The Integration of Interactive Whiteboard Technology Into Regular Lesson Instruction
Bibliographic record
Abstract
This research project focused on looking into whether or not teachers were integrating interactive whiteboards into regular lesson instruction. I wanted to learn from current teachers as to how they were using interactive whiteboards whenever they had access to one. I am interested in seeing how this piece of equipment is perceived by teaching staff and learning whether or not they have seen a difference in student academic learning. My main reason for looking into this is because I noticed interactive whiteboards being used quite often while I was teaching full-time in South Korea. As I mention later in Chapter 1, I am cognizant that this may be a bias I have in relation to the use of interactive whiteboards in the classroom. However, I do recognize that this is not the reality in North American classrooms and I was mindful of this throughout my research. I felt a need to look into this to learn more about how this piece of equipment could be used in a classroom and to understand its' benefits as well as limitations as perceived by teachers who have actually had experience using interactive whiteboards. My research and findings have shown that interactive whiteboards could potentially be beneficial in classrooms, however there are still some limitations. My participants have noticed increased engagement in students during lesson instruction, and teachers felt enjoyment increased when they taught with the assistance of an interactive whiteboard as well. These can become positives for the use of interactive whiteboards in classrooms, but I also recognize this study was very small and my sample of participants do not represent all teachers across the province. This study is only a start of potential future studies related to the integration of interactive whiteboard technology in regular lesson instruction.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".