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  Department of Electrical Engineering and Computer Science Theses Master

"Engage First, Learn Second": Co-Designing Short-Form Video Learning with Social Media Natives

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dc.contributor.advisor 김선준 -
dc.contributor.author Sangeun Seo -
dc.date.accessioned 2026-01-23T10:56:06Z -
dc.date.available 2026-01-23T10:56:06Z -
dc.date.issued 2026 -
dc.identifier.uri https://scholar.dgist.ac.kr/handle/20.500.11750/59697 -
dc.identifier.uri http://dgist.dcollection.net/common/orgView/200000944642 -
dc.description short-form video, co-design, learner-centered design, generative AI, educational technology -
dc.description.tableofcontents List of Contents
Abstract i
List of contents ii
List of tables iv
List of figures iv

I. INTRODUCTION 1
II. RELATED WORK
2.1 Short-form Videos as an Emerging Educational Medium 3
2.2 Revisiting Learning Theories in the Context of Short-form Videos 4
2.3 Participatory and Co-design Approaches for Educational AI 5
2.4 Identifying Gaps and Emerging Design Patterns for SFV Learning 6
2.5 The Engineering Challenge: Computational Formulation of Qualitative Insights 6
2.5.1 The Limitations of Conventional Computational Approaches 7
2.5.2 The Justification for Case-Based Reasoning (CBR) as the Optimal Solution ·· 7
III. METHOD
3.1 Participants 9
3.2 Co-Design Workshop Protocol 10
3.2.1 Phase 1: Sensitization and Critical Discussion 11
3.2.2 Phase 2: Contextualization through Persona Creation 12
3.2.3 Phase 3: Team-Based Storyboarding 13
3.2.4 Phase 4: Rationale Interviews 13
3.3 Workshop Materials and Scaffolds 14
3.4 Data Collection and Analysis 15
III. FINDINGS
4.1 What Makes a "Good" Educational SFV? 18
4.1.1 Balancing Engagement and Comprehension 18
4.1.2 The Need for Pedagogical Transparency in SFVs 19
4.1.3 The Role of Audio and Narration in Perceived Trustworthiness 20
4.2 "For Whom and In What Context?": Defining Learning Scenarios 20
4.2.1 Subject-Specific Motivations for Learning 22
4.2.2 Cognitive Depth of Learning Goals 23
4.3 Synthesizing Insights into Design: Social Media Native Design Patterns 24
4.3.1 Design Pattern 1: Engage First, Learn Second 24
4.3.2 Design Pattern 2: Design for Mobility 26
4.3.3 Design Pattern 3: Adopt Social Media Native Genres 27
4.4 Exemplar Case: Applying the Design Patterns to an Adaptive Learner Persona ·· 29
III. DISCUSSION
5.1 Pattern 1: "Engage First, Learn Second" 30
5.1.1 Meaning and Significance 30
5.1.2 Dialogue with Theory (Cognitive Load Theory) 30
5.1.3 Inherent Tension (Engagement vs. Depth) 31
5.2 Pattern 2: "Design for Mobility" 31
5.2.1 Meaning and Significance 31
5.2.2 Dialogue with Theory (Modality Principle) 31
5.2.3 Inherent Tension (Empowerment vs. Habit Reinforcement) 32
5.3 Pattern 3: "Adopt Native Genres" 32
5.3.1 Meaning and Significance: The Pursuit of Cultural Legibility 32
5.3.2 Dialogue with Theory (Remediation Theory) 32
5.3.3 Inherent Tension (Authenticity vs. Scalability) 33
5.4 The Challenge of Scalability: A Bridge to Technical Contribution 33
VI. PROPOSED SYSTEM ARCHITECTURE: A CBR/k-NN ENGINE
6.1 Justification: Why Case-Based Reasoning (CBR)? 34
6.2 The "CBR/k-NN" Engine: A 4-Component Design 35
6.2.1 Component 1: The Probabilistic Query Encoder (The "Input") 36
6.2.2 Component 2: The Expert Case Base (The "Database") 36
6.2.3 Component 3: The CBR Core (k-NN Matching & Pattern Abstraction) 36
6.2.4 Component 4: The Scaffolding HCI Layer (The "Interface") 37
VII. VALIDATION ROADMAP AND FUTURE WORK
7.1 Limitations: The "N=24 Seed" Problem 38
7.2 A 3-Phase Roadmap for Validation and Extension 38
7.2.1 Phase 1 (Implementation): Building the Working Prototype 38
7.2.2 Phase 2 (Validation): Controlled Experiment (A/B Test) 39
7.2.3 Phase 3 (Extension): The Vision for a Continually Learning System 40
VIII. CONCLUSION 40
REFERENCES 42
SUMMARY (국문요약) 48		 -
dc.format.extent 48 -
dc.language eng -
dc.publisher DGIST -
dc.title "Engage First, Learn Second": Co-Designing Short-Form Video Learning with Social Media Natives -
dc.title.alternative "선(先)몰입 후(後)학습": 소셜 미디어 네이티브와의 숏폼 비디오 학습 공동 디자인 -
dc.type Thesis -
dc.identifier.doi 10.22677/THESIS.200000944642 -
dc.description.degree Master -
dc.contributor.department Department of Electrical Engineering and Computer Science -
dc.contributor.coadvisor Jean Y. Song -
dc.date.awarded 2026-02-01 -
dc.publisher.location Daegu -
dc.description.database dCollection -
dc.citation XT.IM 서52 202602 -
dc.date.accepted 2026-01-19 -
dc.contributor.alternativeDepartment 전기전자컴퓨터공학과 -
dc.subject.keyword short-form video, co-design, learner-centered design, generative AI, educational technology -
dc.contributor.affiliatedAuthor Sangeun Seo -
dc.contributor.affiliatedAuthor Sunjun Kim -
dc.contributor.affiliatedAuthor Jean Y. Song -
dc.contributor.alternativeName 서상은 -
dc.contributor.alternativeName Sunjun Kim -
dc.contributor.alternativeName 송진영 -
dc.rights.embargoReleaseDate 2026-08-31 -
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