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Here are a few specific examples you can apply to your instructional design process to help learners with metacognition: Model the metacognitive process during instruction (or in one-on-one consultations) to ask and reflect on big picture questions such as: “What questions can you answer with this tool?” “What can you not do with this tool?” Keep in mind some answers may be simple (e.g., this tool can only work with data in this way, so it is excluded automatically). Also, “Did I get the results I expected? What could I have done differently?” Start with inquiry and build conversations based on the learner’s answers. “Is it the data that does not work? Or is the research question fundamentally wrong to begin with?” Collaborate with faculty to teach together, modelling your practices while demonstrating a specific tool. This could include thinking aloud as you make decisions so learners can self-correct assumptions. Also, be aware of your own expert bias so you can demonstrate how to clear obstacles. Ask learners to specifically define what is difficult for them during the process of instruction. Digital humanities tools are complex and are based on complex methodologies and research questions. By constructing opportunities for learners to self-question as they move from one task to another, they learn to self-assess their progress and adjust accordingly. There are several instructional design activities that promote metacognition: think-pair-share, one minute paper (“share a key concept learned” or “what comes next?”), and case studies.

There are specific strategies we can implement to help learners escape the recursive spiral of the liminal state they experience while managing complex digital projects: One of the most challenging aspects of teaching digital tools is forgetting what it is like to be a novice learner. Sometimes being a near-novice oneself helps you better prepare for the basic problems and frustrations learners are facing. But recognizing liminality is a reminder to you as a teacher that the learning process is not smooth, and it requires anticipating common difficulties and regularly checking in with learners to make sure you are not leaving them behind. When meeting with learners one-on-one, make sure to use your in-depth reference interview skills to engage in methods discussions. When a learner is in the liminal state, they are not always able to “see the forest for the trees.” Your directed questions will illuminate the problems they are having and the solutions they had not seen. Pay close attention to the digital humanities work and discussions happening on your own campus, as well as across the academic community. Working through the liminal space may require helping learners make connections to others facing similar problems. Also follow online discussions in order to point your learners to a wide variety of group learning opportunities, such as the active digital humanities community on Slack.9 When designing instructional opportunities, such as workshops and hackathons, pay particular attention to outreach strategies that may bring like-minded learners together, as well as diverse voices. For example, invite the scholar whose project was completed last year to add a more experienced voice to the conversation. By encouraging the formation of learning communities on your campus, you are creating safe spaces to help learners navigate the liminal state with others who may be on the other side of struggling with specific digital project issues. In designing instructional activities, guide learners through visualization exercises that help to identify “stuck” places. Making graphic representations of one’s thoughts (e.g., concept maps) can highlight areas that require clarification.

Video games, like work, are basically a series of quests comprised of mundane and repetitive tasks: Receive an assignment, travel to a location, overcome some obstacles, perform some sort of search, pick up an item, and then deliver it in exchange for a reward—and, usually, another quest, which starts the cycle all over again. You are not playing the game so much as following its orders. The game is your boss; to succeed, you have to do what it says.

Instead of working, they are playing video games. About three quarters of the increase in leisure time among men since 2000 has gone to gaming. Total time spent on computers, including game consoles, has nearly doubled. You might think that this would be demoralizing. A life spent unemployed, living at home, without romantic prospects, playing digital time wasters does not sound particularly appealing on its face. Yet this group reports far higher levels of overall happiness than low-skilled young men from the turn of the 21st century. In contrast, self-reported happiness for older workers without college degrees fell during the same period. For low-skilled young women and men with college degrees, it stayed basically the same. A significant part of the difference comes down to what Hurst has called "innovations in leisure computer activities for young men." The problems come later. A young life spent playing video games can lead to a middle age without marketable skills or connections. "There is some evidence," Hurst pointed out, "that these young, lower-skilled men who are happy in their 20s become much less happy in their 30s or 40s." So are these guys just wasting their lives, frittering away their time on anti-social activities? Hurst describes his figures as "staggering" and "shocking"—a seismic shift in the relationship of young men to work. "Men in their 20s historically are a group with a strong attachment to the labor force," he writes. "The decline in employment rates for low-skilled men in their 20s was larger than it was for all other sex, age, and skill groups during this same time period." But there's another way to think about the change: as a shift in their relationship to unemployment. Research has consistently found that long-term unemployment is one of the most dispiriting things that can happen to a person. Happiness levels tank and never recover. One 2010 study by a group of German researchers suggests that it's worse, over time, for life satisfaction than even the death of a spouse. What video games appear to do is ease the psychic pain of joblessness—and to do it in a way that is, if not permanent, at least long-lasting. For low-skilled young men, what is the alternative to playing games? We might like to imagine that they would all become sociable and highly productive members of society, but that is not necessarily the case.

