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Integrating Industry-Crowdsourced Projects in University Capstone Courses: A Comparative Study Using Parametric Statistics and Sentiment Analysis

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Title: Integrating Industry-Crowdsourced Projects in University Capstone Courses: A Comparative Study Using Parametric Statistics and Sentiment Analysis
Language: English
Authors: Kenneth David Strang, Narasimha Rao Vajjhala (ORCID 0000-0002-8260-2392)
Source: Industry and Higher Education. 2024 38(6):574-582.
Availability: 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
Peer Reviewed: Y
Page Count: 9
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Industry, Universities, Higher Education, Capstone Experiences, College Students, School Business Relationship, Student Projects, Artificial Intelligence, Algorithms, Learning Analytics, Grades (Scholastic), Academic Achievement, Classification
DOI: 10.1177/09504222241249894
ISSN: 0950-4222
2043-6858
Abstract: This study explores integrating industry-crowdsourced projects within capstone courses of a 4-year Bachelor of Science program at an accredited American university. A unique business consulting model was developed for the final year course, aligning students with 16-weeks industry projects that reflected their academic goals and the program's learning objectives. The study aimed to evaluate the efficacy of this pedagogical approach compared to traditional capstone courses. This evaluation involved collecting data from grading systems and anonymous course surveys. A novel aspect of the research design was the synergetic combination of nonparametric and parametric statistical techniques with modern machine learning (ML) algorithms to analyse the students' grades, survey comments and third-party course opinion comments. Additionally, independent third-party course ratings were examined to triangulate the results. Findings revealed that while the academic performance in the industry-crowdsourced capstone course mirrored that of the traditional course, the industry-crowdsourced variant elicited significantly more positive responses in course surveys. Furthermore, ML sentiment analysis of comments from third-party forums indicated a stronger positive reception for the industry-crowdsourced course over the traditional approach.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1445528
Database: ERIC
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  Data: Integrating Industry-Crowdsourced Projects in University Capstone Courses: A Comparative Study Using Parametric Statistics and Sentiment Analysis
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  Data: <searchLink fieldCode="AR" term="%22Kenneth+David+Strang%22">Kenneth David Strang</searchLink><br /><searchLink fieldCode="AR" term="%22Narasimha+Rao+Vajjhala%22">Narasimha Rao Vajjhala</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8260-2392">0000-0002-8260-2392</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Industry+and+Higher+Education%22"><i>Industry and Higher Education</i></searchLink>. 2024 38(6):574-582.
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  Data: 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
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  Data: 10.1177/09504222241249894
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  Data: 0950-4222<br />2043-6858
– Name: Abstract
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  Data: This study explores integrating industry-crowdsourced projects within capstone courses of a 4-year Bachelor of Science program at an accredited American university. A unique business consulting model was developed for the final year course, aligning students with 16-weeks industry projects that reflected their academic goals and the program's learning objectives. The study aimed to evaluate the efficacy of this pedagogical approach compared to traditional capstone courses. This evaluation involved collecting data from grading systems and anonymous course surveys. A novel aspect of the research design was the synergetic combination of nonparametric and parametric statistical techniques with modern machine learning (ML) algorithms to analyse the students' grades, survey comments and third-party course opinion comments. Additionally, independent third-party course ratings were examined to triangulate the results. Findings revealed that while the academic performance in the industry-crowdsourced capstone course mirrored that of the traditional course, the industry-crowdsourced variant elicited significantly more positive responses in course surveys. Furthermore, ML sentiment analysis of comments from third-party forums indicated a stronger positive reception for the industry-crowdsourced course over the traditional approach.
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      – Text: English
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      – SubjectFull: Universities
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      – SubjectFull: Student Projects
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      – SubjectFull: Algorithms
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