نوع مقاله : مقاله علمی پژوهشی
نویسندگان
1 'استاد مدعو، دانشگاه تهران
2 گروه زبان و ادبیات انگلیسی، دانشکده زبان ها و ادبیات خارجی، دانشگاه تهران
چکیده
This conceptual article advances STELLA-U, a universal computer-assisted language learning mechanism framework explaining how digital storytelling fosters durable learning across languages, disciplines, and platforms. STELLA-U integrates six constructs (Narrative Transduction, Polysemiotic Consonance, Dialogic Critique Loop, Re-Authoring Spiral, Participation Field, and Voice Trajectory) into four pathways (Consonance Advantage, Deepen-to-Complexify, Canalized Uptake, Voice-Forward) that connect story design to outcomes. We define five theoretical constructs that function as portable yardsticks, namely Consonance Index (CI), Revision Depth (RDI), Dialogic Uptake Rate (DUR), Structure Integration Quotient (SIQ), and Voice Trajectory Stage (VTS). Two boundary conditions, the Over-Modalization Threshold (OMT) and an Opacity Buffer for ethical opacity and accessibility, specify when added modes help or hinder learning and how consent and identity are protected. We state falsifiable propositions, for example, that higher CI predicts SIQ, that DUR mediates Participation Field effects on RDI, and that OMT yields segmentable breakpoints. Worked mini-examples and rubric-ready definitions support recognition and replication. Three design moves, Alignment Vector, Audience Anchor, and Revision Torque, translate theory into practice without prescribing any platform or genre. By offering portable constructs and testable claims, STELLA-U reframes digital storytelling as a mechanism-rich research program, enabling cumulative evidence and equitable pedagogy across literature, STEM communication, and professional education.
کلیدواژهها
- Digital Storytelling
- Computer-Assisted Language Learning
- Multimodal learning
- Narrative Transduction
- Polysemiotic Consonance
- Dialogic Critique Loop
- Over-Modalization Threshold
- Learning analytics
موضوعات
عنوان مقاله [English]
Digital Stories that Speak Across Domains: STELLA-U, a Computer-Assisted Language Learning (CALL) Conceptual Model for Humanities and Literary Studies
نویسندگان [English]
- Ahmad Mohamadi Suhrawardi 1
- Seyed Reza Dashtestani 2
1 Visiting Professor, University of Tehran
2 Department of English Language and Literature , Faculty of Foreign Languages and Literatures, University of Tehran
چکیده [English]
This conceptual article advances STELLA-U, a universal computer-assisted language learning mechanism framework explaining how digital storytelling fosters durable learning across languages, disciplines, and platforms. STELLA-U integrates six constructs (Narrative Transduction, Polysemiotic Consonance, Dialogic Critique Loop, Re-Authoring Spiral, Participation Field, and Voice Trajectory) into four pathways (Consonance Advantage, Deepen-to-Complexify, Canalized Uptake, Voice-Forward) that connect story design to outcomes. We define five theoretical constructs that function as portable yardsticks, namely Consonance Index (CI), Revision Depth (RDI), Dialogic Uptake Rate (DUR), Structure Integration Quotient (SIQ), and Voice Trajectory Stage (VTS). Two boundary conditions, the Over-Modalization Threshold (OMT) and an Opacity Buffer for ethical opacity and accessibility, specify when added modes help or hinder learning and how consent and identity are protected. We state falsifiable propositions, for example, that higher CI predicts SIQ, that DUR mediates Participation Field effects on RDI, and that OMT yields segmentable breakpoints. Worked mini-examples and rubric-ready definitions support recognition and replication. Three design moves, Alignment Vector, Audience Anchor, and Revision Torque, translate theory into practice without prescribing any platform or genre. By offering portable constructs and testable claims, STELLA-U reframes digital storytelling as a mechanism-rich research program, enabling cumulative evidence and equitable pedagogy across literature, STEM communication, and professional education.
کلیدواژهها [English]
- Digital Storytelling
- Computer-Assisted Language Learning
- Multimodal learning
- Narrative Transduction
- Polysemiotic Consonance
- Dialogic Critique Loop
- Over-Modalization Threshold
- Learning analytics
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