Embedded Library Guides in Learning Management Systems Help Students Get Started on Research Assignments
Bibliographic record
Abstract
A Review of:
 Murphy, S. A. & Black, E. L. (2013). Embedding guides where students learn: Do design choices and librarian behavior make a difference? The Journal of Academic Librarianship, 39(6), 528-534. http://dx.doi.org/10.1016/j.acalib.2013.06.007 
 
 Abstract
 
 Objective – To determine whether library guides embedded in learning management systems (LMS) get used by students, and to identify best practices for the creation and promotion of these guides by librarians.
 
 Design – Mixed methods combining quantitative and qualitative data collection and analysis (survey, interviews, and statistical analysis).
 
 Setting – A large public university in the United States of America.
 
 Subjects – 100 undergraduate students and 14 librarians.
 
 Methods – The researchers surveyed undergraduate students who were participating in a Project Information Literacy study about their use of library guides in the learning management system (LMS) for a given quarter. At that university, all course pages in the LMS are automatically assigned a library guide. In addition, web usage data about the course-embedded guides was analyzed and high use guides were identified, namely guides that received an average of at least two visits per student enrolled in a course. The researchers also conducted a qualitative analysis of the layout of the high use guides, including the number of widgets (or boxes) and links. Finally, librarians who created high use library guides were interviewed. These mixed methods were designed to address four research questions: 1) Were students finding the guides in the LMS, and did they find the guides useful? 2) Did high use guides differ in design and composition? 3) Were the guides designed for a specific course, or for an entire department or college? and, 4) How did the librarians promote use?
 
 Main Results – Only 33% of the students said they noticed the library guide in the LMS course page, and 21% reported using the guide. Among those who used the guide, the majority were freshmen (possibly because embedding of library guides in the LMS had just started at the university). Library guides with high use in relation to class enrollment did not significantly differ from low use guides in terms of numbers of widgets and links, although high use guides tended to have slightly fewer widgets. Of those guides, 55% were assigned at the course level, 30% at the department level and 13% at the college level. Over half the librarians with at least one high use guide conducted a library instruction session in which they used or promoted that guide. For 39% of the courses with high-use guides, the librarian was actively engaged with the faculty and students via the LMS, but others reported no specific involvement in courses. 
 
 Conclusion – Those students who used library guides reported the guides helped them get started on their research paper or assignment and find research materials, two areas for which previous studies show students have great difficulty. Since the majority of students did not notice the link to the library guide in the LMS, librarians could emphasize it in the news section of the course, which gets much more attention. Within library guides, simpler groupings of links might be easier for students to use, but this conclusion would require further research to confirm. In any case, nearly half of all high use guides were not promoted in any way by librarians, but simply automatically embedded in the LMS, a sign that passive embedding may provide an easy way for the library to reach a large number of students early in their academic career. Since the automatic embedding of guides began, guides have seen a dramatic increase in usage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.646 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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; both teacher heads agree on what is shown here.
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".