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Record W2776042168 · doi:10.18438/b8pq1k

Motivational Design and Problem-Based Learning May Increase Student Engagement in Information Literacy Instruction Sessions

2017· article· en· W2776042168 on OpenAlexvenueno aff
Ann Glusker

Bibliographic record

VenueEvidence Based Library and Information Practice · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSession (web analytics)Test (biology)Information literacyRelevance (law)PsychologyPerceptionLibrary instructionMathematics educationMedical educationLiteracyComputer sciencePedagogyWorld Wide WebMedicine

Abstract

fetched live from OpenAlex

A Review of: Roberts, L. (2017). Research in the real world: Improving adult learners web search and evaluation skills through motivational design and problem-based learning. College & Research Libraries, 78(4), 527-551. https://doi.org/10.5860/crl.78.4.527 Abstract Objective – To determine whether the use of the ARCS (Attention, Relevance, Confidence, and Satisfaction) Model of Motivational Design, combined with the Problem-Based Learning approach, improves the skills, confidence, and perception of workshop relevance among non-traditional students in information literacy sessions. Design – Experimental study, one group pre-test and post-test. Setting – Community college in Denver, Colorado, United States. Subjects – 41 community college students. Methods – A convenience sample of three community college student groups each attended an information literacy session. The session was constructed using principles and strategies outlined in the ARCS Model of Motivational Design and the Problem-Based Learning approach. Pre-test and post-test instruments were developed by the author after a literature review. The students were given the information literacy-related pretest before the session. After receiving instruction, the comparable posttest (with different literacy challenges) was administered. Main Results – A comparison of the pre-test and post-test results showed that there were increases in the students’ search skills; their confidence in their own search skills; and their perceptions of workshop relevance in relation to their needs and to real-world situations. Conclusion – This study focuses on the use of motivational design for information literacy instruction. It addresses a gap in the research literature, as it explicitly examines issues of concern regarding the instruction of non-traditional students. The conjunction of the ARCS Model and Problem-Based Learning is considered to be an effective strategy for improving learning and perceptual outcomes for non-traditional students in information literacy contexts. This is important because: 1) information literacy skills are a central aspect of successfully transitioning from the educational setting to the modern workplace; 2) increased confidence can enhance students’ sense of self-empowerment and self-efficacy, as well as decreasing “library anxiety”; and 3) establishing a sense of the personal relevance of information literacy engages students with tools that they can and will actually use in work and life situations. In addition, the author connects these findings to two other areas. One is the new ACRL (Association of College and Research Libraries) Framework for Information Literacy for Higher Education; the author notes that “threshold concepts”, defined by Roberts as “big picture ideas that are foundational to the field”, relate best to teaching techniques such as problem-based learning. The other is the concept of metacognition, which is an aspect of metaliteracy; the author states that the study’s information literacy session addressed three of four metaliteracy goals being considered. Future avenues of research and collaboration will include librarians working with learning scientists around the Framework content; finding new and engaging methods for teaching literacy concepts and assessing learning; incorporating metacognitive awareness into teaching and assessment; and specifically focusing on transferable skills and knowledge, in the service of preparing non-traditional students for the world of work.

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)0.005
metaresearch head score (Gemma)0.014
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 score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.031
GPT teacher head0.356
Teacher spread0.325 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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