The predictive Ability of Admission Criteria and Student Performance Level in Master Programs in College of Education at Sultan Qaboos University

Abstract

Abstract

Most universities in the world are largely committed to creating credible and transparent admission standards that provide justice in admission and have the ability to predict students' performance in their chosen programs. Hence, this study aimed to reveal the predictive ability of the acceptance criteria for the level of performance of master's students in the College of Education at Sultan Qaboos University. Quantitative data were collected from (115) students' admission documents for those accepted in the postgraduate programs for the academic year 2019-2020, and GPA data was collected from students’ transcripts for the fall semester of 2019. Qualitative data were also collected from the interviews that were conducted with focus groups of (27) graduated students from various disciplines. SPSS software was used for analyzing quantitative data while NVivo software was used for qualitative data. The results of multiple regressions revealed the ability of the interviews to predict the students' GPA in the master’s program. Study results also showed there are many strengths and weaknesses related to the admission exam and interview standards since they measure applicants' ability in the specialization of knowledge and their personal characteristics. The study provides a set of recommendations such as developing admission criteria by introducing non-traditional criteria represented in the applicant's portfolio and job performance.