Students in LEADR are often required to develop digital projects that include digital primary sources, including digital collections and narrative-driven websites. Usage of digital objects can range from a few photographs used to illustrate central concepts in writing to creating exhibits that draw upon large collections of digitized documents, artifacts, and photographs. Because primary source discovery and evaluation is a critical component of nearly all projects, information literacy instruction in conjunction with LEADR often begins with a strong focus on finding, evaluating, and understanding digital information. Librarians at MSU Libraries are often brought into classrooms to fulfill this learning frame, drawing upon their expertise in information literacy and the usage of digital resources.

The American Library Association defines information literacy as “an overarching set of abilities in which students are consumers and creators of information who can participate successfully in collaborative spaces”  [ACRL 2015]. The information literacy objectives used in LEADR are informed by the ACRL’s Framework for Information Literacy for Higher Education, which includes six frames [ACRL 2015]:

The sources students use for their digital projects — images, datasets, documents, film, etc. — can be found all over the web. Many resources, especially images, can be located with incomplete metadata, incorrect information or with questionable provenance. Although studies have shown that students do usually end up in library journal databases when seeking reliable or scholarly sources, based on experiences in the LEADR classroom, students rarely think about similar repositories for accessing primary source material [Asher and Duke 2012]. Source location and evaluation exercises in LEADR have shown that the great majority of students turn directly to Google Image Search when asked to find a historic photograph to use as a source. To alleviate this problem, students are introduced to librarians early on, and have a session on finding and working with primary source material whenever relevant. In addition to learning about available resources through library databases, students are shown local collections in the MSU Museum or the MSU Library Special Collections. This gives students the additional benefit of meeting and speaking with the curators and librarians who possess relevant expertise about the collections, and students are strongly encouraged to visit the locations and conduct more research. These exercises are molded to meet the Authority is Constructed and Contextual and Searching as Strategic Exploration frames.

Legal and ethical intellectual property use is a key component of digital literacy in the classroom. Throughout the first two years of courses in LEADR, a substantial portion of students would correctly cite the sources they used in their writing, but would use images on their projects without citations, even when this was mentioned as a requirement. There is seemingly a disconnect between traditional requirements of paper writing and web project development. In response to this, LEADR staff, librarians, or museum staff put strong emphasis on the Information Has Value frame, including lessons on citation practices in academic writing, copyright, Creative Commons licensing, and fair use in courses by discussing preferred media citation practices for the digital platform being used.

Ithaka S&R’s recent US Faculty Survey shows that information literacy remains both valued and underdeveloped in the Humanities and Social Science classrooms. Over 60% of Humanities faculty and approximately 55% of Social Science faculty strongly agreed with the statement “My undergraduate students have poor skills related to locating and evaluating scholarly information,” while over two thirds of both faculty groups strongly agreed with the statement, “Improving my undergraduate students’ research skills related to locating and evaluating scholarly information is an important educational goal for the courses I teach”  [Wolff et al. 2016, 57–64]. Focused studies on information usage in the classroom have shown that most US faculty find that the greatest issue students have is not in finding appropriate material, but instead in thinking critically and using these sources to augment a larger argument [Foster 2013] [Head 2013] [Dimmock 2013, 12]. In-class exercises go beyond access, and engage students in deeper evaluations of the sources they have found. These exercises, which draw on deeper engagement with resources, allow MSU Libraries and LEADR staff to build on the aforementioned frames, as well as frames such as Research as Inquiry, Scholarship as Conversation, and Authority is Constructed and Contextual.

Potential Information Literacy Topics: Determine the extent of information needed; access and evaluate secondary and primary sources; understand source biases and evaluation; understand economic, legal, and social issues surrounding the use of information

