Critical Thinking and Writing Informational Texts in a Grade Three Classroom
Bibliographic record
Abstract
In this chapter, the authors present a case study that explores grade three students' work with informational text over a month-long unit in order to document the students' developing thinking skills about text structures and features. Students were introduced to informational mentor texts to discover insight into expository text structures and create their own “All About…” books using their own background knowledge and interests. In writing their own informational texts, the students were encouraged to use a variety of visual representation formats such as lists, checklists, and diagrams. They also used common expository text structures found in informational trade books including description, sequence, and comparison. These structures provided an overall framework for students to organize their writing and use the skills of conceptualizing, applying, synthesizing, and evaluating their knowledge. One of the primary successes of the unit for developing students' critical thinking was the opportunity to teach others about an area of expertise. Scaffolding for student success in a variety of ways throughout the writing process was also important for student learning. Choosing mentor texts with text features and visuals that were desired in the students' finished pieces provided concrete examples for the class. Overall, the reading and writing of informational text was successful in promoting the development of important thinking skills that support students' need to critically evaluate information from a variety of sources.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".