Issues and Challenges in Preparing Teachers to Teach in the Twenty-First Century
Bibliographic record
Abstract
Preservice teachers need to acquire both technological skill and understanding about how technology rich environments can develop subject-specific knowledge as a part of their teacher education programs. The purpose of the research project, as described in this case study, was to examine the impact that immersion in technology-infused social studies pedagogy courses had on preservice teachers’ willingness to use computer and online tools as well as how they used them during their student teaching. Teacher education students enrolled in two pedagogy courses were surveyed at the beginning and end of the courses and interviewed over the duration of the courses regarding the nature and extent of their technological knowledge and skill. Following the completion of the pedagogy courses, six volunteered to have their technology use tracked during their nine-week practice teaching experience. Findings showed that while the preservice pedagogy courses did increase the student teachers’ knowledge of and skill with a variety of computer and online tools as well as their desire to use them during their student teaching, the elementary schools in which they were placed for their practicum were poorly equipped and the mentor teachers were not using the tools that were modeled on campus. If preservice teachers are to truly understand the benefits of learning and teaching with technology, teacher education institutions and school districts need to work together to present a consistent vision of technology integration, and schools need to provide environments that encourage and support technology use.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".