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What do 21st-century journalism educators need to know?

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This year marks the tenth anniversary of the publication of the Knight-Carnegie Initiative report on the Future of Journalism Education. A collaboration between two foundations and 11 prominent journalism schools and institutes, the initiative was formed in 2005 to address both long-standing concerns about the intellectual depth and breadth of journalism education and contemporary questions about how to add digital skills and entrepreneurship to the curriculum.

In 2007, I wrote my own short essay for Nieman Reports calling for a new approach to journalism education. I argued that, “The challenge for journalists—and journalism educators—is to think about ways to create dynamic curricula to enhance the practice of journalism. Such a challenge lends itself to the development of new and closer partnerships among journalists, technology specialists involved with communications tools, economists looking at new business models, and educators working with the next generation of potential journalists. ” Specifically, I called for:

  • the creation of an undergraduate journalism education major and related certification that would support the teaching of media literacy and multimedia technology skills in secondary schools,
  • the infusion of what has come to be known as computational thinking (the ability to formulate, analyze, and solve problems in ways that optimize what humans and computers do best) in our undergraduate and graduate curricula,
  • an alertness to the ways in which emerging and evolving computing and communications technologies will reshape our industry. I urged journalism educators to engage in interdisciplinary research and teaching collaborations and called for attention to the likely impact of artificial intelligence on our news distribution and consumption practices.

Some of this seems obvious now. No one argues whether computational thinking is important or whether newsrooms need technologists and multimedia specialists, for example. Similarly, no one argues the need for greater media literacy to combat disinformation and to foster greater civic participation. Every journalism program understands the need for digital and multimedia faculty – and that big data and AI matter. Data journalism and interactive journalism are recognized occupational specialties, and a fertile area of scholarship (See Nikki Usher, C.W. Anderson). Something akin to the open source software movement is emerging among journalism educators focused on innovation, with texts on such urgent topics as media entrepreneurship, data journalism, coding pedagogy, and verification. And no one argues the need for the new forms of interdisciplinary collaboration and research partnerships.

Despite this, none of the articles in that Nieman special issue on Teaching Journalism in the Digital Age anticipated the degree to which the knowledge, values and skills required to deliver a 21st century journalism education require a fundamental rethinking of the requirements journalism educators should be expected to meet. What we are confronted with goes beyond the need to incorporate new specialties within the existing structure of journalism and mass communications departments. The transformation of our news and information ecosystems changes the nature of everything we do, and the contexts in which we do it.

We debate whether aspiring journalism educators actually need a PhD (and if so, in what?) , and how we should value professional experience, vs. academic credentials. We’re trying to get at the question of what all journalism educators should be expected to know, how specialties within the field can be defined, and come to conclusions about the academic and professional preparation best suited to the tasks.

The purpose of this essay is twofold: to instigate a conversation about areas of practice and pedagogy that are central to the challenges confronting us, and to highlight examples of work intended to confront these challenges. In particular, I want to focus on three areas that have been a focus of my own re-education over the last 30 years: journalism’s epistemologies, ontologies, and literacies. Ultimately, I hope this will inform our approach to designing curricula for graduate programs for journalism educators, and for thinking through strategies and standards for their ongoing professional development.

A bit about me: I’ve been teaching Journalism and Professional Writing for 30 years in a small undergraduate program. (We have a separate Communication Studies department with whom we work closely.) I came to academia with a background in science communications, corporate PR, and freelance magazine writing, During the 1980s, I witnessed the pervasive effects of hollowing out of the manufacturing economy. I was also part of a generation of people with minoritized identities (in my case, Black, female, a mother, urban, newly middle-class, newly disabled) who had gained entrée to spaces previously denied them (in my case, the Ivy League, the tech industry, affluent suburbs). I thought I might be able to help journalism and PR students understand what this emerging economy would look like and what it would demand of them.

I was also impelled by a larger set of public policy debates crystallized in a 1987 report by the US Department of Labor – Workforce 2000: Work and Workers for the 21st Century. Based on its demographic analysis, the report’s writers concluded that the America’s future required finding, cultivating and making space for people with backgrounds like mine. Specifically, it argued:

If the United States is to continue to prosper, policymakers must find ways to accomplish the following: stimulate balanced growth; accelerate productivity increase in service industries; maintain the dynamism of an aging workforce; reconcile the conflicting needs of women, work and families; integrate Black and Hispanic workers fully into the economy; and improve the educational preparation for all workers.

William B. Johnston et. al., https://files.eric.ed.gov/fulltext/ED290887.pdf

I started teaching online journalism in 1996 and co-founded a department of Interactive Multimedia in 2003, partially hoping to create an environment for interdisciplinary learning and reflective practice. What I know about communications theory and journalism history beyond my Master’s degree is largely self-taught. What I know about programming, scripting, and design is either self-taught, a by-product of the eight years I spent at AT&T in the 1980s where even word processing required writing in Unix, or the product of two decades of research and teaching collaborations with computer scientists, media scholars and designers.

I do recognize that I am writing this at a time of existential crisis for journalism as a civic institution and as a financially viable industry. I’m also writing at a time when a global pandemic has accelerated and exacerbated threats to the existence of many colleges and universities. However, if democracy and civil society is to survive, some form of independent journalism must be part of the social contract. Whatever form that journalism takes, these questions will need to be addressed.

Challenge 1: Journalism’s epistemologies

How do we know what’s worth reporting and how do we get and vet what we report? These are essential questions that we attempt to address by hewing to a discipline of verification. The computational turn in journalism is the latest of a series of movements that can create more comprehensive, engaging, and more accurate reporting through the development and deployment of new tools for mining, analyzing and presenting information. It’s also a movement whose failings highlight the ways in which the news industry continues to grapple with unresolved historical rifts over objectivity, fairness, and the nature and needs of the publics journalists serve. And it’s empowered malign actors with tools to amplify violent ideologies, lies, and dangerous fringe ideas.

This June, 2019 Pew Research poll is just the latest of a long list of studies confirming that news consumers recognize the credibility crisis in journalism.

A chart showing Journalists are not blamed most for creating made-up news and information, but Americans say the news media are most responsible for fixing it

Writing in the Columbia Journalism Review, Avid Ovadya advances a framework for he calls the credibility assessment model: “In summary,” he writes, ‘the answer to the question ‘What should we investigate if we want to determine whether something is credible?’ is that we need to investigate the evidence supporting the claims, the reputation of the network purveying the information, or some combination of the two. Journalists have created a number of fact-checking and verification operations to guide news consumers to credible information, But the problems of fake news and public skepticism persist.

Computational journalist and educator Jonathan Stray notes that the strategies that governmental agencies, platforms and news organizations vary widely in their effectiveness in combating misinformation. The most effective strategies and tools may be incompatible with the values of a free press and free speech. In 2019 paper (.pdf) presented to an international conference on the issue, Stray argued, “In societies with a free press, there is no one with the power to direct all media outlets and platforms to refute or ignore or publish particular items, and it seems unlikely that people across different sectors of society would agree on what is disinformation and what is not.” You can see Stray’s presentation to an international conference on countering misinformation.

Ovadya hopes the framework that his team is developing will be used to create fact-checking systems that can be run by networks of bots on large data sets. But finding reliable measures of evidence and reputation that are free of implicit bias and accepted in a wide range of contexts is proving to be a wicked problem. As Cathy O’Neil notes in her book, Weapons of Math Destruction, data that appears unbiased is often anything but:

The models being used today are opaque, unregulated, and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his zip code), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.” Welcome to the dark side of Big Data.

