Incorporating Computer-Based Learning Into Preservice Education Courses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it