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Record W4389063786 · doi:10.55016/ojs/ajer.v69i1.71768

Elementary Students’ Online Information Problem Solving (IPS) in a Science Classroom

2023· article· en· W4389063786 on OpenAlexafffundvenue
Qingna Jin, Mijung Kim, Suzanna Wong

Bibliographic record

VenueAlberta Journal of Educational Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsThink aloud protocolFieldnotesPsychologyMathematics educationQualitative researchPedagogyProcess (computing)Computer scienceSociologyEthnography

Abstract

fetched live from OpenAlex

Supporting students to become critical consumers of online information is one of the most urgent agendas in education today. In schools around the world, students are engaging in online information problem solving (IPS) tasks to develop critical thinking and problem-solving skills by searching and evaluating online information. This qualitative case study explored how 5th and 6th Grade students experienced online IPS using their metastrategic knowledge—that is, knowing why and how to use certain strategies in specific instances. Data collection methods included classroom observation, classroom video recording, fieldnotes, the think-aloud strategy, interviews, and students’ reflections about their writing and artifacts. The study’s findings indicated that students actively engaged their metastrategic knowledge during the online IPS processes to understand and examine the validity of information and sources and to effectively communicate their IPS results to others. In the process, students also developed ownership and responsibilities for problem solving with reliable information. Based on the study’s findings, this article summarizes the process and discusses the pedagogical implications of elementary students’ online IPS. Keywords: information problem solving; metastrategic knowledge; information evaluation; elementary science; qualitative research Aider les élèves à devenir des consommateurs critiques d'information en ligne est actuellement l'un des objectifs les plus urgents en éducation. Dans les écoles du monde entier, les élèves s'engagent dans des tâches de résolution de problèmes d'information en ligne pour développer leur esprit critique et leurs compétences en résolution de problèmes en recherchant et en évaluant des informations en ligne. Cette étude de cas qualitative s’est penchée sur l'expérience d'élèves de 5e et 6e années en matière de résolution de problèmes d'information en ligne alors qu’ils utilisaient leurs connaissances métastratégiques, c'est-à-dire qu’ils déterminaient pourquoi et comment utiliser certaines stratégies dans des cas spécifiques. Les méthodes de collecte de données comprenaient l'observation de la classe, l'enregistrement vidéo de la classe, les notes de terrain, la stratégie de réflexion à voix haute, les entretiens, ainsi que les réflexions des élèves sur leurs écrits et leurs artefacts. Les résultats de l'étude indiquent que les élèves ont activement utilisé leurs connaissances métastratégiques au cours des processus de résolution de problèmes d'information en ligne afin de comprendre et d'examiner la validité des informations et des sources ainsi que de communiquer efficacement ces conclusions aux autres. Au cours de ce processus, les élèves ont également développé l'appropriation et la responsabilité de la résolution de problèmes à l'aide d'informations fiables. Sur la base des résultats de l'étude, cet article résume le processus et discute des implications pédagogiques de résolution de problèmes d'information en ligne des élèves du primaire. Mots clés : résolution de problèmes d'information ; connaissances métastratégiques ; évaluation de l'information ; sciences élémentaires ; recherche qualitative

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)0.004
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.095
GPT teacher head0.482
Teacher spread0.387 · 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".

Quick stats

Citations1
Published2023
Admission routes3
Has abstractyes

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