Blurb for Cathy O’Neil’s Weapons of Math Destruction

While the definition of journalistic objectivity has never been stable, there’s no current consensus on what it means and whether it is attainable and desirable. David Mindich, Pamela Newkirk, Natalie Byfield and Lewis Raven-Wallace are among those who have persuasively argued that even in the good old days when journalism was more trusted, news organizations often failed to meet their own standards for credible sourcing, representative coverage and political independence. These failures, in large measure, were failures to accord epistemic authority to those on the less privileged side of the Robert Maynard’s “fault lines” of race, class, gender, geography and generation. (As Mark Dery has noted, this failure persisted has into the digital era.)

All journalism educators should be conversant with these debates and the evolving standards of practice that emerge from them. This especially applies to professors of the practice who might have absorbed the facile notion that the most objective reporting “plays it down the middle.”

Challenge 2: Journalism’s ontologies

Ontology is that branch of philosophy that seeks to name what “is” in the world. To say that in the past, journalists were gatekeepers is to make an ontological statement. So are definitional distinctions between “news,” “features,” “entertainment,” etc.

Similarly, In computer science, ontology refers to the act of defining the categories and hierarchies that are used to structure information in algorithms and databases. In a digital age, journalism’s ontologies are represented in both the human and technological structures of news ecosystems.

David Ryfe argues that the fundamental crisis facing American journalism in particular, and Western journalism, generally, is ontological:

Today, in the field they once dominated, journalists find themselves standing cheek by jowl with a vast array of other news producers: from community blogs to corporate communications offices, and from nonprofit organizations to advocacy groups. Many of these organizations have little interest in or knowledge of journalism. Yet, they produce and distribute as much if not more news as journalists.

In this context, the question of what journalism is, and is for, and how it is to be distinguished from an array of other news produces, is raised anew.

Ryfe, D. (2019). The ontology of journalism. Journalism, 20(1), 206–209. https://doi.org/10.1177/8756087918809246

One of the challenges to journalists’ efforts to convey those distinctions is that many members of the public they seek to serve don’t see journalists the way they see themselves. Brendan Nyhan and George Lakoff are among the researchers who have taught us that the simplest claims of fact can land differently with different audiences. To a disturbing degree, substantial percentages of Americans agree with former Pres. Donald Trump’s assertion that mainstream news organizations are the “enemy of the people.” Take the topline results of this August, 2018 Ipsos online poll, for example:

While a plurality – 46% — agree “most news outlets try their best to produce honest reporting”, there are very stark splits by the partisan identification of the respondent with most Democrats (68%) generally believing in the good intent of journalists, but comparatively few Republicans (29%). And when we ask questions with specific partisan cues, the political split is very wide. For instance, 80% of Republicans but only 23% of Democrats agree that “most news outlets have a liberal bias,” and 79% of Republicans but only 11% of Democrats agree, “the mainstream media treats President Trump unfairly”. Returning to President Trump’s views on the press, almost a third of the American people (29%) agree with the idea that “the news media is the enemy of the American people,” including a plurality of Republicans (48%).

Americans’ Views on the Media: Ipsos poll shows almost a third of the American people agree that the news media is the enemy, August 7, 2018. Accessed July 18, 2019. https://www.ipsos.com/en-us/news-polls/americans-views-media-2018-08-07

Similarly, public perceptions of the degree to which journalists fulfill such traditional roles as serving as watchdogs holding the powerful to account are divided along sharply partisan lines. This 2018 poll from Pew Research illustrates that division.

In 2017 and 2018, partisan divides in support of the news media's watchdog role largest ever measured

There’s a broad agreement that rebuilding trust in journalism requires finding new ways to connect with communities – and that requires developing products and approaches that respond to specific community needs. Monica Guzman offers this guide for engaging audiences. The Democracy Fund has engaged in a multi-year effort to identify best practices, tools and funding mechanisms for inclusive, community-driven journalism. Free Press offers guides to help communities and news organizations collaborate to improve news coverage.

These efforts are as effective as the depth and breadth of the constituencies they engage. There is evidence that news organizations aren’t meeting that bar now. A May, 2019 Pew survey found that the people most likely to be interviewed by local news outlets are most likely to be older, educated, white men.

The same survey found that the percentage of Americans who had spoken to a local journalist declined between 2016 and 2019.

Sue Robinson has produced a guide for journalists seeking to diversify their sources, drawing upon research conducted in cities across the United States. Resource constraints are major issue, Robinson reports: “With contracting newsrooms, journalists of all colors find themselves stretched thin in reporting on local communities such that all voices and perspectives can be represented.” Noting that many members of marginalized communities have justifiable fears about opening up to the press, she calls for, “rethinking traditional journalistic notions such as “critical distance” and re-conceptualizing established relationships to sources and audiences so that source networks expand. ”

Similarly, improved reporting on rural America also requires a fundamental rethink. Letrell Deshan Crittenden and Andrea Wenzel underscored this in their April 2020 Tow Center report, The road to making small-town news more inclusive. Crittenden and Wenzel conducted focus group research in Chambersburg, Pennsylvania, a rural community that New York Times columnist David Brooks depicted in a 2001 book as uniformly pastoral, conservative and white. But Chambersburg is diverse, and the lack of a common and inclusive local news source has reinforced racial divisions and impeded civic dialogue. Black and Latinx community members argued that news coverage of their community is either stereotypical or nonexistent, with deleterious consequences for local governance. White respondents downplayed racial tensions and decried perceived liberal media bias. Latinx participants called for more Spanish-language news sources.

This kind of qualitative research informs the movement to integrate design thinking into journalism practice and pedagogy. Design thinking can be a great entry point to developing and teaching strategies for more inclusive news reporting, presentation, and community-building. It also opens the door to deeper questions about the affordances and limits of the technologies at our disposal. Computer scientist Ramesh Srinavasan has argued that if we approach the development of information technologies with cultural humility, our databases and content management systems might be reimagined to reflect how the communities we are designing for create and preserve knowledge. Journalists, designers and computer science ought to be in collaborative conversation about the ideas that Srinivasan is advancing. You can learn more by reading the summaries of his projects and the elaboration of his ideas in his books as Whose Global Village? , After the Internet, and Beyond the Valley.

Challenge 3: Journalism’s literacies

In a 2018 Nieman Reports essay, Cindy Royal, a leading scholar and curriculum developer in the area of coding pedagogy for journalism, argues persuasively that too many journalism faculty resist learning and teaching the skills students need to meet employers’ growing needs. Royal notes a 2018 study by Amanda Bright documenting this bottleneck, and noting that innovation, to the degree that it is happening, is concentrated in larger, better funded programs. Royal calls for the inclusion of digital technology design and development in Communications PhD curricula and a reworking of institutional recruitment, tenure, and promotion practices to place greater value on teaching, research and practice in these emerging critical areas. In a separate Nieman essay she argues that, at the very least, every journalism faculty member needs to know how to teach some fundamental technical skills, such as how to scrape a website.

Mindy McAdams agrees. McAdams is a pioneer in online journalism practice and teaching who has influenced thousands of online journalists with her workshops, blog posts and books. In an October, 2019 telephone interview, she observed that too many journalism educators who don’t consider themselves technology specialists “think they don’t need to know about the tech.”

“I feel like if you are In the field of chemistry you take it upon yourself to know what’s happening in the whole field,” McAdams said. For example, “As AI becomes such a buzzword, how many people who are journalism educators even know what is meant by that? They could know more about it if they follow it as a news topic.”

Royal created CodeActually, a coding curriculum for journalism and communications students. And if you are baffled by AI, McAdams has a helpful lecture series on YouTube, AI in Media and Society, that will bring you up to speed.

