Application of Needham’s Five Phase Constructivism Model in (Civil, Electrical and Mechanical) Engineering Subject at Technical Secondary School
Bibliographic record
Abstract
The aim of this study is to find out the application of Constructivism Model based on the Needham’s Five Phase Constructivism Model in (Civil, Electrical and Mechanical) Engineering Studies at Technical Secondary School. The focus of this study is to describe the 5 (five) phases in this model which are orientation phase, generation of ideas, restructuring ideas, application of ideas and reflection phase. Qualitative research methodology is used in this study. A total of 40 teachers who teach Engineering Studies subject at Technical School in Selangor and Malacca were involved as a sample to this study. Questionnaires were used as the research instrument. The data was analyzed using Statistical Package for Social Science (SPSS) version 17.0 and presented in the form of frequency, percentage and mean. This study show that teacher applied the Needham’s Five Phase Constructivism Model in Engineering Studies (mean = 3.63). In the other hand, the most often phase which applied in the teaching and learning process is the restructuring of idea phase (mean = 3.78) and the orientation phase (mean = 3:46) is a phase of less applied by teacher. Although the Needham’s Five Phase Constructivism Model is implemented by the teacher but there is still have some problems in implement the model (mean = 2.85). From the findings, it is suggested to the teachers to be exposed in a variety of teaching models that can improve the quality of teaching and learning process in the classroom.
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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 | 0.030 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".