeta.uu.nl/tdb/fulltext/Tdbeta_6_2_Driver_1988.pdf
Driver, R., & Oldham, V.A. (1986). A constructivist approach to curriculum development in science, Studies in Science Education, 13, 105-122. DOI: 10.1080/03057268608559933
Duit, R., & Treagust, D. (1998). Learning in science: From behaviourism towards social constructivism and beyond, In B.J. Fraser & K.G. Tobin (Eds.) International Handbook of Science Education (pp. 3-25), Kluwer Academic Publishers
Elliott, K. M., & Healy, M. A. (2001). Key factors influencing student satisfaction related to recruitment and retention. Journal of Marketing for Higher Education, 10(4), 1-11. DOI: 10.1300/J050v10n04_01.
Enonbun, O. (2010). Constructivism and web 2.0 in the emerging learning era: A global perspective. Journal of Strategic Innovation and Sustainability, 6(4), 16-25. http://www.na-businesspress.com/JSIS/EnobunWeb.pdf
Ferren, A. S., & McCafferty, J. K. (1992). Reforming college mathematics. College Teaching, 40(3), 87-90. DOI: 10.1080/87567555.1992.10532222
Fok, A., & Watkins, D. (2008). Does a Critical Constructivist Learning Environment Encourage a Deeper Approach to Learning?. The Asia-Pacific Education Researcher, 16(1), 1-10. http://www.dlsu.edu.ph/wpcontent/uploads/pdf/research/journals/taper/pdf/200706/Fok-watkins.pdf
Fortier, M. S., Vallerand, R. J., & Guay, F. (1995). Academic motivation and school performance: Toward a structural model. Contemporary Educational Psychology, 20(3), 257-274. DOI: 10.1006/ceps.1995.1017
Geary, D. C. (2011). Cognitive predictors of achievement growth in mathematics: A 5-year longitudinal study. Developmental Psychology, 47(6), 1539. DOI: 10.1037/a0025510
Graunke, S. S., & Woosley, S. A. (2005). An exploration of the factors that affect the academic success of college sophomores. College Student Journal, 39(2), 367.
Guthrie, L. F., & Richardson, S. (1995). Turned on to language arts: Computer literacy in the primary grades. Educational Leadership, 53(2), 14-18. https://www.learntechlib.org/p/79775/
Hailikari, T., Nevgi, A., & Komulainen, E. (2008). Academic self‐beliefs and prior knowledge as predictors of student achievement in mathematics: A structural model. Educational Psychology, 28(1), 59-71. DOI: 10.1080/01443410701413753
Hannula, M. S. (2002). Attitude towards mathematics: Emotions, expectations and values. Educational Studies in Mathematics, 49(1), 25-46. DOI: 10.1023/A:1016048823497
Hatem, N. (2010). The effect of graphing calculators on student achievement in college algebra and pre-calculus mathematics courses. Available from ProQuest Dissertations & Theses A&I. (847259113). http://search.proquest.com/docview/847259113?accountid=14589
Hemmings, B., Grootenboer, P., & Kay, R. (2011). Predicting mathematics achievement: The influence of prior achievement and attitudes. International Journal of Science and Mathematics Education, 9(3), 691-705. DOI: 10.1007/s10763-010-9224-5
House, J. D. (2001). The predictive relationship between academic self-concept, achievement expectancies, and grade performance in college calculus. Journal of Social Psychology, 135(1), 111–112. DOI: 10.1080/00224545.1995.9711411
Johnston, J., Killion, J., & Oomen, J. (2005). Student satisfaction in the virtual classroom. The Internet Journal of Allied Health Sciences and Practice, 3(2), 1-7. https://nsuworks.nova.edu/ijahsp/vol3/iss2/6/
Kaufman, J. C., Agars, M. D., & Lopez-Wagner, M. C. (2008). The role of personality and motivation in predicting early college academic success in non-traditional students at a Hispanic-serving institution. Learning and Individual Differences, 18(4), 492-496. DOI: 10.1016/j.lindif.2007.11.004
Keller, J. M. (2008). First principles of motivation to learn and e3‐learning. Distance Education, 29(2), 175-185. DOI: 10.1080/01587910802154970
Kim, H. (2006). A model for predicting performance in introductory statistics courses. (Unpublished Doctoral Dissertation). Retrieved from https://etda.libraries.psu.edu/paper/7177/
Kim, H. B., Fisher, D. L., & Fraser, B. J. (1999). Assessment and investigation of constructivist science learning environments in Korea. Research in Science & Technological Education, 17(2), 239-249. DOI: 10.1080/0263514990170209
Kloosterman, P. (1991). Beliefs and achievement in seventh-grade mathematics. Focus on Learning Problems in Mathematics, 13(3), 3-15.