My own thinking about what Royal and McAdams are saying is “yes, and.” The technological transformation of news gathering has changed every aspect of journalism practice, and I can’t conceive of an aspect of the curriculum that can be taught without rethinking tech’s role. That role is more profound than I realized back in 2009, when I argued that journalists needed to understand computational thinking.

But I’ve come to the conclusion that knowing how technology affects what journalists can do and how that’s received is more important than knowing how to “do” the technology. And it’s equally important to understand what the technologies, and the thinking behind them, do – especially to people and communities in information deserts or who are targets of disinformation. (This reading list necessarily starts with Meredith Broussard’s Artificial Unintelligence, but we need journalism-relevant analyses of the work of Simone Browne, Cathy Davidson, Safiya Noble, Ruha Benjamin, Virginia Eubanks,, Mar Hicks, and Andre Brock, for starters.

Here are four examples that might give pause to faculty who think that their teaching and practice are unaffected by technological change :

Journalists have had to rethink the inverted pyramid in light of research on the impact of linguistic framing. Journalists’ assumption that audiences trust a dispassionate recitation of facts in perceived order of importance is no match for a media ecosystem dominated by social platforms that make it easy to publish and amplify all kinds of misinformation and disinformation. That’s why linguist George Lakoff advises that journalists substitute the “truth sandwich” for stories about lies and misinformation:

Truth Sandwich:
1. Start with the truth. The first frame gets the advantage.
2. Indicate the lie. Avoid amplifying the specific language if possible.
3. Return to the truth. Always repeat truths more than lies.
Hear more in Ep 14 of FrameLab w/@gilduran76

Originally tweeted by George Lakoff (@GeorgeLakoff) on December 1, 2018.

Lakoff’s recommendation is grounded in his decades of work in linguistics, cognition and neuroscience. This video provides a helpful introduction.

To create content that will be understood as truthful and gathered in good faith, we have to understand how our brains process information and evaluate its credibility.

Our audience engagement strategies and technologies aren’t value neutral. Journalism practitioners, educators and scholars need to be able to engage the questions Andrew Losowsky and Jennifer Brandel raised in their session on audience engagement ethics at the 2018 Online News Association conference:

‘Audience engagement’ is the hot new thing in journalism; but most journalists are pursuing it without understanding the risks – for the community and for the newsroom – of inviting the audience into a conversation. What happens when people who aren’t used to being sources share a sensitive, deeply personal story? What happens when we reach out to communities for information, but don’t stick around to answer questions or address concerns? How do we build trust authentically, without resorting to a journalistic sleight of hand to get reluctant sources to talk? 

We urgently need to establish ethical frameworks around this work, or we risk further erosion of trust in journalism, and could even be endangering people’s lives.

Optimizing engagement tools to promote accurate information and constructive discourse is no mean feat. A March, 2019 article in Recode detailed Twitter’s stymied efforts to create incentives for constructive conversations on the platform. One fundamental challenge is understanding how to gauge the “health” of a conversation.

Technology has breached the church-state divide between the business and editorial sides of news in subtle and overt ways. The Columbia Journalism Review has argued that journalists need to understand how advertising technologies “influence the practice, distribution, and perception of journalism.”

Efforts to assess address the information needs of underserved communities can be facilitated or impeded by the way we deploy audience engagement technologies and related strategies. For example, Sue Robinson studied how mainstream journalists’ narrow sourcing and reliance on social media posts to gauge African American community views of a charter school controversy in Madison Wisconsin contributed to important blind spots in their reporting. She notes, “Despite the optimism that digital networks will diffuse power through entrenched structures, scholarly evidence has shown how online networks act as echo chambers for the powerful. In these spaces, offline inequalities not only persist but are exacerbated in digital spaces,”

We need to analyze journalism technologies and storytelling methods through the lens of critical computing. In his 2013 book Phantasmal Media: An Approach to Imagination, Computation and Expression, D. Fox Harrell explains that

“[C]ritical computing entails critically assessing the potential of the technology being researched and developed to engender conceptual change in users and the potential of the technology to engender real-world change in society….. Critical computing systems are built on cultural computing groundings, which in turn are informed by subjective computing research aims.”

D, Fox Harrell

Because those subjective aims can reinforce oppressive systems, Harrell stresses the importance of bringing diverse perspectives to the tech development process. As we embrace various dynamic storytelling tools for journalism – augmented reality, virtual reality and gamification, for example – we need to be cognizant of these cultural groundings and subjective aims, and we need well-researched pedagogies that address them.

Those pedagogies need to address not only how we tell individual stories in an ethical, accessible and inclusive way, but also how we help members of vulnerable communities harden their media ecosystems against propaganda and disinformation. For example, Howard University’s Truth Be Told News project has been combatting misinformation in and about African Americans for years in the tradition of the historic Black press. But it’s unlikely that the project’s creators ever envisioned the troll-powered Russian disinformation campaigns unleashed on social media in 2016 and 2020 to discourage African Americans and Latinx voters from participating in the electoral process. In December, 2020, Facebook and the National Association of Black Journalists launched a Fact-checking Fellowship that will fund journalists for one year’s work with an established fact-checking outfit. It would be wonderful if there were parallel initiatives supporting basic research on these issues at institutions such as Howard that are deeply connected to the communities being targeted. At the University of Buffalo, Siwei Lyu is doing NSF-funded research on helping people and systems detect deepfakes. Computational journalists need to be part of these research projects.

Conclusion

It’s hard to think about what journalism educators will need to know when we don’t know what journalism will become. Nic Neumann’s January 2021 report on journalism’s trends and predictions for the Reuters Institute identifies far-reaching changes in editorial practices and business structure:

2021 will be a year of profound and rapid digital change following the shock delivered by Covid-19. Lockdowns and other restrictions have broken old habits and created new ones, but it is only this year that we’ll discover how fundamental those changes have been. While many of us crave a return to ‘normal’, the reality is likely to be different as we emerge warily into a world where the physical and virtual coexist in new ways.

Nic Neumann, Journalism, media, and technology trends and predictions 2021

However, we do know that the survival of democratic civic values requires a cadre of educators and leaders who can serve as advocates, defenders and innovators of the best that journalism has to offer. This will require more robust research and debate that goes beyond the “Do you need a PhD?” debate.

A 21st-century journalism department or school requires faculty with newsroom experience and expertise in the rhetorical and literary forms underpinning storytelling across platforms and in social environments. (Yes, that means writing and shoe-leather reporting skills are still necessary. They’re just no longer sufficient.) It also requires both data journalists and computational journalists who understand and adhere to principles of algorithmic justice. It needs social scientists who can teach the quantitative and qualitative skills needed to assess community information needs and translate those needs into reporting that will be seen as trustworthy and useful. It needs specialists in media psychology and ecology who are conversant with emerging technologies. It continues to need legal specialists and ethicists. It needs specialists in entrepreneurship and tech management, because news organizations are tech enterprises now. And it still needs historians – although the journalism history canon needs substantial revision and expansion. I don’t know of a Masters or Ph.D. program that does all of those things well. However, the areas I’ve outlined here may be a way of invigorating the imagination about what’s needed and what’s possible.

Serving Diverse Audiences With Science Writing

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It was an honor to be a panelist for this November 17, 2020 conversation, sponsored by the DC chapter of the National Association of Science Writers and moderated by Emily Conover.

