e-Literate

Present is Prologue

Category: Ed Tech

The “Ed Tech” category includes posts about educational technology products themselves, including LMSs and other learning platforms, adaptive learning and other digital curricular materials products, learning analytics, and educational apps of all types. It also includes technical aspects of ed tech products, especially interoperability.

  • Asahi Net International Acquires the Sakai Division of rSmart

    Today Asahi Net International acquired the Sakai Division of rSmart. rSmart CEO Chris Coppola will join the Ashai Net International Board creating interlocking boards. The financial arrangements are not known.

    rSmart is a well known contributor to Apereo Inc.’s Sakai learning management system and to the Kuali suite of administrative software applications. rSmart has enhanced, implemented, and supported Sakai. It has also implemented the Kuali Financial System for colleges and universities.

    This reorganization of effort may represent the changes in higher education: The relentless promotion of online learning and demand for more productive administrative systems is being advocated as a solution to the rising cost of higher education in developed countries. (more…)

  • Six Ways the edX Announcement Gets Automated Essay Grading Wrong

    Six Ways the edX Announcement Gets Automated Essay Grading Wrong

    Last week, edX made a splashy spectacle of an announcement about automated essay grading, leaving educators fuming. Let’s rethink their claims. 

    “Give Professors a break,” the New York Times suggested in this joint press release from edX, Harvard, and MIT. The breathless story weaves a tale of robo-professors taking over the grading process, leaving professors free to kick back their feet and take a nap, and subsequently inviting universities, ever-focused on the bottom-line, to fire all the professors. If I had set out to write an article intentionally provoking fear, uncertainty, and doubt in the minds of teachers and writers, I don’t think I could have done any better than this piece.

    Anyone who’s seen their claims published in science journalism knows that the popular claims bear only the foggiest resemblance to academic results. It’s unclear to me whether the misunderstanding is due to edX intentionally overselling their product for publicity, or if something got lost in translation while writing the story. Whatever the cause, the story was cocksure and forceful about auto-scoring’s role in shaping the future of education.

    I was a participant in last year’s ASAP competition, which served as a benchmark for the industry; the primary result of this, aside from convincing me to found LightSIDE Labs, is that I get email; a lot of email. I’ve been told that automated essay grading is both the salvation of education and the downfall of modern society. Naturally, I have strong opinions about that, based both on my experience with developing the technology and participating in the contest, as well as in the conversations I’ve had since then.

    Before we resign ourselves to burning the AI researchers at the stake, let’s step back for a minute and think about what the technology actually does. Below, I’ve tried to correct the most common fallacies I’ve seen coming both from articles like the edX piece, as well as the incendiary commentary that it provokes. (more…)

  • Insight on MOOC student types from ELI Focus Session

    Michael and I had the privilege of leading off the ELI Online Spring Focus Session on MOOCs (taking place today, Apr 3, and tomorrow). Thanks to Stephen Downes, we have a good set of notes on our presentation “Everything You Thought You Knew About MOOCs Could Be Wrong” (program & resources, notes) as well as the other sessions already on each of the sessions on his blog. You know, I think he actually believes this stuff about learner-generated content and self-forming communities . . .  In all seriousness, this is a great session by ELI, great notes by Stephen, and it’s instructive to see the various channels such as chat, Twitter, Google+, and blogs tied to the session (most using #elifocus).

    There was an interesting presentation from Seth Anderson (Duke), Amy Collier (Stanford), Cassandra Horlii (California Institute of Technology) titled “Designing and Implementing MOOCs that Maximize Student Learning” (program & resources, notes) that gives additional insight into MOOC student types. Quoting from Stephen’s notes:

    In most of the MOOCs Stanford has offered, fewer than half the students have come from North America. Nearly half the people in a MOOC may not have a knowledge of English as a first language. They have a more varied educational and cultural background than in traditional courses.

    The majority of active users say they’re taking the course for fun or a challenge, rather than a credential. The tendency to judge MOOCs based on completion rates overlooks the reasons why people join a MOOC. The majority engage in sampling behaviour – like the people in this webinar. Many are MOOC auditors, but they don’t engage, and they aren’t motivated by completion records.

    This is an important aspect of MOOCs and open courses in general that gets missed by popular media and even by some MOOC faculty. The students have varying goals and behavior within a course, and it is a mistake to assume they should converge on a common goal and set of behaviors.

    (more…)

  • Upcoming Events for Phil Hill and Michael Feldstein

    Michael and I will be speaking at and attending a few events coming up in April.

    ELI Online Spring Focus Session – Michael and I will jointly kick off the EDUCAUSE Learning Initiative (ELI) Online Spring Focus Session “Learning and the MOOC” on April 3 – 4. Our presentation is April 3 from 12:15 – 12:50pm ET:

    Everything You Know About MOOCs Could Be Wrong  There are four things that we think we know about MOOCs: They are “massive,” they are “open,” they are “online,” and they are “courses.” But what happens if we start playing with those “truths”? How massive do MOOCs need to be? How open? Do they need to be fully online? Do they even need to be courses? As institutions determine whether and how to incorporate new online models into their repertoire, Phil Hill and Michael Feldstein will explore the boundaries of the current definition of the MOOC and discuss how it is likely to evolve in the next 12-24 months.

