“They hear it from me”: Student voices on critically assessing digital multimodal composing
Bibliographic record
Abstract
• Grade 8 youth composed digital stories and video essays to counter social injustices. • Students used digital, multimodal and disciplinary tools to enhance critical actions. • Their rubric criteria, reflections and interviews highlighted critical DMC practices. • Student perspectives on DMC practices fostered transparency, reflection, and action. • Justice-oriented assessment helps reorient DMC practices toward social justice. This ethnographic case study investigates how student perspectives on digital multimodal composing (DMC) practices can inform a justice-oriented approach to assessment in English Language Arts (ELA). While existing research often privileges teacher perspectives in assessing DMC, this study centres the voices of two culturally and linguistically diverse Grade 8 students in a Canadian ELA classroom. Drawing on multimodality, critical digital literacies, and disciplinary literacies, we examine interviews, self-assessment reflections, student-suggested rubrics, and final DMC projects to understand how youth critically apply digital, multimodal and disciplinary tools to design digital stories and video essays that challenge social injustices. Our thematic analysis reveals that students remix digital tools, such as voiceovers, music, and visual design, not only to meet assignment expectations but also to critically engage their audience. Student perspectives on their DMC practices emphasize their multimodal and audience awareness, including intentional choices about sound, pacing, and visual impact. Students also engage in disciplinary weaving, combining self and world connections with judicious sourcing to denounce racial, cultural, and gender discrimination. These insights fostered transparency and enabled the classroom teacher to provide formative, student-responsive feedback and honour diverse ways of knowing. This study contributes to current scholarship by advancing a justice-oriented model of DMC assessment that values student agency and encourages educators to assess how students reorient digital, multimodal and disciplinary tools toward social justice. By foregrounding student voices, this study positions justice-oriented DMC assessment as a pedagogical tool capable of disrupting hegemonic norms in disciplinary classrooms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".