projects outside of classroominstruction[19].Before we develop instruction plans and curricular materials, it is helpful to consider whatapproaches are being taken in higher education. While this topic is emerging, many of thecurrent examples include courses that focus on English composition using AI tools suchChatGPT [20], [21] and efforts to teach prompt engineering [22], however our project aims to gobeyond those specific tools and skills. Following are examples of AI literacy instruction that hasbeen integrated into existing courses.Fyfe included AI literacy instruction in a course titled “Data and the Human.”[23] With supportfrom the University Library in using AI tools, students wrote an essay that integrated contentfrom a text-generating
), one tool deployed to help students navigatethe information landscape is IF I APPLY. This method uses two parts, the personal and theresource, to help users negotiate source evaluation. Reviewing a researcher’s own emotions andintellectual courage helps to limit the confirmation bias and reach students in a novel way.Through this paper, the authors plan to review selected evaluation methods before diving into adeeper explanation of the IF I APPLY tool. Finally, some examples from use in the classroomare shared. By exploring the changing face of source evaluation in the Penn State Universityengineering classroom, readers will better understand why it is important to put the student at thecenter of their own evaluation.Literature ReviewCRAAPOver
to understand howsearchers use particular mental models to find the information they need [25]. To collect thisvital information, it is necessary to expand this study’s design beyond self-reporting and includeseveral different knowledge elicitation tools in order to more accurately understand cognition incontext.Conclusion This exploratory CTA study provides data that highlight several areas where engineeringlibraries may focus their efforts to improve student search outcomes. It also confirms andextends existing literature in this area [7, 10]. We are planning a follow up study that willexplore how a larger group of undergraduate students use public search tools, the library’s searchtool, what they expect from each one, and where
Conference.References[1] P. Hernon et al., Statistics for Library Decision Making: A Handbook, Norwood, NJ, USA:Ablex Publishing Company, 1989.[2] J. Marquez & A. Downey, “Service Design: An Introduction to a Holistic AssessmentMethodology of Library Services,” Weave: A Journal of Library User Experience, vol. 1, no. 2,2014, doi: 10.3998/weave.12535642.0001.201.[3] “College Facts,” College of Engineering, https://engineering.uiowa.edu/college/college-facts(retrieved Jan. 18, 2024).[4] L.R. Horowitz, “Assessing Library Services: A Practical Guide for the Nonexpert,” LibraryLeadership & Management, vol. 23, no. 4, pp. 193-203, Fall 2009.[5] S. Hiller & J. Self, “From Measurement to Management: Using Data Wisely for Planning andDecision-Making
in their classes. One stated, “I definitelyencourage them to go online and search, there’s so much information out there.” Another stated,“it’s important for [the students] to understand the difference between just searching the web andgoing into a more reliable source.” Faculty from both engineering and business pointed out avariety of resources they talk about with their students ranging from databases like Factiva andWeb of Science to other free and governmental resources, such as EuroMonitor and the Bureauof Labor Statistics. Another faculty member confirmed that they do take class time to talk aboutthe research process as a whole, “how to plan for it, how to search, how to screen results andnarrow down.”Others expressed it is an area
andpublishing patterns.Journal articles and conference proceedings were originally planned as the formats to beexamined but as the research proceeded it was determined that a) there was sufficient material toexamine the patterns focusing only on the journals and b) that publishing in conference venueswas potentially different enough to warrant a separate study. Finally, as neither of the authorswas fluent enough in other languages to warrant including them, only articles published entirelyin English were included, i.e. an English abstract alone was insufficient for a study to beincluded. This filter was applied inconsistently by the database vendors so some of the originalnumbers include papers that were written in a language other than English but
editions. This demonstrates theneed for clearer communication of what books are required and what editions are acceptable toavoid unnecessary costs.ConclusionsThough the survey indicates that students are spending $100 per quarter or less, we feel that thisis not representative of what students are being asked to spend on textbooks for their courses.Because of this, we are still pursuing ways to make textbooks more affordable to students in theschool of engineering and across Dartmouth. We conclude that there are several ways to reducestudent textbook costs at the school of engineering.For professors interested in reducing costs but still planning to use a textbook from a publisher,considering the upfront cost of the current edition and being open
data scientist would be thenatural instructor to lead the data-analysis side of the project, an academic librarian would bemost appropriate to discuss the information literacy skills. Accordingly, we developed a programwhere the data science projects would also include an information literacy facet to be guided byan engineering librarian.This work-in-progress paper discusses the lessons learned from this initial collaboration,feedback from the students who participated in the project, and our plans to continue thecollaboration into the Spring 2025 semester. Quantitative surveys of students in the projectsexhibited positive trends with respect to their familiarity with statistical concepts and increasedconfidence in their data science skills
theyare at the author’s institution). Indeed, in previous instructional plans (see [13]) the author foundthat combining search strategy in the same lesson as source evaluation limited the depth withwhich this important element could be explored and practiced by students.To remedy this, the author has created an EDM for a pre-class assignment that focusesexclusively on source evaluation principles and thus provides for a look at a wider variety ofengineering-related sources and prompts students to think more deeply about a series ofquestions to ask when evaluating sources. This approach assumes all discussion of searchstrategy can take place during an in-class session. A simplified version of this EDM is shown inFigure 3.As shown, the initial
in academic and research libraries.Keywords: Bibliometric Analysis, Automated Reporting, Data Extraction, Research Impact,Academic LibrariesIntroductionEvaluating research impact and analyzing scientific collaborations are fundamental tasks thatcontribute to the advancement of knowledge and strategic planning in academic institutions.Bibliometric analysis is a crucial tool in this context, as it provides quantitative measures ofscholarly output and influence 1,2 . Bibliometrics uses citation counts, publication metrics, andcoauthorship networks, allowing researchers and funding agencies to assess the dissemination andreception of scientific work 3,4 .Bibliometric analysis has evolved significantly over the past decades. Foundational works