Analysis of English Language Textbooks of the Iran Language Institute In Order to Specify the Student’s Involvement Index of the Teaching Learning Process
Bibliographic record
Abstract
Increasing students’ involvement in learning activities is one of the most important methods in effective teaching and learning process. When students are actively involved in the learning task, they learn more than when they are passive recipients of instruction. Text booksare the most important elements and have a very crucial effect in the process of language teaching and learning while prepared and developed by considering the potential educational usage and designed based research studies and modern educational technologies to meet the needs of diverse learners and enable pupils to engage actively with lessons to develop their thinking and interpreting power. The purpose of this study was analyzing the text content of the Iran English language textbook at the language institute to identify whether the contents in the textbook appropriate for involving learners in the learning task and may contribute to improve learning? The content of English language textbook for the adult learners was analyzed by criteria derived from adapting the involvement developed based on William Rummy technique. The content analysis covered the components of the textbook such as texts, activities, questions, and figures and diagrams. The results revealed that only the texts, questions, and activities given in the textbook encourage student’s involvement in the teaching and learning process.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".