The Predictive-Observation-Explanatory (POE) Technology based Learning Management Results to Promote Scientific Explanations Making about the Change of the Substance for Primary School Students
Bibliographic record
Abstract
The research aimed to 1) build and assess suitability of the learning management plan of the predictive-observation-explanatory (POE) technology based learning approach to promote scientific explanation making about the change of the substance for primary school students, 2) to distill the lesson learnt of the predictive-observation-explanatory (POE) technology based learning approach to promote scientific explanation making about the change of the substance for primary school students. The research was action research. The sample in the research consisted of: (1) a group of experts assessing the learning management approach, namely staff of teachers, teachers of science and experts of science learning management, accounting for 9 people and (2) the experimental group of learning management, namely 3 science teacher and 40 Year 5 primary school students. Purposive sampling was used to come up with a total of 52 people. The study results revealed that: 1) Building and assessing suitability of the learning management plan of the predictive-observation-explanatory (POE) technology based learning approach to promote scientific explanation making about the change of the substance for primary school students has brought about the learning management plan for 3 learning management plans by using the total of 3 hours for learning. There is the process in organizing learning activities for 7 steps called “7P POE Technology based Learning Model” consisting of (1) Positive Stimulate, (2) Pre-debate, (3) Predict, (4) Post-debate, (5) Participant Observation, (6) Phenomenon Explanation and (7) Practice and the result of suitability assessment was at the highest level. 2) Distilling the lesson learned of the predictive-observation-explanatory (POE) technology based learning approach to promote scientific explanation making about the change of the substance for primary school students has found that scientific learning competency called (Scientific Explanations Making Concepts consists of (1) Positive Definition, (2) Scientific Phenomenon Prediction and (3) Logical Thinking. This is important scientific learning competency which should be developed to primary school students in the future.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".