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Record W2765562911 · doi:10.5539/ies.v10n11p129

Relationship between Students’ Perception toward the Teaching and Learning Methods of Mathematics’ Lecturer and Their Achievement in Pre-University Studies

2017· article· en· W2765562911 on OpenAlexvenueno aff
Nor Amalina Ahmad, Farah Liyana Azizan, Nur Fazliana Rahim, Nor Hayati Jaya, Norhunaini Mohd Shaipullah, Emmerline Shelda Siaw

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsLikert scaleMathematics educationPerceptionPsychologyTeaching methodScale (ratio)Developmental psychology

Abstract

fetched live from OpenAlex

The academic performance of students is affected by many factors, including effectiveness in teaching, the subjects taught and the environment as well as the facilities provided. The purpose of this study is to determine the relationship between students’ perceptions of the teaching and learning towards the lecturers with their achievements in Mathematics at the Centre for Pre University Studies. The study was a descriptive study in which a survey research design was adopted. A total of 841 students from the centre participated in the study. The data were collected through student’s questionnaire. The questionnaires consisted of 26 questions. 5-Likert Scale questionnaires used in this study focused on the five categories of students’ perceptions; teaching, evaluations, subjects, guidance and environment dimensions. The findings revealed that there is no significant correlation between the average scores of students’ perceptions of teaching and learning towards the Mathematics lecturer with the average scores Mathematics achievement of the students. The study also revealed that there are no significant differences between the average scores of male and female students’ perceptions of the effectiveness of teaching and learning of the Mathematics lecturer. The findings of this study show that the lecturer can improve their teaching skills and techniques that are appropriate to the students.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.165
GPT teacher head0.515
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2017
Admission routes1
Has abstractyes

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