e-Literate

Present is Prologue

Tag: EEP

  • Instructure DIG and Student Early Warning Systems

    Instructure DIG and Student Early Warning Systems

    EdSurge‘s Tony Wan is first out of the blocks with an Instructurecon coverage article this year. (Because of my recent change in professional focus, I will not be on the LMS conference circuit this year.) Tony broke some news in his interview with CEO Dan Goldsmith with this tidbit about the forthcoming DIG analytics product:

    One example with DIG is around student success and student risk. We can predict, to a pretty high accuracy, what a likely outcome for a student in a course is, even before they set foot in the classroom. Throughout that class, or even at the beginning, we can make recommendations to the teacher or student on things they can do to increase their chances of success.

    Instructure CEO Dan Goldsmith

    There isn’t a whole lot of detail to go on here, so I don’t want to speculate too much. But the phrase “before they even set foot in the classroom” is a clue as to what this might be. I suspect that the particular functionality he is talking about is what’s known as an “student retention early warning system.”

    Or maybe not. Time will tell.

    Either way, it provides me with the thin pretext I was looking for to write a post on student retention early warning systems. It seems like a good time to review the history, anatomy, and challenges of the product category since I haven’t written about them in quite a while and they’ve become something of a fixture. The product category is also a good case study in why tool that could be tremendously useful in supporting students who need help the most often fails to live up to either its educational or commercial potential.

    The archetype: Purdue Course Signals

    The first retention early warning system that I know of was Purdue Course Signals. It was an experiment undertaken by Purdue University to—you guessed it—increase student retention, particularly in the first year of college, when students tend to drop out most often. The leader of the project, John Campbell, and his fellow researchers Kim Arnold and Matthew Pistilli, looked at data from their Student Information System (SIS) as well as the LMS to see if they could predict and influence students. Their first goal was to prevent them from dropping courses, but they ultimately wanted to prevent those students from dropping out.

    They looked at quite a few variables from both systems, but the main results they found are fairly intuitive. On the LMS side, the four biggest predictors they found for students staying in the class (or, conversely, for falling through the cracks) where

    1. Student logins (i.e., whether they are showing up for class)
    2. Student assignments (i.e., whether they are turning in their work)
    3. Student grades (i.e., whether their work is passing)
    4. Student discussion participation (i.e., are they participating in class)

    All four of these variables were compared to the class average, because not all instructors were using the LMS in the same way. If, for example, the instructor wasn’t conducting class discussions online, then the fact that a student wasn’t posting on the discussion board wouldn’t be a meaningful indicator.

    These are basically four of the same very generic criteria that any instructor would look at to determine whether a student is starting to get in trouble. The system is just more objective and vigilant in applying these criteria than instructors can be at times, particularly in large classes (which is likely to be the norm for many first-year students). The sensitivity with which Course Signals would respond to those factors would be modified by what the system “knew” about the students from their longitudinal data—their prior course grades, their SAT or ACT scores, their biographical and demographic data, and so on. For example, the system would be less “concerned” about an honors student living on campus who doesn’t log in for a week than about a student on academic probation who lives off-campus.

    In the latter case, the data used by the system might not normally be accessible, or even legal, for the instructor to look at. For example, a disability could be a student retention risk factor for which there are laws governing the conditions under which faculty can be informed. Of course, instructors don’t have to be informed in order for the early warning system to be influenced by the risk factor. One way to think about a way that this sensitive information could be handled is like a credit score. There is some composite score that informs the instructor that the student is at increased risk based on a variety of factors, some of which are private to the student. The people who are authorized to see the data can verify that the model works and that there is legitimate reason to be concerned about the student, but the people who are not authorize are only told that the student is considered at-risk.

    Already, we are in a bit of an ethical rabbit hole here. Note that this is not caused by the technology. At least in my state, the great Commonwealth of Massachusetts, instructors are not permitted to ask students about their disabilities, even though that knowledge could be very helpful in teaching those students. (I should know whether that’s a Federal law, but I don’t.) Colleges and universities face complicated challenges today, in the analog world, with the tensions between their obligation to protect student privacy and their affirmative obligation to help the students based on what they know about what the students need. And this is exactly the way John Campbell characterized the problem when he talked about it. This is not a “Facebook” problem. It’s a genuine educational ethical dilemma.

    Some of you may remember some controversy around the Purdue research. The details matter here. Purdue’s original study, which showed increased course completion and improved course grades, particularly for “C” and “D” students, was never questioned. It still stands. A subsequent study, which purported to show that student gains persisted in subsequent classes, was later called into question. You can read the details of that drama here. (e-Literate played a minor role in that drama by helping to amplify the voices of the people who caught the problem in the research.)

    But if you remember the controversy, it’s important to remember three things about it. First, the original research about persistence was not ever called into question. Second, the subsequent finding was not disproven; rather, there was a null hypothesis. We have proof neither for nor against the hypothesis that the Perdue system can produce longer term effects. And finally, the biggest problem that controversy exposed was with university IR departments releasing non-peer-reviewed research papers that staff researchers have no power to respond to on their own when they get criticized. That’s worth exploring further some other time, but for now, the point is that the process problem was the real story. The controversy didn’t invalidate the fundamental idea behind the software.

    Since then

    Since then, we’ve seen lots of tinkering with the model on both the LMS and SIS sides of the equation. Predictive models have gotten better. Both Blackboard and D2L have some sort of retention early warning products, as do Hobsons, Civitas, EAB, and HelioCampus, among others. There were some early problems related to a generational shift in data analytics technologies; most LMSs and SISs were originally architected well before the era when systems were expected to provide the kind of high-volume transactional data flows needed to perform near-real-time early warning analytics. Those problems have increasingly been either ironed out or, at least, worked around. So in one sense, this is a relatively mature product category. We have a pretty good sense of what a solution looks like and there are a number of providers in the market right now with variations on on the theme.

    In a second sense, the product category hasn’t fundamentally changed since Purdue created Course Signals over a decade ago. We’ve seen incremental improvements to the model, but no fundamental changes to it. Maybe that’s because the Purdue folks pretty much nailed the basic model for a single institution on the first try. What’s left are three challenges that share the common characteristic of becoming harder when converted from an experiment by a single university to a product model supported by a third-party company. At the same time, They fall on different places on the spectrum between being primarily human challenges and primarily technology challenges. The first, the aforementioned privacy dilemma, is mostly a human challenge. It’s a university policy issue that can be supported by software affordances. The second, model tuning, is on the opposite end of the spectrum. It’s all about the software. And the third, which is the last mile problem from good analytics to actual impact, is somewhere in the messy middle.

    Three significant challenges

    I’ve already spent some time on the student data privacy challenge specific to these systems, so I won’t spend much more time on it here. The macro issue is that these systems sometimes rely on privacy-sensitive data to determine—with demonstrated accuracy—which students are most likely to need extra attention to make sure they don’t fall through the cracks. This is an academic (and legal) problem that can only be resolved by academic (and legal) stakeholders. The role of the technologists is to make the effectiveness and the privacy consequences of various software settings both clear and clearly in the control of the appropriate stakeholders. In other words, the software should support and enable appropriate policy decisions rather than obscuring or impeding them. At Purdue, where Course Signals was not a product that was purchased but a research initiative that had active, high-level buy-in from academic leadership, these issues could be worked through. But a company selling the product into as many universities as possible with differing levels of sophistication and policy-making capability in this area, the best the vendor can do is build a transparent product and try to educate their customers as best as they can. You can lead a horse to water and all that.

    On the other end of the human/technology spectrum, there is an open question about the degree to which these systems can be made accurate without individual hand tuning of the algorithms for each institution. Purdue was building a system for exactly one university, so it didn’t face this problem. We don’t have good public data on how well its commercial successors work out of the box. I am not a data scientist, but I have had this question raised by some of the folks who I trust the most in this field. That, in turn, means that each installation of the product would require a significant services component, which would raise the cost and make these systems less affordable to the access-oriented institutions that need them the most. This is not a settled question; the jury is still out. I would like to see more public proof points that have undergone some form of peer review.

    And in the middle, there’s the question of what to do with the predictions in order to produce positive results. Suppose you know which students are more likely to fail the course on Day 1. Suppose your confidence level is high. Maybe not Minority Report-level stuff—although, if I remember the movie correctly, they got a big case wrong, didn’t they?—but pretty accurately. What then? At my recent IMS conference visit, I heard one panelists on learning analytics (depressingly) say, “We’re getting really good at predicting which students are likely to fail, but we’re not getting much better at preventing them from failing.”

    Purdue had both a specific theory of action for helping students and good connections among the various program offices that would need to execute that theory of action. Campell et al believed, based on prior academic research, that students who struggle academically in their first year of college are likely to be weak in a skill called “help-seeking behavior.” Academically at risk students often are not good at knowing when they need help and they are not good at knowing how to get it. Course Signals would send students carefully crafted and increasingly insistent emails urging them to go to the tutoring center, where staff would track which students actually came. The IR department would analyze the results. Over time, the academic IT department that owned the Course Signals system itself experimented with different email messages, in collaboration with IR, and figured out which ones were the most effective at motivating students to take action and seek help.

    Notice two critical features to Purdue’s method. First, they had a theory about student learning—in this case, learning about productive study behaviors—that could be supported or disproven by evidence. Second, they used data science to test a learning intervention that they believed would help students based on their theory of what is going on inside the students’ heads. This is learning engineering. It also explains why the Purdue folks had reason to hypothesize that the effects of using Course Signals might persist with students after they stopped using the product. They believed that students might learn the skill from the product. The fact that the experimental design of their follow-up study was flawed doesn’t mean that their hypothesis was a bad one.

    When Blackboard built their first version of a retention early warning system—one, it should be noted, that is substantially different from their current product in a number of ways—they didn’t choose Purdue’s theory of change. Instead, gave the risk information to the instructors and let them decide what to do with it. As have many other designers of these systems. While everybody that I know of copied Purdue’s basic analytics design, nobody that I know—at least no commercial product developers that I know of—copied Purdue’s decision to put so much emphasis on student empowerment first. Some of this has started to enter product design in more recent years now that “nudges” have made the leap from behavioral economics into consumer software design. (Fitbit, anyone?) But the faculty and administrators remain the primary personas in the design process for many of these products. (For non-software designers, a “persona” is an idealized person that you imagine that you’re designing the software for.)

