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Record W7010453313

The influence of information use strategies on ill structured-domain graduate learning tasks

2013· dissertation· en· W7010453313 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2013
Typedissertation
Languageen
FieldComputer Science
TopicInformation Retrieval and Search Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsConstruct (python library)Process (computing)Set (abstract data type)Task (project management)Information systemInformation scienceHigher educationActive learning (machine learning)Learning sciences
DOInot available

Abstract

fetched live from OpenAlex

This dissertation presents a user-centred, interdisciplinary study on information use strategies. In complex learning contexts such as ill-structured-domains, there are three ways of using information. The first is to make sense of the information fragments to construct a cohesive whole. The second is to integrate this newly constructed whole into the learner's existing knowledge structures. The third is to make use of this integrated entity to achieve the learning goals. The Information Science literature refers to this three-fold process as "using information". A number of strategies can be applied to using information in order to help learners address the challenges of using information within these complex contexts. This dissertation study is a qualitative inquiry on the influence such strategies can have on using information to complete ill-structured-domain learning tasks.This was a naturalistic study designed to better understand both the general nature of the information use process and the influence of strategies on this process. The study consisted of a user-centred study that examined the information use process of nine graduate students enrolled in the faculty of Education at McGill University over the period of an academic semester. This investigation focused on two graduate level ill-structured-domain learning tasks. The first was to produce a set of reflective critiques on the required course readings over the course of the term, one for each theory studied. The second task was a final term paper that proposed research based on the theories studied in the course. Learners were required to synthesize the theories and the course content in order to propose a research study relevant to the field of Education. Both tasks required the use of higher order thinking skills. The information use strategies used by students during these two tasks were studied in order to better understand their nature and thus make an original contribution towards theory and practice in the Information Science. The two frameworks developed by this study represent theoretical contributions. The first framework provides an overview of the information use process in an ill-structured-domain learning task. The second framework demonstrates the influence of strategies on this process. These two frameworks could be used as springboards for future Information Science research.These frameworks could also be used to design instructional programs in such a way as to improve student performance. This represents a contribution to professional practice. The user-centered approach provides insights on factoring in user subjectivity into these programs. The framework for information use strategies can help inform learners on how to construct dynamic and optimal strategies and plans that are best adapted to their learning goals and to the progressive stages of using information.A number of interdisciplinary propositions guided this investigation. The investigation of the information use process was predominantly guided by Dervin's (1983) “sensemaking approach” and Spiro's (1987) "cognitive flexibility theory". The analysis of strategies in the information use process relied mostly on the Education literature and employed Pressley's (1989) "strategy oriented perspective" as the key theoretical concept.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.011
Open science0.0020.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.253
Teacher spread0.233 · 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 designTheoretical or conceptual
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

Citations0
Published2013
Admission routes1
Has abstractyes

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