Here’s DCSWA’s description of the panel that accompanies the YouTube video:

Audiences for many forms of science communication skew predominantly white. This panel will discuss the origins of the lack of racial and ethnic diversity in audiences and strategies for how newsrooms and individual writers can work to remove barriers and better serve and engage communities of color. Panelists: Juliet M. Beverly, Content Manager at BrainFacts.org Mónica Feliú-Mójer, Director of Communications and Science Outreach for Ciencia Puerto Rico Kim Pearson, Associate Professor of Journalism at The College of New Jersey Andrea Wenzel, Assistant Professor of Journalism at Temple University Moderator: Emily Conover, Physics Reporter at Science News (Note: the gray panelist image indicates a co-host present to help with logistics.)

Resources shared by the panelists:

Teaching race across disciplines using interdisciplinary collaboration

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In the wake of widespread protests against police violence during a time when the Covid-19 epidemic’s racially disparate impacts highlight the inequities in our health systems and economy, academics are having searching conversations about how we talk about racism with our students. It has also elicited painful testimony about the experiences of college educators who have dared to teach about racism. Scholars in African American Studies, Ethnic Studies and related disciplines have been studying and teaching about racism for decades, often at great professional cost and without adequate institutional support. Faculty outside of those areas who hadn’t seen a connection between what they teach and systemic racism are making connections and searching for ways to respond. Any attempt to address how we should teach right now must acknowledge that while some faculty are approaching this subject with idealism, interest, and concern about getting it right, others – disproportionately faculty of color – are weary and wary.

Much of what’s been offered to faculty focuses on strategies for talking about race and racism in class. There’s advice from civil rights educators, webinars, and tips from the Chronicle of Higher Education. But Brenda Leake, director of the Center for Teaching and Learning and professor of Education at The College of New Jersey, cautions faculty who are looking for simple solutions to the problem, such as adding a learning goal to a syllabus along with a related reading or activity. Individually, and collectively, faculty need to be clear about what they are trying to accomplish in talking about race, to know the content and to have instructional strategies matched to the content and the students.

It’s a false separation to say, oh, here’s the curriculum without having instructional strategies to deliver or to have instructional strategies, knowing techniques and strategies for instruction, but not really understanding the content. Well, that it’s a false separation. If you want to talk about effective learning scenarios, you have to understand both. You can have content that’s wonderfully rich in terms of its potential, but if it’s delivered ineffectively or in ways that counter the content that’s being taught, then it’s really a waste.

Personal interview, June 29, 2020

That’s why some scholars who work on race argue that professors who haven’t studied these issues shouldn’t be teaching them. Instead, they argue, students should be encouraged to take classes in African American Studies and related fields, and college should provide the support and recognition that work deserves. Ohio State University economist Trevon Logan made this argument in a recent Twitter thread about his discipline’s failure to value this type of scholarship. (See this cautionary tale about economist Lisa Cook’s decade-long struggle to publish her “groundbreaking” work on the role of racial terrorism in suppressing Black innovation between 1870-1940 for an example of that failure.)

Logan’s perspective has particular salience amid reports that in the face of the financial crisis precipitated by the Coronavirus pandemic, academic leaders are cutting programs in African American Studies, Women’s and Gender Studies and Ethnic Studies, as well as contingent faculty, where women and people of color are over-represented.

Meanwhile, there are real consequences to producing graduates who are allowed to think that race and social justice issues are extraneous to their fields of study. The recent controversy over a respected science publisher’s reported acceptance of paper whose authors claimed they’d developed a system to predict “criminality” based on facial images is just one of many examples of the problem. As an open letter signed by more that 2,000 scholars from diverse disciplines argued:

“This upcoming publication… is emblematic of a larger body of computational research that claims to identify or predict “criminality” using biometric and/or criminal legal data.[1] Such claims are based on unsound scientific premises, research, and methods, which numerous studies spanning our respective disciplines have debunked over the years.[2] Nevertheless, these discredited claims continue to resurface, often under the veneer of new and purportedly neutral statistical methods such as machine learning, the primary method of the publication in question.[3] In the past decade, government officials have embraced machine learning and artificial intelligence (AI) as a means of depoliticizing state violence and reasserting the legitimacy of the carceral state, often amid significant social upheaval.[4] Community organizers and Black scholars have been at the forefront of the resistance against the use of AI technologies by law enforcement, with a particular focus on facial recognition.[5] Yet these voices continue to be marginalized, even as industry and the academy invests significant resources in building out “fair, accountable and transparent” practices for machine learning and AI.[6]] “

Coalition for Critical Technology. “Abolish the #TechtoPrisonPipeline: Crime prediction technology reproduces injustices and causes real harm” Medium, June 23, 2020

Here, the Coalition argues that computer scientists need to be better educated about the historical context and social implications of the work they do and the ways in which they do it:

Computer scientists can benefit greatly from ongoing methodological debates and insights gleaned from fields such as anthropology, sociology, media and communication studies, and science and technology studies, disciplines in which scholars have been working for decades to develop more robust frameworks for understanding their work as situated practice, embedded in uncountably infinite[30] social and cultural contexts.[31] 

Coalition for Critical Technology. “Abolish the #TechtoPrisonPipeline: Crime prediction technology reproduces injustices and causes real harm” Medium, June 23, 2020

Questioned by MIT Technology Review, the publisher, Springer Nature, said that the paper had actually been rejected during peer review. As reported, the Springer statement does not address the Coalition’s other demands – that the criteria used to evaluate the paper be made public, that Springer “issue a statement condemning the use of criminal justice statistics to predict criminality, and acknowledging their role in incentivizing such harmful scholarship in the past,” and that other publishers announce that they, too, will reject submissions employing similar methods.

Would more robust exposure to the kinds of scholarship that the Coalition advocates prevent such misconceived projects in the future? Computer science educators would need to be part of the conversation to find out. In a recent blog post, Mark Guzdial, a leading figure in computer science education, admitted he has a lot to learn: “I know too little about race, and I have not considered the historic and systemic inequities in CS education when I make my daily teaching decisions…. Let’s learn about race in CS education and make change to improve learning for everyone.”

The soul-searching isn’t limited to computer science. For example, the Journal of Chemical Education published a statement June 19, 2020 on Confronting Racism in Chemistry Journals. A related editorial calls on chemistry educators to do a number of things, including:

“Educate yourself and your co-workers on the scientific literature that shows how systemic and insidious bias is in science. Some valuable resources on both explicit and implicit bias can be found here: https://advance.umich.edu/stride-readings/. Use these data to refute claims that science is purely a meritocracy, that the playing field is inherently equal for everyone, and that scientists are being hired/promoted solely on their merits.”

Melanie S. Sanford, ACS Cent. Sci. 2020, XXXX, XXX, XXX-XXX
Publication Date:June 17, 2020 https://doi.org/10.1021/acscentsci.0c00784
Copyright © 2020 American Chemical Society

Ideally, faculty such as Guzdial who are beginning to learn about structural racism in their own disciplines would be able collaborate with relevant campus experts to ensure that racial literacy and a commitment to racial equity is reinforced across the curriculum. What I propose in this essay is that there are models of interdisciplinary collaboration that can be equitably deployed to deepen students understanding of institutional racism across the curriculum. The models I am going to discuss were developed as a result of research funded by the National Science Foundation over the course of the past dozen years, for the purpose of deepening students’ computational fluency and science literacy. (While I was and am part of these research teams, the opinions offered here are my own.) These models – Distributed Expertise and Collaborating Across Boundaries – provide structures for both reciprocal learning and grappling with real-world issues.

With proper institutional support -flexibility to schedule classes concurrently and logistical support for community engaged learning, for example – these research-based models could be implemented without perpetuating the marginalization of social justice scholarship.