    Why? Two reasons. First, students don’t buy enterprise academic software. So however much the companies that design these products may genuinely want to serve students well, their relationship with them is inherently mediated. The second reason is the same as with the previous two challenges in scaling Purdue’s solution. Individual institutions can do things that companies can’t. Purdue was able to foster extensive coordination between academic IT, institutional research, and the tutoring center, even though those three organizations live on completely different branches of the organizational chart in pretty much every college and university that I know. An LMS vendor has no way of compelling such inter-departmental coordination in its customers. The best they can do is give information to a single stakeholder who is most likely to be in a position to take action and hope that person does something. In this case, the instructor.

    One could imagine different kinds of vendor relationships with a service component—a consultancy or an OPM, for example—where this kind of coordination would be supported. One could also imagine colleges and universities reorganizing themselves and learning new skills to become better at the sort of cross-functional cooperation for serving students. If academia is going to survive and thrive in the changing environment it finds itself in, both of these possibilities will have to become far more common. The kinds of scaling problems I just described in retention early warning systems are far from unique to that category. Before higher education can develop and apply the new techniques and enabling technologies it needs to serve students more effectively with high ethical standards, we first need to cultivate an academic ecosystem that can make proper use of better tools.

    Given a hammer, everything looks pretty frustrating if you don’t have an opposable thumb.

  • Learning Engineering: A Caliper Example

    Learning Engineering: A Caliper Example

    In my recent IMS update post, I wrote,

    [T]he nature and challenges of interoperability our sector will be facing in the next decade are fundamentally different from the ones that we faced in the last one. Up until now, we have primarily been concerned with synchronizing administration-related bits across applications. Which people are in this class? Are they students or instructors? What grades did they get on which assignments? And how much does each assignment count toward the final course grade? These challenges are hard in all the ways that are familiar to anyone who works on any sort of generic data interoperability questions. 
    But the next decade is is going to be about data interoperability as it pertains to insight. Data scientists think this is still familiar territory and are excited because it keeps them at the frontier of their own profession. But this will not be generic data science, for several reasons.

    I then asserted the following positions:

    • Because learning processes are not directly observable, blindly running machine learning algorithms against the click streams in our learning platforms will probably not teach us much about learning.
    • On the other hand, if our analytics are theory-driven, i.e., if we start with some empirically grounded hypotheses about learning processes and design our analytics to search for data that either support or disprove those hypotheses, then we might actually get somewhere.
    • Because learning analytics expressions written in the IMS Caliper standard can be readily translated into plain English, Caliper could form a basis for expressing educational hypotheses and translating them into interoperable tools for testing those hypotheses across the boundaries of tech tools and platforms.
    • The kind of Caliper-mediated conversation I imagined among learning scientists, practicing educators, data scientists, learning system designers, and others, is relevant to a term coined and still used heavily at Carnegie Mellon University—”learning engineering.”

    In this post, I’m going to explore the last two points in more detail.

    What the heck is “learning engineering”?

    The term “learning engineering” was first used by Nobel laureate and Carnegie Mellon University polymath Herbert Simon in 1966. It has been around for quite a while. But it is a term whose time as finally has come and, as such, we are seeing the usual academic turf wars over its meaning and value. On the one hand, some folks love it, embrace it, and want to apply it liberally. IEEE has an entire group devoted to defining it. As is always the case, some of this sort of enthusiasm is thoughtful, and some of it is less so. At its worst, there is a tendency for people to get tangled up in the term because it provides a certain je ne sais quoi they’ve been yearning for to describe the aspects of their jobs that they really want to be doing as change agents rather than the mundane tasks that they keep being dragged back into doing, much like the way some folks are wrapping “innovation” and “design” around themselves like a warm blanket. It’s perfectly understandable, and I think it attaches to something real in many cases, but it’s hard to say exactly what that is. And, of course, where there are enthusiasts in academia, there are critics. Again, some thoughtful, while others…less so. (Note my comment in the thread on that particularly egregious column.)

    If you want to get a clear sense of the range of possible meanings of “learning engineering” as used by people who actually think about it deeply, one good place to start would be Learning Engineering for Online Education: Theoretical Contexts and Design-Based Examples edited by Chris Dede, John Richards, and Bror Saxberg. (I am still working on getting half a day’s worth of Carnegie Mellon University video presentations on their own learning engineering work ready for posting on the web. I promise it is coming.) There are a lot of great take-aways from that anthology, one of which is that even the people who think hard about the term and work together to put together something like a coherent tome on the subject don’t fully agree on what the term means.

    And that’s really OK. Let’s just set a few boundary conditions. On the one hand, learning engineering isn’t an all-encompassing discipline and methodology that is going to make all previous roles, disciplines, and methodologies obsolete. If you are an instructional designer, or a learning designer, or a user experience designer; if you practice design thinking, or ADDIE; be not afraid. On the other hand, learning engineering is not creeping Stalinism either. Think about learning engineering, writ large, as applying data and cognitive sciences to help bring about desired learning outcomes, usually within the context of a team of colleagues with different skills all working together. That’s still pretty vague, but it’s specific enough for the current cultural moment.

    Forget about your stereotypes of engineers and their practices. Do you believe there is a place for applied science in our efforts to improve the ways in which we design and deliver our courses, or try to understand and serve our students needs and goals? If so, what would such an applied science look like? What would a person applying the science need to know? What would their role be? How would they work with other educators who have complementary expertise?

    That is the possibility space that learning engineering inhabits.

    Applied science as a design exercise

    One of the reasons that people have trouble wrapping their heads around the notion of learning engineering is that it was conceived of by very unusual mind. Some of the critiques I’ve seen online of the term position “learning engineering” in opposition to “learning design.” But as Phil Long points out in his essay in the aforementioned anthology, Herb Simon both coined the term “learning engineering” and is essentially the grandfather of design thinking:

    Design science was introduced by Buckminster Fuller in 1963, but it was Herbert Simon who is most closely associated with it and has established how we think of it today. “The Sciences of the Artificial” (Simon, 1967) distinguished the artificial, or practical sciences, from the natural sciences. Simon described design as an ill-structured problem, much like the learning environment, which involves man-made responses to the world. Design science is influenced by the limitations of human cognition unlike mathematical models. Human decision-making is further constrained by practical attributes of limited time and available information. This bounded rationality makes us prone to seek adequate as opposed to optimal solutions to problems. That is, we engage in satisficing not optimizing. Design is central to the artificial sciences: ‘Everyone designs who devises courses of action aimed at changing existing situations into desired ones.’ Natural sciences are concerned with understanding what is; design science instead asks about what should be. this distinction separates the study of the science of learning from the design of learning. Learning scientists are interested in how humans learn. Learning engineers are part of team focused on how students ought to learn.”

    Phil Long, “The Role of the Learning Engineer”

    Phil points out two important dichotomies in Simon’s thinking. The first one: is vs. ought. Natural science is about what is, while design science is about what you would like to exist. What you want to bring into being. The second dichotomy is about well structured vs. poorly structured. For Simon, “design” is a set of activities one undertakes to solve a poorly structured problem. To need or want is human, and to be human is to be messy. Understanding a human need is about understanding a messy problem. Understanding how different humans with different backgrounds and different cognitive and non-cognitive abilities learn, given a wide range of contextual variables like the teaching strategies being employed, the personal relationships between students and teacher, what else is going on in the students’ lives at the time, whether different students are coming to class well fed and well slept, and so on, is pretty much the definition of a poorly structured problem. So as far as Herb Simon is concerned, education is a design problem by definition, whether or not you choose to use the word “engineer.”

    In the next section of his article, Phil then makes a fascinating connection between the evolution of design thinking, which emerged out design science, and learning engineering. The key is in identifying the central social activity that defines design thinking:

    Design thinking represents those processes that designers use to create new designs, possible approaches to problem solutions spaces where none existed before. A problem-solving method has been derived from this and applied to human social interactions iteratively taking the designer and/or co-design participants from inspiration to ideation and then to implementation. The designer and design team may have a mental model of the solution to a proposed problem, but it is essential to externalize this representation in terms of a sketch a description of a learning design sequence, or by actual prototyping of the activities which the learner is asked to engage. [Emphasis added.] All involved can see the attributes of the proposed design solution that were not apparent in the conceptualization of it. this process of externalizing and prototyping design solutions allows it to be situated in larger and different contexts, what Donald Schon called reframing the design, situating it in contexts other than originally considered.

    Phil Long, “The Role of the Learning Engineer”

    So the essential feature that Phil is calling out in design thinking is putting the idea out into the world so that everybody can see it, respond to it, and talk about it together. Now watch where he takes this:

    As learning environments are intentionally designed in digital contexts, the opportunity to instrument the learning environment emerges. Learners benefit in terms of feedback or suggested possible actions. Evaluators can assess how the course performed on a number of dimensions. The faculty and others in the learning-design team can get data through the instrumented learning behaviors, which may provide insight into how the design is working, for whom it is working, and in what context.

    Phil Long, “The Role of the Learning Engineer”

    Rather than a sketch, a wireframe, or a prototype, a learning engineer makes the graph, the dashboard, or the visualization into the externalization. For Herb Simon, as for Phil Long, these design artifacts serve the same purpose. They’re the same thing, basically.

    If you’re not a data person, this might be hard to grasp. (I’m not a data person. This is hard for me to grasp sometimes.) How can you take numbers in a table and turn them into a meaningful artifact that a group of people can look at together, discuss, make sense of, debate, and learn from? What might that even look like?

    Well, it might look something like this, for example:

    Higher ed LMS market share for US and Canada, January 2019
    Phil Hill’s famous squid diagram

    Phil Hill has a graduate degree in engineering. Not learning engineering. Electrical. (Also, he’s not a Stalinist.)

    By the way, when we externalize and share data with a student about her learning processes in a form that is designed to provoke thought and discussion, we have a particular term of art for that in education. It’s called “formative assessment.” If we do it in a way such that the student always has access to such externalizations, which are continually updating based on the student’s actions, we call that “continuous formative assessment.” When executed well, there is evidence that it can be an effective educational practice.

    Caliper statements as learning engineering artifacts

    So here’s where we’ve arrived at this point in the post:

    • Design is a process by which we tackle ill-defined problems of meeting human needs and wants, such as needing or wanting to learn something.
    • Engineering is a word that we’re not going to worry about defining precisely for now, but it relates to applying science to a design problem, and therefore often involves the measurement and numbers.
    • One important innovation in design methodology is the creation of external artifacts early in the design process so that various stakeholders with different sorts of experience and expertise can provide feedback in a social context. In other words, create something that makes the idea more “real” and therefore easier to discuss.
    • Learning engineering includes the skills of creation and manipulation of design artifacts that require more technical expertise, including expertise in data and software engineering.

