It Doesn’t Matter How Many “Doses”: One-Shots Aren’t Cures

Santamaria, Michele; Schomberg, Jessica

doi:10.5860/crl.83.5.851

Cited by 2 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Plus, by participating in this process, academic librarianship would be able to more effectively contextualize the learning it wants to foster and which has "prevented an analysis of how individual students in specific contexts and communities encounter information generally and the library specifically" (Elmborg 2006, 193). By establishing a signature pedagogy that clearly outlines their pedagogical values, then, academic librarians can begin to accurately situate themselves as active participants in an information ecosystem that requires work from everyone, not just those whom we claim to be lacking a dose of information literacy (Santamaria and Schomberg 2022).…”

Section: Concluding Thoughts: a Signature Pedagogy Is Not The End Goalmentioning

confidence: 99%

Visions of the Possible

Arteaga

2023

Can. J. Acad. Lib.

View full text Add to dashboard Cite

In academic libraries, library instruction often takes the form of one-shot instruction and is not always deeply linked to the broader curricula. This article will argue that if library instruction in an academic setting is to be perceived as beneficial to student learning, it needs to coalesce around a set of specific values and teaching practices. To do so, this article will build on scholarship about signature pedagogies to continue the work of identifying a signature pedagogy for academic libraries engaged in information literacy instruction. To Lee S. Shulman, signature pedagogies outline the teaching practices used to educate new professionals, and they are essential to understanding how a profession develops. By participating in the process of developing a signature pedagogy, academic librarians would be able to engage in questions about the nature and purpose of their work, as well as set a foundation for how practicing and developing library professionals are educated and continue to be educated.

show abstract

Section: Concluding Thoughts: a Signature Pedagogy Is Not The End Goalmentioning

confidence: 99%

Visions of the Possible

Arteaga

2023

Can. J. Acad. Lib.

View full text Add to dashboard Cite

show abstract

Predicting student success with and without library instruction using supervised machine learning methods

Harker,

Hargis,

Rowe

2024

PMM

View full text Add to dashboard Cite

PurposeThe main purpose of this analysis was to demonstrate the value of predictive modeling of student success and identify the key groups of students for which library instruction could provide the most impact.Design/methodology/approachData regarding the attendance of library instruction associated with a first-year writing course were combined with student demographic and academic data over a four year period representing over 10,000 students. We applied supervised machine learning methods to determine the most accurate model for predicting student outcomes, including course outcome, persistence and graduation. We also assessed the impact of library instruction on these outcomes.FindingsThe gradient-boosted decision tree model provided the most accurate predictions. The impact of library instruction was modest but still was second only to the previous grade point average (GPA). The value of this metric, however, was greatest for students who were struggling, especially those who were first-generation students, regardless of ethnicity. More notably, the impact of library instruction was substantially greater for specific student demographics, including students with lower cumulative GPAs.Research limitations/implicationsFeatures of the models were limited to high-level academic metrics, some of which may not be very useful in predicting outcomes. Measures more closely related to learning styles, the course or course of study could provide for greater accuracy.Practical implicationsPrediction modeling could allow for a more selective approach to outreach and offers information that the librarian can use to customize instruction sessions and reference interactions.Social implicationsTargeting students who may be at risk of not succeeding in a course has ethical implications either way. If used to bias the subjective assessments, these predictions could produce self-fulfilling prophecies. Conversely, to ignore indicators of possible difficulties the student may have with the material is a disservice to the education of that student.Originality/valueThere are few studies that have incorporated library instruction into models of predicting student outcomes. Library resources and services can play a major role in the success of students, particularly those who have had less exposure to the resources and skills needed to use these resources.

show abstract

It Doesn’t Matter How Many “Doses”: One-Shots Aren’t Cures

Cited by 2 publications

References 3 publications

Visions of the Possible

Visions of the Possible

Predicting student success with and without library instruction using supervised machine learning methods

Contact Info

Product

Resources

About