Krumrei-Mancuso, E. J., Newton, F. B., Kim, E., & Wilcox, D. (2013). Psychosocial factors predicting first-year college student success. Journal of College Student Development, 54(3), 247-266. DOI: 10.1353/csd.2013.0034
Kulik, J.A. (1994). Meta-analytic studies of findings on computer-based instruction. In E.L. Baker, & H.F. O'Neil, Jr. (Eds.). Technology assessment in education and training. Lawrence Erlbaum.
Larwin, K. H. (2010). Reading is fundamental in predicting math achievement in 10th graders. International Electronic Journal of Mathematics Education, 5(3), 131-145. https://www.iejme.com/download/reading-is-fundamental-in-predicting-math-achievement-in-10th-graders.pdf
Lipnevich, A. A., MacCann, C., Krumm, S., Burrus, J., & Roberts, R. D. (2011). Mathematics attitudes and mathematics outcomes of US and Belarusian middle school students. Journal of Educational Psychology, 103(1), 105. DOI: 10.1037/a0021949.
Lizzio, A., Wilson, K., & Simons, R. (2002). University students' perceptions of the learning environment and academic outcomes: Implications for theory and practice. Studies in Higher Education, 27(1), 27-52. DOI: 10.1080/03075070120099359
Lorenzo, G. (2012). Internet learning. Policy Studies, 1(1), 45-54. http://www.ipsonet.org/internet-learning/wp-content/uploads/2012/10/A-Research-Review-about-Online-Learning-wcover-page-copy1.pdf
Ma, X. (1997). Reciprocal relationships between attitude toward mathematics and achievement in mathematics. The Journal of Educational Research, 90(4), 221-229. DOI:10.1080/00220671.1997.10544576
Ma, X., & Kishor, N. (1997). Assessing the relationship between attitude toward mathematics and achievement in mathematics: A meta-analysis. Journal for Research in Mathematics Education, 28(1), 26-47. DOI:10.2307/749662
Mann, D., Shakeshaft, C., Becker, J., & Kottkamp, R. (1999). West Virginia's basic skills/ computer education program: An analysis of student achievement. Milken Family Foundation.
Martínez-Caro, E., & Campuzano-Bolarín, F. (2011). Factors affecting students’ satisfaction in engineering disciplines: Traditional vs. blended approaches. European Journal of Engineering Education, 36(5), 473-483. DOI: 10.1080/03043797.2011.619647
Mata, M. D. L., Monteiro, V., & Peixoto, F. (2012). Attitudes towards mathematics: Effects of individual, motivational, and social support factors. Child Development Research, 49(1), 1-10. http://downloads.hindawi.com/archive/2012/876028.pdf
McKenzie, K., & Schweitzer, R. (2001). Who succeeds at university? Factors predicting academic performance in first year Australian university students. Higher Education Research and Development, 20(1), 21-33. DOI: 10.1080/07924360120043621
McRobbie, C. J., & Fraser, B. J. (1993). Associations between student outcomes and psychosocial science environment. Journal of Educational Research, 87(2), 78-85. DOI: 10.1080/00220671.1993.9941170
Minato, S., & Yanase, S. (1984). On the relationship between students attitudes towards school mathematics and their levels of intelligence. Educational Studies in Mathematics, 15(3), 313-320.
Moody, S. (2010). Students’ attitudes toward their major discipline: Implicit versus explicit measure of attitude (Unpublished Doctoral dissertation) Western Carolina University, North Carolina.
Mouw, J. T., & Khanna, R. K. (1993). Prediction of academic success: A review of the literature and some recommendations. College Student Journal, 27(3), 328-336.