    The twist with Caliper is that, rather than using visualizations and dashboards as the externalization, we can use human language. This was the original idea of behind the Semantic Web, which is still brilliant in concept, even if the original implementation was flawed. Let’s review that basic idea as implemented in Caliper:

    • You can express statements about the world (or the world-wide web) in three-word sentences of the form [subject] [verb] [direct object] e.g., [student A] [correctly answers] .
    • Because English grammar works the way it does, you can string these sentences together to form inferences, e.g., [tests knowledge of] [multiplying fractions]; therefore, [student A] [correctly answers] [a question about multiplying fractions].
    • We can define mandatory and optional details of every noun and verb e.g., it might be mandatory to know that question 13 was a multiple choice question, but it might be optional to include the actual text of the question, the correct answer, and the distractors.

    That’s it. Three-word sentences, which work the way they do in English grammar, and definitions of the “words.”

    A learning engineer could use Caliper paragraphs as a design artifact to facilitate conversations about refining the standard, the products involved, and the experimental design. I’ll share a modified version of an example I recently shared with an IMS engineer to illustrate this same point.

    Suppose you are interested in helping students become better at reflective writing. You want to do this by providing them with continuous formative assessment, i.e., in addition to the feedback that you give them as an instructor, you want to provide them an externalization of the language in their reflective writing assignments. You want to use textual analysis to help the students look at their own writing through a new lens, find the spots where they are really doing serious thought work, and also the spots where maybe they could think a little harder.

    But you have to solve a few problems in order to do give this affordance to your students. First, you have to develop the natural language analysis tool that can detect cues in the students’ writing that indicate self-reflection (or not). That’s hard enough, but the research is being conducted and progress is being made. The second problem is that you are designing a new experiment to test your latest iteration and need some sort of summative measure to test against. So maybe you design a randomized controlled trial where half the students in the class use the new feedback tool, half don’t, and all get the same human-graded final reflective writing assignment. You compare the results.

    This is an example of theory-driven learning analytics. Your theory is that student reflection improves when students become more aware of certain types of reflective language in their journaling. You think you can train a textual analysis algorithm to reliably distinguish—externalize—the kind of language that you want students to be more aware of in their writing and point it out to them. You want to test that by giving students such a tool and see if their reflective writing does, in fact, improve. Either students’ reflective writing will improve under the test condition, which will provide supporting evidence for the theory, or it won’t, which at the very least will not support the theory and might provide evidence that tends to disprove the theory, depending on the specifics. There are data science and machine learning being employed here, but they are being employed more selectively than just shotgunning an algorithm at a data set and expecting it to come up with novel insights about the mysteries of human cognition.

    Constructing theory-driven learning analytics of the sort described here is challenging enough to do in a unified system that is designed for the experiment. But now we get to the problem for which we will need the help of IMS over the next decade, which is that the various activities we need to monitor for this work often happen in different applications. Each writing assignment is in response to a reading. So the first thing you might want to do, at least for the experiment if not in the production application, is to control for students who do the reading. If they aren’t doing the reading, then their reflective writing on that reading isn’t going to tell you much. Let’s say the reading happens to take place in an ebook app. But their writing takes place in a separate notebook app. Maybe it’s whatever notebook app they normally use—Evernote, One Note, etc. Ideally, you would want them to journal in whatever they normally use for that sort of activity. And if it’s reflective writing for their own growth, it should be an app that they own and that will travel with them after they leave the class and the institution. On the other hand, the final writing assignment needs to be submittable, gradable, and maybe markable. So maybe it gets submitted through an LMS, or maybe through a specialized tool like Turnitin.

    This is an interoperability problem. But it’s a special one, because the semantics have to be preserved through all of these connections in order for (a) the researchers to conduct the study, and then (b) the formative assessment tool to have real value to the students. The people who normally write Caliper metric profiles—the technical definitions of the nouns in Caliper—would have no idea about any of this on their own. Nor would the application developers. Both groups would need to have a conversation with the researchers in order to get the clarity they need in order to define the profiles for this purpose.

    The language of Caliper could help with this if a person with the right role and expertise were facilitating the conversation. That person would start by eliciting a set of three-word sentences from the researchers. What do you need to know? The answers might include statements like the following:

    • Student A reads text 1
    • Student A writes text alpha
    • Text alpha is a learning reflection of text 1
    • Student A reads text 2
    • Text 2 is a learning reflection of texts 1 and 2
    • Etc.

    The person asking the questions of the researcher and the feature designer—let’s call that person the learning engineer—would then ask questions about the meanings and details of the words, such as the following:

    • In what system or systems is the reading activity happening?
    • Do you need to know if the student started the reading? Finished it? Anything finer grained than that?
    • What do you need to know about the student’s writing in order to perform your textual analysis? What data and metadata do you need? And how long a writing sample do you need to elicit in order to perform the kind of textual analysis you intend and get worthwhile results back?
    • What do you mean when you say that text 2 is a reflection of both text 1 and 2, and how would you make that determination?

    At some point, the data scientist and software systems engineers would join in the conversation and different concerns would start to come up, such as the following:

    • Right now, I have no way of associating Student A in the note-taking system with Student A in the reading system.
    • To do the analysis you want, you need the full text of the reflection. That’s not currently in the spec, and it has performance implications. We should discuss this.
    • The student data privacy implications are very different for an IRB-approved research study, an individual student dashboard, and an instructor- or administrator-facing dashboard. Who owns these privacy concerns and how do we expect them to be handled?

    Notice that the Caliper language has become the externalization that we manipulate socially in the design exercise. There are two aspects of Caliper that make this work: (1) the three-word sentences are linguistically generative, i.e., they can express new ideas that have never been expressed before, and (2) every human-readable expression directly maps to a machine-readable expression. These two properties together enable rich conversations among very different kinds of stakeholders to map out theory-driven analytics and the interoperability requirements that they entail.

    This is the kind of conversation by which Caliper can evolve into a standard that leads to useful insights and tools for improving learning impact. And in the early days, it will likely happen one use case at a time. Over time, the working group would learn from having enough of these conversations that design patterns would emerge, both for writing new portions of the specification itself and for the process by which the specification is modified and extended.

    Copyright Carnegie Mellon University, CC-BY

  • EEP 2019: The Invisible Miracle of Learning

    Today I am sharing the first video out of the Empirical Educator Project (EEP) 2019 summit, and with it, a central concerns of the project. Much of the basic machinery our learning processes work so naturally and automatically so much of the time that they are invisible to us. So pervasively invisible, in fact, that most of us are barely aware that it even exists. And that’s a problem. If you believe that the job of education is to work within what psychologist Lev Vygotski called the “zone of proximal development”—the kind of learning challenge that would be too hard for a student to learn on her own but not so hard that she can’t learn it at all—then we have to have a very finely tuned understanding of that learning machinery, to the point where we can accurately find each student’s zone of proximal development with a high level of consistency.

    We fail to do this all the time. Some students are bored while others struggle. The more heterogeneous the student population is, the bigger a problem this is. As higher education as a sector becomes more committed to serving post-traditional students, first-generation students, and students with 40-year educational relationships to the school rather than 4-year relationships, then this need to be able to see and understand these invisible learning processes becomes more acute. For this reason among others, fostering academic literacy around the mental machinery of learning—making the invisible visible—is one of the central goals of EEP. I therefore wanted to start the 2019 EEP summit by highlighting this challenge. So I invited three Carnegie Mellon University (CMU) professors with complementary areas of expertise to participate in a panel that could highlight several dimensions of the problem.

    This wasn’t the first time I had interviewed these three particular academics. I had been fortunate enough to be invited to a CMU press fellowship three years earlier. I brought my video camera along and happened to be able to get some air time with these very three people, two of whom I had never met before. The interviews turned out to be formative for me, particularly with regard to my thinking about EEP. I’m going to write a little about the complimentary insights that these three academics gave to me and then share both the interview video from the summit and the original interview videos from two years ago.

    Expert blind spots

    As we get old and forgetful, we like to joke that our minds have to make room for the new information by clearing out old information. It turns out that there’s truth behind this joke in multiple ways. First, we have different kinds of memory. If I asked you to list the steps required to tie your shoe, those steps would probably not come tripping off your tongue. Does that mean that you don’t know how to tie your shoe? No, it doesn’t. It means that you’ve moved that knowledge to a more efficient memory space in your brain. One that’s quick and efficient enough that you can easily bend down and tie your shoes while performing other, more demanding cognitive tasks. But that knowledge is not accessible to your conscious mind. It is “tacit” knowledge. Your brain is very efficient at shunting information that it needs to access but does not need to consciously examine into a different compartment than the one it was in when you were learning a skill.

    There was a time when you could list the steps in tying your shoe, because that was how you first learned those steps. Your brain put that information into a box once it no longer needed conscious access to it. Chances are good that you don’t remember that time well and that you don’t remember the experience of those steps fading from your conscious memory. I tried to recreate this experience recently for myself. I am learning to swim. In the first weeks, I was thinking about about very basic aspects of moving my arms and, separately, moving my legs. That period was about nine months ago. I decided to try a little experiment with memory encoding in the process. Every two weeks, I would try to remember the steps that I learned in my first lesson. I didn’t try to memorize those steps. That would be triggering a different memory process and would invalidate the experiment. I just tried to reconstruct the steps in my mind. Meanwhile, I spent most of my time at the pool learning to be a better swimmer.

    As the weeks went on, I found myself thinking less about what my arms and legs were doing separately and more about what my whole body was doing. I also found it harder and harder to remember what the original steps were that I learned in my first lesson. Nine months in, I barely remember anything about how I first thought about what I was doing. If I had to teach somebody to swim from scratch, I couldn’t just reproduce the lesson that was taught to me. I’d have to make something up. Nor could I reproduce the learning steps I took—many of which I made on my own, without my instructor—to get from my beginner’s understanding to the level of expertise I have achieved as of today. I might be able to draw on some of my knowledge and experience, but I would have to invent more of my teaching moves than most teachers like to admit, through trial and error, by working with students.

