Communicating the relevance of the library in the age of Google: Improving undergraduate research skills and information literacy though new models of library instruction
Bibliographic record
Abstract
Most academic librarians have long been aware that the ascent of the Internet has posed a challenge to the primacy of the library as information hub. Recent studies have shown that the majority of undergraduate students do not begin their research in the library, but with Google and Wikipedia - and many students end their research here as well (Connaway, Dickey, & Radford, 2011). This trend would seem to bode ill for the quality of the research skills and the level of information literacy among current undergraduates, as many students privilege convenient access to information over quality of content (Colón-Aguirre & Fleming-May, 2012; Connaway, et al., 2011). But how do we prepare undergraduate students for the rigours of academic research given this circumstance? The library instruction session has been the path to information literacy traditionally taken by colleges and universities, but increasingly, librarians have begun questioning the value of these sessions. Many undergraduates do not find library instruction sessions relevant to their practical information needs and to changing modes of information access, and many students do not come away from library information sessions feeling fully prepared - or even fully willing - to move beyond Google and into the library in order to carry out quality information searches (Colón-Aguirre & Fleming-May, 2012). Indeed, many librarians also now feel that the classic model of library instruction no longer fully meets the information needs of undergraduates nor anticipates their Internet-focused research habits, and that library instruction needs to change dramatically in order to do so (Colón-Aguirre & Fleming-May, 2012; Farkas, 2012). Such means of improving library instruction include: breaking away from the single-session model and moving toward a multiple-session model (Farkas, 2012); incorporating discussion of Internet-based and electronic resources more fully into instruction sessions (Colón-Aguirre & Fleming-May, 2012); tailoring library instruction to course curricula and assignments (Smith, et al., 2012); and incorporating active, student-centred learning into library instruction sessions (Abate, Gomes, & Linton, 2011). The successful implementation of these measures is ultimately dependent upon communication and collaboration among library staff, faculty, and students. Implementing major changes to library instruction can be challenging for all stakeholders; such challenges will be explored in a discussion of the implementation of a prototype library instruction model developed at Selkirk College, a small undergraduate-focused institution in British Columbia, Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".