1.Distributed expertise models

These models are intended to facilitate inquiry-based learning and cross-disciplinary collaboration in a way that does not require team teaching. Then collaborating courses have separate learning goals, deliverables, and grading. Below, I will include descriptions of these models, links to some of research that has been published, and a description of our current research project, which is entering its second year. I’ll follow this with some ideas of how the collaborations might work in practice.

What they are. From 2008-2013, I was part of a team led by Villanova University researchers Lillian Cassel and Thomas Way that developed and tested three models for teaching computing across departments and institutions. Our work was funded by NSF Award #0829616. These models were called Remote Experts with Local Facilitator, Cooperative Experts, and the Special Resource model.

Remote Expert Model. Under the remote expert model, one class with deeper expertise in a particular area contributes to a another class’s project, often at a different institution. In an example we described in this paper, game design students at Villanova contributed code to a game engine being designed at at TCNJ and TCNJ Interactive Storytelling students analyzed the story bible for the TCNJ game implementation class to identify plot holes before the story was implemented in code. The game implementation class gave the Interactive Storytelling class an interactive storytelling engine that the storytelling class used in order create their midterm projects. Then the Interactive Storytelling students shared their projects with Villanova software engineering students who did a code review.

Potential application: A chemistry class might have a unit in which students learn to detect contaminants in water. They might provide such an analysis to a class that investigates environmental justice or public health issues. The chemistry students would be exposed to the scholarship on the systemic failures behind such events as the water crises in Flint, Michigan, Newark, New Jersey and elsewhere. The environmental justice students would be exposed to a practical application of scientific research.

Special Resource Model. The Special Resource Model involves bringing in subject matter experts from a different field in to collaborate with the STEM class. The Gumshoe project, a collaboration between TCNJ professors Monisha Pulimood (Computer Science), Donna Shaw (Journalism) and Philadelphia Inquirer reporter Emilie Lounsberry is a great illustration of this model. (Lounsberry became a full-time faculty faculty member in the TCNJ journalism program after this project was published.) Lounsberry had been covering the Philadelphia courts for a long time, and had observed that many cases of firearms possession never seemed to go to trial. Shaw obtained court records of nearly 700 people arrested for unlicensed gun possession in January and February, 2006. A subset of these individuals were also accused of violent felonies. Pulimood’s students created a database to help her and her students manage the data. They tracked these cases through the courts and found that nearly half the cases were withdrawn by the DA’s office or dropped, that witnesses often failed to appear, and that only a small percentage of the arrestees charged with both illegal gun possession and violent felonies received significant jail time. Presented with the results, the Inquirer did its own analysis, reaching similar conclusions. Lounsberry and a team of reporters ultimately produced a four-part series that led to significant reforms in the Pennsylvania court system. You can read a detailed description of the project in this 2011 paper for the Special Interest Group for Computer Science Education for the ACM (SIGSCE).

Potential application. This model is well-suited to collaborations in such areas as journalism, education and public information. For example, chemistry or physics class could collaborate with a writing, education, or health communication class on producing material about climate change or environmental justice. Social scientists could collaborate with education majors or artists to produce works that elucidate issues related to race, power, privilege and trauma.

Cooperative Experts Model. The cooperative experts model differs from the special resource or remote experts model in that each the two collaborating classes are conceived as genuinely collaborating, as opposed to operating in a provider- client relationship. In this model, each class has distinct areas of expertise that they bring to the collaboration. Ideally, the classes are scheduled simultaneously, and there are periodic joint meetings at the beginning, middle and end of the project where students can brainstorm ideas and develop and implement team projects. This requires considerable communication and modeling between the instructors, but it can be very fruitful.

Potential application. Imagine a biomedical engineering class taking on “race correction” in medicine in collaboration with a class focused on some aspect of critical technology studies. Race correction is the practice of creating medical devices and treatment protocols that rely on algorithms that use race as part of their criteria, despite the fact that race is a social, not biological category that serves as a poor proxy for genetically-defined populations.This June 2020 New England Journal of Medicine article has a concise overview of the controversy, and this handy chart illustrates the scope of the problem. Drawing on the work of physiologist Lundy Braun, Ruha Benjamin describes one insidious outcome of employing race correction in a common medical device, the spirometer, when 15,000 asbestos workers filed a class-action workplace safety lawsuit against a major insulation manufacturer.

“[T]he idea [of] race correction [is] so normalized that there is a button that produces different measures of normalcy by race – the company made it more difficult for Black workers to qualify for workers’ compensation. Black workers were required to demonstrate worse lung function and more severe clinical symptoms than White workers owing to this feature of the spirometer…”

Ruha Benjamin, Race After Technology: Abolitionist Tools for the New Jim Code, Polity Press, 2019, p. 286

This 1999 Baltimore Sun article confirms that the company, Owens Corning asked a judge to remove Black plaintiffs from the suit, even though their scores would have been accepted as indicating lung damage had they been white.

I can envision a collaboration in which the students interrogate the impacts of these practices, and perhaps consider criteria for more equitable tools. For example, the algorithm used to determine the likelihood of successful vaginal birth after a cesarean section known as the VBAC caculator, includes race corrections for both Black and Hispanic women. As this 2019 article from the Women’s Health Issues Journal notes, the inclusion of the non-biological category of race in the algorithm not only lacks scientific justification, it also evokes discredited ideas about the supposed anatomical differences between Black, white and Hispanic women. In an interview with the investigative reporting podcast, Reveal, the lead developer of the VBAC said the inclusion of race in the algorithm was based on empirical observation. However, the authors of the Women’s Health Issues article argue:

The danger of including race in this manner within a clinical algorithm is in implicitly accepting these categories as natural rather than historical and socially constructed. More often, race is included as a proxy for other variables that reflect the effect of racism on health: factors like income, educational level, or access to care.

Darshali Vyas, et. al. Challenging the Use of Race in the Vaginal Birth after Cesarean Section Calculator, Women’s Health Issues 29-3 (2019) 201–204

2. Collaborating Across Boundaries Model (CAB)

Phase one: CABECT. The CAB model builds upon these distributed expertise models by adding a community-engaged learning component. Our study, Collaborating Across Boundaries to Engage Undergraduates in Computational Thinking (CABECT), was supported by he National Science Foundation DUE Award #1141170. As Project PI Sarah Monisha Pulimood explains, “The primary goal of the project is to develop a model for students and faculty to collaborate across diverse disciplines and with a community organization to develop technology-based solutions to address complex real-world problems. ” I served as co-PI.

Students in successive classes in computer science, journalism, and interactive media worked with our local chapter of Habitat for Humanity to develop tools that would make it easier for both the agency and potential homeowners to understand what pollutants might be on their properties, along with the the associated cleanup costs.

This project resulted in the creation of the SOAP database (Students Organized Against Pollution), including maps of brownfields, data on contaminants that could be accessed via maps or tables, links to relevant state legislation and other explanatory content. The journalism and media students also developed content on Ushahidi’s Crowdmap platform for eventual incorporation into the SOAP database, and they used Sanborn Fire maps, old industrial directories and Google images to build tables identifying the locations of polluted sites that might have been torn down and repurposed before the establishment of environmental regulatory authorities in the 1970s. They also built an alternate reality game, #TrentonTrending, to allow community members to deliberate over and propose solutions to the challenges presented to the community by years of economic disinvestment and environmental injustice.

