Tagged Critical Pedagogy

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Data Literacy in Media Studies: Strategies for Collaborative Teaching of Critical Data Analysis and Visualization

Abstract

This essay addresses challenges of teaching critical data literacy and describes a shared instruction model that encourages undergraduates at a large research university to develop critical data literacy and visualization skills. The model we propose originated as a collaboration between the library and an undergraduate media and cultural program, and our specific intervention is the development of a templated data-visualization instruction session that can be taught by many people each semester. The model we describe has the dual purpose of supporting the major and serving as an organizational template, a structure for building resources and approaches to instruction that supports librarians as they develop replicable pedagogical strategies, including those informed by a cultural critical lens. We intend our discussion for librarians who are teaching in an academic setting, and particularly in contexts involving large-scale or programmatic approaches to teaching. The discussion is also useful to faculty in the disciplines who are considering partnering with the library to interject aspects of data or information literacy into their program.

Learning that emphasizes data literacy and encourages analysis within multimedia visualization platforms is a growing trend in higher education pedagogy. Because data as a form of evidence holds a privileged position in our cultural discourse, interdisciplinary undergraduate degree programs in the social sciences, humanities, and related disciplines increasingly incorporate data visualization, thus elevating data literacy alongside other established curricular outcomes. When well-conceived, critical data literacy instruction engenders a productive blend of theory and practice and positions students to examine how race-based bigotry, gender bias, colonial dominance, and related forms of oppression are implicated in the rhetoric of data analysis and visualization. Students can then create visualizations of their own that establish counternarratives or otherwise confront the locus of power in society to present alternative perspectives.

As scholarship in media, communications, and cultural studies pedagogy has established, data visualizations “reflect and articulate their own particular modes of rationality, epistemology, politics, culture, and experience,” so as to embody and perpetuate “ways of knowing and ways of organizing collective life in our digital age” (Gray et al. 2016, 229). Catherine D’Ignazio and Lauren F. Klein (2020, 10) explain this dialectic more pointedly in Data Feminism, arguing, “we must acknowledge that a key way power and privilege operate in the world today has to do with the word data itself,” especially the assumptions and uses of it in daily life. Critical instruction positions undergraduates to question how data, in its composition, analysis, and visualization, can often perpetuate an unjust socio-cultural status quo. Undergraduates who are introduced to frames for interpreting culture also need to be exposed to tools—literal and conceptual—that help them critique data visualizations. The goal is to enable a holistic critical literacy, through which students can find data, structure it with a research question in mind, and produce accurate, inclusive visualizations.

However, data instruction is challenging, and planning data learning within the context of an existing course requires an array of skills. Effective data visualization pedagogy demands that instructors locate example datasets, clean data to minimize roadblocks, and create sample visualizations to initiate student engagement with first-order cultural-critical concepts. These steps, a substantial time investment, are necessary for teaching that enables data novices to contend with the mechanics of data manipulation while remaining focused on social and political questions that surround data. When charged with developing data visualization assignments and instructional assistance, faculty often seek the support and expertise of librarians and educational technologists, who are located at the nexus of data learning within the university (Oliver et al. 2019, 243).

Even in cases where librarians and instructional support staff are well-positioned to assist, the demand for teaching data visualization can be overwhelming. It can become burdensome to deliver in-person instruction to cohort courses with a large student enrollment, across many sections and in successive semesters. In order to initiate and maintain an effective, multidisciplinary data literacy program, teaching faculty, librarians, and educational technologists must establish strong teaching partnerships that can be replicated and reimagined in multiple contexts.

This essay addresses some challenges of teaching critical data literacy and describes a shared instruction model that encourages undergraduates at a large research university to develop critical data literacy and visualization skills. Although anyone engaged in teaching critical data literacy can draw from this essay, we intend our discussion for librarians who are teaching in an academic setting, and particularly in contexts involving large-scale or programmatic approaches to teaching. In addition, we believe our essay is particularly pertinent to those designing program curricula within discipline-specific settings, as our ideas engage questions of determining scale, scope, and learning outcomes for effective undergraduate instruction.

The teaching model we propose originated as a collaboration between the New York University Libraries and NYU’s Media, Culture, and Communications (MCC) department, and our specific intervention is the development of an assignment involving data visualization for a Methods in Media Studies (MIMS) course. The distributed teaching model we describe has the dual purpose of supporting the major and serving as an organizational template, a structure for building resources and approaches to instruction that supports librarians as they develop replicable pedagogical strategies, including those informed by a cultural critical lens. In this regard, we believe that collaborative instruction empowers librarians and faculty from many disciplines to develop their own data literacy competency while growing as teachers. And, it enables the library to affect undergraduate learning throughout the university.

There is already an extensive body of research about the role of critical data literacy instruction, including critical approaches to the technical elements of data visualization (Drucker 2014; Sosulski 2019; Engebresten and Kennedy 2020). While we draw from that scholarly discussion, we focus instead on the upshot of programmatic, extensible teaching partnerships between libraries and discipline-specific undergraduate programs. Along the way, we engage two crucial questions: What is the value of creating replicable lesson plans and materials, to be taught by an array of library staff repeatedly? How can the librarians who design these materials strike a balance between creating a step-by-step lesson plan that library instructors follow and structuring a guided lesson that is flexible and capacious enough for instructors to experience meaningful teaching encounters of their own?

