Literaturnachweis - Detailanzeige
| Autor/inn/en | Zimmermann, Judith; Brodersen, Kay H.; Heinimann, Hans R.; Buhmann, Joachim M. |
|---|---|
| Titel | A Model-Based Approach to Predicting Graduate-Level Performance Using Indicators of Undergraduate-Level Performance |
| Quelle | In: Journal of Educational Data Mining, 7 (2015) 3, S.151-176 (26 Seiten)
PDF als Volltext |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 2157-2100 |
| Schlagwörter | Undergraduate Students; Academic Achievement; Prediction; Models; Computer Science Education; Regression (Statistics); Sampling; Statistical Inference; Data Analysis; Grade Point Average; College Admission; Foreign Countries; Statistical Analysis; Switzerland |
| Abstract | The graduate admissions process is crucial for controlling the quality of higher education, yet, rules-of-thumb and domain-specific experiences often dominate evidence-based approaches. The goal of the present study is to dissect the predictive power of undergraduate performance indicators and their aggregates. We analyze 81 variables in 171 student records from a Bachelor's and a Master's program in Computer Science and employ state-of-the-art methods suitable for high-dimensional data-settings. We consider regression models in combination with variable selection and variable aggregation embedded in a double-layered cross-validation loop. Moreover, bootstrapping is employed to identify the importance of explanatory variables. Critically, the data is not confounded by an admission-induced selection bias, which allows us to obtain an unbiased estimate of the predictive value of undergraduate-level indicators for subsequent performance at the graduate level. Our results show that undergraduate-level performance can explain 54% of the variance in graduate-level performance. Significantly, we unexpectedly identified the third-year grade point average as the most significant explanatory variable, whose influence exceeds the one of grades earned in challenging first-year courses. Analyzing the structure of the undergraduate program shows that it primarily assesses a single set of student abilities. Finally, our results provide a methodological basis for deriving principled guidelines for admissions committees. (As Provided). |
| Anmerkungen | International Working Group on Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://www.educationaldatamining.org/JEDM/index.php/JEDM/index |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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