This activity involves students working in groups to construct and organize an evaluative annotation paragraph. Each group focuses on different aspects of an article, including summarizing sections, evaluating content and target audience, and researching the author. This collaborative approach reduces intimidation, promotes teamwork, and builds essential research and documentation skills.

The two learning activities outlined in this PowerPoint will aid in developing students’ understanding of spatial epidemiology and the intersectionality of socio-economic and environmental factors. Task 1 involves a short presentation on the software SaTScan covering cluster analysis and including the data types needed for the analysis. Following this, students receive a scenario-based task using a pre-designed hypothetical dataset of the spread of a contagion in the UK. Students input the appropriate text files, developed from contagion datasets, into SaTScan to produce a cluster analysis of unusually high rates of contagion in the region. This task allows for many different manipulations and outputs from the analysis by changing the parameters, such as cluster size and type of analysis in the software. Guidance on the use of SaTScan for the lesson is provided for teachers to help students understand and interpret output using multiple parameter settings. In brief, SaTScan tests the null hypothesis that cases of disease are randomly distributed. Statistical significance suggests that unusual spatial clustering is unlikely to have occurred by chance. This method has been used previously to identify clusters of contagious diseases, such as malaria,23 HIV,24 tuberculosis,25 as well as chronic diseases.26 The output of this session will be a cluster analysis Keyhole Markup Language (.kml) file which shall be used in task 2.Task 2 utilises the cluster outputs produced in task 1. Students then import the .kml cluster analysis layer produced by SaTScan into ArcGIS (or QGIS if preferred) and overlay this layer over a publicly available dataset that contains multiple spatial indexes. This socio-economic dataset uses real-world data on a region of the UK. This process allows students to visually explore the possible characteristics of a region that may explain where clusters fall. For example, students can choose to layer a measure of deprivation (The Index of Multiple Deprivation) over cluster output to visually examine the socio-economic characteristics of individual clusters. The assessment for this task is a student-led presentation and discussion based on their own critical thinking about which factors may predict cluster membership as well as maps that reflect these ideas. This activity is designed to help students develop their visual presentation and interpretation skills and become familiar with linking spatial factors to epidemiological trends.This activity is associated with a chapter in Spatial Literacy in Public Health: Faculty-Librarian Teaching Collaborations (ACRL, 2024).

Public health concerns are often multi-faceted, complex problems. Any exploratory analysis conducted to support public health concerns should be scalable to this multi-attribute nature. This activity pairs with a chapter focused on spatial clustering techniques for multivariate analysis, which can reveal the locations with unusually high or low occurrences of multiple diseases. We will use obesity and insufficient sleep, which often occur together, as conditions to analyze considering their serious impacts on public health. Once contributing factors are determined, policymakers can be informed so that they can begin to address the negative impacts.In the activity, students will use SaTScan to find spatiotemporal hotspots and coldspots in obesity and insufficient sleep data representing American children. After a brief Q&A session, students will follow the walkthrough to complete the spatial clustering with SaTScan (Version 9.4). (SaTScan is a free software designed to detect clusters of spatial, temporal, or spatiotemporal data using scan statistics. This allows for complex relationships within the data to be revealed and explored. You can download SaTScan at https://www.satscan.org/.) The lecture can be closed with a discussion session where students will evaluate the statistically significant hotspots where they are found.LEARNING OUTCOMESBy the end of this lesson, students will be able to• analyze multivariate spatiotemporal data on obesity and insufficient sleep among children by using SaTScan;• interpret and evaluate hotspots and coldspots regions; and• create spatial cluster maps by handling spatial data files.This activity is part of Spatial Literacy in Public Health: Faculty-Librarian Teaching Collaborations (ACRL, 2024).

The lesson covers using PolicyMap and Social Explorer to visualize socioeconomic variables. These two mapping tools let learners view geographic distribution of variables (e.g., evidence) and begin their exploration of spatial literacy. In addition, the visualizations can lead learners to question assumptions and examine the impact of social determinants of health among other issues. The lesson walks learners through using these tools to examine issues, and combine a narrative and visualizations to write a policy report.

The worksheet, activity, slides, and library instruction session outline for this assignment are a methodology for integrating information literacy and library research into testimonio writing in a first-year undergraduate Introduction to Higher Education (IHE) course in the College of Education at California State University, Los Angeles. In the testimonio, students reflect upon and write about their educational experiences while integrating academic sources into their work.

A group of four librarians from varied disciplinary backgrounds came together to examine issues of artificial intelligence and large language models. We are of the opinion that Pandora's box has been opened. Students will use ChatGPT, so it is important that we engage our students to promote a deeper learning and awareness of this technology and its limitations. As a result, we participated in a semester-long ChatGPT workshop sponsored by our institution's writing center. We explored various aspects of generative pre-trained transformers (GPTs), particularly where it intersects with information literacy, visual literacy, digital literacy, and privacy literacy. We created learning activities closely tied to learning outcomes derived from the Association of College and Research Libraries’ (ACRL) Information Literacy Framework and ACRL's Framework for Visual Literacy in Higher Education. Each centers on a frame and contains an overview of the information or visual literacy issue as it relates to ChatGPT or AI tools. We designed each with customizations appropriate for the different approaches taken in humanities, social sciences, and science courses.

Using social media examples, helping students understand how scholarship is not done in isolation but shared and a conversation. This is a lesson plan for a single class session. Included is a Learning Objectives doc for behind the scenes use, Questions Reading Activity for sharing with students, and Assessment for capturing data.

Easier access to research data is changing the research landscape. Investigate the data available for your research topic through the library’s catalog and open-access sources. 

Set in a broader internship program as a key component of an archival program and following a backwards design approach, interns (undergraduate students) develop complimentary archival exhibits in both physical and digital environments. Students are also tasked with anticipating their information needs. The supervisor draws on the Scholarship as Conversation Frame of the ACRL Framework to encourage conceptualizing both the archives/special collections/library environment they are in as a place of active conversation, and as a basis for encouraging reflection on the information component of their internship.

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