    So our brains do, in fact, make room for new information by boxing up old information and putting into storage. In addition to the memory changes, we also process information differently as our domain knowledge gets more sophisticated. When you’re learning math, or cooking, or yoga, or any other discipline with integrated skills that build on each other, at first, you’re learning each skill separately. Over time, your mind integrates steps and makes general rules. As novice cooks become expert cooks, their way of thinking about cooking looks less like meticulously following one out of hundreds of completely separate recipes and more like following some generalized principles that they’ve drawn from their experience of making so many recipes. They stop thinking algorithmically and start thinking heuristically.

    We don’t generally notice these changes in our cognition as we move from novices to experts in a topic. They’re not directly observable and not usually consciously experienced. They just happen. This is a problem for teaching because professors, as experts, have undergone all of these changes in their learning processes. They no longer think they way their students do. They don’t think about cooking as following individual recipes. Further, because their evolution as thinkers was largely silent, and because most professors have no professional development in these processes, it’s not always obvious to them the extent to which their brains process information in fundamentally different ways than those of their students. Ironically, it is their very expertise that causes them to struggle sometimes to understand how their students think about their subjects or how to work with them in that zone of proximal development. CMU Professor Ken Koedinger, Director of LearnLab at the Pittsburg Science of Learning Center, is an expert in this conundrum.

    Expert teaching blind spots

    There’s a related phenomenon that I’ll call an expert teaching blind spot, even though I don’t think that’s an official term of art. Just as it is possible to not consciously know what you know in any domain of knowledge, it’s possible to have tacit knowledge specifically in teaching. In addition to the reasons above, I’ll add another one: Interpersonal skills, including teaching skills, are somewhere in the middle of learning spectrum between things that we are hardwired to learn without anyone specifically teaching us (like spoken language as young children), and something that is an intellectual creation which must be consciously learned (like political science). Many educators have what we colloquially refer to as teaching “instincts,” and that word is not far from the truth. We have tacit interpersonal knowledge, sometimes including tacit knowledge about learning processes of our students. We know some things about how to teach in a very real sense, but that knowledge is not fully consciously accessible to us.

    As a result, it can be very difficult to talk to even highly skilled teachers about what they do, because in many cases they’ve never even tried to put what they do into language. They just do what seems right and obvious to them. And if they do verbalize what they’re doing, they usually aren’t using terms of art because they usually haven’t been taught any. Their insights seem personal because nobody has talked to them that beyond the personal and phenomenological there could be a sharable, learnable, teachable body of knowledge that their instincts are tapping into. CMU’s Marsha Lovett, Director of Eberly Center for Teaching Excellence & Educational Innovation is an expert in this problem domain.

    If we don’t have a coherent answer, then we make one up

    If you put all of this together, it adds up to a very significant challenge to serious educators. They don’t have easy ways of knowing how they think differently than their students or easy access to their own cognitive journeys that got them from novice learners to expert learners. And yet, most of us have vivid memories of our formative experiences as students. On top of that, teachers teach, and students learn. It happens all the time. Humans are such incredible learning machines, and the machinery is so well hidden from us, that many people tend to assume that there really isn’t much to it (when nothing could be further from the truth). Most professors are good at academic learning. That’s how they ended up as professors.

    And they usually had at least one experience that really inspired them to learn about their chosen field. That association is often all it takes for educators to attribute causality. “Well, I had an amazing experience in Professor Smith’s class, and Professor Smith did X, so X must be a great way to teach.” Given that most professors diligently worked through five to seven years of graduate school without being exposed to the tiniest hint of any of the above and then were expected to somehow magically know how to teach well, what tends to happen is that professors make up their own stories about what effective teaching is based on their own personal experiences—which is the only data they have, really—and they go on that. And they don’t change their minds about it very much or very easily. CMU anthropologist and Simon Research Faculty Lauren Herckis has conducted some fascinating research in this area.

    We have a literacy problem

    If you put all of this together, it’s clear that we’re not going to make substantial progress on improving education until educators are taught to see that which is currently invisible. We have to develop a common cultural understanding that learning involves a complex set of cognitive processes, that being an expert in a knowledge domain is not sufficient to be a good teacher of novices, that good teaching instincts are often based on tacit knowledge which we can make explicit and therefore more sharable and useful. Only by doing this together, as a sector, can we make substantial progress on improving student success. One of the main goals of Empirical Educator Project is to begin fostering the cultural infrastructure that we need in order to do that.

    Here are the three original video interviews I conducted of Marsha, Ken, and Lauren two years ago:

    e-Literate TV CMU Interviews

    I got lucky with those interviews. The coherence in the interviews is a product of the coherent body of work at CMU’s Simon Initiative as represented by the three people who happened to be available to interview rather than through some master plan of mine.

    At the summit, I chose to frame up both the discussion and the event more consciously. In addition to their work, I asked the three to reflect on their personal journeys as educators to embrace views about teaching and learning that may have seemed surprising or even counter-intuitive to them:

    EEP Summit 2019: Empirical Education 1.0 beta Panel

    The journeys that these experts describe are emblematic of the bigger picture that EEP is all about. And not just in classroom work specifically, but in every aspect of serving students.

    I have said before that academia needs to move from a philosophical commitment to student success toward operational excellence at supporting student success. The implied gap is knowhow. It will show up differently in the classroom than it will in, say, advising, but the pattern is going to be the same, and I think academics will be most comfortable thinking about it as starting with a literacy problem. There is some discipline, either new or existing, that they must learn to some degree of competence in order to serve their students well. They might not have to be expert in it—they don’t have to have PhDs in cognitive psychology, for example—but they do need to be literate in it.

  • The IMS at an Inflection Point

    The IMS at an Inflection Point

    A few weeks back, I had the pleasure of attending the IMS Learning Impact Leadership Institute (LILI). For those of you who aren’t familiar with it, IMS is the major learning application technical interoperability organization for higher education and K12 (and is making some forays into the corporate training and development world as well). They’re behind specifications like LIS, which lets your registrar software automagically populate your LMS course shell with students, and LTI, which lets you plug in many different learning applications. (I’ll have a lot more to say about LTI later in this post.)

    While you may not pay much attention to them if you aren’t a technical person, they have been and will continue to be vital to creating the kind of infrastructure necessary to support more and better teaching and learning affordances in our educational technology. As I’ll describe in this post, I think the nature of that role is likely to evolve somewhat as the interoperability needs of the sector are beginning to evolve.

    The IMS is very healthy

    I’m happy to report that the IMS appears to be thriving by any obvious measure. The conference was well attended. It attracted a remarkably diverse group of people for an event hosted by an organization that could easily be perceived as techie-only. Furthermore, the attendees seemed very engaged and the discussions were lively.

    On more objective measures, the organization’s annual report bears out this impression of strong engagement. They have strong international representation across a range of organization types.

    From the IMS Global 2018 Annual Report

    Whether your measure is membership, product certifications, or financial health, the IMS is setting records.

    From the IMS Global 2018 Annual Report

    This state of affairs is even more remarkable given that, 13 years ago, there was some question as to whether the IMS was financially sustainable.

    From the IMS Global 2018 Annual Report

    If you look carefully at this graph, you’ll see three distinct periods of improvement: 2005-2008, 2009-2013, and 2013-2018. Based on what I know about the state of the organization at the time, first period can most plausibly be attributed to immediate changes implemented by Rob Abel, who took over the reins of the organization in February of 2006 and likely saved it from extinction. Likewise, the magnitude of growth in the second period is consistent with that of a healthy membership organization that has been put back on track.

    But that third period is different. That’s not normal growth. That’s hockey stick growth.

    I am not a San Franciscan. By and large, I do not believe in heroic entrepreneur geniuses who change the world through sheer force of will. Whenever I see that kind of an upward trend, I look for a systemic change that enabled a leader or organization—through insight, luck, or both—to catch an updraft.

    There is no doubt in my mind that the IMS has capitalized on some major updrafts over the last decade. That is an observation, not a criticism. That said, the winds are changing, in part because the IMS has helped move the sector through an important period of evolution and is now helping to usher in the next one. That will raise some new challenges that the IMS is certainly healthy enough to take on but will likely require them to develop a few new tricks.

    The world of 2005

    In the first year of the chart above, when the IMS was in danger of dying, there was very little in the way ed tech to interoperate. There were LMSs and registrar systems (a.k.a. SISs). Those were the two main systems that had to talk to each other. And they did, after a fashion. There was an IMS standard at the time, but it wasn’t a very good one. The result was that, even with the standard, there was a person in each college or university IT department whose job it was to manage the integration process, keep it running, fix it when it broke, and so on. This was not an occasional tweak, but a continual effort that ran from the first day of class registration through the last day of add/drop. If you picture an old-timey railroad engineer shoveling coal into the engine to keep it running and checking the pressure gauge every ten minutes to make sure it didn’t blow up, you wouldn’t be too far off. As for reporting final grades from the LMS’s electronic grade book automatically to the SIS’s electronic final grade record, well, forget it.

    If you ignore some of the older content-oriented specifications, like QTI for test questions and Common Cartridge for importing static course content, then that was pretty much it in terms of application-to-application interoperability. Once you were inside the LMS, it was basically a bare-bones box with not much you could add. Today, the IMS lists 276 officially certified products that one can plug into any LMS (or other LTI-compliant consumer), from Academic ASAP to Xinics Commons. I am certain that is a substantial undercount of the number of LTI-compatible applications, since not all compatible product makers get officially certified. In 2005, there were zero, because LTI didn’t exist. There were LMS-specific extensions. Blackboard, for example, had Building Blocks. But with a few exceptions, most weren’t very elaborate or interesting.

    My personal experience at the time was working at SUNY Systems Administration and running a search committee for an LMS that could be centrally hosted—preferably on a single instance—and potentially support all 64 campuses. For those who aren’t familiar with it, SUNY is a highly diverse system, with everything from rural (and urban) community colleges to R1s to everything in between, with some specialty schools thrown into the mix like the Fashion Institute of Technology, a medical school or two, an ophthalmology school, and so on. Both the pedagogical needs and the on-campus support capabilities across the system were (and presumably still are) incredibly diverse. There simply was not any existing LMS at the time, with or without proprietary extensions, that could meet such a diverse set of needs across the system. We saw no signs that this state of affairs was changing at pace that was visible to the naked eye, and relatively few signs that it was even widely recognized as a problem.

