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Record W2304933863 · doi:10.14288/1.0094096

The performance of second year primary children on missing addend sentences

2010· article· en· W2304933863 on OpenAlexaboutno aff
Heather Kelleher

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsnot available
Fundersnot available
KeywordsPrimary (astronomy)Missing dataDemographyStatisticsMathematicsPhysicsAstrophysicsSociology

Abstract

fetched live from OpenAlex

This study examined the accuracy, solution strategy use, and level of response use of second year children when solving missing addend problems at four levels of difficulty and in two placeholder positions. The relationship between these aspects of Missing Addend performance and performance on a measure of Class Inclusion ability, was then examined. Subjects of the study were 40 year two students from an urban community in British Columbia, Canada. A Missing Addend Test and a Class Inclusion Test were administered individually to all subjects. The level of difficulty of the Missing Addend Test items (as defined by the magnitude of the constants) affected accuracy. Process errors were more common than conceptual errors as the difficulty of the item increased. The level of difficulty of the item also affected the child's level of response. Children tended to use more externalized processes and concrete aids as the difficulty increased. The level of difficulty did not, however, appear to affect strategy choice to the same degree. Placeholder position was found to have little or no effect on children's accuracy, strategy choice, or level of response use. Children interpreted the missing addend as a situation requiring an incrementing, or additive process in 68% of the cases. In 9% of the cases they used decrementing or subtractive processes. Children used recall of basic fact combinations to solve 9% of the items. Conceptual misinterpretations of the number sentence, as indicated by the use of an incorrect sentence transformation, occurred in 8% of the examples. Children omitted items or used unidentifiable processes in 6% of the examples. Of the 248 items where an additive or subtractive process was used, by far the preferred process was a counting procedure. Two counting procedures were particularly popular: Counting-All and Counting-On. Other identified strategies were Semi-Guesses, Substitution procedures, and procedures involving Associative reasoning. Concrete materials were used in 45% of the examples, and usually in association with Semi-Guess, Substitution, and Counting-All strategies. Internalized reasoning procedures were used in 37% of the examples, and usually in association with the Recall and Associative strategies. Fingers were used as aids for 17% of the examples, and were used almost exclusively with the Counting-On strategy. It was concluded that the use of fingers provided a valuable transition between external and internalized solution procedures. It was also concluded that the ability to count-on was key to the development of more sophisticated solution processes. Class Inclusion performance was found to be positively related (p<.05) to Missing Addend performance. Class Inclusion performance was significantly related to the use of two solution strategies. The use of Recall was positively related (p<.01) and the use of an Incorrect Transformation was negatively related (p<.01). Class Inclusion performance was not related to the use of any of the identified levels of response.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.008
GPT teacher head0.190
Teacher spread0.182 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations1
Published2010
Admission routes1
Has abstractyes

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