Effective and Engaging OR Pointless and Problematic? Integrating Technology into the High School History Classroom
Bibliographic record
Abstract
This research study explored how a sample of high school History teachers in Ontario are integrating technology into their classrooms. The study was conducted using a qualitative research approach that involved reviewing the relevant literature and existing research surrounding technology integration in History, as well as conducting one-on-one, semi- structured, and face-to-face interviews with two high school History teachers in a southern Ontario school board. The study revealed that there are several benefits to technology integration in the high school History class, such as increased student engagement and motivation, increased access to historical resources and perspectives, more student-centered learning, increased collaboration, and more opportunities for differentiated instruction. At the same time, however, the study also found that integrating technology into the History classroom does not come without its challenges, as things like access to too much historical information online can be overwhelming, confusing, and time-consuming for both students and teachers alike. Finally, and perhaps most importantly, the study found that simply incorporating technology into the History classroom does not just automatically translate into sound practice; rather, technology must be meaningfully and intentionally incorporated into the classroom in order to engage students and potentially transform or redefine their learning. Overall, findings suggest that further professional development in the area of technology integration is needed, especially in subject- specific areas. Teachers may then feel more comfortable and confident introducing digital tools 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".