    To be honest, I came to the realization of the need fairly slowly myself, one conversation at a time. A couple of art history professors dragged me excitedly to Columbia University to see an open source image annotation tool, only to be disappointed when they discovered that the tool was developed to teach clinical histology, which uses image annotation to teach in an entirely different way than is typically employed in art history classes. An astronomy professor at a community college on the far tip of Long Island, where there was relatively little light pollution, wanted to give every astronomy student in SUNY remote access to his telescope if only we could figure out how to get it to talk to the LMS. Anyone who has either taught a been an instructional designer for a few wildly different subjects has a leg up on this insight (and I had done both), but even so, there are levels of understanding. The art history/histology thing definitely took me by surprise.

    A colleague and I, in an effort to raise awareness about the problem, wrote an article about the need for “tinkerable” learning environments in eLearn Magazine. But there were very few models at the time, even in the consumer world. The first iPhone wasn’t released until 2007. The first practically usable iPhone wasn’t released until 2008. (And we now know that even Steve Jobs was secretly skeptical that apps on a phone were a good idea.) It is a sign of just how impoverished our world of examples was in January of 2006 that the best we could think of to show what a world of learning apps could be like was Google Maps:

    There are several different ways that software can be designed for extensibility. One of the most common is for developers to provide a set of application programming interfaces, or APIs, which other developers can use to hook into their own software. For example, Blackboard provides a set of APIs for building extensions that they call “Building Blocks.” The company lists about 70 such blocks that have been developed for Blackboard 6 over the several years that the product version has been in existence. That sounds like a lot, doesn’t it? On the other hand, in the first five months after Google made the APIs available for Google Maps, at least ten times that many extensions have been created for the new tool. Google doesn’t formally track the number of extensions that people create using their APIs, but Mike Pegg, author of the Google Maps Mania weblog, estimates that 800-900 English-language extensions, or “mash-ups,” with a “usable, polished Google Maps implementation” have been developed during that time—with a growth rate continuing at about 1,000 new applications being developed every six months. According to Pegg, “There are about five sites out there that facilitate users to create a map by taking out an account. These sites include wayfaring.com, communitywalk.com, mapbuilder.net—each of these sites probably has hundreds of maps for which just one key has been registered at Google.” (Google requires people who are extending their application to register for free software “keys.” Perhaps for this reason, Chris DiBona, Google’s own Open Source Program Manager, has heard estimates that are much higher. “I’ve seen speculation that there are hundreds or thousands,” says DiBona, noting that estimates can vary widely depending on how you count.

    Nevertheless, even the most conservative estimate of Google Maps mash-ups is higher than the total number of extensions that exist for any mainstream LMS by an order of magnitude.

    There seemed little hope for this kind of growth any time in the foreseeable future. By early 2007, having failed to convince SUNY to use its institutional weight to push interoperability forward, I had a new job working at Oracle and was representing them on a specification development committee at the IMS. It was hard, which I didn’t mind, but it was also depressing. There was little incentive for the small number of LMS and SIS vendors who dominated specification development at that time to do anything ambitious. To the contrary, the market was so anemic that the dominant vendors had every reason to maintain their dominance by resisting interoperability. Every step forward represented an internal battle within those companies between the obvious benefit of a competitive moat and the less obvious enlightened self-interest of doing something good for customers. This is simply not the kind of environment in which interoperability standards grow and thrive.

    And yet, despite the fact that it certainly didn’t feel like it, change was in the air.

    Glaciers are slow, but they reshape the planet

    For starters, there was the LMS, which was both a change agent in of itself and an indicator of deeper changes in the institutions that were adopting them. EDUCAUSE data shows that the US LMS market became saturated some time roughly around 2003. At that time, Blackboard and WebCT had the major leads as #1 and #2, respectively. The dynamic for the next 10 years was a seesaw, with new competitors rising and Blackboard buying and killing them off as fast as it could. Take a look at the period between 2003 and 2013 in Phil’s squid graph: ((By the way, if you haven’t subscribed to Phil’s new blog yet, then you really, really should. Like, right now. I’ll wait.))

    It was absolutely vicious.

    None of this would materially affect the standards making process inside the IMS until, first, Blackboard’s practice of continually buying up market share eventually failed (thus allowing an actual market with actual market pressures to form) and, second, until the management team that came up with this decidedly anti-competitive strategy…er…chose to spend more time with their respective families. (I’ll have more to say about Heckle and Jeckle and their lasting impact on market perceptions in a future post.)

    But the important dynamic during this period is that customers kept trying to leave Blackboard (even if they found themselves being reacquired shortly thereafter) and other companies kept trying to provide better alternatives. So even though we didn’t have a functioning, competitive market that could incentivize interoperability, and even though it certainly didn’t feel like we had one, some of the preconditions for one were being established.

    Meanwhile online education growth was being driven by no fewer than three different vectors. First, for-profit providers were hitting their stride. By 2005, the University of Phoenix alone was at over 400,000 enrollments. Second, public access-oriented institutions, many of which had been seeded a decade earlier with grants from the Sloane Foundation, were starting to show impressive growth as well. A couple were getting particular attention. UMUC, for example, may not have had over 400,000 online enrollments in 2005, but they had well over 40,000, which is enough to get the attention of anyone in charge of an access-oriented public university’s budget. More quietly, many smaller schools were having online success that were proportional to their sizes and missions. For example, when I arrived at SUNY in 2005, they had a handful of community colleges that had self-sustaining online degree programs that supported both the missions and the budget of the campuses. Many more were offering individual courses and partial degrees in order to increase access for students. (Most of New York is rural, after all.)

    The third driver of online education, which is more tightly intertwined with the first two than most people realize, is that Online Program Management companies (OPMs) were taking off. The early pioneers, like Deltak (now Wiley Education Services), Embanet, Compass Education (now both subsumed into Pearson), and Orbis (recently acquired by Grand Canyon University) had proved out the model. The second wave was coming. Academic Partnerships and 2Tor (now 2U) were both founded in 2008. Altius Education came in 2009. In 2010, Learning House (now also owned by Wiley) was founded.

    Counting online enrollments is a notoriously slippery business, but this chart from the Babson survey is highly suggestive and accurate enough for our purpose:

    If you’re a campus leader and thirty percent of your students are taking at least one online class, that becomes hard for you to ignore. Uptime becomes far more important. Quality of user experience becomes far more important. Educational affordances become far more important. Obviously, thirty percent is an average, and one that is highly unevenly distributed across segments. But it’s significant enough to be market-changing.

    And the market did change. In a number of ways, the biggest one being that it became an actual, functioning market (or at least as close to one as we’ve gotten in this space).

    When glaciers recede

    Let’s revisit that second growth period in the IMS graph—2008 to 2013—and talk about what was happening in the world during that period. For starters, online continued its rocket ride. The for-profits peaked in 2010 at roughly 2 million enrollments (before beginning their spectacular downward spiral shortly thereafter). Not-for-profits (and odd mostly-not hybrids) ramped up the competition. ASU launched its first online 4-year degree in 2006. SNHU started a new online unit in 2009. WGU expanded into Indiana in 2010, which was the same year that Embanet merged with Compass Knowledge and was promptly bought by Pearson. (Wiley acquired Deltak two years later.)

    Once again, the more online students you have, the less you are able to tolerate downtime, a poor user interface that drives down productivity, or generic course shells that make it hard to teach students what they need to learn in the ways in which they need to learn. Instructure was founded in 2008. They emphasized a few distinctions from their competitors out of the gate. The first was their native multitentant cloud architecture. Reduced downtime? Check. The second was a strong emphasis on usability. The big feature that they touted which was their early runaway hit was Speed Grader. Increased productivity? Check.

    Instructure had found their updraft to give them their hockey stick growth.

    But they also emphasized that they were going to be a learning platform. They weren’t going to build out every tool imaginable. Instead, they were going build a platform and encourage others to build the specialized the tools that teachers and students need. And they would aggressively encourage the development and usage of standards to do so. On the one hand, this fit from a cultural perspective. Instructure was more like a Silicon Valley company than its competitors, and platforms were hot in the Valley. On the other hand, it was still a little weird for the education space. There still weren’t good interoperability standards for what they wanted to do. There still hadn’t been an explosion of good learning tools. This is one of those situations where it’s hard to tell how much of their success was prescience and how much of it was luck that higher ed caught up with their cultural inclination at that exact moment.

    Co-evolution

    The very same year that Brian Whitmer and Devlin Daley founded Instructure, Chuck Severence and Mark Alier were mentoring Jordi Piguillem on a Google Summer of Code project that would become the initial implementation of LTI. In 2010, the same year that Instructure scored its first major win with the Utah Education Network, IMS Global released the final specification for LTI v1.0. All this time that the market had felt like it had been standing still, it had actually been iterating. We just hadn’t been experiencing the benefits of it. Chuck, who had been thinking about interoperability in part through his work on Sakai, had been tinkering. Students like Brian and Devlin, who had been frustrated with their LMS, had been tinkering. The IMS, which actually had a precursor specification before LTI, had been tinkering. While conditions hadn’t become visible on the surface of the glacier, way down, a mile below, the topology of the land was changing.

    Meanwhile in Arizona, in 2009, the very first ASU+GSV summit was held. I admit that I have had writer’s block regarding this particular conference the last few years. It has gotten so big that it’s hard to know how to think about it, much less how to sum it up. In 2009, it was an idea. What if a university and a company that facilitates start-ups (in multiple ways) got together to encourage ed tech companies to work more effectively with universities? That’s my retrospective interpretation of the original vision. I wasn’t at many of those early conferences and I certainly wasn’t an insider. It was hard for me, with my particular background, to know what to make of it then and even harder now.

    But something clicked for me this year when it turned out that IMS LILI was held at the same hotel that the ASU+GSV summit had been at a couple of months earlier. How does the IMS get to 523 product certifications and $8 million in the bank? A lot of things have to go right for that to happen, but for starters, there have to be 523 products to certify and lots of companies that can afford to pay certification fees. That economy simply did not exist in 2008. Without it, there would be no updraft to ride and consequently no hockey stick growth. ASU+GSV’s phenomenal growth, and the ecosystem that it enabled, was another major factor influenced what I saw at IMS LILI this month.

    There is a lot of chicken-and-egg here. LTI made a lot of this possible, and the success LTI (and IMS Global) have experienced would not have been possible without a lot of this. The harder you stare at the picture, the more complicated it looks. This is what “systems thinking” is all about. There isn’t a linear cause-and-effect story. There are multiple interacting feedback loops. It’s a complex adaptive system, which means that it doesn’t respond in linear or predictable ways.

