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Record W2766113159

Science Students' Document Literacy Skills

2006· article· en· W2766113159 on OpenAlexaboutno aff
Silvia d'Apollonia

Bibliographic record

VenueeScholarship (California Digital Library) · 2006
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsnot available
Fundersnot available
KeywordsScientific literacyMathematics educationConstruct (python library)Test (biology)Variety (cybernetics)Cognitive skillLiteracyPsychologyCognitionTask (project management)Representation (politics)Information literacyComputer scienceScience educationPedagogyArtificial intelligenceEngineering
DOInot available

Abstract

fetched live from OpenAlex

Science Students’ Document Literacy Skills Silvia d’Apollonia (sdapollonia@place.dawsoncollege.qc.ca) Dawson College and Concordia University 3040 Sherbrooke W., Montreal, QC, H3Z 1A4, Canada Introduction Science students are expected to interpret, reason, and construct charts, graphs, and flow charts in many of their science courses. These are cognitively complex skills, involving interactions among three factors: the cognitive skills of the student, the properties of the graphical representation, and the task demands (Peebles & Cheng, 2003). In most science courses, students are exposed to a variety of graphical representations, but are rarely explicitly taught the underlying structure of such representations. graphs. Moreover, directions. many have difficulty following Table1. Documentary Literacy for College Science Students N The recent advances in graphical technologies have stimulated interest in external cognition (Scaife & Rogers, 1996). Moreover, several instruments (ALLS, IALS, TOWES) measuring document literacy (i.e., the knowledge and skills required by adults to locate and use information from complex documents containing graphical representations such as tables, maps, diagrams, and flow charts) have been developed. Lev Methodology As part of a larger study, investigating students’ co- construction of conceptual understanding of mechanics, we explored students’ document literacy. Subjects Forty-one students (between the ages of 17 and 19) at an urban CEGEP, volunteered to take a document literacy test. Of these, 31 completed the test. Measures Twenty tasks (5 questions assessing each of four levels) were taken from the TOWES (TOWES, 2004). Their written responses were then compared to the answer key provided by TOWES. Students were required to score at least 80% in order to be categorized as achieving each level. Task Characteristics locating a single piece of information by matching the information required with information presented in an identical form; entering a specific piece of information into a given form; locating multiple pieces of information by repeating a limited search. In all tasks there is no ambiguity and students are not required to make any inferences. locating and entering information by comparing the information given and the information required; locating a single piece of information by matching ambiguous information or eliminating distractors; locating multiple pieces of information and making some limited analysis; locating one piece of information using low level inference. . In all tasks students are required to use work with multiple pieces of information and go slightly beyond what is given. comparing and analyzing information from multiple searches from multiple document types; integrating information from different parts of a document or from different document types. integrating and synthesizing information using high-level inferences; locating information in one format and reorganizing it in another format satisfying several conditions Acknowledgments Results and Discussion This research was funded by Programme d'aide a la recherche sur l'enseignement et l'apprentissage and Fonds quebecois de la recherche sur la societe et la culture. Most of the science students had surprisingly low levels of document literacy (see Table 1). More than 90% of the students were only at level 2, indicating that they could only deal with graphical representations which were clear, simple, and/or explicitly described. Although these students have adapted their literacy skills to everyday life, they have great difficulty with many of the reading tasks found in university science courses or in jobs requiring science degrees. Interviews with the students suggest that many students have only a superficial understanding of tables and References Peebles, D., & Cheng, P.C.-H. (2003). Modeling the effects of task and graphical representation on response latency in a graph reading task. Human Factors, 45, 28-45. Scaife, M. & Rogers, Y. (1996). External cognition: how do graphical representations work? Int. J. Human-Computer Studies, 45, 185-213. Towes (2004)http://measureup.towes.com/english/index.asp

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)0.004
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 score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.002

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 head0.009
GPT teacher head0.298
Teacher spread0.290 · 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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Citations0
Published2006
Admission routes1
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