ICT facilitating learning for all : investigating how interactive whiteboards can support teaching and learning for diverse learners in an elementary, language arts classroom
Bibliographic record
Abstract
Changing information and communication technologies (ICT) are radically altering how people navigate their world. Our students are adept at sending emails, texting friends and surfing the World Wide Web, but only within the last five to ten years have schools begun to improve the ICT available to them. As new ICT are incorporated into schools, it is important for educators to understand how to utilize their capabilities. Recently, Vancouver schools have begun to purchase interactive whiteboards (IWB). Initial observations from teachers reveal that it seems like student engagement is increasing with IWB use. Perhaps more important is the question is teaching and learning shifting in response to IWB capabilities? Current research discusses the IWBs multimodal potential, including visual, auditory and tactile, that support student's learning. In addition, the research shows that teachers need a certain level of technological competency in order to fully incorporate these learning modalities and maximize the interactivity. This interactive nature has the potential to support our students' meaning making process in language arts by supporting critical literacy strategies, multi-literacies and reader response theory. Teachers who are knowledgeable about these possibilities will be able to more fully resource their students and support their learning. As an example, a design for IWB unit plans is included to assist other teachers who wish to incorporate the learning modalities and interactive nature of the IWB into their classrooms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".