An abandoned factory site in Trenton New Jersey that was part of the focus of the SOAP project. Students in computer science, journalism and media developed tools to help Habitat for Humanity identify pollutants and cleanup costs in properties they might acquire for low income housing.
An abandoned factory site in Trenton New Jersey that was part of the focus of the SOAP project. Students in computer science, journalism and media developed tools to help Habitat for Humanity identify pollutants and cleanup costs in properties they might acquire for low income housing.

Assessment outcomes for the CABECT project were encouraging: students made gains in both computational thinking and civic engagement. More details on the study and the assessment data are available here, here, and here.

CAB: The current work. Our current project, Collaboration Across Boundaries to Engage Undergraduates in STEM Learning, expands the CABECT model across the campus. As we explained in this video for the 2020 STEM for all Video Showcase, by the end of the project, about a dozen faculty, 700 students and perhaps a dozen community partners will have participated in the project by the end of its three-year run. We’ve just concluded our first year.

As you can see from the poster below, the structure of the CAB model readily accommodates collaborations focused on addressing historical and contemporary inequities. Our research questions are focused on STEM learning, but they also include questions related to community engagement and STEM diversity that are best addressed by attention to systemic inequities faced by the community partners, as well as in our classes and curricula. The collaborations we are testing span the range of disciplines and disciplinary combinations, forming a community of practice that is contributing to broader deliberations and actions across campus.

CAB project research design.

We hope that what we learn will be a useful tool in the broader effort to improve both students’ STEM literacy and constructive civic engagement Our project website will report on our progress.

Not just STEM. CAB PI Monisha Pulimood has extended our model beyond STEM in her capacity as the Barbara Meyers Pelson Chair in Faculty-Student Collaboration. Pelson CAB collaborators also complete an interdisciplinary project. They also participate in the CAB training sessions and workshops, but their outcomes are not assessed as part of the research project.

This past spring, English professor Glenn Steinberg’s Bible as Literature class worked with Music professor John Leonard’s College Chorale class on a performance of Arthur Honegger’s symphonic psalm King David. Steinberg’s students supplied extensive program notes based on their research. Although the Covid-19 shutdown kept the concert from being staged, both the program and a virtual performance of selections from the work will go online later this year.

In the 2020-2021 academic year, Computer Science professor Sherif Ferdous and Communication Studies professor Yifeng Hu will lead teams of advanced research students from their respective departments in a unique collaborative course.

As Hu explained in an email, the course is titled: ‘Virtual Reality for Social, Cultural, and Health Issues’, and multiple student groups will explore different topics surrounding social/cultural/health issues… There will be projects that use VR to raise awareness of racial and/or cultural understanding as well as meeting health communication needs, and to potentially bring about social/cultural/behavioral changes.” 

Personal email, July 14, 2020

Conclusion

The challenge of fostering anti-racist pedagogy across the curriculum is both institutional and instructional. At the level of the institution, there must be, as Logan says, respect for the “body of knowledge” generated by scholars on race, racism, and racial inequality. This includes matters that are well beyond the scope of this essay, such as ensuring that support for academic units focused upon these areas is a strategic planning priority as administrators make hard choices during hard times. It also includes fostering faculty deliberation and action on the best ways to ensure that students understand the relevance of these issues across the curriculum.

This essay posits that collaborative classes with interdisciplinary community-engaged projects might be one way to develop both effective instructional strategies and relevant content. The institutional resources required to support such collaborations are already in place in many colleges in universities where offices of community-engaged learning, instructional design and Centers for Teaching and Learning are common. TCNJ’s Center for Teaching and Learning sponsors learning communities that allow faculty to deepen their understanding of issues that affect their pedagogy, often leading to specific actions in the form of campus programming, and administrative initiatives. This could be an ideal place to incubate ideas for teaching collaborations along the lines of the CAB model.

While the protests (or at least the media attention) may subside, the need to address these issues in our classrooms will persist. We have an opportunity to address longstanding inequities and give our students a more comprehensive understanding of their fields of study that will positively inform the kind of professionals and citizens they become.

Acknowledgements

Whenever I talk about this work, I am reminded of the debt of gratitude I owe to many current and former colleagues at TCNJ. I must thank Ursula Wolz, CEO of Riversound Solutions and my former colleague at TCNJ, for inviting me into the world of interdisciplinary computing collaboration. Ursula, Phil Sanders and I collaborated to write the initial proposal for the Interactive Multimedia Major at TCNJ. Ursula was the PI for the first NSF grant for which I was co-PI with Monisha, Broadening Participation in Computing via Community Journalism, which led to the creation of the Interactive Journalism Institute for Middle Schoolers (IJIMS). Reaching back further, I am grateful to TCNJ colleagues Elizabeth Mackie and Terry Byrne. In the early 1990s, we undertook a number of teaching collaborations to give students the experience of creating advertising and merchandising campaigns and launch both print and online magazines.

On measuring the presence of absence

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After decades of researching my family history, I’ve learned a lot about the people and circumstances who brought me into being. But the effort to destroy, distort and deny the histories of both enslaved and indigenous people means that I’ll probably never know the answers to some basic questions about my ancestry. That has consequences.

When I started researching my family history back in the 1970s, I had to rely on family stories to help me understand the gaps and errors in official documents. Family lore guided me to the name of one of the men who enslaved my paternal ancestors, and that led to his census records. Family lore helped me untangle family secrets – an ancestor born out of wedlock and raised by an uncle, for example, as well as the likely names of that ancestor’s birth parents.

But there are bits of family lore that are difficult to verify because of deliberate erasure by those who were in power at the time. One of the big mysteries has to do with my purported Native American ancestry. To be clear, I know many Black families have these stories – and as the unfortunate example of Sen. Elizabeth Warren shows – so do many White ones. The claims about Native ancestry and their possible meaning both fascinated me and made me uneasy, because I was not one of those Black people trying to claim non-Black ancestors to escape my Blackness. As Zora Neale Hurston put it in her famous essay, “How it Feels to be Colored Me:”

I am colored but I offer nothing in the way of extenuating circumstances except the fact that I am the only Negro in the United States whose grandfather on the mother’s side was not an Indian chief.

“How it Feels to Be Colored Me,” Zora Neal Hurston

Here’s the thing: my maternal grandmother did say that her maternal grandfather, George Ashton, was an Indian community leader, if not an actual chief. She also said her mother, who had died when Grandmom was a little girl, was a full-blooded Native American. My grandmother talked about attending pow wows as a child, but she had little detail to offer. My mother showed me a newspaper clipping about Grandpa Ashton, but of course I couldn’t find it in her things when she died. He died when my mother was only four, so the memories she and her siblings had were slight.

Edna Ashton Farrell, who died in 1925, was my mother’s maternal grandmother
My mother’s mother and one of her babies. My mother was probably a toddler when this was taken.
Mommy with her Aunt Gladys
Mom and me

Despite this, the claim to Native ancestry was an important part of the way both my grandmother and mother saw themselves. For a long time, I regarded all of this with mild curiosity and some skepticism. My understanding of what it means to be Native American had been largely shaped by college friends who’ve lived on and off of reservations, who were forced to attend boarding schools, and who have been involved in their tribe’s battles over treaty rights and decent living conditions. To my mind, my grandmother and mother lived lives shaped by the geographies South Philadelphia, Camden, and rural New Jersey and their iterations of African American culture – foodways, religious practices, forms of entertainment, etc.

However, having reconsidered Grandmom’s life and genealogy, I see those memories — and her– differently. My grandmother, Eileen Barnes, was a resourceful matriarch whose character was forged by learning to survive amid ongoing trauma and dislocation. This I knew. I also knew that her grandfather, George Ashton, had been an important person in her life. As I have begun to learn about George Ashton, I’ve begun to realize that I may have missed an important clue about how my grandmother’s identity was formed by George Ashton’s experiences as a Native American in an area where Native identity was ignored or erased.

