Title

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

Alternative Title

"선(先)몰입 후(後)학습": 소셜 미디어 네이티브와의 숏폼 비디오 학습 공동 디자인

DGIST Authors

Sangeun Seo ; Sunjun Kim ; Jean Y. Song

Advisor

김선준

Co-Advisor(s)

Jean Y. Song

Issued Date

2026

Awarded Date

2026-02-01

Type

Thesis

Description

short-form video, co-design, learner-centered design, generative AI, educational technology

Table Of Contents

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

URI

https://scholar.dgist.ac.kr/handle/20.500.11750/59697
http://dgist.dcollection.net/common/orgView/200000944642

DOI

10.22677/THESIS.200000944642

Degree

Master

Department

Department of Electrical Engineering and Computer Science

Publisher

DGIST

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