The Investigation of the Effects of Authentic Assessment Approach on Prospective Teachers’ Problem-Solving Skills
Bibliographic record
Abstract
The purpose of this study was to investigate the effect of authentic assessment, an approach used in Scientific Research Methods, on problem solving skills of prospective classroom teachers. The participant groups of the study consisted of sophomore prospective teachers who study at Dicle University in the Ziya Gökalp Education Faculty Classroom Teaching Department during 2013-2014 academic spring term. The two classrooms in the department were randomly assigned as experimental group (Group B) and control group (Group A). The experimental group was given authentic tasks and asked to do them group work. The authentic tasks fulfilled by prospective teachers were analyzed in accordance with the authentic assessment approach. Authentic assessment tools such as self-assessment, group assessment, portfolio assessment, teacher-peer assessment, weekly performance assessment, and student journals were used in the experimental group. Meanwhile, control group activities were based on a subject-oriented curriculum design and teacher-centered traditional practices and assessment were carried out. Methods like verbal lectures, discussions, and question-answers were used. In addition, the evaluation process was conducted on the mid-term exam essay in traditional sense. While the pre-test and post-test results of the experimental group indicate a statistically significant positive difference for the post-test, the difference between pre- and post-test results for the control groups were not found to be statistically significant. Moreover, a comparative analysis of adjusted post-test results based on pre-test results of experimental and control groups indicated a statistically significant positive difference in favor of experimental group.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".