“Educational Data Mining and Learning Analytics”

This week, Ryan Baker posted a link to a piece, co-written with George Siemens, that is meant to function as an introduction to the fields of Educational Data Mining (EDM) and Learning Analytics (LA). “Educational Data Mining and Learning Analytics” is book chapter primarily concerned with methods and tools, and does an excellent job of summarizing some of the key similarities and differences between the two fields in this regard. However, in spite of the fact that the authors make a point of explicitly stating that EDM and LA are distinctly marked by an emphasis on making connections to educational theory and philosophy, the theoretical content of the piece is unfortunately quite sparse.

The tone of this work actually brings up some concerns that I have about EDM/LA as a whole. The authors observe that EDM and LA have been made possible, and have in fact been fueled, by (1) increases in technological capacity and (2) advances in business analytics that are readily adaptable to educational environments.

“The use of analytics in education has grown in recent years for four primary reasons: a substantial increase in data quantity, improved data formats, advances in computing, and increased sophistication of tools available for analytics”

The authors also make a point of highlighting the centrality of theory and philosophy in informing methods and interpretation.

“Both EDM and LA have a strong emphasis on connection to theory in the learning sciences and education philosophy…The theory-oriented perspective marks a departure of EDM and LA from technical approaches that use data as their sole guiding point”

My fear, however, which seems justified in light of the imbalance between theory and method in this chapter (a work meant to introduce, summarize, and so represent the two fields), is that the tools and methods that the fields have adopted, along with the technological- and business-oriented assumptions (and language) that those methods imply, have actually had a tendency to drive their educational philosophy.  From their past work, I get the sense that Baker and Siemens would both agree that the educational / learning space differs markedly from the kind of spaces we encounter in IT and business more generally. If this is the case, I would like to see more reflection on the nature of those differences, and then to see various statistical and machine learning methods evaluated in terms of their relevance to educational environments as educational environments.

Donkey-Carried-by-the-CartAs a set of tools for “understanding and optimizing learning and the environments in which it occurs” (solaresearch.org), learning analytics should be driven, first and foremost, by an interest in learning. This means that each EDM/LA project should begin with a strong conception of what learning is, and of the types of learning that it wants to ‘optimize’ (a term that is, itself, imported from technical and business environments into the education/learning space, and which is not at all neutral). To my mind, however, basic ideas like ‘learning’ and ‘education’ have not been sufficiently theorized or conceptualized by the field. In the absence of such critical reflection on the nature of education, and on the extent to which learning can in fact be measured, it is impossible to say exactly what it is that EDM/LA are taking as their object. How can we measure something if we do not know what it is? How can we optimize something unless we know what it is for? In the absence of critical reflection, and of maintaining a constant eye on our object, it becomes all too easy to consider our object as if its contours are the same as the limits of our methods, when in actual fact we need to be vigilant in our appreciation of just how much of the learning space our methods leave untouched.

If it is true that the field of learning analytics has emerged as a result of, and is driven by, advancements in machine learning methods, computing power, and business intelligence, then I worry about the risk of mistaking the cart for the horse and, in so doing, becoming blind to the possibility that our horse might actually be a mule—an infertile combination of business and education, which is also neither.

Four (Bad) Questions about Big Data

A colleague recently sent me an email that included four questions that he suggested were the most concerning to both data management companies and customers: *

  • Big Data Tools – What’s working today? What’s next?
  • Big Data Storage – Do organizations have a manageable and scalable storage strategy?
  • Big Data Analytics – How are organizations using analytics to manage their large volume of data and put it to use?
  • Big Data Accessibility – How are organizations leveraging this data and making it more accessible?

These are bad questions.

I should be clear that the questions are not bad on account of the general concerns they are meant to address. Questions about tools, scalable storage, the ways in which data are analyzed (and visualized), and the availability of information are central to an organization’s long-term information strategy. Each of these four questions addresses a central concern that has very significant consequences for the extent to which available data can be leveraged to meet current informational requirements, but also future capacity. These concerns are good and important. The questions, however, are still bad.

