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HomeDAVAO RESEARCH JOURNALvol. 11 no. 2 (2016)

A Conjoint Analysis on Students’ Choice of Mathematics Instruction

Doris Del Salasinas

Discipline: Education

 

Abstract:

Individuals’ differences in terms of their interest, intellectual capacity, and valuing excellent teaching of mathematics contribute to the toughness in designing and developing effective strategies. This study analyzes students’ preference of mathematics instruction using conjoint analysis. It was sometimes called “trade-off analysis” which reveals on how individuals’ draw critical judgements on a certain product or service. There are 271 respondents in the survey asking them to choose different factors of mathematics instruction. The respondents ranked the four attributes as follows: instructional method, assessment type, instructional medium/media, and instructional activity. The set of instruction that students prefer is the instruction profile that is composed of lecture discussion, chalk/marker and board, problem solving, and learner focused. Demographic and psychographic segmentation showed that freshmen female BSEDMand sophomore female BSCE students, BSEDM who are members of SGO with an average GPA of 1.99, and non-scholar BSCE students choose all instruction profiles. Another group of students taking BSMBF and are STUFAPS scholars with average GPA of 1.95 chose instruction Profile 1 and the other group of senior and sophomore male non-scholar civil engineering students choose instruction Profile 5. Instruction profile 1 is composed of lecture-discussion, chalk/marker and board, problem solving, and learner focused while instruction Profile 5 is composed of cooperative learning, chalk/marker and board, problem solving, and learner focused. In the simulation, the profile that gained the greatest share is instruction Profile 6 which is composed of lecture-discussion, chalk/marker, solving mathematics expression, and learner-focused. Evident information found in this study conclude that students look forward for a set of instruction that would make the class interactive, challenging, and of course informative.



References:

  1. Agbenyegah, D. (2014). Tell me what you want: Conjoint analysis made simple using SAS. Retrieved on October 18, 2015 from
  2. Alijosiene S., & Gudonaviciene R. (2010). Analyzing price-quality relationship using conjoint analysis. Retrieved on August 20, 2015 from
  3. Amarchinta, H. (2006). Multi-Attribute optimization based on conjoint analysis. Retrieved on July 21, 2015 from
  4. Black, P. & Wiliam, D (1998). Assessment and classroom learning. Assessment in education. Retrieved on October 14, 2015 from .
  5. Bonilla, T. (n.d). Analysis of consumer preferences toward 100% fruit juice packages and labels. Retrieved on August 25, 2015 from . DOI:
  6. Chrzan K. & Orme, B. (2000). An overview and comparison of design strategies for choice-based conjoint analysis. Retrieved on October 13, 2015 from
  7. Crowell, E. (2015). Instructional strategies: Hands-on, interactive, expository & collaborative. Retrieved on October 14, 2015 from strategies hands on interactive expository collaborative.html
  8. Day, M. (n.d.). A synthesis of research on effective mathematics instruction. Retrieved on September 1, 2015 from Day.pdf
  9. Florez, L.J. (2009). Teacher variables and student mathematics learning related to manipulative use. Retrieved on September 1, 2015 from .
  10. FSU Handbook, (2011). Instruction. Retrieved on January 29, 2016 from
  11. Garcia-Ros R., Perez, F., & Talaya, I. (2008). New university students’ instructional preferences and how these relate to learning styles and motivational strategies. Retrieved on June 21, 2015 from
  12. Green, P. & Srinivasan, V. (1978). Conjoint analysis in consumer research–issues and outlook. Retrieved on July 4, 2015 from . DOI:
  13. Hagos, L. & Dejarme, E. (2008). Enhancing curriculum in Philippine schools in response to global community challenges. Retrieve on October 13, 2015 from
  14. Hodara, M. (2013). Improving students’ college math readiness. Retrieved on June 21, 2015 from
  15. Horng E. (2005). Retaining teachers by understanding the tradeoffs they make: An application for conjoint analysis in educational reform. Retrieved on August 25, 2015 from
  16. Hudson P., Miller S., & Butler F., (2006). Adapting and merging explicit instruction within reform based mathematics classrooms. Retrieved on September 1, 2015 from
  17. Hunt, N. & Tyrrell, S. (2001). Stratified sampling. Retrieved on August 8, 2015 from
  18. IBM SPSS 20 (2011). Retrieved on February 1, 2016 at
  19. Interpca (n.d). Interpreting the results of conjoint analysis. Retrieved on March 31, 2016 from
  20. Kanetkar, V., (n.d.). Choice and discrete conjoint design for pricing research. Retrieved on January 29, 2016 from
  21. Krahmer T., & Staht E. (n.d.). An illustration of conjoint analysis in educational research with measuring epistemological judgments. Retrieved on August 25, 2015 from
  22. Kuhfeld, W. (2010). Conjoint analysis. Retrieve on July 4, 2014 from
  23. Kuzmanovic M., Savic G., Popovic M., & Martic M. (2012). A New approach to evaluation of university teaching considering heterogeneity of students’ preferences. DOI: 012 9596 2
  24. Lithner, J. (2011). Education inquiry. Retrieved on September 27, 2015 from .
  25. Lonial S., Menezes D., and Zaim S., (2000). Identifying purchase driving attributes and market segments for PCs using conjoint and cluster analysis. Retrieved on January 29, 2016 from
  26. Louviere J., Flynn T., & Carson R. (2010). Discrete choice experiments are not conjoint analysis: Journal of choice modeling. Retrieve on August 25, 2015 from
  27. Macro Consulting Inc. (2014). A method for handling a large number of attributes in full profile trade-Off studies. Retrieved on October 18, 2015 from
  28. Makeshwari, V.K. (2013). Expository teaching - A direct instructional strategy. Retrieved on October 14, 2015 from
  29. Petrina (nd). Instructional methods and learning styles. Retrieved on June 21, 2015 fromhttp://people.uwplatt.edu/~steck/Petrina%20Text/Chapter%204.pdf.
  30. Popovic, M., Vagic M., Kuzmanovic M., & An膽elkovic Labrovic J. (2015). Understanding heterogeneity of students’ preferences towards English medium instruction: A conjoint analysis approach. Retrieve on August 25, 2015 from DOI:
  31. Populus (nd). Conjoint analysis. Retrieved on July 4, 2015 from
  32. Protheroe, N. (2007). What mathematics instruction looks like? Retrieved on September 1, 2010 from .
  33. Qualtrics (2011). Conjoint analysis: Explaining full profile and self-explicated approaches. Retrieved on July 4, 2015 from
  34. Safer N., & Fleischman S. (2005). Research matters / how student progress monitoring improves instruction. Retrieved on July 20, 2015 from
  35. Sawtooth Software Inc., (2013). The CBC system for conjoint-based analysis. Retrieved on June 5, 2015 from
  36. Siegler R. (1983). The Psychology of Mathematics for Instruction, vol. 91 p. 374-377. Retrieved on June 19, 2015 from DOI:
  37. Stipek D., Givvin K., Salmon J., & MacGyvers V. (2000). Teachers’ beliefs and practices related to mathematics instruction. Retrieved on September 1, 2015 from .
  38. Suh, J. (2005). Third graders’ mathematics achievement and representation preference using virtual and physical manipulative for adding fractions and balancing equations. Retrieved on September 1, 2015 from
  39. Survey Analytics (2016). Conjoint analysis market research. Retrieved on January 29, 2016 from .
  40. Tavakol M. & Dennick R. (2011). Making sense of cronbach’s alpha. Retrieved on March 32, 2016 from
  41. W5 Insights (2009). W5 on conjoint analysis. Retrieved on July 21, 2015 from insight.com/cms/wp-content/uploads/W5-on-Conjoint-Analysis.pdf
  42. Wedel, M. & Kamakura, W. (2000). Product- specific unobservable bases. Conjoint analysis. Market conceptual and methodological foundations Boston. Retrieved on June 19, 2015 from
  43. Wilson, M. (2011). Students’ learning style preferences and teachers’ instructional strategies: Correlations between matched styles and academic achievement. Retrieved on September 1, 2015 from