A military shooter might offer a simulation of being a crack special forces soldier. A racing game might simulate learning to handle a performance sports car. A sci-fi role-playing game might simulate becoming an effective leader of a massive space colonization effort. But what you're really doing is training yourself to effectively identify on-screen visual cues and twitch your thumb at the right moment. You're learning to handle a controller, not a gun or a race car. You're learning to manage a game's hidden stats system, not a space station. A game provides the sensation of mastery without the actual ability. "It's a simulation of being an expert," Wolpaw says. "It's a way to fulfill a fantasy." That fantasy, ultimately, is one of work, purpose, and social and professional success.

At the Digital Pedagogy Lab Institutes where we’ve taught, there’s one exercise in particular we return to again and again. In our “crap detection” exercise (named for Rheingold’s use of the term), participants use a rubric to assess one of a number of digital tools. The tools are pitted, head to head, in a sort of edtech celebrity deathmatch. Participants compare Blackboard and Canvas, for instance, or WordPress and Medium, Twitter and Facebook, Genius and Hypothes.is. We start by seeing what the tools say they do and comparing that to what they actually do. But the work asks educators to do more than simply look at the platform’s own web site, which more often than not says only the very best things (and sometimes directly misleading things) about the company and its tool. We encourage participants to do research — to find forums, articles, and blog posts written about the platform, to read the tool’s terms of service, and even to tweet questions directly to the company’s CEO.

Here’s the rubric for the exercise: Who owns the tool? What is the name of the company, the CEO? What are their politics? What does the tool say it does? What does it actually do? What data are we required to provide in order to use the tool (login, e-mail, birthdate, etc.)? What flexibility do we have to be anonymous, or to protect our data? Where is data housed; who owns the data? What are the implications for in-class use? Will others be able to use/copy/own our work there? How does this tool act or not act as a mediator for our pedagogies? Does the tool attempt to dictate our pedagogies? How is its design pedagogical? Or exactly not pedagogical? Does the tool offer a way that “learning can most deeply and intimately begin”? Over time, the exercise has evolved as the educators we’ve worked with have developed further questions through their research. Accessibility, for example, has always been an implicit component of the activity, which we’ve now brought more distinctly to the fore, adding these questions: How accessible is the tool? For a blind student? For a hearing-impaired student? For a student with a learning disability? For introverts? For extroverts? Etc. What statements does the company make about accessibility? Ultimately, this is a critical thinking exercise aimed at asking critical questions, empowering critical relationships, encouraging new digital literacies.

Unfortunately, the enemy is complacency. Tech workers trust their founders, find labor organizing distasteful, and are happy to leave larger ethical questions to management. A workplace free of 'politics' is just one of the many perks the tech industry offers its pampered employees. So our one chance to enact meaningful change is slipping away. Unless something happens to mobilize the tech workforce, or unless the advertising bubble finally bursts, we can expect the weird, topsy-turvy status quo of 2017 to solidify into the new reality. But even though we're likely to fail, all we can do is try. Good intentions are not going to make these structural problems go away. Talking about them is not going to fix them. We have to do something.

Can we fix it? Institutions can be destroyed quickly; they take a long time to build. A lot of what we call ‘disruption’ in the tech industry has just been killing flawed but established institutions, and mining them for parts. When we do this, we make a dangerous assumption about our ability to undo our own bad decisions, or the time span required to build institutions that match the needs of new realities. Right now, a small caste of programmers is in charge of the surveillance economy, and has broad latitude to change it. But this situation will not last for long. The kinds of black-box machine learning that have been so successful in the age of mass surveillance are going to become commoditized and will no longer require skilled artisans to deploy. Moreover, powerful people have noted and benefited from the special power of social media in the political arena. They will not sit by and let programmers dismantle useful tools for influence and social control. It doesn’t matter that the tech industry considers itself apolitical and rationalist. Powerful people did not get to be that way by voluntarily ceding power. The window of time in which the tech industry can still act is brief: while tech workers retain relatively high influence in their companies, and before powerful political interests have put down roots in the tech industry.