Weaving together media, technologies and people
Bibliographic record
Abstract
Purpose Students in flipped classrooms are challenged to orchestrate an increasingly heterogeneous collection of learning objects, including audiovisual materials as well as traditional learning objects, such as textbooks and syllabi. This study aims to examine students' information practices interacting with and synthesizing across learning objects, technologies and people in flipped classrooms. Design/methodology/approach This grounded theory study explores the information practices of 12 undergraduate engineering students as they learned in two flipped classrooms. An artifact walkthrough was used to elicit descriptions of how students conceptualize and work around interoperability problems between the diverse and distributed learning objects by weaving them together into information tapestries. Findings Students maintained a notebook as an information tapestry, weaving fragmented information snippets from the available learning objects, including, but not limited to, instructional videos and textbooks. Students also connected with peers on Facebook, a back-channel that allowed them to sidestep the academic honesty policy of the course discussion forum, when collaborating on homework assignments. Originality/value The importance of the interoperability of tools with elements of students' information space and the significance of designing for existing information practices are two outcomes of the grounded theory approach. Design implications for educational technology including the weaving of mixed media and the establishment of spaces for student-to-student interaction are also discussed.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".