The Constructivist Approach? I have Heard about it but I have never Seen it “An Example of Exploratory Sequential Mixed Design Study”
Bibliographic record
Abstract
The present study was undertaken to investigate the quality of education based on the views of the students attending social studies education departments at the Faculties of Education and to determine the existing problems and present suggestions for their solutions. The study was conducted according to exploratory sequential mixed method. In line with this, first of all, qualitative data was collected and based on qualitative data, a quantitative data collection tool was developed. The qualitative study group was chosen via criterion sampling, and two-phase cluster sampling method was used to determine the quantitative study group. The qualitative study group is composed of 6 participants and the quantitative group is composed of 1670 students. In line with the responds the participants provided the content was structured and presented under 3 themes. These themes are: 1- Findings with regard to instructional activities of lecturers, 2- Behaviorism instead of constructivism. 3- Findings related to physical, social and cultural environment. It was concluded that quantitative data collected indicate that qualitative data can be generalized and that various factors are influential on education provided at the faculties.
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 | 0.153 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".