    Update: I got a note from Rob Abel noting that a lot of the growth in the last leg came from an explosion of participation in the K12 space. That’s good color and consistent with what I’ve seen in my last couple of LILI conference visits. It’s also consistent with the rest of this analysis. K12 benefitted from all of the dynamics above—the maturation of the LMS market, the dynamics in higher education online that pushed toward SaaS and usability, the massive influx of venture funding, and so on. All of those developments, plus the work inside IMS, made the K12 growth possible, while the dynamics inside K12 added another feedback loop to this complex adaptive system.

    But respond it finally did. We have some semblance of a functioning market, and with its rise, blockers preventing the formation of a vibrant interoperability standards ecosystem of the type we have today have largely fallen. Now we have to address the blockers of the formation of the vibrant interoperability ecosystem that we will need tomorrow. Because it will be qualitatively different. Tomorrow’s blockers are not market formation problems but rather collaboration methodology problems. They are about creating meaningful learning learning analytics, which will require solving some wicked problems that can only be tackled through close and well structured interdisciplinary work. That most definitely includes the standards design process itself.

    After the glacier comes the flood

    What I saw at the IMS LILI this year was, I think, a milestone. The end of an era. Market pressures now favor interoperability. The same companies that were the most resistant to developing and implementing useful interoperability standards in 2007 are among the most aggressive champions of interoperability today. This is not to say that foundational interoperability work is “over.” Far from it. Rather, the conditions finally exist where it can move forward as it should, still hard but relatively unimpeded by the distortions of a dysfunctional market.

    That said, the nature and challenges of interoperability our sector will be facing in the next decade are fundamentally different from the ones that we faced in the last one. Up until now, we have primarily been concerned with synchronizing administration-related bits across applications. Which people are in this class? Are they students or instructors? What grades did they get on which assignments? And how much does each assignment count toward the final course grade? These challenges are hard in all the ways that are familiar to anyone who works on any sort of generic data interoperability questions.

    But the next decade is is going to be about data interoperability as it pertains to insight. Data scientists think this is still familiar territory and are excited because it keeps them at the frontier of their own profession. But this will not be generic data science, for several reasons. (I will tell you right now that some of them disagree with me on this. Vehemently.) First, even in the most richly instrumented fully online environments that we have today, they are highly data impoverished relative to what we need to make good inferences about teaching and learning. For heaven’s sake, Amazon still recommends things that I have already bought. If I just bought a toaster oven last month, then how likely is it that I want to buy another one now? And I buy everything on Amazon. If they don’t know enough to make good buying recommendations on consumer products, then there’s no way that our learning environments are going to have enough data to make judgements that are orders of magnitude more sophisticated.

    Well then, some answer, we’ll just collect more data! More more more! We’ll collect everything! If we collect every bit of data, then we can answer any question. (That is a pretty close paraphrase of what one of the IMS presenters said in one of the handful of learning analytics talks I went to.)

    No. You won’t collect “everything”—even if we ignore the obvious, glaring ethical questions—because you don’t know what “everything” is. Computer folks, having finally freed themselves from the shackles of SQL queries and data marts, are understandably excited to apply that newfound freedom to the important problem space of learning. But it is not a good fit, because we don’t have a good understanding of the basic cognitive processes involved in learning. As I wrote about (at length) in a previous post, we have to employ multiple cutting-edge machine learning techniques just to get glimpses of learning processes even when we are directly monitoring students’ brain activity because these are extraordinarily complex processes with multiple hidden variables. Trying to tease out learning processes inside a student’s head based on learning analytics from running machine learning algorithms on LMS data is a little like trying to monitor the digestive processes of a flatworm on the bottom of the Marianas Trench based on studying the wave patterns on the surface of the ocean. There are too many invisible mediating layers to just run a random forest algorithm on your data lake—it all sounds very organic, doesn’t it?—and pop out new insights about how students learn.

    That doesn’t mean we should just throw up our hands, by any means. To the contrary, IMS Global has some extraordinarily good tools close at hand for tackling this problem. But it does mean that they are going to have to take some of the stakeholder engagement strategies they’ve been working at diligently to the next level, to the point where the standards-making process itself may evolve over time.

    Theory-driven interoperability

    There is an excellent data and processing resource that the learning analytics folks have yet to think deeply about how to leverage, as far as I can tell from the conference. The computational power is impressive (and impressively parallel). It is the collective intelligence of educators and learning scientists. Because there are too many confounds to making useful direct inferences from the data, educational inferencing needs to be theory-driven. You need to start with at least some idea of what might be going on inside the learner’s head. One that can be either supported or disproven based on evidence. And you need to know what that evidence might look like. If you can spell all that out, then you can start doing interesting things with learning analytics, including machine learning. There is room for learning science, data science, and on-the-ground teaching expertise at the table. In fact, you need all those kinds of expertise. But the folks with those respective kinds of know-how need to be able to talk to each other and work together in the right ways, which is really hard.

    The IMS has an outstanding foundation for this sort of work, because their Caliper specification turns out to provide the basis for a perfectly lovely lingua franca. To begin with, its fundamental structure is triples, which is the same basic idea as the original concept behind the semantic web. If you’re not a computer person and this is starting to make your eye’s glaze over, don’t worry, because this is plain English. Three-word sentences, in fact. Noun, verb, direct object. Student takes test. Question assesses learning objective. Student highlights sentence. Sentence discusses Impressionism.

    IMS Caliper expresses learning analytics in statements that can easily be translated into three-word plain-English sentences. These sentences can be strung together into coherent paragraphs. Notice, for example, how the last two example sentences are related. Three-word sentences in this format can be chained together to form longer thoughts. New thoughts. With this one, very simple grammatical structure, we have a language that is generative in the linguistic sense. As long as you have words to put into these grammatical placeholders, you can string thoughts together. Or “chain inferences,” to sling the lingo. And it turns out, unsurprisingly, that Caliper has a mechanism for defining these words in ways that both humans and machines can understand them.

    That has to be the bridge. Humans have to understand the utterances well enough to be able express their theories on the front end and understand whatever the machine is telling them it may have learned on the back end. Machines have to understand them specifically enough to be able to parse the sentences in their own, literal, machine-y way. Theoretically, Caliper could be an ideal language to enable educators and computer scientists to discuss theories about how to better support students as well as how to test those theories together.

    The challenge is that the IMS community, at least based on what I saw in the sessions I attended, is not using the specification as an interdisciplinary communication tool in this way yet. What I saw happening instead was a lot of very earnest data scientists pumping as much Caliper data as the can into their data lakes. They come to the conference, give a talk and, to their credit, shrug their shoulders and admit that they really don’t know what to do with those data yet. But then they go home and build bigger pipes, because that’s their job. That’s what they do.

    It’s not their fault. I’ve been friends with some of these folks for a very long time indeed. There are good people here. But if you work in the IT department, and you’re not a learning scientist or a classroom educator, and the faculty are somewhere between dismissive and disdainful of the idea of talking to you about working together to improve teaching and learning, then what can you do? You do what you know how to do and hope that things will change for the better over time.

    It’s not the IMS’s fault either. The conference I attended was called the IMS Learning Impact Leadership Institute. That’s not a new name. Caliper has board that helps guide its direction. That board includes educators who are the kind of advocates that I would like to see on such a body. They are productive irritants in the best possible way. But that’s not enough anymore. This is just a really hard problem. It’s the challenge of the next decade. To meet it, we need to do more than just make sure the right people are in the room together. We need to develop new ways of working together. New roles, methodologies, ways of talking with each other, and ways of seeing the world.

    I’m going to preview a bit of a post that I have in my queue for…I’m not sure when, but some time soon…by mentioning “learning engineering.” This term has gotten a lot of buzz lately, along with some criticism. I’ll be writing up my own take on it, but for now I’ll say that one reason I think the term is gaining some currency is that it represents a set of skills for being a mediator in the kind of collaboration that I’m describing here.

    As it turns out, it was coined by Nobel prize-winning polymath and Carnegie Mellon luminary Herb Simon, after whom Carnegie Mellon University’s Simon Initiative was named. And, as it also turns out, the Simon Initiative hosted this year’s EEP summit and made some news in the process by contributing $100 million worth of open source software that they use in their research and pratice of…wait for it…learning engineering.

    Here’s a slide that they used in their talk explaining what the heck learning engineering is and what they are doing when they are doing it:

    Copyright Carnegie Mellon University, CC-BY

    (By the way, the videos of all talks from the summit will be posted online, as promised. Please be patient a little longer.)

    This post has already run long, so rather than unpacking the slide, I’ll leave you with a question or two. Think about this graphic as representing a data-informed continuous improvement methodology involving multiple people with multiple types of expertise. What would that methodology need to look like? Who would have to be at the table, what kinds of conversations would they have to have, and how would they have to work together?

    I’m not suggesting that “learning engineering” is a magical conjuring phrase. But I am suggesting that we need new approaches, new competencies, and likely a new role or two if we are going to get to the next updraft.

  • EEP 2019 Will Be Live Audiostreamed

    Interest in the Empirical Educator Summit (EEP) has been off the charts. We want and intend to include everybody, but only when we can include people in a way that is useful to them. So we are being intentional about the pace and ways in which we are growing.

    That said, we know a lot of people are very interested. We had already planned to release video of much of the summit after the fact. We’ve decided that we’re going to try to live stream the audio as well. (My experience with live-streaming video is that there isn’t much value in the visuals unless your setup is better than we will be able to manage, so we’d rather focus on trying to get you a solid audio stream.)

    We have a placeholder page set up at http://empiricaleducators.net/2019-eep-summit/. Between now and Monday, we will be posting an agenda of the summit and putting up a widget for the audio streaming on that page. Check there periodically for updates. For planning purposes, I can tell you now that the audio streaming will be from 9 AM to 3:30 PM EST on Monday, May 6th and from 9 AM to 12 PM EST on Tuesday, May 7th. Again, the agenda will be posted on the EEP summit page soon. This is a last-minute addition driven by demand, so we’re winging it a bit.

    We also invite you to discuss the summit on Twitter as it is streamed. We will not have the luxury of a dedicated social media person to monitor and respond to the conversation live, but we will be encouraging the on-site community to participate and will definitely be looking at what you have to say afterward to see what we can learn from your input. The hashtag for the event is #EEP2019.

    We’re adding two more hashtags for more specific input, since EEP is ultimately about doing things together. If you use these, please be sure to catch the early sessions on Monday that explain the goals of EEP so that your input is on point. The first hashtag, #EEP2019ideas, is for suggestions about how EEP members—both current and prospective—can work together to accomplish the goals of the network. The second, #EEP2019challenges, is for obstacles you want us to be aware of as we think about how to build out the collaborative network.

