Literaturnachweis - Detailanzeige
| Autor/in | Takele, Silabat |
|---|---|
| Titel | Validity Strength of College Entrance Assessment Score and High School Academic Records in Predicting College Academic Performance |
| Quelle | In: Journal of Education and Practice, 8 (2017) 7, S.67-75 (9 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 2222-1735 |
| Schlagwörter | Validity; Scores; College Entrance Examinations; Multiple Regression Analysis; High Schools; Academic Achievement; Grade Point Average; Teacher Education Programs; Undergraduate Students; Prediction; College Admission; Admission Criteria; Student Records; Foreign Countries; Predictor Variables; Statistical Significance; Statistical Analysis; Ethiopia |
| Abstract | This is a predictive validity study which aimed at examining the validity strength of College Entrance Assessment (CEA) scores, EGSLCE (Ethiopian General School Leaving Certificate Examination) and High School Average Transcript (HSAT) in predicting college students' academic success as measured by first year CGPA. For this purpose, 2015 regular program entrants (2nd year regular program students in the year 2016) of Gondar College of Teachers Education were comprehensively taken as participant of the study. Data collected from 716 out of 954 participants were found useful and employed for analysis. The collected data were processed using SPSSv16.0. Standard multiple regression and hierarchical multiple regression were used as data analysis methods. Standard multiple regression was employed to estimate the individual and combined contributions of CEA, EGSLCE and HSAT as predictor variables in predicting students' college academic performance. While hierarchical multiple regression analysis was used to check if the addition of EGSLCE and HSAT as admission criteria up on CEA score during the final admission decision enhances prediction of college academic performance. The results indicated that 38.5percent of the variance in college academic performance as measured by first year CGPA is accounted for by the combined predictor variables (CEA, EGSLCE and HSAT). Implying in, jointly these three predictor variables contribute 38.5 percent in predicting college academic achievement. F-test result also indicated that the contribution of these predictor variables in predicting first year college CGPA was found statistically significant F (3,712) =148.689, p<0.05. Of these, CEA scores where final admission decision so far merely relied on accounted 4.9 percent while EGSLCE and HSAT accounted 3.2 percent and 30.4 percent, respectively. Indicating that, HSAT took the largest share of contribution to predict first year CGPA. In the same token, it was found that HSAT has the largest regression coefficient or "ß weight" (0.510) as compared to CEA (0.140) and EGSLCE (0.099). All these evidence showed that HSAT was found a statistically significant best predictor of college academic performance as measured by first year CGPA. Hierarchical regression and F-test results disclosed that EGSLCE and HSAT do add a statistically significant increment (20.3 percent and 6.2 percent, respectively) to the prediction of first year college academic performance if they were used as predictor variables along with CEA scores during the final admission decision. Based on the findings it was recommended that admission guide lines needs to be revised and mere reliance on CEA score during final admission decisions need to be terminated. And EGSLCE and HSAT have to be used along with CEA score during final admission decision. When combined predictor variables were considered during the final admission decision, maximum weight needs to be placed for HSAT. (As Provided). |
| Anmerkungen | IISTE. No 1 Central, Hong Kong Island, Hong Kong SAR. Tel: +852-39485948; e-mail: JEP@iiste.org; Web site: http://iiste.org/Journals/index.php/JEP |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
Literaturbeschaffung und Bestandsnachweise in Bibliotheken prüfen
Permalink als QR-Code
Inhalt auf sozialen Plattformen teilen (nur vorhanden, wenn Javascript eingeschaltet ist)
Teile diese Seite:
