Learning Analytics as Teaching Practice

Too often, it seems, conversations about learning analytics focus too much on means, and not enough on ends. Learning analytics initiatives are always justified by the promise of using data as a way of identifying students at risk, in order to develop interventions that would increase their chances of success. In spite of the fact that the literature almost always holds such intervention out as a promise, a surprising lack of attention is paid to what these interventions might look like. A recent paper presented by Wise, Zhao, and Hausknecht at the 2013 Conference on Learning Analytics and Knowledge (LAK’13) goes a long way in putting learning analytics in perspective, taking some crucial first steps in the direction of a model of learning analytics as a pedagogical practice.

Analytics About Learning

Analytics ABOUT Learning

Like so many, I often find myself being sucked into the trap of thinking of learning analytics as a set of tools for evaluating learning, as if learning and analytics inform one another as processes that are complementary, but nonetheless distinct. In other words, it is easy for me to think of learning analytics as analytics ABOUT learning. What this group of researchers from Simon Fraser University show, however, is that it is possible to think of learning analytics as a robust pedagogical practice in its own right. From analytics ABOUT learning, Wise, Zhao, and Hausknecht encourage us to think about analytics AS learning.

Analytics AS Learning

Analytics AS Learning

The paper is ostensibly interested in analytics for online discussions, and is insightful in its emphasis on dialogical factors, like the extent to which students not only actively contribute their own thoughts and ideas, but also engage in ‘listening’-type behaviors (i.e. thoughtful reading) that would engender engagement in community and a deeper level of discussion. More generally, however, two observations struck me as generally applicable to thinking of learning analytics as a pedagogical practice.

1. Embedded Analytics are also Interventions

Wise et al make a distinction between embedded analytics, which are “embedded in the discussion interface and can be used by learners in real-time to guide their participation,” and extracted analytics, which involve the collection of traces from learning activity in order to interpret them apart from the learning activity itself. Now, the fact that student-facing activity dashboards are actually also (if not primarily) intervention strategies is perhaps fairly obvious, but I have never thought about them in this way before. #mindblown

2. Analytics are Valued, through and through

By now we all know that, whatever its form, research of any kind always involves values, no matter how much we might seek to be value neutral. The valued nature of learning analytics, however, is particularly salient as we blur the line between analysis (which concerns itself with objects) and learning (which concerns itself with subjects). Regardless of the extent to which we realize how our use of analytics reinforces values and behaviors beyond those explicitly articulated in a curriculum, THAT we are using analytics and HOW we are using them DO have an impact. Thinking carefully about this latent curriculum and actively identifying the core values and behaviors that we would like our teaching practices to reinforce allows ensure consistency across our practices and with the larger pedagogical aims that we are interested in pursuing.

Wise, Zhao, and Hausknecht identify six principles (with which I am generally sympathetic) that guide their use of analytics as, and for the sake of, pedagogical intervention:

  1. Integration – in order for analytics to be effectively used by students, the instructor must present those analytics are meaningfully connected to larger purposes and expectations for the course or activity. It is incumbent upon the ‘data-driven instructor’ to ensure that data are not presented merely as a set of numbers, but rather as meaningful information of immediate relevance to the context of learning.
  2. Diversity (of metrics) – if students are presented with too few sources of data, it becomes very easy for them to fixate upon optimizing those few data points to the exclusion of others. Sensitive also to the opposite extreme, which would be to overload students with too much data, it is important to present data in such a say as to encourage am holistic approach to learning and learning aims.
  3. Agency – students should be encouraged to use the analytics to set personal goals, and to use analytics as a way of monitoring their progress relative to these. Analytics should be used to cultivate sutonomy and a strong sense of personal responsibility. The instructor must be careful to mitigate against a ‘big-brother’ approach to analytics that would measure all students against a common and rigid set of instructor-driven standards. The instructor must also act to mitigate against the impression that this is what is going on, which has the same effect.
  4. Reflection – encouraging agency involves cultivating habits of self-reflection. The instructor should, therefore, provide explicit time and space for reflection on analytics. The authors, for example, use an online reflective journal that is shared between students and instructor.
  5. Parity – activities should be designed to avoid a balance of power situation in which the instructor collects data on the students, and instead use data as a reflective and dialogic tool between the instructor and students. In other words, data should not be used for purposes of evaluation or ranking, but rather should be used as a critical tool for the purpose of identifying and correcting faults or reinforcing excellences.
  6. Dialogue – just as analytics are used as an occasion for students to cultivate agency through active reflection on their behavior, the instructor should “expose themselves to the same vulnerability as the students.” Not only should instructors attend to and reflect upon their own analytics, but do so in full view of the class and in such a way as to allow students to criticize him/her in the same way as s/he does them.

References

How Big Data Is Taking Teachers Out of the Lecturing Business

A Summary and Response

In his Scientific American article, How Big Data is Taking Teachers Out of the Lecturing Business” Seth Fletcher describes the power of data-driven adaptive learning for increasing the efficacy of education while also cutting the costs associated with hiring teachers. Looking specifically at the case of Arizona State University, where computer-assisted learning has been adopted as an efficient way to facilitate the completion of general education requirements (math in particular), Fletcher describes a situation in which outcomes for students scores increase, teacher satisfaction improves (as teachers shift from lecturing to mediating), and profit is to be made by teams of data-scientists for hire.

Plato_Aristotle_della_Robbia_OPA_FlorenceThere are, of course, concerns about computer-assisted adaptive learning, including those surrounding issues of privacy and the question of whether such a data-driven approach to education doesn’t tacitly favor STEM (training in which can be easily tested and performance quantified) over the humanities (which demands an artfulness not easily captured by even the most elaborate of algorithms). In spite of these concerns, however, Fletcher concludes with the claim that “sufficiently advanced testing is indistinguishable from instruction.” This may very well be the case, but his conception of ‘instruction’ needs to be clarified here. If by instruction Fletcher means to say teaching in general, then the implication of his statement is that teachers are becoming passé, and will at some point become entirely unnecessary. If, on the other hand, instruction refers only to a subset of activities that take place under the broader rubric of education, then there remains an unquantifiable space for teachers to practice pedagogy as an art, the space of criticism and imagination…the space of the humanities, perhaps?

As the title of Fletcher’s piece suggests, Big Data may very well be taking teachers out of the lecturing business, but it is not taking teachers out of the teaching business. In fact, one could argue that lecturing has NEVER been the business of teaching. In illustrating the aspects of traditional teaching that CAN be taken over by machines, big data initiatives are providing us with the impetus to return to questions about what teaching is, to clarify the space of teaching as distinct from instruction, and with respect to which instruction is of a lower-order even as it is necessary. Once a competence has been acquired and demonstrated, the next step is not only to put that competency to use in messy, real-world situations–situations in which it is WE who must swiftly adapt–but also to take a step back in order to criticize the assumptions of our training. Provisionally (ALWAYS provisionally), I would like to argue that it is here, where technê ends and phronesis begins, that the art of teaching begins as well.

Using Data You Already Have

I-WANT-DATA-NOWThis is a rich (and at times quite dense), article by authors from the University of Central Florida that effectively demonstrates some of the potential for developing predictive models of student (non-)success, but also some of the dangers. It emphasizes the fact that the data do not speak for themselves, but require interpretation at every level. Interpretation not only guides researchers in the questions they ask and the ways that certain insights become actionable, but also their interventions.

Dziuban, Moskal, Cavanagh & Watts (June 2012) “Analytics that Inform the University: Using Data You Already Have”

An interesting example from the article is the observation that, when teaching modalities are compared (ex Blended, Online, Face-To-Face, Lecture Capture), the blended approach is found to produce greater success (defined as a grade of C or higher) and fewer withdrawals. Lecture Capture, on the other hand, sees the least success and the most withdrawals, comparatively. This is a striking observation (especially as institutions invest more and more in products like Echo360, and as MOOC companies like coursera begin to move into the business of providing lecture-capture technology). When modality is included in a logistic regression, however, that includes other variables (ex. Cumulative GPA, High School GPA, etc), it is found to have nearly no predictive power. The lesson here, is that our predictive models need to be carefully assessed, and interventions carefully crafted, so that we are actually identifying students at risk, and that our well-meaning, but some-what mechanically generated, interventions do not have unexpected and negative consequences (i.e. What is the likelihood that identifying a PARTICULAR student as ‘at risk’ may in fact have the effect of DECREASING their chances of success? )

Personal Activity Monitors and the Future of Learning Analytics

Jawbone Up and Learning AnalyticsIn Spring 2013, while discussing the details of his final project, a gifted student of mine revealed that he was prone to insomnia. In an effort to understand and take control of his sleeping habits, had began wearing a device called a ‘Jawbone UP.’ I recently started wearing the device myself, and have found it an exciting (and fun) technology for increasing behavioral awareness, identifying activity patterns (both positive and negative), and motivating self-improvement. Part of the movement toward a quantification of self, this wearable technology not only exemplifies best practice in mobile dashboard design, but it also opens up exciting possibilities for the future of learning analytics.

Essentially, the UP is a bracelet that houses a precision motion sensor capable of recording physical activity during waking hours, and tracking sleep habits during the night. The wearable device syncs to a stunning app that presents the user with a longitudinal display of their activity and makes use of an ‘insight engine’ that identifies patterns and makes suggestions for positive behavioral improvements. The UP is made even more powerful by encouraging the user to record their mood, the specifics of deliberate exercise, and diet. The motto of the UP is “Know Yourself, Live Better.” In the age of ‘big data,’ an age in which it has become possible to record and analyze actual behavioral patterns in their entirety rather than simply relying upon samples of anecdotal accounts, and in which our mobile devices are powerful enough to effortlessly identify patterns of which we, ourselves, would otherwise be quite ignorant, the UP (and its main competitor, the Fitbit Flex) are exemplary personal monitoring tools, and represent exciting possibilities for the future of learning analytics.

Personal activity monitors like the UP effectively combine three of the six “technologies to watch,” as identified in the 2013 Higher Education Edition of the NMC Horizon Report: Wearable Technology, Learning Analytics, and Games and Gamification.

Wearable Technology. As a bracelet, the UP is obviously a wearable technology. This kind of device, however, is strikingly absent from the list of technologies listed in the report, which tend to have a prosthetic quality, extending the user’s ability to access and process information from their surroundings. The most interesting of these, of course, is Google’s augmented-reality-enabled glasses, Project Glass. In contrast to wearable technologies that aim at augmenting reality, motivated by a post-human ambition to generate a cyborg culture, the UP has an interestingly humanistic quality. Rather than aiming at extending consciousness, it aims at facilitating self-consciousness and promoting physical and mental well-being by revealing lived patterns of experience that we might otherwise fail to recognize. The technology is still in its infancy and is currently only capably of motion sensing, but it is conceivable that, in the future, such devices might be able to automatically record various other bodily activities as well (like heart-rate and geo-location, for example).

Learning Analytics. Learning Analytics is variously defined, but it essentially refers to the reporting of insights from learner behavior data in order to generate interventions that increase the chances of student success. Learning analytics takes many forms, but one of the most exciting is the development of student dashboards that identify student behaviors (typically in relation to a learning management system like Blackboard) and make relevant recommendations to increase academic performance. Acknowledging the powerful effect of social facilitation (the social-psychological insight that people often perform better in the presence of others than they do alone), such dashboards often also present students with anonymized information about class performance as a baseline for comparison. To the extent that the UP and the Fitbit monitor activity for the purpose of generating actionable insights that facilitate the achievement of personal goals, they function in the same way as student dashboards that monitor student performance. Each of these systems are also designed as application platforms, and the manufacturers strongly encourage the development of third-party apps that would make us of and integrate with their respective devices. Unsurprisingly, most of the third-party apps that have been built to date are concerned with fitness, but there is no reason why an app could not be developed that integrated personal activity data with information about academic behaviors and outcomes as well.

Games and Gamification. The ability to see one’s performance at a glance, to have access to relevant recommendations for improvement according to personal goals, and to have an objective sense of one’s performance relative to a group of like individuals can be a powerful motivator, and it is exactly this kind of dashboarding that the UP does exceptionally well. Although not aimed at academic success, but on physical and mental well-being, the UP (bracelet and app) functions in the same way as learning analytics dashboards, but better. To my mind, the main difference between the UP and learning analytics dashboards–and the main area in which learning analytics can learn from consumer products such as this–is that it is fun. The interface is user-friendly, appealing, and engaging. It is intentionally whimsical, like a video game, and so encourages frequent interaction and a strong desire to keep the graphs within desired thresholds. The desire to check in frequently is further increased by the social networking function, which allows friends to compare progress and encourage each other to be successful. Lastly, the fact that the primary UP interface takes the form of a mobile app (available for both  is reflective of the increasing push toward mobile devices in higher education. Learning analytics and student dashboarding can only promote student success if the students use it. More attention must be placed, then, on developing applications and interfaces that students WANT to use.

Screen shot of the iOS dashboard from the Jawbone UP app

Personal activity monitors like the UP should be exciting to, and closely examined by, educators. As a wearable technology that entices users to self-improvement by making performance analytics into a game, the UP does exactly what we are trying to do in the form of student activity dashboards, but doing it better. In this, the UP app should serve as an exemplar as we look forward to the development of reporting tools that are user-focused, promoting ease, access, and fun.

Looking ahead, however, what is even more exciting (to me at least) is the prospect that wearable devices like the UP might provide students with the ability to extend the kinds of data that we typically (and most easily) correlate with student success. We have LMS information, and more elaborate analytics programs are making effective use of more dispositional factors. Using the UP as a platform, I would like to see someone develop and app that draws upon the motion, mood, and nutrition tracking power of the UP and that allows students to relate this information to academic performance and study habits. Not only would such an application give students (I would hesitate to give personal data like this to instructors and / or administrators) a more holistic vision of the factors contributing or detracting from academic success, but it would also help to cultivate healthy habits that would contribute to student success in a way that extends beyond the walls of the university and into long-term relationships at work, to family, and with friends as well.

Bacon, Vico, and the “Long Tail”

[Image Creative Commons licensed / Flickr user spratmackrel]

[Image Creative Commons licensed / Flickr user spratmackrel]

In his essay, “The Long Tail,” Chris Anderson observes that the our ability to overcome the ‘tyranny of physical space’ through the use of a combination of online databases and streaming services has fundamentally altered business models and, as a consequence, has radically increased our access to information. In the past, limited by the physical constraints of location and shelf space, retailers were forced to carry a small selection of material that appealed to the greatest proportion of a local market. In terms of the book industry, these limitations mean that books rapidly go out of print and become very difficult to come by after a relatively short period of time. In contrast, however, the ability of companies like Amazon to replace small store fronts with massive warehouses, and to leverage the internet to reach global markets, has allowed them to carry and generate significant revenue from products in a way that smaller markets would not produce. This model becomes even more profitable when the products themselves are digital (as in the case of music, movies, books, software, etc), since a single stored copy can be infinitely licensed and distributed, which is to say, sold. The first of three ‘rules’ that Anderson offers businesses in this new digital economy is “Make Everything Available.” Since there is a market for everything, and since the cost of storage is so incredibly low, there is profit to be made even from the most obscure (and awful) material: “In the Long Tail economy, it’s more expensive to evaluate than to release. just do it!”

We do indeed seem to be moving more and more from an economy of scarcity to one of abundance. From a scholarly perspective, this means the development of a rich, extensive, inexpensive (Anderson’s second rule is “cut the price in half, then lower it”), and easily accessible archive of material. As Anderson observes, “it is a fair bet that children today will grow up never knowing the meaning of “out of print.” On the other hand, however, I wonder what an economically driven abundance (concerned with quantity over quality) will have on our ideas about the value of tradition.

In the early days of the Enlightenment, there was some discussion about the scarcity of available information relative to the total amount of material that had presumably been produced. In this, there seems to have been two primary perspectives. From Francis Bacon we learn that it was common for scholars at that time to believe that the works that had survived had done so by virtue of their importance and, consequently, represented the best that the history of ideas had to offer. In contrast to this dominant position, Bacon argued that it was not in fact the best that had endured, but rather the most trivial.

Another error, that hath also some affinity with the former, is a conceit that of former opinions or sects after variety and examination the best hath still prevailed and suppressed the rest; so as if a man should begin the labour of a new search, he were but like to light upon somewhat formerly rejected, and by rejection brought into oblivion; as if the multitude, or the wisest for the multitude’s sake, were not ready to give passage rather to that which is popular and superficial than to that which is substantial and profound for the truth is, that time seemeth to be of the nature of a river or stream, which carrieth down to us that which is light and blown up, and sinketh and drowneth that which is weighty and solid. (Bacon, The Advancement of Learning)

In essence, Bacon advocates an approach that would abandon tradition entirely, and systematically create new repositories of knowledge built upon firm foundations. Since the best has been lost, and what remains has little value, Bacon leaves us with little choice in the matter. Responding to Bacon, who is notorious for his rejection of the value of tradition, Giambattista Vico supports the former view, the view that Bacon insists is in error:

There is, therefore, more wit than truth in Bacon’s statement that in the tidal wave of the barbarians’ invasions, the major writers sank to the bottom, while the lighter ones floated on the surface. In each branch of learning, instead, it is only the most outstanding authors who have reached us, by virtue of being copied by hand. If one or another was lost, it was purely by chance. (Vico 1990, 73)

For Vico, Bacon makes a mistake in accounting for scarcity by emphasizing what falls away. For Bacon, it seems, knowledge persists unless something happens to it, and it just so happens that the finest knowledge is the first to be lost. In contrast, Vico argues that the opposite is the case: that knowledge naturally decays over time, and that its endurance is only made possible through the active (providential) intervention of scribes motivated by an interest in preserving the finest and best. Both authors are resigned to the fact that what’s lost is lost. When it comes to accounting for the scarce intellectual resources that have persisted through time, however, Bacon views this scarcity as evidence of inferiority, and Vico of eminence.

Under conditions of scarcity, whether high (in the case of Vico) or low (in the case of Bacon), knowledge has value, and this valuable nature of knowledge demands a response. If received knowledge is of high value, then it ought to be preserved; if not, then it ought to be jettisoned. But what kind of value does knowledge have within a ‘long-tail’ information economy characterized by abundance? (This would, of course, be an obvious place to bring in Walter Benjamin and his comments on the Work of Art in the Age of Mechanical Reproduction, and for that reason I will resist the temptation).

A central part of digital literacy (something that we, in higher education, are increasingly encouraged to incorporate into our learning outcomes, at least latently) is the ability to evaluate and judge the quality of sources that are found online. With so much information at our fingertips, and the flattening of value that comes as a result of an approach to content delivery that would release rather than evaluate, are we entering a period that, with Vico, is appreciating tradition more and more by virtue of the fact that we have more and more of it? Or is tradition quickly being stripped of its value as a consequence of the fact that all knowledge is lumped together as equally valuable within the marketplace of ideas? In other words, does our increased access to the past (and other marginal material) give it more importance, more of a voice, in the present? Or does this abundance justify its dismissal (a la Bacon) in the face of a present and future that really count?

Showing Appreciation

In conversations with several people over the last few days, the theme of appreciation has been among the most prominent. Questions like the following are difficult to answer, and in fact don’t seem to lend themselves easily to the application of general ‘rules of thumb:’

  • How do I recognize those vital individuals who work tirelessly behind the scenes, contributing to the success of an organization or event, and who are uncomfortable with public form of acknowledgement (the exceptions to the ‘praise in public, criticize in private’ rule)
  • How do I show appreciation for things my partner does that I could not begin to reciprocate in kind?
  • How do I motivate my staff to continue to perform despite limits placed upon my ability to promote or increase salaries?

[Image Creative Commons licensed / Flickr user http://www.flickr.com/photos/carlos_maya/C!...]

[Image Creative Commons licensed / Flickr user C!…]

People don’t leave jobs because of money. They leave because of poor management, which is to say that they leave either because they are not fully utilized (a problem of unrecognized potential) or because their work is going unnoticed (a problem of unrecognized activity). Assuming that salaries are reasonable, the best way of ensuring employee retention is a positive work environment in which ability and activity are recognized and encouraged.

The same principle of appreciation applies in education. Tinto (1975) has convincingly argued that Social Integration (through informal peer group associations, semi-formal extracurricular activities, and interaction with faculty / personnel) is the most directly associated factor when predicting what he calls ‘persistence.’ In other words, a student who feels appreciated at their college, by their peers, teachers, and administrators, is more likely to persist despite other factors like poor grades or dissatisfaction with other more institutional aspects. On the other hand, an otherwise successful student (i.e. a student with top grades) who is not successfully integrated into a social group is likely to voluntarily withdraw.

Appreciation is important. In fact, I would argue (provisionally, or course) that financial rewards are meaningful only to the extent that they are (1) expressions of appreciation, and/or (2) perceived as a means through which to achieve additional appreciation. When viewed through the lens of appreciation, a powerful yet unquantifiable driving factor (a factor that is powerful because it is unquantifiable), open information phenomena like open access journals, open source software, and creative commons begin to make sense.

In his book, The Public Domain: Enclosing the Commons of the Mind (available to read online HERE), James Boyle discusses several paradoxical effects of copyright and patent law. In particular, he observes the fact that, although patents and copyrights are ostensibly in place in order to encourage innovation, the lengthy and increasing terms actually have the opposite effect (by blocking the use of a great deal of material for the sake of future creative efforts). The glorious thing about open access initiatives, creative commons, open source, etc., is that they demonstrate that the profit motive is not in actual fact necessary to encourage innovation. In fact, ‘distributed creativity’ models have proven so effective that they are being increasingly embraced by major technology companies.

What is remarkable is not merely that the software works technically, but that it is an example of widespread, continued, high-quality innovation. The really remarkable thing is that it works socially, as a continuing system, sustained by a network consisting both of volunteers and of individuals employed by companies such as IBM and Google whose software “output” is nevertheless released into the commons. (187)

What, then, is the motivation for innovation and creativity here. Of course, when looking to Google and IBM, the profit motive is anything but absent. From the perspective of the larger community of authors in open source programming communities, however, the primary motivations appear to be two-fold. First, there seems to be an intrinsic motivation: there is something about programming that is game-like, that absorbs individuals in a task because of a pleasure derived from solving a problem for its own sake. There is something about the mere acquisition of mastery, apart from external rewards, that feels good in itself. Second, however, there is also an extrinsic motivation. But this external incentive is not for profit, but for recognition. Through creative commons licensing, authors give up potential financial rewards in exchange for an acknowledgment of their efforts that is transmitted through every iteration of its use. It may seem tautological, but a creation that is used is a useful creation. In other words, the fact that a program or piece of code (or song, or blog post, or book, or…) is put to use is a sign that it is valuable and that, in turn, the author is valuable as well. Even if the author is not thanked directly, but merely acknowledged, the use of some piece of intellectual property is itself an implicit gesture of appreciation. What we have, then, is an alternative economic model, an economy of appreciation that seemingly has all the social benefits intended by copyright law (encouraging innovation), without the unfortunate intellectual hoarding and orphaned works that we see as a result of copyright law in practice.

How can we establish an open economy of appreciation in the classroom? Too often, instructors lean too heavily on grades (the classroom equivalent of the cash economy) in order to produce results. “Perform this exercise / show up for class / participate, or else I’ll dock marks.” The problem with grades, however, is that they have a tendency to produce compliance rather that creativity. The key, then, is to structure class time and assignments in such a way as to maximize intrinsic motivation while also cultivating an economy of appreciation, wherein students can freely encourage one another, recognizing each other’s contributions to a common project (or range of projects) in a way that can be praised and further built upon. Assignments like digital storytelling projects and blogs can be powerful means by which to encourage this type of environment. Developing these assignments with a view to cultivating appreciative environments, however, is hard work, just as the development of the infrastructure necessary to encourage open source code sharing and a creative commons took (and continues to take) a lot of hard work. As we are beginning to see, however, the potential benefits for community and innovation are tremendous.


References
Boyle, James. The Public Domain: Enclosing the Commons of the Mind. New Haven: Yale University Press, 2008.

Tinto, Vincent. “Dropout from Higher Education: A Theoretical Synthesis of Recent Research.” Review of Educational Research 45, no. 1 (1975): 89-125.

Revisioning Argument?: Notes on “Theory in the Machine”

In her recent talk at Georgia Institute of Technology (February 13, 2013), entitled “Theory in the Machine: Or a Feminist in the Software Lab,” Tara McPherson described how she came to the digital humanities, her work as a founding editor of Vectors, and her current involvement in the development of Scalar, “a semantic web authoring tool that brings a considered balance between standardization and structural flexibility to all kinds of material.” McPherson prefaced her presentation with the disclaimer that it would not be deeply theoretical. Nonetheless, the talk was informative, introducing a variety of exciting new digital approaches to scholarship, and providing a wide variety of jumping off points for further inquiry.

[Image Creative Commons licensed / Flickr user FilPho

[Image Creative Commons licensed / Flickr user FilPhoto]

One aspect of McPherson’s talk that was particularly irksome, however, were common variations on the theme of “revisiting scholarly argument.”  From the perspective of screen theory, McPherson spoke about the possibility of “playing an argument like a video game,” or “watching an argument like a film.”  She talked about “refracting arguments through multi-modal lenses,” and adopting a non-linear approach. What I would like to claim here is that McPherson’s use of the term ‘argument,’ betrays a lack of clarity about what an argument is and, consequently, a failure to recognize that what is being proposed by many projects in the digital humanities is not a new approach to argument, but rather something else, a return to an old form of expression, namely the mythic.

