Students’ Perception of Teachers’ Characteristics and Their Attitude towards Mathematics in Oron Education Zone, Nigeria
Bibliographic record
Abstract
The study sought to find out the relationship between how students perceive their teachers’ in respect of knowledge of Mathematics content, communication ability, use of appropriate teaching strategies and teachers’ classroom management skills and students’ attitude towards mathematics. The population of the study comprised all the second year students in senior secondary schools in Oron Education Zone. The study sample consisted of 640 students selected through cluster and simple random sampling techniques. Two instruments – Students’ Perception of Teacher Characteristics Questionnaire (SPTCQ) and Students’ Attitude towards Mathematics Questionnaire (SATMQ) were developed and administered on the respondents. A trial test of 50 students using split-half reliability test was carried out which yielded reliability coefficients of 0.86 and 0.94 for SPTCQ and SATMQ respectively. Pearson Product Moment Correlation and t-statistics were used to answer the research questions and test the hypotheses respectively. Findings show that the way students’ perceive their teachers’ in terms of knowledge of mathematics contents, communication ability, teaching methods and classroom management skills has a significant relationship with students’ attitude towards mathematics. When the students’ perception of their teachers’ characteristics is low, students’ attitude towards mathematics tends to be negative.
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.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".