    To prepare you for the streaming of the event, I’m going to assign you some homework. The main reading is very short. I just published a piece in Forbes about Carnegie Mellon’s contribution. It’s not what you’re used to reading from me in that Forbes required the piece to be only about 800 words and strictly enforced a requirement that readers shouldn’t need to have any knowledge of higher education or software whatsoever in order to understand the article. The downside of these requirements is that I had to flatten and truncate some details and nuances that e-Literate readers are used to getting from me. (One example that I particularly want to get off my chest is that I briefly described the fruits of Lumen Learning’s collaboration with Carnegie Mellon but wasn’t able to give them proper credit.) But there were some benefits to those restrictions too. I think the piece captures something of the sense of professional identity and culture that both Carnegie Mellon and EEP seek to foster. Also, did I mention that it’s probably the shortest piece by me that you will ever see? Go read it.

    Beyond that, if you want to get a deeper sense of the train of thought behind the effort, take a dip into the archive of EEP-related blog posts here at e-Literate.

  • Carnegie Mellon and Lumen Learning Announce EEP-Relevant Collaboration

    Late last week, Carnegie Mellon University (CMU) and Lumen Learning jointly issued a press release announcing their collaboration on an effort to integrate the Lumen-developed RISE analytical framework for curricular materials improvement analysis into the toolkit that Carnegie Mellon announced it will be contributing via open licenses (and unveiling at the Empirical Educator Project (EEP) summit that they are hosting in May).

    To be clear, Lumen and Carnegie Mellon are long-time collaborators, and this particular project probably would have happened without either EEP or CMU’s decision to contribute the software that they are now openly licensing. But it is worth talking about in this context for two reasons. First, it provides a great, simple, easy-to-understand example of a subset of the kinds of collaborations we hope to catalyze. And second, it illustrates how CMU’s contribution and the growth of the EEP network can amplify the value of such contributions.

    RISE

    The RISE framework is pretty easy to understand. RISE stands for Resource Inspection, Selection, and Enhancement. Their focus is on using it to improve Open Educational Resources (OER) because that’s what they do, but there’s nothing about RISE that only works with OER. As long as you have the right to modify the curricular materials you are working with—even if that means removing something proprietary and replacing it with something of your own making—then the RISE framework is potentially useful.

    From the paper:

    In order to continuously improve open educational resources, an automated process and framework is needed to make course content improvement practical, inexpensive, and efficient. One way that resources could be programmatically identified is to use a metric combining resource use and student grade on the corresponding outcome to identify whether the resource was similar to or different than other resources. Resources that were significantly different than others can be flagged for examination by instructional designers to determine why the resource was more or less effective than other resources. To achieve this, we propose the Resource Inspection, Selection, and Enhancement (RISE) Framework as a simple framework for using learning analytics to identify open educational resources that are good candidates for improvement efforts.


    The framework assumes that both OER content and assessment items have been explicitly aligned with learning outcomes, allowing designers or evaluators to connect OER to the specific assessments whose success they are designed to facilitate. In other words, learning outcome alignment of both content and assessment is critical to enabling the proposed framework. Our framework is flexible regarding the number of resources aligned with a single outcome and the number of items assessing a single outcome.


    The framework is composed of a 2 x 2 matrix. Student grade on assessment is on the y-axis. The x-axis is more flexible, and can include resource usage metrics such as pageviews, time spent, or content page ratings. Each resource can be classified as either high or low on each axis by splitting resources into categories based on the median value. By locating each resource within this matrix, we can examine the relationship between resource usage and student performance on related assessments. In Figure 2, we have identified possible reasons that may cause a resource to be categorized in a particular quadrant using resource use (x-axis) and grades (y-axis).

    Figure 2. A partial list of reasons OER might receive a particular classification within the RISE framework.

    By utilizing this framework, designers can identify resources in their courses that are good candidates for additional improvement efforts. For instance, if a resource is in the High Use, High Grades quadrant, it may act as a model for other resources in the class. If a resource falls into the Low Use, Low Grades quadrant, it may warrant further evaluation by the designers to understand why students are ignoring it or why it is not contributing to student success. The goal of the framework is not to make specific design recommendations, but to provide a means of identifying resources that should be evaluated and improved.

    Let’s break this down.

    RISE is designed to work with a certain type of common course design, where content and assessment items are both aligned to learning objectives. This design paradigm doesn’t work for every course, but it works for many courses. The work of aligning the course content and assessment questions with specific learning objectives is intended to pay dividends in terms of helping the course designers and instructors gain added visibility into whether their course design is accomplishing what it was intended to accomplish. The 2×2 matrix in the RISE paper captures this value rather intuitively. Let’s look at it again:

    Each box captures potential explanations that would be fairly obvious candidates to most instructors. For example, if students are spending a lot of time looking at the content but still scoring poorly on related test questions, some possible explanations are that (1) the teaching content is poorly designed, (2) assessment questions are poorly written, or (3) the concept is hard for students to learn. There may be other explanations as well. But just seeing the correlation that students are spending a lot of time on particular content are still doing poorly on particular related assessment learning questions leads the instructor and the content designer (who may or may not be the same person) to ask useful questions. And then there is some craft at the end about thinking through how to deal with the content that has been identified as potentially problematic.

    This isn’t magic. It’s not a robot tutor in the sky. In fact, it’s almost the antithesis. It’s so sensible that it verges on boring. It’s hygiene. Everybody who teaches with this kind of course design should regularly tune those courses in this way, as should everybody who builds courses that are designed this way. But that’s like saying everybody should brush their teeth at least twice a day. It’s not sexy.

    Also, easy to understand and easy to do are two different things. Even assuming that your curricular materials are designed this way and that you have sufficient rights to modify them, different courses live in different platforms. While you don’t need to get a lot of sophisticated data to do this analysis—just basic Google Analytics-style page usage and item-level assessment data—it will take a little bit of technical know-how, and the details will be different on each platform. Once you have the data, you will then need to be able to do a little statistical analysis. There isn’t much math in this paper and what little there is isn’t very complicated, but it is still math. Not everybody will feel comfortable with it.

    The typical way the sector has handled this problem has been to put pressure on vendors as consumers to add this capability as a feature to their products. But that process is slow and uncertain. Worse, each vendor will likely implement the feature slightly differently and non-transparently, which creates a greater challenge for the last point of friction. Features like this require a little bit of literacy to use well. Everybody knows the mantra “correlation is not causation,” but it is better thought of as the closest thing that Western scientific thinking can get to Zen koan. ((Outside of quantum mechanics, at least.)) If you think you’ve plumbed the depths of meaning of that phrase, then you probably haven’t. If we want educators to understand both the value and the limitations of working with data, then they need to have absolute clarity and consistency regarding what those analytics widgets are telling them. Having ten widgets in different platforms telling them almost but not quite the same things in ways that are hard to differentiate will do more harm than good.

    And this is where we fail.

    While the world is off chasing robot tutors and self-driving cars, we are leaving many, many tools like RISE just lying on the floor, unused and largely unusable, for the simple reason that we have not taken the extra steps necessary to make them easy enough and intuitive enough for non-technical faculty to adopt. And by tools, I mean methods. This isn’t about technology. It’s about literacy. Why should we expect academics, of all people, to trust analytical methods that nobody has bothered to explain to them? They don’t need to understand how to do the math, but they do need to understand what the math is doing. And they need to trust that somebody that they trust is verifying that the math is doing what they think it is doing. They need to know that peer review is at work, even if they are not active participants in it.

    Making RISE shine

    This is where CMU’s contribution and EEP can help. LearnSphere is the particular portion of the CMU contribution into which RISE will be integrated. I use the word “portion” because LearnSphere itself is a composite project consisting of a few different components that CMU collectively describes as “a community data infrastructure to support learning improvement online.” I might alternatively describe it as a cloud-based educational research collaboration platform. It is probably best known for its DataShop component, which is designed to share research learning research data sets.

    One of the more recent but extremely interesting additions to LearnSphere is called Tigris, which provides a separate research workflow layer. Suppose that you wanted to run a RISE analysis on your course data, in whatever platform it happens to be in. Lumen Learning is contributing the statistical programming package for RISE that will be imported into Tigris. If you happen to be statistically fluent, you can open up that package and inspect it. If you aren’t technical, don’t worry. You’ll be able to grab the workflow using drag-and-drop, import your data, and see the results.

    Again, this kind of contribution was possible before CMU decided to make its open source contribution and before EEP existed. They have been cloud hosting LearnSphere for collaborative research use for some time now.

    But now they also have an ecosystem.

    By contributing so much under open license, along with the major accompanying effort to make that contribution ready for public consumption, CMU is making massive declaration to the world about their seriousness regarding research collaboration. It is a magnet. Now Lumen Learning’s contribution isn’t simply an isolated event. It is an early leader with more to come. Expect more vendors to contribute algorithms and to announce data export compatibility. Expect universities to begin adopting LearnSphere, either via CMU’s hosted instance or their own instance, made possible the full stack being released under an open source license. This will start with the group that will gather at the EEP summit at CMU on May 6th and 7th, because one has to start somewhere. That is the pilot group. But it will grow. (And LearnSphere is only part of CMU’s total contribution.)

    With this kind of an ecosystem, we can create an environment in which practically useful innovations can spread much more quickly (and cheaply) which vendors regardless of size or marketing budget can be rewarded in the marketplace based on their willingness to make practical contributions of educational tools and methods that can be useful to customers and non-customers alike. Lumen Learning has made a contribution with the RISE research. They now want to make a further contribution to make that research more practically useful to customers and non-customers alike. CMU’s contributed infrastructure and the EEP network will give us an opportunity reward that kind of behavior with credit and attention.

    That is the kind of world I want to live in.

  • EEP, EDwhy, and Seeds

    So the news broke today about the Empirical Educator Project’s (EEP’s) year two experimental design, which we’re calling EDwhy. The “ED” stands for Educational Design,” so the full name means, basically, “Why is your educational design the way that it is?” It invites educators to interrogate their own designs and aspires to give them the tools to do so. Here is the press release.