Data Literacy in Undergraduate Education

Several curricular initiatives and assessment rubrics in higher education pedagogy recognize the need for students to develop fluidity with digital media and quantitative reasoning, a precursor to effective data visualization. In 2005, Association of American Colleges and Universities (AAC&U) began a decade-long initiative called Liberal Education and America’s Promise (LEAP), which resulted in an inventory of 21st century learning outcomes for undergraduate education. Quantitative literacy is on the list of outcomes (Association of American Colleges and Universities 2020). A corresponding AAC&U rubric statement asserts that “[v]irtually all of today’s students … will need basic quantitative literacy skills such as the ability to draw information from charts, graphs, and geometric figures, and the ability to accurately complete straightforward estimations and calculations.” The rubric urges faculty to develop assignments that give students “contextualized experience” analyzing, evaluating, representing, and communicating quantitative information (Association of American Colleges and Universities 2020). The substance of the LEAP initiative informed the development of our collaborative teaching model, for it allowed us to ground our curricular interventions within larger university curricular trends that had already emerged.

Although quantitative literacy is important, there are other structures for teaching that see data fluidity and visualization as being tied to larger information seeking practices. For this reason, we also turned to the Framework for Information Literacy for Higher Education, developed by the Association of College and Research Libraries (ACRL). The Framework embraces the concept of metaliteracy, which promotes metacognition and a critical examination of information in all its forms and iterations, including data visualization. One of the six frames posed by the document, “Information Creation as a Process,” closely aligns with data competency, including data visualization. This frame emphasizes that the information creation process can “result in a range of information formats and modes of delivery” and that the “unique capabilities and constraints of each creation process as well as the specific information need determine how the product is used.” Within the Framework, learning is measured according to a series of “dispositions,” or knowledge practices that are descriptive behaviors of those who have learned a concept. Here, the Framework is apropos, as students who see information creation as a process “value the process of matching an information need with an appropriate product” and “accept ambiguity surrounding the potential value of information creation expressed in emerging formats or modes” (ACRL 2016). The Framework recognizes that evolved undergraduate curricula must incorporate active, multimodal forms of analysis and production that synthesize information seeking, evaluation, and knowledge creation.

Other organizations and disciplines also advocate for quantitative literacy in the undergraduate curriculum. For instance, Locke (2017) discusses the relevance of data in the humanities classroom and points to ways undergraduate digital humanities projects can incorporate data analysis and visualization to extend inquiry and interpretation. And Beret and Phillips (2016, 13) recommend that every journalism degree program provide a foundational data journalism course, because interdisciplinary data instruction cultivates professionals “who understand and use data as a matter of course—and as a result, produce journalism that may have more authority [or] yield stories that may not have been told before.” In sum, LEAP, the ACRL Framework, and movements for data literacy in the disciplines influenced the Libraries’ collaboration with the Media and Cultural Communications department, and this informed the effort to create and support a meaningful learning experience for students in this major.

Learning-by-Teaching: Structured, Programmatic Instruction and Libraries

Our collaborative model evolved with the conviction that structured, programmatic teaching can foster professional growth for librarians and library technologists. In addition to creating impactful learning for students, programmatic teaching provides a structure that allows for educators to expand the contexts in which they can teach. In many cases, librarians who specialize in information literacy are less adroit regarding the concepts and mechanics of working with data. Teaching data as a form of information, then, necessarily requires a baseline technical expertise.

Several studies published within the past decade indicate that learning with the intent to teach can lead to better understanding, regardless of the content in question. One such study finds that learners who were expecting to teach the material to which they were being introduced show better acquisition than learners who were expecting only to take a test, theorizing that learning-by-teaching pushes the learner beyond essential processing to generative processing, which involves organizing content into a personally meaningful representation and integrating it with prior knowledge (Fiorella and Mayer 2013, 287). Another study finds that learners who were expecting to teach show better organizational output and recall of main points than those who were not expecting to teach, which suggests that learners who anticipate teaching tend to put themselves “into the mindset of a teacher,” leading them to use preparation techniques—such as concept organizing, prioritizing, and structuring—that double as enhancements to a learner’s own encoding processes (Nestojko, et al. 2014, 1046). This evidence boosts our belief that learning-by-teaching is a good strategy for librarians to build foundational data literacy skills, and it informed the development of our program.

Development and Implementation of the Collaborative Teaching Model

Situated in NYU’s Steinhardt School of Culture, Education, and Human Development, the MCC program covers global and transcultural communication, media institutions and politics, and technology and society, among other related fields. MCC program administrators, who were looking to incorporate practical skills into what had previously been a theory-heavy degree, approached the library to co-develop instructional content that would expose students to applied data literacy and multimedia visualization platforms. The impetus for the program administrators to reach out to the library was their participation in a course enhancement grant program, which testifies to the lasting effects that school or university-based curriculum initiatives can have on undergraduate learning. In this case, what emerged was a sustained teaching partnership. Though the support was refined over time, its core remained constant: individual sections of a media studies methods class would attend a librarian-led class session that prepares students to evaluate data and construct a visualization exploring some element of media and political economy, grounded in an assigned reading that asserts ownership of or access to media and communications infrastructure is intrinsically related to the well-being and development of countries around the world.

The class is a first-year requirement in Media, Culture, and Communication, one of NYU’s largest majors. The course tends to be taught by beginning doctoral students, and is by design a highly fluid teaching environment. In early iterations of library support, we designed a module that attempted to have students perform a range of analysis and visualization tasks. Students were introduced to basic socio-demographic datasets and were invited to create a visualization that investigates a research question of their choosing, provided that the question adhered generally to the themes of media and political economy. The assignment as initially constituted expected the student to frame a question, find a dataset and clean it, choose a visualization platform, and generate one or more visualizations that imply a causal relationship between variables that they had identified.

The learning outcomes and assignment developed in this initial sequence turned out to be too ambitious. The assignment had fairly loose parameters, which proved problematic, and the 75-minute class session could not provide sufficient preparation. Students struggled with developing viable research questions, finding data sets, and cleaning the data (the multivalent process of normalizing, reshaping, redacting, or otherwise configuring data to be ingested and visualized in online platforms without errors). Also, we had pointed them to an overwhelming array of data analysis software tools, including ESRI’s ArcMap, Carto, Plot.ly, Raw, and Tableau. We found they had great difficulty with both selecting a tool and learning how to use it, in addition to the connected process of finding a dataset to visualize within it. The Libraries tried to accommodate, but ultimately realized that the module needed significant adjustment going forward, especially since the MCC department decided to expand the project to include up to 10 sections of the course each semester.

Besides struggling with research questions, datasets, and tools, it was also apparent that students had trouble connecting this work to the broader ideas of media and political economy intrinsic to the assignment. Informed by these first-round outcomes, we came together again to revise the instructional content and assignment. Taking our advice into account, the MCC teaching faculty and program administrators refined the learning outcomes as such:

  • Become familiar with the principles, concepts, and language related to data visualization
  • Investigate the context and creation of a given dataset, and think critically about the process of creating data
  • Emphasize how online visualization platforms allow users to make aesthetic choices, which are part and parcel of the rhetoric of visualization

The librarians also created a student-facing online guide as a home base for the module and decided to distribute the teaching load by inviting Data Services specialists from the Libraries’ Data Services department to help teach the library sessions (MCC-UE 2019). And to provide a better lead-in to the library session, a preparatory lesson plan was developed for the MCC instructors to present in the class prior to the library visit.

After further feedback from program administrators and consideration, we inserted a scaffolding component into the library session lesson plan to better prepare students for their assignment. The component involved comparing four sample visualizations created from the very same data, and it included questions for eliciting a discussion about the origins and constructions of data. Scenario-based exercises for creating visualizations in Google sheets and Carto were also incorporated into the lesson, giving students practice before tackling the actual assignment. The assignment was also redesigned with built-in support. Students would no longer be expected to find their own dataset and attempt to clean extracted data, tasks that had caused them frustration and anxiety. Instead, they would choose from a handful of prescribed and pre-cleaned datasets. Data Services staff worked to remediate a set of interesting datasets to anticipate the kind of visualization students would attempt. Also, rather than having to choose from a confusing array of data visualization tools, they would be directed to use Google sheets or Carto only. Assuming the task of identifying, cleaning, and preparing datasets meant extra front-loaded work on the Libraries’ part, but it also freed students to focus on the higher order activity of investigating the relationship between visualizing information and examining social or political culture.

Instructional Support from a Wide Community of Teachers: Growing a Base

Another issue at hand was the strain the project was having on the members of the Data Services team and Communications Librarian, who taught all ten library sessions that were offered each semester. To achieve sustainability going forward, a broader group of librarians would be needed to help teach the library sessions. Moving forward, the Data and Communications librarians decided to recruit other NYU librarians to participate as instructors. Most of the recruits were data novices, but they viewed the invitation as an opportunity to learn data basics, expand their instruction repertoire, and strengthen their teaching practice. Calling on colleagues to teach outside their comfort zone is a big ask, one that requires strong support and administrative buy-in. So recruits were provided with a thorough lesson plan, a comprehensive hands-on training session, and the opportunity to shadow more experienced instructors before teaching the module solo (MCC-UE 2019).

By including a more robust roster of instructors, the structure also gave us the ability to further tie our lesson to what was planned in the MIMS curriculum. A new reading was chosen by the media studies faculty, “Erasing Blackness: the media construction of ‘race’ in Mi Familia, the first Puerto Rican situation comedy with a black family,” by Yeidy Rivero. The article grounds the students’ exploration of the relationship between media and political economy within the MIMS class, and it also provides a good entry point to explore critical data literacy concepts. According to Rivero, the show Mi Familia, deliberately represents a “flattened,” racially homogeneous “imagined community” of lower-middle class black family life that erases Puerto Rico’s hybrid racial identity. This flattening, Rivero argues, is part and parcel of multidimensional efforts to “Americanize” Puerto Rico and align its culture with the interests of the U.S. Furthermore, since the Puerto Rican media is regulated by the U.S. Federal Communication Commission (FCC) and owned by U.S. corporations, Puerto Ricans themselves had little recourse to question the portrayal of constructed racial identities in the mainstream culture (Rivero 2002).

Students were instructed to complete the reading prior to the library session. During the session, the library instructor referred to the reading and introduced a dataset with particular relevance to it. The instructor engaged students in a discussion about the importance of reviewing the dataset description and variables in order to form a question that can be reasonably asked of the data. With students following along, the instructor then modeled how to use Google sheets to manipulate the data and create a visualization that speaks to the question.

The selected dataset resulted from a study of the experiences and expressions of racial identity by young adults who lived in first and second-generation immigrant households in the New York City area during the late 1990s (Mollenkopf, Kasinitz, and Waters 2011). The timeframe of this article and the dataset line up well. The sitcom mentioned in the article first aired in 1994, but had been picked up in Telemundo’s NYC area affiliates by the late 1990s, so it is highly possible that this sitcom would have been on the air in the homes of study participants. The dataset, which is aggregated at the person level, includes variables about participants’ family and home context, patterns of socialization, exposure to media, and sense of self. In order to foreground the analytic process of looking at data, ascertaining its possibilities, and gesturing at potential visualizations, we created a simplified version of the raw data, which omits some columns and imputes other variables for easier use. To accompany this dataset, we also created some simple data visualizations in Google Sheets, ArcGIS Online, and Tableau, which are intentionally “impoverished,” thus designed to elicit discussion from students about the claims made by the visualizations.

Undoubtedly, these adjustments to the module led to students performing better on the assignment. Improvements to the lead-in session provided by the MCC instructors ensured that the students were prepared with context for the library workshop and an understanding of why the library was supporting the assignment. Basing the assignment on a specific article made it possible for librarians to model a way of bridging the theoretical concepts of the class to a question that could be asked of data. There was also more time for two pair-and-share discussions and group work in Google Sheets and Carto, which addressed a fundamental and recurring frustration in the students’ understanding of the assignment: the ability to ask an original question of a dataset, and to ask a question that would address a larger theme of media and political economy.

From the standpoint of instructors in NYU Libraries, we also found that the model provided a strengthened group of teachers. Several people who worked with sections of MIMS contributed ideas to the instructor manual and created ancillary slides and examples that are tailored to their own interest in the claims about racial and national identity that the Rivero article makes. For us, this flexibility is an important element of the collaborative teaching model; it offers both the structure for those who are new to data analysis and visualization to teach effectively, yet it also contains enough pathways for discussion to be meaningful and personal, should individual instructors want to branch out in their own teaching.

Conclusion

Despite being familiar with technology, many students arrive at college without a holistic ability to interpret, analyze, and visualize data. Educators now recognize the need to provide foundational data literacy to undergraduates, and many teaching faculty look to the library for support in instructional design and implementation. In this article, we recognize that creating integrated, meaningful data learning lessons is a complex task, yet we believe that the collaborative teaching model can be applied in various disciplinary contexts. Sustainability of this model depends on equipping a wide range of librarians with necessary data literacy skills, which can be achieved with a learning-by-teaching approach. After developing a teaching model that calls upon the expertise of teachers across the library, we gained some important insights on maintaining the communication and support to make it sustainable, building the workshop itself, and balancing the labor that all of this requires.

Good communication and organization between the MCC department and librarians was also key in maintaining the scalability of this instruction program. Given the heavy rotation of new teachers on both the library and MCC side, we needed to provide module content that was streamlined and assignment requirements that were clear cut in order to quickly on-board teachers to the goals, process, and output of the module. When recruiting library instructors, we emphasized that volunteers will not only build their data literacy skill set, but will also expand their pedagogical knowledge and teaching range. Finally, to ensure that volunteer instructors have a successful experience, we also provide support mechanisms such as a step-by-step lesson plan, thorough train-the-trainer sessions, opportunities to observe and team-teach before going solo, and a point person to contact with questions and concerns.

There is much hidden labor in all of this work. Robust student support for the course was also crucial, and really took off when the MCC department created a dedicated student support team from graduate assistants in the program. On the library side, communicating regularly with the MCC department, assessing and revising the learning objects, organizing and hosting train the trainer sessions, and scheduling all of the library visits takes many hours of time and planning. This work should not be overlooked when considering a program of this scale.

A collaboration at this level can provide rich data literacy at scale to undergraduates, while also offering the chance for instructors in the library and in disciplinary programs to develop their own skills in numeracy and data visualization as they learn by teaching. Through time, effort, and dedicated maintenance, a program like this becomes a successful partnership that has a broad and demonstrated impact on student learning, strengthens ties between the library and the departments we serve, and allows librarians and data services specialists the opportunity to learn and grow from each other.

Related to the learning objects themselves, we had the most success when we matched the scope of the assignment closely with the time and support the students would have to complete it, and preparing a small selection of data sets for the students in advance was very helpful in this regard. We also built in a full class session of preparation before the library visit, in which MCC teachers introduced the assignment, some principles of data visualization (via a slide deck prepared by the library’s Data Services department), and how this method can connect to broader concepts of media analysis. This led to more effective learning for students. These changes to the student assignment, learning outcomes, and library lesson plan were developed through regular and structured assessments of the workshop: a survey to the instructors teaching the course, classroom visits to see the students’ final projects, and in-depth conversations with instructors on which aspects of the lesson plan were successful and which fell flat. Following each assessment the MCC administrators and the librarians would get together to discuss and iterate on the learning objects. This process of gathering feedback on the workshop, reflecting on that information and then revising the assignment enabled us to improve the teaching and learning experience over the years.

Bibliography

Association of American Colleges and Universities. n.d. “Essential Learning Outcomes.” Accessed June 2, 2020. https://www.aacu.org/essential-learning-outcomes.

Association of American Colleges and Universities (AAC&U). n.d. “VALUE Rubrics.” Accessed June 2, 2020. https://www.aacu.org/value/rubrics/quantitative-literacy.

Association of College & Research Libraries. 2016. “Framework for Information Literacy for Higher Education. “ Accessed June 2, 2020. http://www.ala.org/acrl/standards/ilframework.

Berret, Charles and Cheryl Phillips. 2016. Teaching Data and Computational Journalism. New York: Columbia Journalism School. https://journalism.columbia.edu/system/files/content/teaching_data_and_computational_journalism.pdf.

D’Ignazio, Catherine and Lauren F. Klein. 2020. Data Feminism. Boston: MIT Press. ProQuest Ebook Central.

Drucker, Johanna. 2014. Graphesis: Visual Forms of Knowledge Production. Cambridge, Massachusetts: Harvard University Press.

Engebretsen, Martin and Helen Kennedy, eds. 2020. Data Visualization in Society. Amsterdam: Amsterdam University Press. Project MUSE.

Fiorella, Logan, and Richard E. Mayer. 2013. “The Relative Benefits of Learning by Teaching and Teaching Expectancy.” Contemporary Educational Psychology 38, no. 4: 281–288. https://doi.org/10.1016/j.cedpsych.2013.06.001.

Gray, Jonathan, Lillian L. Bounegru, Stefania Milan, and Paolo Ciuccarelli. 2016. “Ways of Seeing Data: Toward a Critical Literacy for Data Visualizations as Research Objects and Research Devices.” In Innovative Methods in Media and Communication Research edited by Sebastian Kubitschko and Anne Kaun, 227–252. Cham, Switzerland: Palgrave Macmillan. ProQuest Ebook Central.

Locke, Brandon T. 2017. “Digital Humanities Pedagogy as Essential Liberal Education: A Framework for Curriculum Development.” Digital Humanities Quarterly 11, no. 3. http://www.digitalhumanities.org/dhq/vol/11/3/000303/000303.html.

Nestojko, John F., Dung C. Bui, Nate Kornell, and Elizabeth Ligon Bjork. 2014. “Expecting to Teach Enhances Learning and Organization of Knowledge in Free Recall of Text Passages.” Memory & Cognition 42, no. 7: 1038–1048. https://doi.org/10.3758/s13421-014-0416-z.

Mollenkopf, John, Phillip Kasinitz, and Mary Waters M. 2011. Immigrant Second Generation in Metropolitan New York. Ann Arbor: Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/ICPSR30302.v1/.

“MCC-UE 14 Media & Cultural Analysis.” 2019. New York University. https://guides.nyu.edu/mims/.

Oliver, Jeffry, Christine Kollen, Benjamin Hickson, and Fernando Rios. 2019. “Data Science Support at the Academic Library.” Journal of Library Administration 59, no. 3: 241–257. https://doi.org/10.1080/01930826.2019.1583015.

Rivero, Yeidy. M. 2002. “Erasing Blackness: The Media Construction of ‘Race’ in Mi Familia, the First Puerto Rican Situation Comedy with a Black Family.” Media, Culture & Society 24, no. 4: 481–497. https://doi.org/10.1177/016344370202400402.

Sosulski, Kristen. 2018. Data Visualization Made Simple: Insights into Becoming Visual. London: Routledge. ProQuest Ebook Central.

Acknowledgments

This teaching partnership, data, and associated resources would not have been possible without the work of many people in NYU Libraries and Data Services, as well as the NYU Steinhardt Methods in Media Studies program including: Bonnie Lawrence, Denis Rubin, Dane Gambrill, Yichun Liu, and Jamie Skye Bianco.

About the Authors

Andrew Battista is a Librarian for Geospatial Information Systems at New York University and teaches regularly on data visualization, geospatial software, and the politics of information.

Katherine Boss is the Librarian for Journalism and Media, Culture, and Communication at New York University, and specializes in information literacy instruction in media studies.

Marybeth McCartin is an Instructional Services Librarian at New York University, specializing in teaching information literacy fundamentals to early undergraduates.

Teaching Twentieth Century Art History with Gender and Data Visualizations

Nancy Ross, Dixie State University

Abstract

In this article, the author draws on her experience teaching an undergraduate art history course using student-built interactive data visualizations to explore the social relationships of 20th century women artists. This approach increased student engagement despite the conservative environment of Dixie State University. Students learned to critique secondary sources, used digital tools to find results, and engaged in transformative learning advocated by critical pedagogy (Freire et al. 2000). This evidence supports the argument that digital tools and methods should be used not only in advanced scholarly research, but in undergraduate classrooms as well.

 

 

Art history, in my opinion, is a surprisingly traditional field. Art history textbooks are full of Western European men who were deified by later Western European men employing some variant of the Great Man theory (Carlyle 1888, 2). Today, many art historians employ contemporary methodologies that move art history away from its past, but some art historians still teach the gender biases of the past.

The discipline of art history has a lot to gain from employing digital methods, but has not yet reached a level of digital sophistication. In his blog post on the future of digital art history, Bob Duggan (2013) asks, “Can the study of art history stop looking like ancient history itself?” Murtha Baca and Anne Helmreich (2013) believe that it can and outline five phases of development in digital humanties, which they offer as inspiration for digital art history. Phase one began with digitizing works of art and texts related to art. Phase two involved building new tools like Zotero and Omeka. The third phase focused on using new technology to create visualizations and recreations and the fourth phase implemented open peer review. In the fifth phase, scholars have engaged in research enabled by “computational analytics.”

Many institutions are diligently working on the first phase. A good example of this is The Getty, which recently released a number of high-resolution images of works of art in its collections to the public domain (Cuno 2013). There are some second phase tools available, such as ARTstor and the Google Art Project, but digital art history has stalled in the third phase.

Perhaps the fastest way to change the discipline of art history is to teach the change you want to see, to rephrase Gandhi. Art historians need to embrace digital tools, but they also have other challenges, such as addressing long-held gender biases. Critical pedagogy in a university setting addresses the question, “How can university teachers practice pedagogy which is attentive to how their students might as citizens of the future influence politics, culture and society in the direction of justice and reason?” (McLean 2006, 1). In approaching the teaching of Twentieth Century Art at Dixie State, a conservative university in southern Utah, this question was foremost in my mind. I knew most of my students before the semester started, having had them in previous classes. These students openly and privately expressed concerns over issues of gender and sexuality. Many reported that they had experienced outright discrimination or social or family difficulty when their actions did not match the traditional gender roles or heterosexual norms to which many in southern Utah subscribe. In the community and in the university, there were too few venues for students to discuss these issues. I decided that the class would tackle these topics with an unconventional approach to the art history of the twentieth century. I thought that if I could put their personal issues with gender and sexuality into a larger context, that would validate their experiences. Students might even begin having further conversations about gender and sexuality in our conservative community, closing the loop of critical pedagogy.

A typical class on twentieth century art would normally focus on the canon of that century, meaning the major works that appear in most textbooks on the topic. A good example of such a textbook is Arnason and Mansfield’s History of Modern Art (2013). Unfortunately, the canon of twentieth century art, like the canon of every other period in art history, contains very few works of art by women. “Most schools continue to run a male-centered curriculum, and a survey showed work by women artists makes up only 3%-5% of major permanent collections in the US and Europe” (Chicago 2012). I changed the focus of the course from the canon to works of art by women, who had also experienced discrimination on the basis of gender and sexuality.

The main text for the new and revised course was Whitney Chadwick’s Women, Art, and Society (2012). Using that book, we traced the development of women artists’ careers and experiences in the art world. Statements made by male artists, art dealers, and critics about women artists and their work were often very negative. Comments such as the following were typical of art critics throughout history. “The woman of genius does not exist. When she does, she is a man” (quoted in Chadwick 2012, 31). Many male artists in the early twentieth century viewed male sexual energy as the main source of their creative power, leaving no room for the creative power of women artists (ibid., 279). Chadwick tries to rectify the imbalance by focusing on works of art by women. My students reported that they liked Women, Art, and Society and found that it was an engaging text.[1]

This text sensitized my students to issues of gender. At the beginning of the semester, a few students reported that they had not witnessed discrimination based on their gender or sexuality. After two months of reading the Chadwick text, these same students described a shift in their view and reported seeing gender bias in action in their lives.

Early in the course, I was pleased with student engagement. The majority of the class members regularly contributed to in-class discussions. As I had anticipated, students wanted to discuss issues of gender in art and we periodically discussed issues of gender in the lives of the students.

Beyond the indirect measure of the quality and participation levels of class discussions, I had some further evidence that students were engaging with the course material. I set the first major assessment, a slide test, one month into the course. The results of the first major assessments in upper-division classes are often broadly scattered as students try to find their footing in the class, shown in the table below. In the Twentieth Century Art class, the results were still scattered, but the average was high. Moreover, several students’ written answers showed a level of art historical and gender analysis that went beyond class discussions and assigned reading material. This demonstrated a level of student engagement I had not previously seen at that early stage of the semester.

Ross1

In the second month of the course, I travelled to New York to attend THATCamp CAA, where I also visited The Museum of Modern Art (MoMA). At MoMA, I was most interested in seeing works of art from the early Modern period in the exhibition Inventing Abstraction, 1910-1925, which overlapped with the content of my Twentieth Century Art class. At the entrance to the exhibit, there was a large wall showing the social connections between Early Modern artists. This data visualization is reproduced in the exhibition’s interactive website, with photos of the artists and short biographies, and explained in The Modern Art Notes Podcast (Green and Dickerman 2013). I knew that the interactive online material would interest my students and I was interested in this example of Phase Three digital art history (Baca and Helmreich 2013).

My excitement about the MoMA visualization was reinforced by a talk I heard a few days later at THATCamp CAA. Paul B. Jaskot (2013) spoke about “Digital Visualizations as Art Historical Research: The Question of Scale.” Jaskot works on the Spatial History Project in the area of Holocaust Geographies. I was intrigued by how data visualizations gave him insight into the building activities at concentration camps, insights he had not gained through conventional study.

Ted Underwood (2013) has had similar insights, but claims that his colleagues in English literature “just don’t think it’s plausible that quantification will uncover fundamentally new evidence, or patterns we didn’t previously expect.” I think it is fair to say that many art historians would agree with Underwood’s colleagues. Underwood employs text mining in his work, a new methodology that uses computers and algorithms to analyze large bodies of texts. He asks a simple question of literary history, for which there are no answers in current scholarship, and shows how text mining can begin to answer the question. Using data-driven methodologies, he argues, scholars can make new discoveries in the humanities that can reshape our understanding of our disciplines.

Underwood’s work is the literary equivalent of Baca and Helmreich’s Phase Five. Jaskot’s work is part of Phase Three digital art history. It is at this phase that digital tools no longer serve as organizational assistants, but as real drivers of research outcomes. If only scholars could see their work represented differently, not as an extensive series of notes but as data visualizations, they could understand their work differently.

Before attending THATCamp, I did not think about my academic work as data collection or interpretation. I thought of my work, as a medieval art historian, as a matter of identifying and connecting written sources with works of art in a conventional way using my memory. I saw how reliance on my memory was a limited method, as I forgot important details, only to rediscover them later. I was primarily trying to hold tables of information in my mind and making only minimal use of tables in spreadsheets.

As a graduate student, I saw many of my peers approaching humanities research in the same way. I thought about my work in this conventional way even though I regularly used and created lists and tables in the process of research. I was using these tools in a Phase Two way, as organizational assistants, instead of in a Phase Three way, to help me reach new conclusions. My computer scientist husband even helped me create a diagram for my PhD dissertation, technically a data visualization. This visualization summarized my research but did not enhance it. When I heard Jaskot’s talk, I realized that I was missing out on a new and interesting approach to art history. I had previously used technology to record, organize, and even represent my work as part of a larger conventional framework. I had not used technology to help me better understand my work or to help me draw new conclusions.

After visiting THATCamp and MoMA, I was interested in seeing if data visualizations could help my students further engage in the course content. I hypothesized that through research and using graphics to visualize their research, I could help my students better understand gender bias in art history. They were already aware of it, having learned about it through our textbook, but I wanted to see if they could further internalize these lessons and detect it on their own. The data visualization would be the visible proof of their conclusions.

