What are Alberta’s K-12 Students Saying about Learning with Technologies?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Students in Alberta, Canada expect rich opportunities to learn with technologies—opportunities that allow them to use technologies to improve their productivity when learning; to facilitate more complex, collaborative and authentic learning experiences; and to personalize their learning with respect to location, time and pace. While students in schools in Alberta share common expectations for learning with technologies, they do not report common experiences, citing individual preferences and/or contexts as their reasons. These findings derive from an analysis of student voice data collected through research projects and student engagement activities conducted in the province’s K-12 community from 2006 to 2010. In this chapter the authors summarize the collected data and discuss themes common to students’ expectations for learning with technologies as well as reasons why students’ experiences using technologies for learning differ. The authors also outline ways in which Alberta’s K-12 community is evolving to meet students’ expectations for learning with technologies. In closing, the authors challenge the reader to consider what can be done to ensure that students have a voice in designing relevant, technology-rich learning environments that meet their expectations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it