The critical production and consumption of digital media is much more effective with an understanding of both the technical principles and theoretical affordances of the media. Digital communication is not only important in the disciplinary sense, as History and Anthropology scholarship is increasingly being produced digitally, but also as an essential skill for participation in civic society. In LEADR, students learn how to write for a web audience in a variety of ways — identifying audiences and users, using appropriate rhetoric, structuring narratives to function well on the web, integrating multimedia, and using HTML/CSS or a content management system (CMS) to publish and distribute scholarship. The American Library Association defines digital literacy as “the ability to use information and communication technologies to find, understand, evaluate, create, and communicate digital information, an ability that requires both cognitive and technical skills”  [ALA 2013, 1]. There is a great deal of overlap between the ALA’s Information Literacy and Digital Literacy frameworks, but their Digital Literacy report can be instructive for the production, reuse, and communication of digital information. ALA’s digital literacy report stresses that academic libraries play a key role, as undergraduate students are not nearly as digitally literate as often assumed [ALA 2013, 5–17]. “Generally, neither children nor adults acquire critical thinking skills about mass media, popular culture or digital media just by using technology tools themselves…One thing is certain: simply buying computers for schools [and libraries] does not necessarily lead to digital and media literacy education”  [Hobbs 2010, 25] [ALA 2013]. The critical evaluation of the “digital-ness” of an object promotes a better use of the affordances of the media and information, while also understanding external implications, such as ethical and legal rights, privacy, permanence and impermanence, audience reception, and feedback.

Metaliteracy, the expansion of traditional information skills (determine, access, locate, understand, produce, use) as described by Thomas P. Mackey and Trudi E. Jacobson, plays a key role in teaching students to produce and remix digital content [Mackey and Jacobson 2014]. Mackey and Jacobson have expanded the traditional notions to include “the collaborative production and sharing of information in participatory digital environments” that, crucially, “requires an ongoing adaptation to emerging technologies and an understanding of the critical thinking and reflection required to engage in these spaces as producers, collaborators, and distributors”  [Mackey and Jacobson 2014, 1]. Mackey and Jacobson list the following metaliteracy learning goals and objectives:

Metaliteracy also addresses the challenges of maintaining literacy over time. Technologies change, and the (mostly) proprietary platforms that make production and publication easy will require sustained monetary contributions and will likely fade away in lieu of new ones. This means there should be a focus on communication using open and sustainable tools that allow for total freedom and ownership of content, including the creation of video and visualizations and the production of content on flat HTML files, or at least an open source CMS. It should also include the metacognition that is at the center of Mackey and Jacobson's metaliteracy. Metacognition allows for an understanding of content creation within a system of moving and evolving parts, enabling (at least partially) an ability to adapt to new technologies and environments [Mackey and Jacobson 2014, 41–44].

Potential Digital Literacy Topics: Web publication (HTML/CSS and CMS), user experience, multimodal communication, film composition and editing, critical engagement with technology, social media, and third party data collection

As “open data” and “big data” become increasingly common phrases, the ability to access, refine, manipulate, critically evaluate, and share data becomes more of an essential skill. Librarians and data curators have made efforts in recent years to move curation “up the stream,” and to train researchers and scholars to start data collection with good practices in mind. This greatly increases reusability and clarity of the data, and reduces the work required in preparing the data for sharing. Training often occurs in university-wide workshops, brief seminars to train researchers in data curation practices, or perhaps a lab-specific workshop faculty, staff, and graduate student researchers [Carlson et al. 2011]. These sessions teach attendees about establishing metadata standards early on in the research process, documenting their data processing, and using sustainable formatting, among other curation practices, but are often brief and divorced from content and community practices [Carlson and Johnson 2015, 2–3]. Instead, teaching data principles in courses allows for a greater focus on uses of the data that take full consideration of the context and the potential questions that could be asked of the data.

The Data Information Literacy (DIL) initiative, led by Jake Carlson and Lisa R. Johnson, is an extension of the ACRL Information Literacy Framework that focuses on both the creation and consumption of data. The authors describe DIL as “...merg[ing] the concepts of researcher-as-producer and researcher-as-consumer of data products. As such it builds upon and reintegrates data, statistical, information and science data literacy into an emerging skill set”  [Carlson et al. 2011, 634]. Data information literacy is a framework designed to be integrated into courses and graduate research processes in the context of subject-specific data and domain-based community practices. DIL is comprised of 12 primary frames:

The target community for data literacy is typically limited to those doing research for peer-reviewed publication — faculty, academic staff, graduate students, and undergraduate research assistants [Carlson and Johnson 2015] [Shorish 2015]. However, these are valuable skills for all citizens, and basic introductions to concepts should be taught broadly at the undergraduate level. Data skills and literacies are becoming crucial, as terms like ‘big data’ become more and more pervasive in the news, scholarly research, and across industries, and governments and other entities increasingly make data open in the name of transparency. Without the skills to access and evaluate data, there can be great hurdles to successfully use and analyze these resources as scholars, citizens, and employees.