The events of Grandmom’s early life are stark. She was her parents’ oldest daughter. A brother and two sisters who came quickly after, so she was her mother’s helper. Her mother died when Grandmom was eight.

Her father was a man of grand ambitions, summary judgments, and deep resentments. He told me that around the time my grandmother was born, he got a car and driver’s license. Back then, he said, you just had to send in a fee to the motor vehicles’ office. For a time, he was on the road in search of work and adventure. Eventually, he came back to Philadelphia and plied his trade as a barber, ultimately owning his own shop. After his first wife died, he remarried and had another child. He spoke of his sons with pride; he critiqued his daughters. At the end, it was my Grandmother who coordinated his care, and her sisters who visited. One brother had died decades before. The other sent money but kept his distance.

For a time after her mother’s death, Grandmom lived with her Grandfather Ashton and was embraced by him and his wife. Grandfather George identified as Algonquin. This is when she went to powwows. Years later, when my daughter interviewed her for an elementary school assignment, this was the time that she talked about when asked about her childhood. My grandmother’s identification with her Native ancestry was born of intimate association with the people who had nurtured her.

By the time she was 20, Grandmom was a young wife and mother trying to survive the Great Depression. By 30, she was a widow with five children, her young Army husband having succumbed in a VA hospital after a mysterious illness. By then, Grandpa Ashton was gone, too. For years, I’d heard about her going back to the area where he’d lived and visiting with various people, although no one seemed to know much about who they were.

Grandmom lived for another four decades primarily devoted to her ever-growing family. I would sit with her and count up the number of children (16), grandchildren, great-grandchildren, and great-great-grandchildren. It made her chuckle with wonder. It seemed she always had a child or grandchild staying with her. On Sundays, and especially holidays, she would cook enormous meals and her brood would pass through after church. She learned to play church organ and became a poet as well. Her faith became central to her life, and she worked hard to impart that faith to all of her descendants.

When she died, she was buried in one of several family plots that Grandpa Ashton had purchased decades before. Except for my grandmother and mother, the graves are unmarked. Cemetery records verify that the bodies of my great-great Grandfather George, Great-Grandmother Edna, and one of George’s wives are buried there. (I’m guessing this was the wife he married in 1924 – the one my grandmother knew. The first wife, Edna’s mother, disappears from the public records around the turn of the century.) My mother is interred next to her mother. Grandpa George was still providing for us.

Mom and grandma, together for eternity. Grandma’s mother and grandparents are in unmarked plots behind them.

Part of what my grandmother’s life teaches me is that even hidden connections leave legacies. The nurturing my grandmother experienced during her short time among the people she understood to be her indigenous relatives seemed to have forged a lifelong connection, even though there didn’t seem to be a physical community or written history to point to. Like Zora, my grandmother was not “tragically colored.” She was matter-of-fact in saying that her mother was Native and her father had a Black mother and a White father who had not been in his life. She forged her own family and community from those broken pieces.

Sill, it seems that the fragmenting of the Lenni-Lenape might be part of her legacy of intergenerational trauma. It leaves me to wonder whether there’s some set of Ashton relatives cut off from George’s descendants who might know more about his role the hidden history of the Lenni-Lenape in Burlington County, New Jersey.

What all the records confirm is that George Ashton was from Shamong, New Jersey, an area historically settled by the Lenni-Lenape. We have a photograph of a woman identified as his sister Lucy Bell. [2022 update: I did find find information on Lucy Bell that led me to census records with the names of their parents and a possible grandmother. Their father’s name was Isaak and his mother, Mary, appears to have come from New York. It also turns out that one my grandmother’s sisters lived with Lucy Bell’s family at the same time that my grandmother lived with Grandpa George. I have been in touch with that sister’s granddaughter, and she is also trying to piece together the same mysteries.]

Great-great grandaunt Lucy Bell. The notes were written by another of my grandmother’s sisters.

Official histories say the Lenni-Lenape were pushed out decades before Grandpa Ashton was born in 1861. Only the 1940 census record identifies him as Indian; earlier records identify him as Black or White. However, as the website of the Nanticoke Lenni-Lenape attests, there were tribal members who remained in New Jersey and insisted on maintaining their cultural identity even as the majority of their people were forced to go to Oklahoma and elsewhere.

Some helpful insight came from in an email from my friend and TCNJ colleague Dr. Rachel Delgado-Simmons, an anthropologist who formerly worked for the National Museum of the American Indian and who has researched questions surrounding the authenticity of Native American art. According to her, the failure to identify Grandpa George and his family as Indian could have resulted census workers’ ignorance or from government policy not to recognize the existence of Indians in that region.

And some hid their identities to avoid persecution. In a 2007 history of the Nanticoke Lenni Lenape, Rev. Dr. John R. Norwood wrote:

[B]ecause of racial persecution, many eastern tribal families remained in isolated communities and did not seek unwanted attention from outsiders. Cultural activities were not open to the public. Sometimes, even racial misidentification occurred in an effort to clear state and federal obligations to remaining tribal citizens. It was not until the civil rights protections from the 1960s and 1970s that many, previously hidden, eastern tribal communities and their leaders began to openly advocate for their people and promote their heritage to the public. 

Indeed, I’ve been told that Grandpa George was aggrieved because he felt he’d been denied certain benefits accorded to tribal members by treaty. Norwood notes treaties between Native American nations in the eastern United States predated the establishment of the nation, and were often breached by subsequent federal and state governments.

Genealogical records, histories such as Norwood’s, and a 1904 list of names of Indians drawn from 18th and 19th century documents have suggestive clues, but no firm evidence of George Ashton’s connection to the tribe. Could “Ashton” have been an anglicization of Ashitaman? If so, he could have been descended from someone who was party to a 1715 contract deeding land to one “Isaac McCow, of Burlington, for a tract on a run called Timaqueekahung…” (p.27) Could he have been related to the woman Norwood identifies as Ann Ashatama Roberts, known as “Indian Ann?” Her father, Elisha Ashatama, was a Lenni Lenape born about 1780. According to Patricia Martinelli’s book, New Jersey Ghost Towns, Ann lived from 1805 to 1894, and was hailed as the last Indian in the area at the time of her death. She married twice. Her formerly enslaved first husband disappeared from the historical record. Her second husband, with whom she had seven children, died in 1852. One of her sons, Paul Roberts, Jr. served with the United States Colored Troops during the Civil War, entitling her to a pension.There are no other identified descendants for Elisha.

Eligibility for tribal membership requires proof of descent from a list of founding families. It also requires proof of at least 25 percent native ancestry and the ability to trace one’s genealogy four generations back. That would mean that the surviving members of my mother’s generation would be eligible if they could identify George Ashton’s parents. There’s a rumor that at least one of my grandmother’s sisters did have tribal citizenship. Her birth certificate says her mother was Indian. By the time I heard the rumor and saw the birth certificate, she was too ill for me to ask her about it.

So, my next steps are to learn more about George Ashton’s first wife, to confirm his mother’s identity, and to enlist the help of a Lenni-Lenape scholar. Reading the Nanticoke Lenni-Lenape website, it’s clear that tribal leaders are working hard to reconstruct their stories and culture, so maybe there’s room for collaboration. In the meantime, I share this video from the Nanticoke Lenni-Lenape website. It enlarged my perspective on what it means to be Native American in a nation that only sees Black and White.

