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

Autor/in	Yanagiura, Takeshi
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 Verfügbarkeit
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 + Suchen Sie Ihr Suchwort? 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