The reason these questions are bad (okay, maybe they’re not bad…maybe I just don’t like them) is that they are unclear about their terms and definitions. In the first place, they imply that there is a separation between something called ‘Big Data’ and the tools, storage, analytics (here used very loosely), and accessibility necessary to manage it. In actual fact, however, there is no such ‘thing’ as Big Data in the absence of each of those four things. Transactional systems (in the most general sense, which also includes sensors) produce a wide variety of data, and it is an interest in identifying patterns in this data that has always motivated empirical scientific research. In other words, it is data, and not ‘Big Data’ that is our primary concern.

The problem with data as objects is that, until recently, we have been radically limited in our ability to capture and store them. A transactional system may produce data, but how much can we capture? How much can we store? For how long? Until recently, technological limitations have radically limited our ability to capture, store, and analyze the immense quantities of data that are generated, and have meant working with samples, and using inferential statistics to make probable judgements about a population. In the era of Big Data, these technological limitations are rapidly disappearing. As we increase our capacity to capture and store data, we increasingly have access to entire populations. A radical increase in available data, however, is not yet ‘Big Data.’ It doesn’t matter how much data you can store if you don’t also have the capacity to access it. Without massive processing power, sophisticated statistical techniques, and visualization aids, all of the data we collect is for naught, pure potentiality in need of actualization. It is only once we make population data meaningful in its entirety (not sampling from our population data) through the application of statistical techniques and sound judgement that we have something that can legitimately be called ‘Big Data.’ A datum is a thing given to experience. The collection and visualization of a population of data produces another thing given to experience, a meta-datum, perhaps.

In light of these brief reflections, I would like to propose the following (VERY) provisional definition of Big Data (which resonates strongly, I think, with much of the other literature I have read):

Big Data is the set of capabilities (capture, storage, analysis) necessary to make meaningful judgements about populations of data.

By way of closing, I think it is also important to distinguish between ‘Big Data’ on the one hand, and ‘Analytics’ on the other. Although the two are often used in conjunction with each other, it is important to note that using Big Data is not the same as doing analytics. Just as the defining characteristic of Big Data above in increased access (access to data populations instead of samples), so to does analytics. In the past, the ability to make data-driven judgements meant either having some level of sophisticated statistical knowledge oneself, or else (more commonly) relying upon a small number of ‘data gurus,’ hired expressly because of their statistical expertise. In contrast to more traditional approaches to institutional intelligence, which involve data collection, cleaning, analysis, and reporting (all of which took time), analytics toolkits quickly perform these operations in real-time, and make use of visual dashboards that allow stakeholders to make timely and informed decisions without also having the skills and expertise necessary to generate these insights ‘from scratch.’

Where Big Data gives individuals access to all the data, Analytics makes Big Data available to all

Big Data is REALLY REALLY exciting. Of course, there are some significant ethical issues that need to be addressed in this area, particularly as the data collected are coming from human actors, but from a methodological point of view, having direct access to populations of data is something akin to a holy grail. From a social scientific perspective, the ability to track and analyze actual behavior instead of relying on self-reporting about behavior on surveys can give us insight into human interactions that, until now, was completely impossible. Analytics, on the other hand, is something about which I am a little more ambivalent. There is definitely something to be said to encouraging data-driven decision-making, even by those with limited statistical expertise. Confronted by pretty dashboards that are primarily (if not exclusively) descriptive, without the statistical knowledge to ask even basic questions about significance (just because there appears to be a big difference between populations on a graph, it doesn’t necessarily mean that there is one), and with no knowledge about the ways in which data are being extracted, transformed, and loaded into proprietary data warehousing solutions, I wonder about the extent to which analytics do not, at least sometimes, just offer the possibility of a new kind of anecdotal evidence justified by appeal to the authority of data. Insights generated in this way are akin to undergraduate research papers that lean heavily upon Wikipedia because, if it’s on the internet, it’s got to be true.

If it’s data-driven, it’s got to be true.

Analytics Four Square Diagram

I’m not really happy with this diagram. Definitely a work in progress, but hopefully it capture’s the gist of what I’m trying to sort out here.

* The source of these questions is an event that was recently put on by the POTOMAC Officer’s Club entitled “Big Data Analytics – Critical Support for the Agency Mission”, featuring Ely Kahn, Todd Myers, and Raymond Hensberger.

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.


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?