    We have some good coverage to start you off from Inside Higher Ed and EdSurge. At IHE, Lindsay McKenzie goes broad. She starts with some good shoe leather work at Carnegie Mellon with some interviews. Pay close attention to the interview with Ken Koedinger, as he talks about (but does not name) a research finding called the doer effect, which I’m going to use as an example later in this blog post. She also provides a good refresher of the open source versus proprietary question that universities often face with substantial software intellectual property that they develop, and then touches lightly on EEP’s role with the EDwhy announcement at the end (although with a clutch statement from Duke’s Matthew Rascoff, who always seems to say the right thing with a lot of intellectual and moral clarity in very few words). If you’re looking to find a way into this story from the beginning in a compact way, Linday’s story one good route in.

    Meanwhile, Jeff Young at EdSurge has dug a little deeper into significance behind the EDwhy idea and mechanics. I think the question that is on everyone’s minds is, “OK, $100 million dollars, lots of software, cool learning science-y things, but really, how is this going to be made useful?” Jeff begins to explore that question, and I’m going to take a deeper dive in this post. He also has some commentary from me about why we chose the name we did. You’ll have to go read it on EdSurge to get those details, but I’ll say this much here: On e-Literate, where one of our major roles is to critique hype and protect against the dangers of  bad actors, we have an ethical obligation to throw some sharp elbows. With EEP, where we are not watching from the sidelines but actually entering the fray, we are mindful that our obligation shifts as our role shifts. We take the e-Literate lessons to heart while also attempting to be humble both about the accomplishments of those before us and how easy it is for us to fall into the same traps that very smart people before us have fallen victim to.

    But I don’t want to write about the naming decision too much here. Instead, I want to write about how we are going to attempt to live up to the humbling confidence that Carnegie Mellon expressed in us when they chose us as a partner in their grand project. Obviously, when they offered to make their enormous contribution through our fledgling organization, it both forced and empowered us to rethink how we would go about the project in Year 2. We had always planned to stop, evaluate, and iterate on the design after the first year, but this opportunity demanded a pretty dramatic rethink in approach which, to be honest, is still ongoing. We have an idea that I’m going to share with you now that I believe makes sense in concept but does not yet have a fine-grained implementation plan. We are working hard with our Carnegie Mellon friends to have a foundation in place by the time of the summit. We will also workshop the idea at the summit with the cohort to refine our approach. This is going to be a year-long project. So we expect to spend some time after the summit continuing to put pieces in place and fine-tuning as we go. At the end of the year, we will do a progress check, evaluate, and iterate.

    The Hackathon

    I am always mindful about appropriating terms from Silicon Valley culture because I think it tends to be reflexively idealized. That said, there is a lot to like about the educational value of a hackathon. It is a social, time-bounded, self-organizing, problem-based learning exercise. A group of people will get together to solve a defined problem over a period of time. That group is often cross-functional. They might have software engineers, user experience designers, end users, and so on. Hackathons have a tangible and several intangible goals. The tangible goal in the canonical case is a piece of software, but we can think of it more broadly as an artifact that has been tested and demonstrated to solve the problem that was the goal set out at the beginning of the exercise. The intangible goals often include learning how to work in a cross-functional team, learning how to solve difficult problems with unexpected wrinkles, and learning particular craft-related skills necessary to solve the problem (e.g., programming tricks or software testing techniques).

    This is a good model for the kind of culture building that EEP has always aspired to achieve and, I believe that inspired Carnegie Mellon to see us as a good fit for their own ambitions. While I want to be clear that I do not speak for them, my understanding of their goals from our conversations thus far is that it would be a mistake to interpret their primary goal to be broader adoption of their software and other tools. Sure, they want to see that happen. But my read is that they see that as a second-order effect, or maybe as means to an end. What I hear from them in our conversations is that they really want to make their approach to improving education broadly accessible and meaningfully useful. They call that approach “learning engineering,” which they seem comfortable with me characterizing as one flavor or methodology within a broader developing family that we call “empirical education.” The hackathon works to support this goal because it creates an environment in which people habitually self-organize in cross-functional groups to improve educational design in ways that empower greater student success. It brings together the right people around the right kinds of goals and conversations. If we can then empower them with the right tools and methods, we are on your way to promoting learning engineering. If we can achieve that,  we can unlock the real power of the big release, which is to help democratize the science of education.

    While I said I didn’t want to dwell on our name choice here, it’s probably worth spending a little time on the word “design” in the way we are using it in EDwhy. A number of different overlapping but distinct stakeholder groups in academia tend to compete for mindshare around this word—Design Thinking practitioners, Instructional Designers, Learning Designers, User Experience Designers, and others. Making sense of how these all connect yet are distinct from each other is non-obvious even before we get to culturally local differences in usage. To give one example, Herb Simon, in addition to being the father of Learning Engineering, is considered by some to be the grandfather of Design Thinking. These are two compatible but distinct and non-interchangeable disciplines. In most places outside of Carnegie Mellon, their practitioners tend to be either completely ignorant of each other or find themselves cast as rivals in educational solution design.

    “Design” in the EDwhy context is a holistic and colloquial term meaning, simply, the way you decided to put something together. A cross-functional EDwhy hackathon team might include people with knowledge of Design Thinking, Instructional Design, Learning Design, User Experience Design, and/or Learning Engineering. Who is at the table will depend on the specific nature of the challenge being tackled and the kinds of expertise needed to take it on.

    At any rate, as we started thinking about how to help our network digest Carnegie Mellon’s $100 million contribution—never mind the sum of all possible contributions from all current and future EEP participants—we started thinking about both the digestive process and coming up with a form that is digestible. Verbs and nouns.

    The hackathon is the verb. Theoretically, the hackathon is flexible enough to allow for projects of different sizes and ambitions, whether inter- or intra-institutional. We still very much want to encourage inter-institutional collaboration, but one lesson we learned last year is that inter-institutional collaboration is incredibly hard, even with a lot of work done by third parties to lower barriers. We have to build a gentle slope toward that level of collaboration. The hackathon is a form that lets people start small and grow in ambition. At some point, they will outgrow the form and need to form something more like a traditional project with more formal management structures.

    We aspire to reach the point where we have that problem. For now, we are focused on culture-building, and we hypothesize that the hackathon is a good ritual for accomplishing that while also delivering immediate educational utility.

    The Seeds

    The hackathon idea is simple enough to grasp in the abstract. The hard part is putting it together with the right packages that help people identify and solve new problems using the contributions from Carnegie Mellon or other participants. For this, we’ve developed the concept of an EDwhy “seed.” This is one of the pieces I will want to workshop with the EEP cohort, but there’s enough here conceptually that the general idea should be clear.

    We start with a general area of interest where some research has been done but where there are more questions to be answered. For example (and as I mentioned earlier, Ken Koedinger and his CMU colleagues have done some research into something called “the doer effect.” It means pretty much what it sounds like. The researchers were able to demonstrate, using solid, quantitative methods that learning by doing is, for example, about six times more effective than learning by watching a video.

    (Side note for all you liberal arts folks out there who are suspicious of this data stuff: This study more or less just made the case for constructivism. Using numbers and computers and statistics and stuff.)

    That’s an interesting finding, if not a shocking one, but it also highlights a lot that we don’t know. For example, is doing always better than watching a video (or reading) for learning? Should we throw out all books and videos? If not, then how much watching or reading is good? In what order? Does the subject matter make a difference? The expertise of the learner? Other characteristics of the learner? Other characteristics of the overall course design? Or course goals?

    Let’s make this more concrete. One of my favorite course designs is Habitable Worlds by ASU’s Ariel Anbar. There is a lot of learning by doing in that problem-based course, but also liberal use of video. It would be interesting to do some testing and experimentation to find out how to make the most out of the doer effect and find the optimal balance of the course elements.

    As it turns out, Carnegie Mellon’s contributions include the software that was used to conduct the original doer effect research. (The IHE article mentions LearnSphere. Spend a little time exploring that site if you’re curious.) That software includes a data repository with access to (appropriately anonymized) data that could be used to replicate the results (or try to run different analyses on the data), a visual workflow that makes the study easily repeatable with different data, and access to the underlying R packages (for those who can understand them) to make the research methods completely transparent. If you put together the original studies, the software, the workflows, the data to practice reproducing the results, the transparency of the methods, and wrap in some documentation, some training, and a number of suggested starter questions for investigation, you have a seed. A self-organizing community could take up that seed and develop a hackathon project. If there were also a community forum where the hackathon group could ask questions of statisticians, cognitive psychologists, and psychometricians, as well as some technical support folks, as well as share lessons learned with each other, then you could really have something.

    I’m guessing the net result might turn out to be what would call an “intermediate” seed. Not every team would have the capability to self-organize around something this complex. We’d like to develop beginner, intermediate, and advanced level seeds, where beginner seeds are approachable by non-technical groups, intermediate seeds might require some technical skill and some knowledge of experimental design, and advanced seeds are really for folks who have some serious specialist expertise in their groups. The I’ll defer on the final difficulty ratings of each seed, including the one I just described, to the creators and the early adopters. One skill set we will be learning in the EDwhy experiment is how to package up a seed to make it accessible and useful to different sorts of audiences. Eventually, we may develop profiles of hackathon teams that are richer than just beginner/intermediate/advanced.

    At any rate, our goal for the year is to prove out and refine the approach through some pilot seeds and hackathons. We don’t imagine that we will be able to address the entire surface area of Carnegie Mellon’s $100 Million contribution in the one-year time frame, but we do aspire to prove out a novel and sustainable support and diffusion mechanism, not only for the software but for the methods and the culture. And during this time, we will also invite other EEP members to develop and contribute their own seeds, some of which will be less technical or tackle entirely different types of educational problems than Carnegie Mellon’s seeds will. This is a general mechanism we will be trying out. Interestingly, another arrow that CMU has in its quiver is the Open Learning Initiative (OLI) authoring and delivery platforms. So we may very well find their contributions to seed development goes well beyond the open source software code, which I think is the way in which people are naturally tending to think about the contribution at this early stage in the process.

    Both learning and science—or any path to enlightenment, really—starts with a simple admission: “There is so much that I don’t know, and so much that I would like to understand better.” Big announcements like this generally run against the grain of that admission. We have an ingrained cultural notion that, after spending a $100 million, you are supposed know all the answers. After spending 7 years in graduate school, you are supposed to know all the answers. After getting all the press and all the buzz, you are supposed to know all the answers.

    Nope. Sorry. It doesn’t work that way.

    There is so much that we don’t know, and so much that we would like to understand better. If you keep repeating that mantra to yourself every time you hear something new about Carnegie Mellon’s contribution or about EEP or the EDwhy initiative, each new piece of information will make a lot more sense to you.