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Record W1997475288 · doi:10.1037/0022-0663.94.2.278

Relationships between instructional language and primary students' learning.

2002· article· en· W1997475288 on OpenAlexaff
Judith C. Lapadat

Bibliographic record

VenueJournal of Educational Psychology · 2002
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsPsychologyMathematics educationPrimary educationLanguage acquisitionPedagogyCognitive psychology

Abstract

fetched live from OpenAlex

Causal modeling was used to examine how primary students’ language ability interacted with pace and redundancy of instructional language during an expository science lesson to explain students’ attention and learning. Language ability and pace of the instructional language, mediated by students’ attention to the lesson, accounted for significant variance in learning outcomes. Higher language ability related to greater learning. Overall, slow-paced instructional language was positively related to learning, but students attended less to it than to fast-paced talk. Students with special needs attended significantly less and learned less, whereas students not so identified attended slightly less but learned more. Teachers’ talk matters, but because instructional language impacts differentially on students, heuristics for modifying it are not straightforward. In communicative settings involving large groups, most individuals do more listening than talking. This is particularly so for students in many elementary classrooms, where the teacher does up to 80% of the talking, and the remaining 20% of the talking time is shared by all of the students (Cazden, 1988). Despite recent changes in educational practices (Lapadat 2000a, 2000b; Pappas, Kiefer, & Levstik, 1999), this nonreciprocity of talking time persists across educational levels. In such classrooms, students’ receptive language knowledge and the listening strategies they use have a close relationship with their academic learning outcomes. However, even in classrooms where talk is distributed more equitably than in typical whole-class expository teaching, such as in constructivist, small group, and cooperative learning models (Lauritzen & Jaeger, 1997), students’ ability to engage, listen, and respond are still important determinants of both communicative and academic success. Roth and Spekman (1989) called for more research focusing on students’ knowledge of and ability to understand and use language, pointing out that this is crucial in coming to understand how students understand, participate in, and learn from the interactions that make up instruction. Even during expository instruction, talk is constructed interactively (O’Connor & Michaels, 1996; Pappas et al., 1999). Just as young primary school students have the task of attending to and understanding the teacher’s talk (and peers’ talk) in order to learn in school, so too does the teacher need to talk in ways that students understand in order to teach effectively (Derwing, 1991). If students have insufficient linguistic knowledge or do not engage, or if teachers fail to adjust their instructional language, academic learning will be affected. As expository instruction is so frequently used in schools, it is all the more important to do it well. In classrooms in which talk is distributed differently than in wholeclass expository teaching, teachers’ effective use of instructional language in small groups or with individuals, as well as in segments of whole-class instruction, remains a critical component of effective teaching (Lindsay, 1996; Merritt, 1982; O’Connor & Michaels, 1996). This is a challenging task, as students bring diverse understandings to learning, based on their prior knowledge, language skills, cognitive processing abilities, cultural perspectives, motivations, and interests.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.075
GPT teacher head0.348
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2002
Admission routes1
Has abstractyes

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