A college professor’s advice to incoming college students

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Dear College Student,

First, congratulations on embarking on a great adventure. Whether you are just out of high school or you are starting college later in life, you are beginning an endeavor that can open up opportunities that you had not envisioned for yourself and your families. It certainly did that for me.

No doubt, you are anxious about how to make the most of this experience. You are getting a lot of advice – probably too much to take in at one time. I don’t want to pile on, but I do have some perspective, having worked with students for the last 30 years, as well as having experience in the corporate and health care sectors. Some of these tips are things I found personally helpful, and others are things that I’ve learned from some of the highly capable students with whom I’ve had the honor of working. Many are things I wish I had known when I was an undergraduate.

What’s your dream?

This is the first question that I ask students who come to me for advisement. Here’s what I am really asking: What’s the life you are hoping for, and what do you believe or hope college will do to help you achieve that life? Often, students have only a vague idea of what they hope to get out of college, and that’s fine. Occasionally, (often a nontraditional student in either age or life experience), they have very clear ideas of what they want to do and how college fits into their life plan. That’s also fine, although I do encourage an openness to the possibility that your college experiences may lead you to alter your plans.

When I ask about the life you want for yourself, I am not just talking about the work you intend to do. What lifestyle are you looking for? Do you want to experience other cultures? Is it important for you to have strong ties to a faith community? Are you concerned about taking care of family responsibilities? Are you trying to figure out how people negotiate career, marriage and family? Do you need to figure out how to have the life you want while managing a chronic health condition or physical limitation? Are you concerned about how to achieve academic or professional success in a field where you haven’t seen people from your background? There are things you can do during your college years that can help you work out answers to all of these questions.

The value of articulating your dream for your life is two-fold. First, it keeps the focus on college as a means to an end, not the whole of your life. Second, as a faculty advisor, it’s helpful to me to understand what a student’s expectations, interests and considerations are. I might be able to suggest colleagues, alumni or organizations where the student might find people of like mind and interest.

Plan backwards.

You are more likely to reach your goals if you can begin with the ends in mind. That’s why your academic advisors may try to get you to come up with a tentative four-year course plan. It’s good advice, even though we know that it’s common for students to change majors once or twice before graduation. (By the way, changing majors needn’t affect the time it takes you to graduate, in most instances. Your first two years are usually general education courses that you’ll be able to apply to your graduation requirements regardless of major.) Planning backwards is a good way to help you figure out how to fit in the goals that are most meaningful to you. Want to go on exchange or do an internship? Work the eligibility requirements into your timeline.

Plan forwards.

Here’s something I found helpful during my college years: at the beginning of each academic year, I tried to set both academic and personal learning goals for the year. We each come to college with varying degrees of preparation for life. In addition to learning academic content and skills, we may need to learn a variety of practical, social and emotional skills in order to become the people we are striving to be. One of those skills is learning how to take care of our physical and emotional health while carrying a heavy workload. Learning how to be understanding and respectful of other cultures is another. Managing money and time is another. Understanding, setting and enforcing appropriate boundaries may be another.

List the personal development needs that you have noticed or that have been pointed out to you. Each semester, pick one to prioritize and make time in your week to devote some time learn about that thing. How do you do that with all of the other things you have to think about? Here’s a strategy that worked for me:

  • At the beginning of the semester, take all of your syllabi and make out a weekly schedule that includes the due dates for major class assignments. If not already noted, talk to your professor about interim deadlines for major assignments. (For example, if the course has a final paper and there aren’t deadlines for when you should have topic, bibliography and outline, get feedback from your professor about when you should set those deadlines for yourself.)
  • Note any formal recurring obligations, such as class time, lab time, work, practice, and such chores as laundry. If you have trouble staying organized, set aside a 15 minute slot in your day to do some basic straightening up. It will help you and your roommates will appreciate it.
  • Note regular seasonal obligations – office hour appointments with each of your professors and academic advisor at the beginning of the semester and at registration time, deadlines for internship applications, etc.
  • Schedule sleep. Take that seriously.
  • Following the 45/15 rule is a great way to approach study time.
  • Include at least one hour a week for personal time. This can be where you focus on your personal development priority. One semester I took a dance class. In another, I read a guide to personal finance.
  • At the end of each semester, assess your progress and use that to refine your goals for the next semester.
  • Take advantage of your college’s student success office, mentoring programs, or counseling center, as needed, to help you manage this list.

Integrate your academic and social lives.

Many of us got into college by developing a habit of sequestering ourselves away from our peers. Some of us – students of color, first-generation college students – come to college without a peer group that shares our interests or that even understands what we are experiencing. We may find ourselves being the only person from our background in a class. When we’re around people who share our cultural or class background on campus, we are more likely to focus on our common social experience, rather than the particularities of how we’re doing in our classes.

But research shows that collaborative learning really benefits college students, and there may be creative ways that you can integrate your academic study with social concerns. That’s another reason to talk to your professors about how to structure your studies and co-curricular activities in a way that supports your development as a whole person.

You will make mistakes. What matters is what you learn from them.

Years ago, I worked for a major research laboratory whose research VP was a Nobel Prize winner. He used to talk about how, when he visited the labs of his research scientists and asked what they were working on, he was always disappointed when they only told them about their positive results. He’d ask them which experiments weren’t working as expected. That, he said, was where the breakthroughs were.

It’s not unusual to have experiences in college that shake your confidence. I keep a folder in my desk that has my essays from my first-semester writing class at Princeton. I’d placed out of the writing requirement, but took Lit 151 anyway, reasoning that it couldn’t hurt to have more writing instruction. I wasn’t prepared for the critical feedback I got. Similarly, I went into my first-semester psychology midterm feeling confident because I had studied diligently, gone to class, and done the labs. I was gobsmacked when my grade wasn’t an A. What went wrong?

What I learned was that my writing class presumed a greater knowledge of formal rhetoric than I possessed at that time. I’d passed my writing sample because I was a voracious reader, not because I knew as much as I needed to know about how to structure an argument. I lost points on my psych midterm because I didn’t provide enough elaboration in the short answer or essay sections. In my high school, a short answer was a phrase or sentence, and three paragraphs was good enough for the essay section. In college, my instructor expected at least a paragraph for a short answer and 1-2 pages for an essay. I knew the material well enough to do that. What I didn’t have was the social capital to understand the difference between what my teacher wanted and what I thought he wanted.

Even now, I am embarrassed to write about those midterms. You can imagine how mortified I was at the time. When this kind of setback happens to high-achieving students, there’s a temptation to beat yourself up and say you have to work harder. After all, putting in more hours worked in high school. But often, that just leads to more disappointment, frustration, and in the worst cases, disengagement.

What saved me was finding graduate students who understood my background and who could help me bridge the gap between my work and my teachers’ expectations. They affirmed that I wasn’t deficient or lazy. I just needed a cultural translator. Thanks to them, my grades improved as time went on. What’s more, my struggles led me to want to enter higher education to help develop and apply the kinds of collaborative learning models that I’d seen work in industry to the challenge of helping journalism students adapt to the growing need for STEM literacy in the communications professions.

Lots of students struggle in classes where they’ve worked hard and had expected to do well. Sometimes they worry that their life plans will be scuttled if they end up without a high grade. Particularly in your first year, this should not be your worry. When it comes time to apply for jobs and graduate school, hirers and admissions committees will be interested to learn about how you met challenges and recovered from setbacks. That’s part of how they assess your maturity and character.

Well, that’s pretty much it. I hope you have lots of fun. Welcome Week is usually a blast – enjoy. Best wishes for a splendid semester.

Prof. Pearson