I returned to my Twentieth Century Art class and showed them the MoMA visualization. Fully immersed in Chadwick’s book, my students quickly noted that few female artists were included, even though the New York Times reviewer, Roberta Smith, praised the show for its inclusion of female artists (Smith 2012). My students counted a total of 88 artists and only 10 were women. They were not as impressed with the gender balance of the exhibit.

At this point in the semester, we were anticipating another major assessment. In the middle of the semester, I typically let the upper-division students collectively set the essay, while I make the rubric. The students decided to create their own visualization in response to the MoMA one. The student data visualization would show the social connections of women artists to other artists (men and women) from about 1910 through to the 1970s. Each of my fifteen students chose a woman artist covered in Women, Art, and Society, investigated their social circles, and wrote a brief biography.

To create the visualization, each student entered their artist’s social connections into a spreadsheet, pictured below. They used Google Docs because of the ease of sharing and editing as a group. The names of the women artist are in the first column and each of their artistic friends or acquaintances are in the columns to the right. Each individual’s gender is labeled on the spreadsheet, and the sexual orientations of our fifteen primary individuals are also labeled (straight, lesbian, bisexual). Primary individuals are also numbered, both in the first column and wherever else they appear on the spreadsheet.

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In creating the visualization, we were trying to figure out how women artists worked and socialized compared with the men, who met and socialized with each other in clubs and cafés. The men directly influenced each other’s work, inviting each other to their studios. These social relationships became the means by which artistic influence spread. Women artists sometimes participated in these circles, often as partners or spouses of male group members.

We wanted to know if women had parallel artistic networks, meeting together in clubs and cafés, or if they were they isolated from each other. In Women, Art, and Society, Chadwick discusses female artists’ relationships to major movements in the twentieth century. Some women clearly worked independently, such as Romaine Brooks, and rejected the influence of the larger movements that did not accept women. Some worked within movements but struggled to have their work accepted on its own merits, as was the case with Lee Krasner who was married to the superstar Jackson Pollock. We wanted to understand if and how women artists worked with each other and hoped that a data visualization would offer insight into this question.

Even though this assessment involved writing an essay, an activity that does not normally excite students, the level of student engagement increased with the visualization component. I think that the prospect of creating a digital tool was an exciting and novel idea for my arts and humanities-focused students. They demonstrated their increased engagement in a variety of ways. Essay instructions always suggest that students use the library, library databases, and interlibrary loan to find appropriate readings for essays, but students rarely do these things or only do the absolute minimum. For this assessment, many students in the class interlibrary loaned books, all of them used the physical library, and all of them used library databases. I know that they did these things because we dedicated some class time to working on this project and students brought the library and interlibrary loan books with them to class. After reading these outside resources, students shared a number of amusing stories and information that they thought would interest the rest of the class. One student came to class and shared the exhibition reviews she had found on the New York Times website, both of contemporary and historical exhibitions. In the end, several students wrote essays that were in excess of twelve pages, above and beyond the essay requirements.

One problem that students encountered was that the secondary literature mainly discussed women’s artistic production in relation to men’s artistic production. Secondary sources were quick to point out meetings between a female artist and a more famous male artist, but few authors were interested in detailing relationships between female artists, failing a kind of art-historical Bechdel test (Stross 2008). The Bechdel test is a list of three questions that are normally applied to works of fiction to determine whether or not the work of fiction shows significant gender bias. So much of art history, as my students discovered, reveals gender bias and skewed the results for the project.

Students reported that some of the secondary sources they encountered fell into typical traps of interpreting female artists’ work in relation to their biography while ignoring larger social and political contexts (Chadwick 2012, 302). One student researching Georgia O’Keeffe came to the conclusion that views on the artist’s sexuality varied widely. Male authors tended to think she had lesbian relationships, where female authors came to other conclusions. Through these discussions, I saw my students demonstrate a depth of critical thinking I had not previously seen in my upper-division classes.

This project ended at the end of the semester, and left little time for students to draw larger conclusions about patterns of interaction. Nevertheless, we did get to see the interactive data visualization. One of my students was working on an Integrated Studies degree with Art and Visual Technologies. He used the spreadsheet and Flash to create the new data visualization, pictured below.

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It is not as fully interactive as the MoMA visualization, but it’s well-developed for a class project. The gray links are our fifteen primary individuals and the colored lines represent the social connections between artists, with each artist having her own color. If you click on one of the gray links, you can see all of other artists that that person knows. Each individual on the chart has a blue or pink bar next to their name to indicate their gender.

Like many undergraduate projects, it has its problems, including a wide focus, incompleteness, too many spelling errors, mistaken gender caused by unfamiliar French names, and the repetition of the blue/pink gender colors in the line colors. Nevertheless, it was an instructive exercise and my students expressed pride in their contributions and in the resulting visualization. I think that the experience affirmed their ability to conduct research in art history and to engage in meaningful conversations about gender, which was a direct result of the application of critical pedagogy.

In using critical sources on data visualization to evaluate the class project after the semester, there are some clear problems. Jeffrey Heer and Ben Shneiderman (2012) created “a taxonomy of tools that support the fluent and flexible use of visualizations,” which outlines goals, methods, and skills sets necessary for different kinds of projects. Their article serves as a kind of guide book and rubric for data visualization projects. First, we attempted to visualize the entire data set in a single visualization, pictured above. This resulted in a visual mess that makes the larger visualization difficult to use, although this flaw is present in the initial MoMA visualization. It does allow users to select a single artist and filter out the rest, but does not offer other types of filters, as suggested by Heer and Schneiderman. The lack of filters and different views is limiting, as the visualization does not present clear patterns to the viewer.

