Innovating Education: Development and Validation of the Teacher Adviser Systematic Kit (TASK) Using Microsoft Excel for Enhanced Teacher Productivity
Bibliographic record
Abstract
<p><span>Teachers no longer just teach in the classroom but are also compelled to provide reports required by DepEd personnel. The<span> </span>number of tasks to be done is increasing. Without tools and methodologies that can simplify these tasks, it can become a<span> </span>burden<span> </span>to<span> </span>teachers<span> </span>and<span> </span>may<span> </span>cause<span> </span>a<span> </span>problem.<span> </span>This<span> </span>study,<span> </span>funded<span> </span>by<span> </span>the<span> </span>Basic<span> </span>Education<span> </span>Research<span> </span>Fund<span> </span>of<span> </span>the<span> </span>DepEd,<span> </span>attempts<span> </span>to validate a researcher-developed system called Teacher Adviser Systematic Kit (TASK) that can be used by teachers. The<span> </span>methodology of this study involved a systematic tool validation process and tool application using the Guidelines for the<span> </span>Development and Validation of Spreadsheets by Peter M. Esch following the OECD Principles of GLP from an Article in The<span> </span>Quality Assurance Journal 2010. The TASK was subjected to the different steps in the qualification process, which include<span> </span>Installation<span> </span>Qualification<span> </span>(IQ),<span> </span>Operational<span> </span>Qualification<span> </span>(OQ),<span> </span>and<span> </span>Performance<span> </span>Qualification<span> </span>(PQ).<span> </span>The<span> </span>researchers<span> </span>also<span> </span>used<span> </span>a descriptive qualitative approach to know the respondent’s point of view on the usefulness of the TASK. Respondents<span> </span>strongly agree that the TASK is an effective tool in the efficient and simplified preparation of DepEd school forms that are<span> </span>prepared by the advisers at the end of the quarter and can contribute to the improved and simplified work for teachers.</span></p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".