09 SES 03 A, Research into the Predictive Validity of Individual and Contextual Characteristics for Academic Success and Returns on Education
Higher education plays a key role in developing the competitive advantage for the society. Societies are increasingly dependent on skilled and talented workforce. Singapore is facing challenges of rapidly changing population. Society is aging at the same time that the birth rate is falling (National Population and Talent Divisiion (NPTD) 2013). A key pressure felt throughout the educational system is the increasing participation rate of non-traditional students. The fastest growing population in higher education is the working adult student (Finn, 2011).
In 2012, the Singapore government affirms its commitment to continuing higher education sector by growing the University pathways (Ministry of Education [MOE], 2012). The restructuring of higher education pathways is to support the changing needs of Singapore’s workforce. SIM University is Singapore's only privately funded university dedicated to working adults. The University’s pathways provided for many to pursue lifelong learning and higher education while balancing career, family and social responsibilities. How does SIM University inform and modify their strategies and processes to serve the diverse needs of today’s multigenerational students? This study was developed in the context of the SIM University’s enrolment process.
With influx of adult learners in higher education, it is crucial to develop an insight of their enrolment factors and patterns. Enrolment management and decisions often include anecdotal analysis. Understanding the potential academic performance of incoming applicants can support decisions. The University has a voluminous amount of data; however there is a need to convert the data into strategic knowledge for decision-making processes (Koh and Chong, 2014). Student offices are inundated with data from a variety of applications and sources, such as student demographics, professional experience and academic background. Data mining have emerged in higher education’s ability to capture a vast amount of data. Huebner (2013), defined educational data mining as “an emerging discipline that focuses on applying data mining tools and techniques to educationally related data” to “develop models for improving learning
experiences and improving institutional effectiveness”.
This study investigated the entering characteristics and early engagement variables that predict the success of first-year adult learner in a Singapore higher education institution. Data mining techniques are employed as part of the knowledge discovery process in this study. In the study first year academic performance is defined by cumulative grade point average. Through this, the University aims to identify students or applicants who are academically at-risk as early as possible. Decision trees and models are used to construct so that appropriate strategies could be designed and implemented. Specifically the study aimed to achieve the following
- Identify characteristics that are available at enrolment variables of adult learners who are academically (CGPA) at-risk in higher education
- Construct models for early prediction of the academically at-risk (Sem1 CGPA<2.3)
- Evaluate these models using cross-validation
Making informed decisions require accurate data and analysis for evidenced-based insights and knowledge discovery. Building a predictive model to identify the potential at risk students or student success requires a combination of explicit knowledge base with sophisticated analytical approaches to uncover patterns, associations and relationships Modelling development is a reiterative stepwise process. Based on research objectives, the CHAID model was selected for optimum in complexity, accuracy, explanatory power over a range of variables.
The predictive model provided a consistent and equitable approach to enrolment management. Awareness of how potential students may perform academically could lead to a more targeted marketing campaign. Recruitment resources on academic, mentoring, and student success can aid in supporting the adult learners transition to University.
Finn, D. (2011). Principles of adult learning: An ESL context. Journal of Adult Education, 40, 34–39. Huebner, R.(2013), “A.Educational data-mining research”, Research in Higher Edu. Journal, pp.1-13. Koh, H.C. and Chong, S. (2014) Broadening and Deepening of SoTL with Learning Analytics, Available: http://tlc.unisim.edu.sg/research/AdvSoTL/pdf/koh_hian_chye.pdf [20 June 2015]. Geiser, S. and Santelices, M.V. (2007) Validity of High-School Grades in Predicting Student Success Beyond The Freshman Year, Berkeley: Center for Studies in Higher Education: University of California. Ministry of Education (MOE). (2012). Report of the Committee on University Education pathways beyond 2015 (CUEP). Singapore: Author. Available: http://www.moe.gov.sg/media/press/files/2012/08/cuep-report-greaterdiversity-moreopportunities.pdf [20 Dec 2015]. National Population and Talent Divisiion (NPTD) (2013) A Sustainable Population for a Dynamic Singapore: Population White Paper, Available: http://www.nptd.gov.sg/content/NPTD/news/_jcr_content/par_content/download_98/file.res/population-white-paper.pdf [20 Dec 2015].
00. Central Events (Keynotes, EERA-Panel, EERJ Round Table, Invited Sessions)
Network 1. Continuing Professional Development: Learning for Individuals, Leaders, and Organisations
Network 2. Vocational Education and Training (VETNET)
Network 3. Curriculum Innovation
Network 4. Inclusive Education
Network 5. Children and Youth at Risk and Urban Education
Network 6. Open Learning: Media, Environments and Cultures
Network 7. Social Justice and Intercultural Education
Network 8. Research on Health Education
Network 9. Assessment, Evaluation, Testing and Measurement
Network 10. Teacher Education Research
Network 11. Educational Effectiveness and Quality Assurance
Network 12. LISnet - Library and Information Science Network
Network 13. Philosophy of Education
Network 14. Communities, Families and Schooling in Educational Research
Network 15. Research Partnerships in Education
Network 16. ICT in Education and Training
Network 17. Histories of Education
Network 18. Research in Sport Pedagogy
Network 19. Ethnography
Network 20. Research in Innovative Intercultural Learning Environments
Network 22. Research in Higher Education
Network 23. Policy Studies and Politics of Education
Network 24. Mathematics Education Research
Network 25. Research on Children's Rights in Education
Network 26. Educational Leadership
Network 27. Didactics – Learning and Teaching
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