The Application of Behavioral and Constructivist Theories in Educational Technology
Bibliographic record
Abstract
The educational technology designer should know about the learning theories to analyze the needs and design the contents in terms of the target that aimed to reach from learning operations. Educational technology is known as a process that includes many factors, which will provide a good simulation for the students. This research study used a basic qualitative study because it interprets an educational field experience. This is the type of research most commonly used in the fields of education, health, and social work because it is interpretative research. The participants in this study were two college instructors and 10 college students who volunteered to participate in this study. All participants have teaching experience and work in the same school. This study concludes that mixing instructional technology with behavioral theory provides an opportunity to stimulate students through psychological conditions by direct experiences and activities inside the classroom. Mixing instructional technology with constructivist theory allows educators to focus on how students can explore and discover class content and new experiences by explaining their mistakes, ideas, and experiences and the basics of the content.
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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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".