A Software Carpentry lesson to learn how to use Make Make is a tool which can run commands to read files, process these files in some way, and write out the processed files. For example, in software development, Make is used to compile source code into executable programs or libraries, but Make can also be used to: run analysis scripts on raw data files to get data files that summarize the raw data; run visualization scripts on data files to produce plots; and to parse and combine text files and plots to create papers. Make is called a build tool - it builds data files, plots, papers, programs or libraries. It can also update existing files if desired. Make tracks the dependencies between the files it creates and the files used to create these. If one of the original files (e.g. a data file) is changed, then Make knows to recreate, or update, the files that depend upon this file (e.g. a plot). There are now many build tools available, all of which are based on the same concepts as Make.

How Arguments Work takes students through the techniques they will need to respond to readings and make sophisticated arguments in any college class. This is a practical guide to argumentation with strategies and templates for the kinds of assignments students will commonly encounter. It covers rhetorical concepts in everyday language and explores how arguments can build trust and move readers.

With the release of ChatGPT in November 2022, the field of higher education rapidly became aware that generative AI can complete or assist in many of the kinds of tasks traditionally used for assessment. This has come as a shock, on the heels of the shock of the pandemic. How should assessment practices change? Should we teach about generative AI or use it pedagogically? If so, how? Here, we propose that a set of open educational practices, inspired by both the Open Educational Resources (OER) movement and digital collaboration practices popularized in the pandemic, can help educators cope and perhaps thrive in an era of rapidly evolving AI. These practices include turning toward online communities that cross institutional and disciplinary boundaries. Social media, listservs, groups, and public annotation can be spaces for educators to share early, rough ideas and practices and reflect on these as we explore emergent responses to AI. These communities can facilitate crowdsourced curation of articles and learning materials. Licensing such resources for reuse and adaptation allows us to build on what others have done and update resources. Collaborating with students allows emergent, student-centered, and student-guided approaches as we learn together about AI and contribute to societal discussions about its future. We suggest approaching all these modes of response to AI as provisional and subject to reflection and revision with respect to core values and educational philosophies. In this way, we can be quicker and more agile even as the technology continues to change.

We give examples of these practices from the Spring of 2023 and call for recognition of their value and for material support for them going forward. These open practices can help us collaborate across institutions, countries, and established power dynamics to enable a richer, more justly distributed emerging response to AI.

In the April OER and English webinar, many OER resources for English were introduced, but there was not sufficient time for questions and discussion. This webinar will provide an opportunity for further discussion of English OER and literature materials. The OER and English archive is available for viewing and the OER and English resources page has been updated.

In the April OER and English webinar, many OER resources for English were introduced, but there was not sufficient time for questions and discussion. This webinar will provide an opportunity for further discussion of English OER and literature materials. The OER and English archive is available for viewing and the OER and English resources page has been updated.