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Improving Reflection and Professional Development in Public Service Programs: A Deep Learning Approach

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Title: Improving Reflection and Professional Development in Public Service Programs: A Deep Learning Approach
Language: English
Authors: Abdul-Akeem Sadiq (ORCID 0000-0003-4276-4917), Naim Kapucu
Source: Teaching Public Administration. 2026 44(2):146-161.
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: 16
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Reflection, Public Service, Professional Development, Public Administration Education, Leadership Styles, Leadership Training, Graduate Students
DOI: 10.1177/01447394261433006
ISSN: 0144-7394
Abstract: As global challenges--including public health emergencies, racial and ethnic diversity, political division, and persistent economic inequalities--continue to strain communities, the need for public administration leaders equipped with robust skills, competency-based knowledge, and empathy has become increasingly urgent. This paper applies a deep learning approach to strengthen reflection and professional development among students in public service programs. Using deep learning for reflective leadership practice as our guiding framework, we identify five strategies from the literature and illustrate their application in a graduate leadership course. Student feedback indicates meaningful gains in deep learning, particularly through structured reflective assignments. We conclude by outlining future research directions for advancing deep learning in public administration.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1507319
Database: ERIC
FullText Text:
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  Data: Improving Reflection and Professional Development in Public Service Programs: A Deep Learning Approach
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  Data: <searchLink fieldCode="AR" term="%22Abdul-Akeem+Sadiq%22">Abdul-Akeem Sadiq</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4276-4917">0000-0003-4276-4917</externalLink>)<br /><searchLink fieldCode="AR" term="%22Naim+Kapucu%22">Naim Kapucu</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Teaching+Public+Administration%22"><i>Teaching Public Administration</i></searchLink>. 2026 44(2):146-161.
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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: <searchLink fieldCode="DE" term="%22Reflection%22">Reflection</searchLink><br /><searchLink fieldCode="DE" term="%22Public+Service%22">Public Service</searchLink><br /><searchLink fieldCode="DE" term="%22Professional+Development%22">Professional Development</searchLink><br /><searchLink fieldCode="DE" term="%22Public+Administration+Education%22">Public Administration Education</searchLink><br /><searchLink fieldCode="DE" term="%22Leadership+Styles%22">Leadership Styles</searchLink><br /><searchLink fieldCode="DE" term="%22Leadership+Training%22">Leadership Training</searchLink><br /><searchLink fieldCode="DE" term="%22Graduate+Students%22">Graduate Students</searchLink>
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  Data: 10.1177/01447394261433006
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  Data: As global challenges--including public health emergencies, racial and ethnic diversity, political division, and persistent economic inequalities--continue to strain communities, the need for public administration leaders equipped with robust skills, competency-based knowledge, and empathy has become increasingly urgent. This paper applies a deep learning approach to strengthen reflection and professional development among students in public service programs. Using deep learning for reflective leadership practice as our guiding framework, we identify five strategies from the literature and illustrate their application in a graduate leadership course. Student feedback indicates meaningful gains in deep learning, particularly through structured reflective assignments. We conclude by outlining future research directions for advancing deep learning in public administration.
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  Data: 2026
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