Predictive Analysis of factors affecting Life Skills and its Effect on Holistic Development of Students in higher education

Abstract

The current paper aims at researching the issue of predictive analysis to determine which determinants play a major role in shaping life skills and their implication on the overall development of students in higher education. Critical thinking; communication, emotional intelligent, adaptability, decision-making, among other life skills have become overly important in the overall development of the student in an academic environment that has increasingly become competitive and dynamic. This research will also be applied in analyzing the impact of various academic, socio-economic, psychological and institutional factors on the developmental process of such life skills. The research will have a quantitative research design where structured questionnaires will be given to the students at institutions of higher learning. The strength and the direction of the relationship between the independent variables (teaching methodologies, extracurricular participation, digital exposure and family background) and the dependent variables (definers of life skills and holistic development) in assessing the relationship are also determined through the use of statistical techniques like regression analysis and predictive modeling. The findings that follow are that experiential or learning, institutional support and engagement in co-curricular activities result in a massive scooping of life skills that influence the cognitive, emotional and social development of students positively. Also, predictive models, created in this study, assist in achieving an insight into the most effective factors influencing life skills that teachers and policymakers can use to create particular interventions. The paper highlights the mediation qualities of life skills towards achievement of holistic development and the need to integrate the program of skill-based education in the higher education curriculum. The study contributes to existing knowledge, as it presents a predictive analytics model which can be used to associate predictive analytics with academic achievement and hence support the evolvement of evidence based policy within a higher education institution.