Integrating Reading and Technology: The Development of Pamanpintermu
Bibliographic record
Abstract
<p>Reading as one of English skills has paramount features in shaping EFL English competence. Referring to the importance for reading, it is inevitable that teaching method, assessments tools, reading material and activities have indispensable tasks to attain EFL learners’ reading objectives. This study is intended to develop integrated reading material using PHP software. It is designed to react toward the vast development of technology and to reach the attainment of more comprehensive reading objectives in accordance with International Reading Association’s views which is not achieved yet in today’s EFL teaching context. The study utilizes five phases Research and Development model covering need analysis, design, development, Focus Group Discussion and Try out. The development invented 10 units of reading material within <em>Pamanpintermu’s</em> program containing audio vocabulary survey, timed reading, audio reading, comprehension task, writing sections and its integrated auto assessment devices in every unit. The results from FGD and try out revealed that the theoretical foundation and syntax were categorized into high as it reached the average score of 3.7. In addition, content relevance achieved the average score of 3.9 as high and difficulty level reached 2.3 as medium. Meanwhile, the category of integrated reading skills and auto scoring obtained 3.6 and 3.8 and both categories belonged to high level. The last point, software practicality achieved 3.5 is very high as it is also applicable for teacher made reading material automatically through modifying the reading text, audio, exercises and score thoroughly. Toward overall astonishing prototype <em>Pamanpintermu</em>, it remains one problematic point on the error reading detector section which cannot detect users’ errors reading automatically as it requires intensive investigation from different background field of studies.</p>
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".