IMPLICATIONS OF PENTAGON THEORY OF CONTEXTUALIZED MULTIPLE INTELLIGENCES (CMIs) FOR FUTURE JOB PROSPECTS OF UNIVERSITY STUDENTS

Abstract

The Purpose of the study was to test the Pentagon theory of Contextualized Multiple Intelligences (CMIs) in the context of fourth industrial revolution. It also explored the awareness of students regarding the trends and challenges of future job market. Lastly, skills and competences required by graduate to meet the complexities of future job market were also investigated. This study followed the critical paradigm, therefore sequential explanatory mixed method research design was used. All the masters’ level students, heads of departments/ senior faculty members and experts of social sciences were considered as population for this study. Multistage sampling techniques was employed to select the sample size for quantitative phase (n=786). For qualitative phase, purposive sampling technique was used and 12 experts of social sciences were selected. Self-developed questionnaires were used to collect data from respondents. Contextualized Multiple Intelligences questionnaire has 32 statements under 6 factors with Cronbach’s Alpha value of 0.92. Similarly, Future Job Market Prospects questionnaire has 20 statements under 4 factors with Cronbach’s Alpha value of 0.91. Semi-structured Interview protocol was developed and used during the qualitative phase. Study revealed positive and strong correlation between the contextualized multiple intelligences and future job market prospects. However, the public universities students showed relatively larger correlation (r=.631, p=.000) as compared to private universities students (r=.556, p=.000). Furthermore, male and female students have differences in their contextualized multiple intelligences. Besides, students exhibited high level of understanding regarding the need of market driven content and importance of employability skills. Experts (Qualitative data analysis) highlighted the need for active role of universities in re-skilling students with contextualized multiple intelligences to make them more desirable for employability.