The Total Learning Experience Model on the Cloud: TLX Model on the Cloud to Enhance Digital Teaching Skills for Teacher Professional Students
Bibliographic record
Abstract
The total learning experience model on the cloud, or TLX model, is based on the application of concepts concerning the instruction management of teacher professional students, who are required to use technologies and innovations to produce instruction media that enable learners to learn and interact with instructors anywhere and anytime. The instruction management of this style is said to provide challenges and real experiences, leading to new bodies of knowledge and technology skills that help teacher professional students create the more efficient instruction media. The objectives of this research are (1) to design the total learning experience model on the cloud, (2) to develop the total learning experience model on the cloud, and (3) to study the results of the development of the total learning experience model on the cloud. The research tools include (1) the total learning experience process on the cloud, and (2) the assessment form on the suitability of the total learning experience model on the cloud. The results of this research show that (1) the overall suitability of the total learning experience model on the cloud (overall elements) is at the highest level (mean = 4.83, SD = 0.14), and (2) the overall suitability of the total learning experience model on the cloud is at the highest level (mean = 4.77, SD = 0.17). According to the results, it can be summarised that the total learning experience model on the cloud can be employed as a tool to promote learning through cloud technology in order to enhance digital teaching skills for teacher professional students through experiential learning process.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".