Career Decision Support System Using Multiple Intelligences

Tracy N. Tacuban

Abstract


A person’s success is primarily attributed to the just decisions he takes at crucial points in life, and one of these is choosing a bachelor’s degree. The purpose of this study is to design and evaluate the effectiveness of Career Decision Support System Using Multiple Intelligences (CDSS-MI) in determining the dominant intelligence of the student. The general concept is for the system to determine the dominant intelligence of the student, suggest the courses that are most compatible to the student’s dominant intelligence, and verify whether the personal choice of the respondent is included in the list of courses. CDSS-MI is akin to knowledge-driven DSS where knowledge elicitation from guidance counselors provided the system’s knowledge base. Using Visual Basic as the development tool, the researcher chose an object-oriented approach in building the system’s rule-base. The list of courses suggested by the system was validated by 281 students of the Information and Communication Technology Department of Iloilo Science and Technology University in Lapaz, Iloilo City. Majority of the respondents affirmed that the list of courses suggested by the system includes their actual preferred courses, thus, proving that the system can effectively guide the students in making a just and proper decision.


Keywords


Career Choice, Multiple Intelligences, Decision Support System

References


Armstrong, T. (2009). Multiple Intelligence in the Classroom (3rd edn.). ASCD Product.

Aslam, M. Z. and Khan, N. R. (2011). Proposed decision support system/expert system for guiding fresh

students in selecting a faculty in Gomal University. Industrial Engineering Letters, 1 (4), 33–40.

Balogun, V. F. & Thompson, A. F. 2009. Career master: A decision support system (DSS) for guidance and

counseling in Nigeria. Pacific Journal of Science and Technology, 10(2), 337–354.

Ellen, Stephanie. (n.d). Slovin’s Formula sampling techniques. Retrieved from https:/ /www. scribd. com/ doc/124438831/ The- Slovin- Formula.

Hopper, C. H. (2014). Practicing college learning strategies. Retrieved from https:// books. google. com.ph/ books? isbn=1305537971.

Johnston, V. (1998). Why do first year students fail To progress to their second year? An academic staff perspective. Retrieved from http:// www. leeds. ac. uk/ educol/ documents/ 000000453.htm.

Louw, R. E. (2002). Decision support systems. Retrieved from http:// www. umsl. edu/ sauterv/ analysis/ 488 f02 papers/ dss. html

McHugh, M. L. (2013). The chi-square test of independence. Retrieved from http:// www. ncbi. nlm. nih. gov/ pmc/ articles/ PMC3900058/

Pabalinas, S. T. et al. (2015). Career choice: An analysis of multiple intelligences and socio-environmental factors. Retrieved from icehm.org/ upload/ 2466ED0315092. Pdf.

Power, D. J. (2010). Building knowledge driven DSS and mining data. Retrieved from http:// dssresources. com/ subscriber/ password/ dssbookhypertext/ ch10/ contents.html.

Pressman, R. S. (2010). Software engineering : A practitioner’s approach (7th edn). McGraw-Hill Companies, Inc.

Shearer, B. & Luzzo, D. A. (2009). Exploring the Application of multiple intelligences theory to career counseling. Retrieved from http:// onlinelibrary. wiley. com/ doi/ 10.1002/j.2161-0045. 2009. tb00169.x/ abstract.

Tutorialspoint. (2016). Artificial Intelligence Fuzzy Logic Systems. Retrieved from http://www. tutorialspoint .com/ artificial intelligence

/artificial intelligence fuzzy logic systems .htm.

Zwibelman, B. B. & Plant, R. T. (1994). Choosing a college major: A prototype decision support system. Computers in Human Behavior, 10 ( 3), 231–242.


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