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Librarians Aren’t Born with Information Superpowers: Leveling the Playing Field for Incoming Library Science Graduate Students

2016· article· en· W2582556779 on OpenAlexvenueno aff
Annette Lamb

Bibliographic record

VenuePartnership The Canadian Journal of Library and Information Practice and Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsSummative assessmentFormative assessmentInformation literacyLibrary scienceGraduate studentsInstructional designLibrary instructionComputer scienceMathematics educationMedical educationPedagogyPsychologyMedicine

Abstract

fetched live from OpenAlex

Students enter the library science graduate program with a wide range of information and technology skills. Today’s graduate courses require students to be able to build web-based pathfinders, use social media, and search databases. This article examines the design and development of an introductory course for incoming library science graduate students that personalizes instruction and ensures that each student is ready for the rigors of graduate school. Taken during the first semester of the program, this introductory course teaches information and technology skills and concepts that are core to library science. The author explores the process of creating a computer-based course that addresses the diverse needs of this student population. Using a systematic approach to instructional design and development, the author outlines the steps in designing, developing, implementing, and evaluating an online, self-paced graduate course. Based on the Dick and Carey model, the process included identifying the instructional goals, conducting an instructional analysis, analyzing learners and contexts, writing performance objectives, developing assessment instruments, developing instructional strategies, developing and selecting instructional materials, designing and conducting formative evaluation of instruction, revising instruction, and designing and conducting summative evaluation. This process produced effective, efficient, and appealing instructional materials.
 Les étudiants entament un programme d’études supérieures en sciences de l’information avec une panoplie d’habiletés en matière d’information et de technologie. Les cours d’études supérieures requièrent aujourd’hui que les étudiants puissent créer des guides en ligne, utiliser des médias sociaux et interroger des bases de données. Cet article porte sur la conception et le développement d’un cours d’introduction qui est offert aux nouveaux étudiants diplômés en sciences de l’information et qui cherche à individualiser la formation et à s’assurer que chaque étudiant se sent prêt pour les rigueurs d’une école d’études supérieures. Ce cours d’introduction, offert durant le premier semestre du programme, enseigne des habiletés en matière d’information et de technologie essentielles aux sciences de l’information. L’auteure relate le processus entrepris pour créer ce cours informatisé qui répond aux divers besoins des étudiants. En utilisant une approche systématique pour la conception et le développement pédagogique, l’auteure présente les étapes liées à la conception, le développement, la mise en œuvre et l’évaluation d’un cours de maîtrise en ligne adapté au rythme de chacun. Selon le modèle de Dick et Carey, le processus permet d’identifier des objectifs d’apprentissage, mener une analyse pédagogique, mener une analyse des apprenants et des contextes, écrire des objectifs de rendement, développer des instruments d’évaluation, développer des stratégies d’enseignement, développer et choisir du matériel didactique, concevoir et mener une évaluation formative de l’enseignement, réviser l’enseignement ainsi que concevoir et mener une évaluation sommative. Ce processus a fourni du matériel didactique efficace, efficient et attrayant.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0060.336
Open science0.0010.000
Research integrity0.0000.000
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 head0.092
GPT teacher head0.381
Teacher spread0.289 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations2
Published2016
Admission routes1
Has abstractyes

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