Developing the level of adoption survey to inform collaborative discussion regarding educational innovation
Bibliographic record
Abstract
Learning organizations rely on collaborative information and understanding to support and sustain professional growth and development. A collaborative self-assessment instrument can provide clear articulation and characterization of the level of adoption of innovation such as the use of instructional technologies. Adapted from the “Level of Use” (LoU) and “Stages of Concern” indices, the Level of Adoption (LoA) survey was developed to assess changes in understanding of and competence with emerging and innovative educational technologies. The LoA survey, while reflecting the criteria and framework of the original LoU from which it was derived, utilizes a specifically structured on-line, self-reporting scale of “level of adoption” to promote collaborative self-reflection and discussion. Growth in knowledge of, and confidence with, specific emergent technologies is clearly indicated by the results of this pilot study, thus supporting the use of collaborative reflection and assessment to foster personal and systemic professional development. Résumé : Les organisations apprenantes s’appuient sur des informations et une compréhension issues de la collaboration afin de soutenir et d’entretenir la croissance et le perfectionnement professionnels. Un instrument d’auto-évaluation collaboratif permet d’articuler et de caractériser de manière explicite le niveau d’adoption des innovations, comme l’utilisation de technologies éducatives, par exemple. Adapté à partir des indices de « niveau d’utilisation » (ou « LoU » pour Level of Use) et de « niveaux de préoccupation », l’instrument d’enquête sur le niveau d’adoption (ou « LoA » pour Level of Adoption) a été conçu afin d’évaluer les changements qui surviennent dans la compréhension des technologies éducatives émergentes et innovatrices ainsi que dans les compétences relatives à ces technologies. L’instrument d’enquête LoA, bien qu’il reflète les critères et le cadre de l’indice original LoU dont il est dérivé, utilise une échelle d’autodéclaration en ligne du « niveau d’adoption » qui est structurée spécifiquement afin de promouvoir l’autoréflexion et les discussions collaboratives. Les résultats de cette étude pilote démontrent clairement une croissance des connaissances et de la confiance relatives à certaines technologies émergentes en particulier, ce qui vient du même coup appuyer l’utilisation de la réflexion et de l’évaluation collaboratives afin de favoriser le perfectionnement personnel et professionnel systémique.
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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 | 0.028 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".