Feeling the Slope with Augmented Reality Technology: Movements of Consciousness in the Learning of the Derivative
Bibliographic record
Abstract
Abstract In this article, we explore how the concept of derivative is learned through a joint activity involving a tenth-grade student, an instructor, and an augmented reality application. This application, Touch the Derivative, allows users to trace a function graph with their hands and simultaneously displays the derivative function graph as they move their hands on the graph. This study is guided by the theory of knowledge objectification, which considers learning as a social, reflexive, and creative meaning-making dialectical process. The joint activity was qualitatively analyzed to answer the two research questions about the meaning of the derivative concept appearing in the joint activity and the role of contradictions. Focusing on the creative, embodied, and materialist process of becoming conscious of the function–derivative mathematical relations, in the results section we discuss the tenth-grader student’s movement of consciousness and the emerging contradictions outlining the semiotic means involved. The meaning-making process of the student progressed through four interrelated layers of consciousness, which evolved dynamically through the contradictions arising as a movement beyond the opposite perspectives of the student and the instructor. We conclude the article by emphasizing the need to understand the pedagogical potential of augmented reality in terms of the activity in which it is used.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".