Myth is primordial and originary. In the words of Claude Lévi-Strauss, “Myths get thought in man unbeknownst to him” (Levi-Strauss 1978, 3).  In contrast to philosophical thinking, which begins by assuming a difference between the knower and a thing to be known, Ernst Cassirer argues that myth is a function that makes abstract objective thought possible, but in a way that is in itself unmotivated either metaphysically – as if thought served to mirror some pre-existing reality – or psychologically – as a mirror of subjective psychic states or as a response to some set of pre-existing drives. In myth it is “Language itself [that] initiates such articulations and develops them in its own sphere” (Cassirer 1946, 12). The basis of mythological thought is metaphor, or the transmutation of one cognitive or emotional experience into a medium that is foreign to that experience (87). Mythical thought is not representational. It is a function by which relationships between experiences are spontaneously generated in such a way that allows those experiences to come into view. Mythological thought does not bear any relation to reality. It opens up reality, makes reality possible. It totalizes the world because it is the world.

Myth is not argument. Instead, as a mode of cognition and the distinguishing feature of Western philosophy, argumentation emerged and has persisted under a very particular (albeit long-lasting) set of conditions. First, as Marshall McLuhan observed, the written phonetic word is a crucial precondition for the emergence of philosophy. On the one hand, the phonetic alphabet served to sever the mythological identity of word and thing, thereby making it possible to map real relationships through conventional representation. On the other hand, the written word favors linear modes of deductive reasoning in a way that pictographs and strict orality do not, and that is actually alien to our lived experience of consciousness. Prior to widespread phonetic literacy, mythological thought, or what McLuhan also calls “tribal consciousness,” takes place as “an instant vision of a complex process (McLuhan 1964, 38), the communication of a tangled web of emotions and feelings (59) using metaphors meant to produce an effect rather than convey a meaning (85). With the advent of linear-sequential thinking, however, it becomes possible to map the world and determine causal relationships that allow for the prediction and control of the natural world and the progressive rationalization of the social world through the establishment of stable social institutions. With the development of electronic and digital communications technologies in the twentieth century, however, McLuhan insists that our experience is being fundamentally reshaped once again, that the increasing instantaneity made possible by electronic communication marks a return to mythical experience, but in a way that is at odds with institutions that emerged as a result of, and are therefore strongly committed to, discursive thought: “In the mechanical age now receding, many actions could be taken without too much concern. Slow movement insured that the reactions were delayed for considerable periods of time. Today the action and the reaction occur almost at the same time. We actually live mythically and integrally, as it were, but we continue to think in the old, fragmented space and time patterns of the pre-electric age” (20).

Work in the digital humanities, like that of McPherson, is exciting in so far as it is perhaps helping us to reconcile our mythic lives to scholarly modes of thought.  Put differently, revisioning standard forms of scholarly presentation might more accurately reflect the way we live the world. On the other hand, however, my fear is that the claim to ‘rethink argumentation’ may reveal a lack of reflection upon the modes of cognition and consciousness that the digital humanities claim to call into question.  More importantly, misunderstanding the history and character of argumentation is perhaps a symptom of a lack of reflection about the modes of consciousness that some work in the digital humanities are promoting.  Under the auspices of criticism, it is possible that these alternative modes of presentation may actually represent an uncritical embrace of our contemporary digital tribalism and, to that extent, function to promote and legitimate the status quo rather than call it into question.

From the perspective of teaching with technology, this can serve as a reminder of the fact that we, as teachers, are not merely shaping our students’ knowledge, but also the modes of cognition through which that knowledge is processed. If, as a consequence of their ubiquitous exposure to electronic and digital media, our students are increasingly coming to us with McLuhan’s ‘tribal consciousness,’ is it our task to embrace and cultivate a more mythological approach to sense-making? Or, is it in fact the case that the formation of a critical consciousness is important now more than ever, and that we should be more conservative in our use of digital technology in the classroom?  For all the criticisms of strictly empirico-deductive forms of reason (and there are many), what the philosophical / argumentative lens offers is the ability to put a distance between us and the technologies we use in order for use to ask exactly these kinds of critical questions.


References
McLuhan, Marshall. Understanding Media: The Extensions of Man. New York: New American Library, 1964.

Lévi-Strauss, Claude. Myth and Meaning: Five Talks for Radio by Claude Lévi-Strauss. Toronto: University of Toronto Press, 1978.

Cassirer, Ernst. Language and Myth. Translated by Susanne Langer, K. New York: Dover Publications, 1946.