Goshit Nenbunmwa Amos, Aminu Abubakar Danladi, and Agi Abdulahi Owuna
COMPARATIVE ANALYSIS OF EMPLOYABILITY SKILLS AMONG UNDERGRADUATES IN FEDERAL, STATE, AND PRIVATE UNIVERSITIES IN NIGERIA
https://doi.org/10.83151/4wkq-bh79
This study explored the Predictive Validity of Entry Modes on Academic Achievement among Computer science students at AFIT, Kaduna. Two research questions and two null hypotheses guided the study. The study employed the correlational research design. The population of the study comprised 313(298 UTME and 15 IJMB) students of 100 and 200 levels of the 2020 to 2023 academic session of the Department of Computer Science. The sample for the study comprises 175 (125 male and 50 female) students, comprising of 160 (115 male and 45 female) UTME students and 15 (10 male and 5 female) IJMB students. The researcher designed a record of performance of students and their mode of entry (PROMOE), which was used for extracting data from the students’ record files for data collection. Research questions were answered using PPMC. The OLS Regression method statistics at the 0.05 level of significance were used to test the hypotheses. The findings revealed that the students’ scores in UTME significantly predict their CGPA scores in their First year, and also the students' IJMB results significantly predict their CGPA scores in their second year. The study recommended, among others that UTME and IJMB examination syllabi should be given keen attention since the two examinations predict the academic success of students in the university and that therefore, can affect the quality of graduates the university produces.
Keywords: Computer Science, IJMB Scores, UTME Scores, and Students’ Achievement
Goshit Nenbunmwa Amos, Aminu Abubakar Danladi, and Agi Abdulahi Owuna
COMPARATIVE ANALYSIS OF EMPLOYABILITY SKILLS AMONG UNDERGRADUATES IN FEDERAL, STATE, AND PRIVATE UNIVERSITIES IN NIGERIA https://doi.org/10.83151/4wkq-bh79
This study explored the Predictive Validity of Entry Modes on Academic Achievement among Computer science students at AFIT, Kaduna. Two research questions and two null hypotheses guided the study. The study employed the correlational research design. The population of the study comprised 313(298 UTME and 15 IJMB) students of 100 and 200 levels of the 2020 to 2023 academic session of the Department of Computer Science. The sample for the study comprises 175 (125 male and 50 female) students, comprising of 160 (115 male and 45 female) UTME students and 15 (10 male and 5 female) IJMB students. The researcher designed a record of performance of students and their mode of entry (PROMOE), which was used for extracting data from the students’ record files for data collection. Research questions were answered using PPMC. The OLS Regression method statistics at the 0.05 level of significance were used to test the hypotheses. The findings revealed that the students’ scores in UTME significantly predict their CGPA scores in their First year, and also the students' IJMB results significantly predict their CGPA scores in their second year. The study recommended, among others that UTME and IJMB examination syllabi should be given keen attention since the two examinations predict the academic success of students in the university and that therefore, can affect the quality of graduates the university produces. Keywords: Computer Science, IJMB Scores, UTME Scores, and Students’ Achievement