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

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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: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Two Year Colleges
Descriptors: Articulation (Education), Human Factors Engineering, Algorithms, Educational Planning, Community Colleges, Courses, Transfer Policy, Student Attitudes, Student Participation, Planning, Degree Requirements, Community College Students, College Transfer Students, Student College Relationship
Geographic Terms: California
DOI: 10.1080/10668926.2024.2395277
ISSN: 1066-8926
1521-0413
Abstract: 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.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1457152
Database: ERIC
FullText Text:
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  Data: Optimal Academic Plan Derived from Articulation Agreements: A Preliminary Experiment on Human-Generated and (Hypothetical) Algorithm-Generated Academic Plans
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  Data: <searchLink fieldCode="AR" term="%22David+Van+Nguyen%22">David Van Nguyen</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9405-9120">0000-0002-9405-9120</externalLink>)<br /><searchLink fieldCode="AR" term="%22Shayan+Doroudi%22">Shayan Doroudi</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0602-1406">0000-0002-0602-1406</externalLink>)<br /><searchLink fieldCode="AR" term="%22Daniel+A%2E+Epstein%22">Daniel A. Epstein</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2657-6345">0000-0002-2657-6345</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Community+College+Journal+of+Research+and+Practice%22"><i>Community College Journal of Research and Practice</i></searchLink>. 2025 49(1):44-54.
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  Data: 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
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  Data: 11
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  Data: <searchLink fieldCode="DE" term="%22Articulation+%28Education%29%22">Articulation (Education)</searchLink><br /><searchLink fieldCode="DE" term="%22Human+Factors+Engineering%22">Human Factors Engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Planning%22">Educational Planning</searchLink><br /><searchLink fieldCode="DE" term="%22Community+Colleges%22">Community Colleges</searchLink><br /><searchLink fieldCode="DE" term="%22Courses%22">Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Transfer+Policy%22">Transfer Policy</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Participation%22">Student Participation</searchLink><br /><searchLink fieldCode="DE" term="%22Planning%22">Planning</searchLink><br /><searchLink fieldCode="DE" term="%22Degree+Requirements%22">Degree Requirements</searchLink><br /><searchLink fieldCode="DE" term="%22Community+College+Students%22">Community College Students</searchLink><br /><searchLink fieldCode="DE" term="%22College+Transfer+Students%22">College Transfer Students</searchLink><br /><searchLink fieldCode="DE" term="%22Student+College+Relationship%22">Student College Relationship</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22California%22">California</searchLink>
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  Data: 10.1080/10668926.2024.2395277
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  Data: 1066-8926<br />1521-0413
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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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  Data: EJ1457152
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      – SubjectFull: Articulation (Education)
        Type: general
      – SubjectFull: Human Factors Engineering
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      – SubjectFull: Algorithms
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      – SubjectFull: Student Participation
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      – SubjectFull: Planning
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      – SubjectFull: Degree Requirements
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      – SubjectFull: Community College Students
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      – SubjectFull: California
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