Pedagogically appropriate integration of informational technology in an elementary preservice teacher education program
Bibliographic record
Abstract
This paper describes ways in which preservice students in an elementary education four-year degree program at the University of Regina (Saskatchewan) learn about and learn with information technology. Students learn specific skills and concepts related to informational technology (IT) and its uses and application to the classroom in specific ways at different stages of the program. Also discussed in the paper is the evolving faculty inservice training to reskill or retool instructors to enable them to provide meaningful IT experiences for students. One particular experiment is outlined, an experiment that addressed the content of and response to a specific set of five IT modules implemented in order to ensure that all students complete their education degrees with a mandatory set of technology concepts and skills, in a program where there is no mandatory computers in education class. Ways that students who have taken the five modules are using IT in pedagogically-appropriate ways are outlined. The paper also describes a vision of the IT future for faculty and students. (Author/AEF) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ******************************************************************************** Pedagogically Appropriate Integration of Informational Technology in an Elementary Preservice Teacher Education Program PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY
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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 | 0.003 |
| 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 | 0.001 |
| 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".