Muis, K. R. (2004). Personal epistemology and mathematics: A critical review and synthesis of research. Review of Educational Research, 74(3), 317-377. DOI: 10.3102/00346543074003317
Murayama, K., Pekrun, R., Lichtenfeld, S., & vom Hofe, R. (2013). Predicting long‐term growth in students' mathematics achievement: The unique contributions of motivation and cognitive strategies. Child Development, 84(4), 1475-1490. DOI: 10.1111/cdev.12036
Murray, J. (2013). The factors that influence mathematics achievement at the Berbice campus. International Journal of Business and Social Science, 4(10),150-164.
NCAT (2008) Six models of course redesign. http://www.thencat.org/PlanRes/R2R_ModCrsRed.htm
NCAT (2015) National Center for Academic Transformation. http://www.thencat.org
NCAT (2015a) Program in course redesign - Carnegie Mellon University, http://www.thencat.org/PCR/R2/CMU/CMU_Overview.htm
NCAT (2015b) Impact on students – Penn State University, http://www.thencat.org/PCR/R1/PSU/PSU_FR1.htm
NCAT (2015c) Impact on students – University of Wisconsin - Madison, http://www.thencat.org/PCR/R1/UWM/UWM_FR1.htm
NCAT (2015d) Program in course redesign – the University of Alabama, http://www.thencat.org/PCR/R2/UA/UA_Overview.htm
NCAT (2015e) Program in course redesign – the University of Idaho, http://www.thencat.org/PCR/R2/UId/UId_Overview.htm
NCAT (2015f) Program in course redesign – Virginia Tech, http://www.thencat.org/PCR/R1/VT/VT_Overview.htm
NCAT (2015g) The roadmap to redesign – Louisiana State University. http://www.thencat.org/R2R/Abstracts/LSU_Abstract.htm
NCAT (2015h) Program in course redesign – Rio Salado College. http://www.thencat.org/PCR/R1/RSC/RSC_Overview.htm
NCAT (2015i) Program in course redesign – Iowa State University. http://www.thencat.org/PCR/R3/ISU/ISU_Overview.htm
NCAT (2015j) Program in course redesign – The Ohio State University. http://www.thencat.org/PCR/R3/OSU/OSU_Overview.htm
NCAT (2015k) Tennessee board of regents: Developmental studies redesign initiative - Austin Peay State University. http://www.thencat.org/States/TN/Abstracts/APSU%20Algebra_Abstract.htm
NCAT (2015l) Missouri course redesign initiative. http://www.thencat.org/States/MO/Abstracts/UMKC%20College%20Algebra_Abstract.html
Palmer, D. (2005). A motivational view of constructivist‐informed teaching. International Journal of Science Education, 27(15), 1853-1881. DOI: 10.1080/09500690500339654
Papanastasiou, C. (2000). Effects of attitudes and beliefs on mathematics achievement. Studies in Educational Evaluation, 26(1), 27-42. https://eric.ed.gov/?id=EJ605934
Passolunghi, M. C., & Lanfranchi, S. (2012). Domain-specific and domain-general precursors of mathematical achievement: A longitudinal study from kindergarten to first grade. British Journal of Educational Psychology, 82(1), 42-63. DOI: 10.1111/j.2044-8279.2011.02039.x
Peters, M. L., & Kortecamp, K. (2010). Rethinking undergraduate mathematics education: The importance of classroom climate and self-efficacy on mathematics achievement. Current Issues in Education, 13(4), 1-34. cie.asu.edu
Pintrich, P. R., & Schunk, D. H. (1996). Motivation in education: Theory, research, and practice. Chapter, 5, 153-197.
Pintrich, P. R., & Schunk, D. H. (2002). Motivation in education: Theory, research, and applications (2nd ed.). Pearson Education.
Pintrich, P. R., Marx, R. W., & Boyle, R. A. (1993). Beyond cold conceptual change: The role of motivational beliefs and classroom contextual factors in the process of conceptual change. Review of Educational Research, 63(2), 167-199. DOI: 10.3102/00346543063002167
Pyzdrowski, L. J., Sun, Y., Curtis, R., Miller, D., Winn, G., & Hensel, R. A. (2013). Readiness and attitudes as indicators for success in college calculus. International Journal of Science and Mathematics Education, 11(3), 529-554. DOI: 10.1007/s10763-012-9352-1
Rickinson, B., & Rutherford, D. (1996). Systematic monitoring of the adjustment to university of undergraduates: A strategy for reducing withdrawal rates. British Journal of Guidance and Counselling, 24(2), 213-225. DOI: 10.1080/03069889608260410
Ridlon, C.L. (1999). How a problem centered curriculum enhanched the learning of low acheivers. In F. Hitt & M. Santos (Eds.), Proceedings of the Twenty First Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education, (pp. 582–587), ERIC Clearinghouse for Science, Mathematics, and Environmental Education, Columbus, OH,.
