“It feels like I have a camera in my eye”: New methods for literacies research in maker-oriented classrooms
Bibliographic record
Abstract
This paper focuses on new data collection methods made possible through first person-perspective or point-of-view (POV) recording technology and how these tools can provide important insights into students’ digital making and learning processes. Observation is a powerful tool, but researchers and educators are limited in what they can observe during a given moment and their inferences about student learning are made through the lens of an “outsider”. Audiovisual recording can supplement classroom observations to provide a more complete picture of students’ learning, but we contend that commonly-used methods are insufficient to capture the dynamic, social processes and literacies at play in a maker-oriented classroom. Through analyses of students’ learning during a digital tutorial-making task, we examine the affordances of and considerations for using POV “spyglasses” in digital literacies research. Spyglasses look and feel like regular glasses that one would wear to improve their vision, augmented with an integrated video camera and recording functionality. Our findings indicate that using tools that allow data to be collected from the student perspective gives access to important, alternate narratives about what students’ final products might show or represent about their digital skills and competencies. We also explore the important technical, ethical and data management considerations associated with using spyglasses as a data collection tool. As physical and digital making practices become more prominent in education and classroom-based research, this study highlights the importance of research tools capable of capturing the nuance and process of learning through making. Future research could explore the gap between researcher interpretation of collected data when it is not “read” alongside, or compared against, documentation from the “insider” perspective.
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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 | 0.100 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".