With growing access to humanities datasets through both library vendors and open repositories, data literacies and skills can greatly benefit students in History and Anthropology courses working on small, course-based or thesis research projects.[4] One promising project for effective information and data literacy pedagogy in the humanities is the Library-Led DH Pedagogy: Modeling Paths Toward Information and Data Literacy symposium [Padilla et al. 2015]. Librarians at Michigan State University specializing in digital humanities, data curation, and information literacy called a symposium of librarians and disciplinary faculty from across the region to discuss and develop library instruction that melds together disciplinary information and data literacy needs.

The ability to evaluate and use data gives students the ability to ask different types of questions, and to utilize growing resources for analysis. Data literacy skills have great utility that extends beyond large research projects, and even beyond academic research. New technologies and the increasingly widespread use (and misuse) of data visualization in journalism and politics make data competencies such as collection, evaluation, and management a crucial skillset. Data skills are essential if we wish to transform students from passive data consumers to critical consumers and producers of data. In LEADR, data literacy lessons are delivered using domain-specific data as well as domain-specific norms for data curation and manipulation, but examples and use-cases from mainstream sources are discussed whenever relevant.

Potential Data Literacy Topics: Data collection, manipulation, cleaning, and structuring; critical evaluation of data; data sharing and publication

Computational analysis is used here to signify a wide-ranging set of tools and methodologies that rely upon computational processes to assist in the asking and exploring of research questions, including but not limited to text mining, network analysis, GIS and web mapping, 3D modeling, desktop fabrication, and topic modeling. These lessons are grounded in disciplinary methodology, and illustrate the ways in which scholars are using computationally-aided methods to ask new questions of their sources, as well as exploring more traditional questions. Once the methodological groundwork is established, students are instructed in the usage of a tool or tools, and then encouraged to experiment with the tool(s) and dataset(s) before reflecting on the experience.

Although computational analysis is often seen as a means to a scholarly end, it holds great value in its ability to challenge students to think differently about a resource — to break down the way we convey information and think about ways to work through those abstractions. The disciplinary benefit is not necessarily the development of computational research skills, but instead in teaching students about research methodologies and new ways of thinking about sources [Mahony and Pierazzo 2012] [Sayers et al. 2016]. Computational methods can serve as valuable aids to think about datasets, electronic representations of materials and cultures, and the affordances that come with each. Stephen Ramsay, long a proponent of teaching programming as a way to help students learn both computational and humanistic thinking, wrote, “The center of digital humanities, after all, is not the technology, but the particular form of engagement that characterizes the act of building tools, models, frameworks and representations for the traditional objects of humanistic study”  [Ramsay 2012]. Emerging practices in physical computing and desktop fabrication allow for the making, breaking and alteration of real and speculative objects, enabling interested parties to engage critically with the form, function, and materiality of objects [Sayers et al. 2016].

Teaching the ubiquity of this kind of analysis, along with critical approaches to the strengths and shortcomings of such analysis, can help to remove the black box that often surrounds such digital outputs, and allows for thoughtful criticism of these methods employed in our everyday lives. For example, the usage of text mining and text analysis in the undergraduate humanities classroom does not only lead to learning more about the content and the humanities methodology, but also about the potential and limits of surveillance analytics [Sinclair and Rockwell 2012]. Likewise, critical makerspaces can contribute to “advanced thinking and policy-development around critical issues like privacy, surveillance, intellectual property, consumerism in education, data exploitation, and sustainability and the environment” and “foster productive thinking on issues of representation, contingency, privilege, and other structural problems in academic labor”  [Sayers et al. 2016, 16].

Potential Computational Analysis: Text analysis, 3D fabrication and modeling, network analysis, mapping