Robbins, S. B., Allen, J., Casillas, A., Peterson, C. H., & Le, H. (2006). Unraveling the differential effects of motivational and skills, social, and self-management measures from traditional predictors of college outcomes. Journal of Educational
Psychology, 98(3), 598-616.DOI: 10.1037/0022-0663.98.3.598 Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., & Carlstrom, A. (2004). Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychological Bulletin, 130(2), 261-288. DOI: 10.1037/0033-2909.130.2.261
Ryser, G. R., Beeler, J. E., & McKenzie, C. M. (1995). Effects of a computer-supported intentional learning environment (CSILE) on students' self-concept, self-regulatory behavior, and critical thinking ability. Journal of Educational Computing Research, 13(4), 375-385. DOI: 10.2190/XLGB-PXEC-BVXG-GRKN
Sartawi, A., Alsawaie, O. N., Dodeen, H., Tibi, S., & Alghazo, I. M. (2012). Predicting mathematics achievement by motivation and self-efficacy across gender and achievement levels. Interdisciplinary Journal of Teaching and Learning, 2(2), 59-77. http://files.eric.ed.gov/fulltext/EJ1056531.pdf
Scardamalia, M., & Bereiter, C. (1991). Higher levels of agency for children in knowledge building: A challenge for the design of new knowledge media. The Journal of the Learning Sciences, 1(1), 37-68. DOI: 10.1207/s15327809jls0101_3
Schoenfeld, A. H. (1989). Explorations of students' mathematical beliefs and behavior. Journal for Research in Mathematics Education, 20(4), 338-355. DOI: 10.2307/749440 Scott, P., Asoko, H., & Driver, R. (1992). Teaching for conceptual change: A review of strategies. In R. Duit, F. Goldberg, & H.
Niedderer (Eds.) Research in physics learning: Theoretical issues and empirical studies (pp. 310-329), Institute for Science Education at the University of Kiel.
Sinclaire, J.K. (2011). Student satisfaction with online learning: Lessons from organizational behavior. Research in Higher Education Journal, 11, 1-20.
Sivin-Kachala, J. & Bialo E. R. (1998). Report on the effectiveness of technology in schools, 1990-1997. Software Publisher's Association.
Solomoniduo, C. (2009). Constructivist design and evaluation of interactive educational software: A research-based approach and examples. Open Education – The Journal for Open and Distance Education and Educational Technology, 5(1), 6-24. DOI: 10.12681/jode.9693
The United States Immigration and Customs Enforcement (2012). STEM – Designated degree program list. Https://www.ice.gov/doclib/sevis/pdf/stem-list-2011.pdf
Wang, M., Haertel, G., & Walberg, H. (1993). Toward a knowledge base for school learning. Review of Educational Research, 63(3), 249-294. DOI: 10.3102/00346543063003249
Warner, R. M. (2014). Applied statistics: From bivariate through multivariate techniques. SAGE Publications
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. DOI: 10.1348/000709902158775
Weinstein, R. (1998). Promoting positive expectations in schooling. In N. Lambert & B. McCombs (Eds.), How students learn: Reforming schools through learner-centered education (pp. 81-111). American Psychological Association. DOI: 10.1037/10258-003
Wentzel, K. (1991). Social and academic goals at school: Motivation and achievement in context. In M. Maehr & P. R. Pintrich (Eds.), Advances in motivation and achievement: Goals and self-regulatory processes (7th ed., pp.185-121). JAI Press.
Wince, M. H., & Borden, V. M. (1995). When does student satisfaction matter? AIR 1995 Annual Forum Paper. (ERIC Document Reproduction Services No. ED 386 990). Association for Institutional Research. https://files.eric.ed.gov/fulltext/ED386990.pdf
Zan, R., & Di Martino, P. (2007). Attitude toward mathematics: Overcoming the positive/negative dichotomy. In B. Sriraman (Ed.), Beliefs and mathematics: Festschrift in honor of Guenter Toerner's 60th birthday (pp.197-214). Information Age Publishing.