A Conjoint Analysis on Students’ Choice of Mathematics Instruction
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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. Demographic and psychographic segmentation showed that freshmen female BSEDM and 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.
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References
Agbenyegah, D. (2014). Tell me what you want: Conjoint analysis made simple using SAS. Retrieved on October 18, 2015 from http://www.mwsug.org
Alijosiene S., & Gudonaviciene R. (2010). Analyzing price-quality relationship using conjoint analysis. Retrieved on August 20, 2015 from http://internet.ktu.lt
Amarchinta, H. (2006). Multi-Attribute optimization based on conjoint analysis. Retrieved on July 21, 2015 from http://cecs.wright.edu/cepro/docs/thesis
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