Examining the literacy component of science literacy: 25 years of language arts and science research
Bibliographic record
Abstract
This review, written to celebrate the 25th anniversary of the International Journal of Science Education, revealed a period of changes in the theoretical views of the language arts, the perceived roles of language in science education, and the research approaches used to investigate oral and written language in science, science teaching, and learning. The early years were dominated by behavioralist and logico-mathematical interpretations of human learning and by reductionist research approaches, while the later years reflected an applied cognitive science and constructivist interpretations of learning and a wider array of research approaches that recognizes the holistic nature of teaching and learning. The early years focus on coding oral language into categories reflecting source of speech, functional purpose, level of question and response, reading research focused on the readability of textbooks using formulae and the reader's decoding skills, and writing research was not well documented since the advocates for writing in service of learning were grass roots practitioners and many science teachers were using writing as an evaluation technique. The advent of applied cognitive science and the constructivist perspectives ushered in interactive-constructive models of discourse, reading and writing that more clearly revealed the role of language in science and in science teaching and learning. A review of recent research revealed that the quantity and quality of oral interactions were low and unfocused in science classrooms; reading has expanded to consider comprehension strategies, metacognition, sources other than textbooks, and the design of inquiry environments for classrooms; and writing-to-learn science has focused on sequential writing tasks requiring transformation of ideas to enhance science learning. Several promising trends and future research directions flow from the synthesis of this 25-year period of examining the literacy component of science literacy - among them are critical listening and reading of various sources, multi-media presentations and representations, effective debate and argument, quality explanation and the role of information and communication technologies/environments.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".