It is telling that in a visualization attempting to understand the relationships between women artists, there is still an overwhelming amount of blue. This study did detect one female artist network, which involved several female artists living in Mexico, including Remedios Varo, Leonora Carrington, and Kati Horna. Female artists living in Paris knew each other, but the male-dominated artistic groups formed the focal point of artistic and social activity. It would have been possible to show this visually with additional filters that showed the geographic locations of female artists and their locations over time. I am also certain that a better-executed project could show further patterns that were not addressed in the scholarship on these women. This would have allowed the class to fully achieve Phase Three digital art history.

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Students learned that the women we studied were generally connected to lots of other male artists, but not necessarily to many other women. Louise Bourgeois was the best-connected woman artist, closely followed by Remedios Varo, who is still relatively unknown. Perhaps Varo was disadvantaged in art history texts by having a higher percentage of women contacts. It would be possible to build on this project, correcting the existing errors and expanding the number of women artists included. This would allow a more thorough exploration of the relationships between women artists and would lead to clearer conclusions.

The project uncovered a lot of sexual scandal: heterosexual affairs, including those with male artists, homosexual affairs, sham marriages, and incest (thank you, Claude Cahun). Still, students revealed a lot of holes in scholarship, especially with Sonia Delaunay and Remedios Varo. Undergraduates often think of scholarship as complete, but my students now know that its not. They learned about the research process, the benefits of visualizing data, biased scholarship, and the problems of gender in the twentieth century.

At the end of the semester, many of the students reported that they thought about gender and twentieth century art differently than they had previously, that they had engaged in transformative learning. Specifically, many reported being more sensitive and aware of issues of gender. One student reported that she no longer assumed that all artists are or were heterosexual. Another student is constructing a senior project that addresses gender and the arts. A third student reported being unhappy with the secondary material on his artist, Meret Oppenheim, and is interested in researching and writing better material about her.

There were a number of successes with this class that I hope to repeat in future courses. Before the semester began, I knew that a group of students in the class were interested in issues of gender and as a result, I changed the focus of the class. Most importantly, the students were involved in shaping the class project, which stemmed from their own observations. Many students expressed interest in the digital and interactive nature of the project. When the students began the project, the outcomes were not clear. Students felt like they were engaging in real research instead of just learning prescribed course materials. All students reported positive experiences with this kind of research-based learning. Many students reported that they did not normally like working on group projects, but each student’s contribution formed a distinct and individual part of the larger project that allowed for full ownership of his or her part. This made group work more engaging and removed the stress that normally accompanies it. As a result of all of this, I will be looking to construct future class projects that are an intersection of digital humanities, course content, and gender studies.

Just as Underwood (2013) suggests that we “don’t already know the broad outlines of literary history,” I would suggest that we don’t already know the broad outlines of art history, in part because of gender bias. Students learned this first from their textbook and then applied their knowledge to a research project, where a data visualization confirmed gender bias in the history of female artists and in the scholarship on them. In class, we talked about art history in terms of data, tables, quantities, and graphics in addition to the more traditional terms of social movements, stylistic trends, and pivotal figures. Art history is changing and adapting to new technology, but this transition will be faster and smoother if digital tools and methods are introduced in undergraduate classrooms and not just in scholarly inquiry.

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Chicago, Judy. 2012. “We women artists refuse to be written out of history.” The Guardian. October 9. http://www.guardian.co.uk/commentisfree/2012/oct/09/judy-chicago-women-artists-history. OCLC 60623878.

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Hopkins, David. 2000. After Modern Art 1945-2000. New York: Oxford University Press. OCLC 43729118.

Jaskot, Paul B. 2013. “Digital Visualizations as Art Historical Research: The Question of Scale.” February 12. Paper presented at ThatCamp CAA, February 11-12, 2013.

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Smith, Roberta. 2012. “When the Future Became Now: ‘Inventing Abstraction: 1910-1925’ at MoMA.” The New York Times, December 20. http://www.nytimes.com/2012/12/21/arts/design/inventing-abstraction-1910-1925-at-moma.html?pagewanted=all

Stross, Charles. 2008. “Bechdel’s Law.” Charlie’s Diary (blog). July 28. http://www.antipope.org/charlie/blog-static/2008/07/bechdels_law.html

Underwood, Ted. 2013. “We Don’t Already Understand the Broad Outlines of Literary History.” The Stone and the Shell (blog). February 8. http://tedunderwood.com/2013/02/08/we-dont-already-know-the-broad-outlines-of-literary-history/

 


[1]In the second half of the course, we read David Hopkins’ After Modern Art (2000). This text applies a more traditional, or masculine, approach to art history, which covers the canon with few references to women artists and their work. Unlike Chadwick, Hopkins references many ideas and historical events that he does not explain. Some students liked the change in style, but many reported that it seemed like the author was trying to pitch the material over their heads in an effort to show off his knowledge. Nevertheless, using the two different texts showed my students two different approaches to the same material. Next time I teach this class, I plan to assign parallel readings from both texts instead of reading them consecutively.

 

About the Author

Nancy Ross graduated from the University of Cambridge in 2007 with a Ph D in the History of Art. She is an Assistant Professor of Art History at Dixie State University in St. George, Utah. She led the TICE ART 1010 development team in 2011-12 and is the Contributing Editor for Medieval Art for Smarthistory at Khan Academy. She blogs about teaching art history at Experiments in Art History.

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