What Do Teacher Candıdates Know About the Limits of the Sequences?
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
candidates. Research is a case study in which qualitative methods are adopted. The first phase of the study wasconducted with a total of 45 teacher candidates taking the course of Analysis III. At this stage, the "Limit KnowledgeTest in Sequences", which was developed by the researchers to investigate the concept knowledge of the sequenceconcept, was used as a data collection tool. At this stage, "Limit Knowledge Test in Sequences", which wasdeveloped by researchers to investigate the limit concept knowledge in sequences of the teacher candidates, included2 open-ended problems were used as data collection tool. In the second phase of the research, individual interviewswere made with 8 teacher candidates selected from the sample to conduct in-depth research. Content analysis wasused to analyze the obtained data. As a result of the analysis of the data, significant shortcomings were found in theknowledge of the concept of the limit topic in the sequences of teacher candidates. It is seen that the candidates donot know the importance of the concept of "accumulation point", which is indispensable to convergence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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