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Autor/in | Yanagiura, Takeshi |
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Titel | How Accurately Can Short-Term Outcomes Approximate Long-Term Outcomes? Examining the Predictive Power of Early Momentum Metrics for Community College Credential Completion Using Machine Learning |
Quelle | In: Community College Review, 51 (2023) 3, S.367-395 (29 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
Zusatzinformation | ORCID (Yanagiura, Takeshi) |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
ISSN | 0091-5521 |
DOI | 10.1177/00915521231163895 |
Schlagwörter | Community Colleges; Community College Students; Educational Indicators; Outcomes of Education; Prediction; Credentials; Graduation Rate; Predictive Measurement; Man Machine Systems; Artificial Intelligence; Gender Differences; Racial Differences; Academic Degrees; Decision Making; Probability; Models; Classification; Robustness (Statistics); Academic Achievement; College Credits Community college; Community College; Community colleges; College students; Collegestudent; Educational indicato; Bildungsindikator; Lernleistung; Schulerfolg; Vorhersage; Studienbuch; Mensch-Maschine-System; Künstliche Intelligenz; Geschlechterkonflikt; Rassenunterschied; Degree; Degrees; Academic level graduation; Akademischer Grad; Hochschulabschluss; Decision-making; Entscheidungsfindung; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Analogiemodell; Classification system; Klassifikation; Klassifikationssystem; Widerstandsfähigkeit; Schulleistung; College; Colleges; Achievement; Performance; Anrechnung; Hochschule; Fachhochschule; Leistung |
Abstract | Objective: This study examines how accurately a small set of short-term academic indicators can approximate long-term outcomes of community college students so that decision-makers can take informed actions based on those indicators to evaluate the current progress of large-scale reform efforts on long-term outcomes, which in practice will not be observed until several years later. Method: Using transcript-level data of approximately 50,000 students at over 30 institutions in two states, I compare the out-of-sample predictive power of the early momentum metrics (EMMs), 13 short-term academic indicators suggested in the literature, to that of more complex, Machine Learning (ML)-based models that employ 497 predictors. Results: This study found that EMMs accurately predict credential completion for 75% to 77% of students in an out-of-sample dataset, with a predictive power largely comparable to that of ML-based models. This study also found similar results among the gender and race/ethnicity groups. However, the predictive power for certificate completion is lower than that for associate and bachelor's degrees by 5 percentage points, implying that this set of EMMs are likely to be less relevant to certificate completion. Contribution: This study validates EMMs as informative predictors of credential completion, confirming that decision makers can use them to understand the probable long-term impact of current reforms on credential outcomes. However, room for continued research and refinement of EMMs remains, especially for certificate. (As Provided). |
Anmerkungen | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2024/1/01 |