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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.

These studies demonstrate some evidence for the existence of disciplinary differences in technology adoption, which suggests that there is not a homogeneous form of “scholarship” within academia.

Instead of burrowing into a silo or vertical on a single webpage, as our Gen Z digital natives do, fact checkers tended to read laterally, a strategy that sent them zipping off a site to open new tabs across the horizontal axis of their screens. And their first stop was often the site we tell kids they should avoid: Wikipedia. But checkers used Wikipedia differently than the rest of us often do, skipping the main article to dive straight into the references, where more established sources can be found. They knew that the more controversial the topic, the more likely the entry was to be "protected," through the various locks Wikipedia applies to prevent changes by anyone except high-ranking editors. Further, the fact checkers knew how to use a Wikipedia article's "Talk" page, the tab hiding in plain sight right next to the article—a feature few students even know about, still less consult. It's the "Talk" page where an article's claims are established, disputed, and, when the evidence merits it, altered.

In the short term, we can do a few useful things. First, let's make sure that kids (and their teachers) possess some basic skills for evaluating digital claims. Some quick advice: When you land on an unfamiliar website, don't get taken in by official-looking logos or snazzy graphics. Open a new tab (better yet, several) and Google the group that's trying to persuade you. Second, don't click on the first result. Take a tip from fact checkers and practice click restraint: Scan the snippets (the brief sentence accompanying each search result) and make a smart first choice.

What if the answer isn't more media literacy, but a different kind of media literacy?

A few years after I graduated my alma mater decided to overhaul their generals program. After much contentious wrangling over what students should or should be forced to study, the faculty tasked with developing the general curriculum settled on an elegant compromise: there would be no generals. Except for a basic primer course in mathematics and writing, general credit requirements were jettisoned entirely. Instead, faculty made a list of all majors, minors, and certificates offered at the university, and placed each into one of three categories: science and mathematics, the humanities, and professional skills. From this point forward all students would be required to gain a separate qualification in each of the three categories.

At some point, though, a presenter began reading a paper that caused me to look up at once from the wiki. This was Anna Kornbluh, of the University of Illinois at Chicago. Her paper was written in the same language as the others, but scythelike; she plied the language with weird skill, as if slicing a path toward some promontory of insight — and I found, to my surprise and unease, that I wanted to follow her there and stand and look out. Advertisement Her thesis was unsparing. “We have rhapsodized demolition as liberation while literally laying ruin to the university,” she argued, “a horror to be beheld by future historians — in the unlikely event there are any.” Literary theorists, by prizing an ethos of destruction in the name of freedom, had ironically aligned themselves with the external forces — political, administrative — that had for years conspired to obliterate the institution in which they work. “Human beings,” though, “are essentially builders,” she noted, channeling Marx — “architects of ideas” as well as topplers of norms. Both gestures, affirmation and dissent, are “life-sustaining”; ideally they coexist,  equipoised, twin components of a fulfilled life. A reconstructed university — and wider world — would depend on recovering the constructive and visionary impulse, which the profession had too long devalued in favor of critique. “Get building,” she enjoined the room.

In my last year’s analysis of the MOOC space, I concluded that there’s been a decisive shift by MOOC providers to focus on “professional” learners who are taking these courses for career-related outcomes. At the recently concluded EMOOCs conference, the then CEO of Coursera, Rick Levin, shared his thoughts on this shift. He thinks that MOOCs may not have disrupted the education market, but they are disrupting the labor market. The real audience is not the traditional university student but what he calls the “lifelong career learner,” someone who might be well beyond their college years and takes these online courses with the goal of achieving professional and career growth.

One of the lessons I learned from running Class Central is that to make money, you need to make others money. By targeting professional learners, MOOC providers are trying to exactly do that. To better serve this audience, every MOOC provider has launched products that range from tens of dollars to tens of thousands of dollars. As a professional learner, I feel a certain amount of comfort knowing that high-quality educational material exists for skills that I would want to learn in the future. But if you are true lifelong learner—the ones that helped start all the hype in the first place—the MOOC experience has largely been reduced to basically a YouTube playlist with a cumbersome user interface. Unless, of course, you are willing to pay.

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.

Although sometimes confused with digital scholarship centers, digital humanities centers are often specialized research centers led by a group of faculty and serving only select disciplines rather than a broad campus community. Also, libraries often play only a peripheral role in digital humanities centers.1 In contrast, libraries or IT organizations have a key role in digital scholarship centers.

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.