Incorporating Computer-Based Learning Into Preservice Education Courses
Bibliographic record
Abstract
Most teachers graduate from teacher education institutions with limited knowledge of the ways technology can be used in their professional prac-tice (Wetzel & Chisholm, 1996). Few preservice teachers have any instruc-tion in actually using technology in the classroom (Vagle, 1995), and yet, being able to effectively apply technology is high on the list of what begin-ning teachers should know and be able to do in today’s classroom (Korte-camp & Croninger, 1995). Transferring technology skills from teacher preparation to classroom practice has been limited and has been identified as the “weakest link of most educational programs ” (Browne & Ritchie, 1991, p. 28). Integrating technology in teacher education programs is a ne-cessity so preservice teachers are able to see the importance of developing and using computer-based lessons in their own teaching (Wiburg, 1991). Including technology modeling in field experience is one possibility for helping preservice teachers to see the importance of integrating technology into their teaching (Hunt, 1995; McGraw & Meyer, 1995). However, stud-ies have found that student teachers tend to make limited use of computers in their school-based practicum experiences (Robinson, 1995; Sunal, Smith, Sunay, & Britt, 1998). Another possibility is through the course work that preservice teachers take as a part of their teacher education programs. Most teacher education programs offer a course or two focused on learning to 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".