Treffer: Optimal Academic Plan Derived from Articulation Agreements: A Preliminary Experiment on Human-Generated and (Hypothetical) Algorithm-Generated Academic Plans

Title:
Optimal Academic Plan Derived from Articulation Agreements: A Preliminary Experiment on Human-Generated and (Hypothetical) Algorithm-Generated Academic Plans
Language:
English
Authors:
David Van Nguyen (ORCID 0000-0002-9405-9120), Shayan Doroudi (ORCID 0000-0002-0602-1406), Daniel A. Epstein (ORCID 0000-0002-2657-6345)
Source:
Community College Journal of Research and Practice. 2025 49(1):44-54.
Availability:
Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed:
Y
Page Count:
11
Publication Date:
2025
Document Type:
Fachzeitschrift Journal Articles<br />Reports - Research
Education Level:
Higher Education
Postsecondary Education
Two Year Colleges
Geographic Terms:
DOI:
10.1080/10668926.2024.2395277
ISSN:
1066-8926
1521-0413
Entry Date:
2025
Accession Number:
EJ1457152
Database:
ERIC

Weitere Informationen

Our preliminary experiment examined a potential pain point with ASSIST, California's database of articulation agreements. That pain point is cross-referencing multiple articulation agreements to manually develop an "optimal" academic plan. Optimal is defined as the minimal set of community college courses that satisfy all transfer requirements for the multiple universities a student is preparing to apply to. Accordingly, we designed a low-fidelity prototype that lists the minimal set of courses a "hypothetical" optimization algorithm would output based on selected articulation agreements. Twenty-four students were tasked with creating an optimal academic plan using either ASSIST (which requires manual optimization) or the optimization prototype (which already provides the minimal set of classes). Prototype users had less optimality mistakes, were faster, and provided higher usability ratings compared to ASSIST users. Going forward, future research needs to move beyond our "proof of value" of a hypothetical optimization algorithm and toward actually implementing an algorithm.

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