    In addition, we will be helping Veronica Diaz and Malcolm Brown with the closing session, April 4 from 4:40 – 5:00pm ET.

    Pearson Cite – I will physically be at the Pearson Cite conference, also April 3 – 4, and will present during the closing session April 4 from 4:45 – 5:45pm CT.

    Online Educational Delivery Models: What to Expect Next For almost two decades, the world of online education has been evolving. Entering 2013, we are presented with a confusing array of educational delivery models. Ranging from relatively traditional classrooms to fully-online courses, online education is being integrated into the college experience for almost one-third of students. Many of these models, such as Massive Open Online Classes (MOOCs), are geared towards accessibility and affordability. Other models focus on personalized learning styles, such as competency-based education. Traditional LMS providers and textbook publishers are also trying to find their places in this new world, promoting yet different models.

    American Association of Colleges of Nursing – I will be the keynote speaker for the AACN Hot Issues conference April 11 from 1:30 – 3:00pm CT.

    Online Educational Delivery Models: A Descriptive View Consider varied approaches to online courses and programs, including fully online programs, School-as-a-Service, educational partnerships, competency-based education, blended/hybrid courses, the flipped classroom, and Massive Open Online Courses (MOOCs).

    Education Innovation Summit – Michael will attend the GSV Advisors / ASU Education Innovation Summit April 15 – 17.

  • SUNY and the Expansion of Prior Learning Assessments

    Last week Steve Kolowich wrote in the Chronicle of Higher Education about the State University of New York (SUNY) system approval of a plan that would target reduced time-to-degree.

    The State University of New York’s Board of Trustees on Tuesday endorsed an ambitious vision for how SUNY might use prior-learning assessment, competency-based programs, and massive open online courses to help students finish their degrees in less time, for less money.

    The plan calls for “new and expanded online programs” that “include options for time-shortened degree completion.” In particular, the board proposed a huge expansion the prior-learning assessment programs offered by SUNY’s Empire State College.

    Although the MOOC angle led the article’s title and online discussion, there was more significant news in terms of structural change regarding Prior Learning Assessments (PLA).

    Even before the SUNY announcement, it had already been a big week for nontraditional models for awarding college credit. The U.S. Education Department on Monday said it had no problem with spending federal student aid on college programs that give credit based on “competency,” not the number of hours students spend in class.

    Empire State College’s prior-learning assessment programs operate on a similar principle. Students who can demonstrate that they have acquired certain skills can get college credit, even if they did not acquire those skills in a college classroom.

    The new SUNY effort will aim to copy the Empire State model across the system, said Nancy L. Zimpher, the chancellor.

    “This resolution opens the door to assurances to our students that this kind of prior-learning assessment will be available eventually on all our campuses,” said Ms. Zimpher in an interview.

    (more…)

  • If you like learning, could I recommend analytics?

    There’s something that drives me a little crazy when I hear about how someone has learned from Netflix, Amazon, Google, etc., and that they’re going to be the Netflix, Amazon, Movie Box or Google of education.  Actually there are a few things.

    There is a natural tendency to want to leverage the work of others.  In the burgeoning space of learning analytics many look to those who have large data sets and algorithms to extract some kind of meaning from those massive stores of information.  We should always be looking to leverage when we can!  This time, though, our takeaways are limited.

    (more…)

  • Blackboard’s New Early Warning Analytics Product

    A couple of weeks ago in my post about the different types of learning analytics, I described retention early warning systems thusly:

    Most people don’t think about early warning systems as being in the same category as adaptive analytics, but if you consider that “adaptive” really just means “adjusting to your personal needs,” then a system like Purdue’s Course Signals is, in fact, adaptive. It sees when a student is in danger of failing or dropping out and sends increasingly urgent and specific suggestions to that student. It does that without “knowing” anything about the content that the student is learning. Rather, it’s looking at things like recency of course login (Are you showing up for class?), discussion board posts (Are you participating in class?), on-time assignment delivery (Are you turning in your work?), and grade book scores (Is your work passing?), as well as longitudinal information that might indicate whether a student is at-risk coming into the class. What Purdue has found is that such a system can teach students metacognitive awareness of their progress and productive help-seeking behavior. It won’t help them learn the content better, but it will help them develop better learning skills.

    Well, last week, Ray Henderson announced Blackboard’s new Retention Center and described it as follows:

    The Retention Center gives critical insight on learning and activity gaps to instructors, within the LMS, that helps them quickly diagnose students that are falling behind. Pre-configured and automatic so they don’t have to hunt for it. No set-up: it automatically calls out students that are at risk while instructors still have time and space to do something about it. With the feature instructors can see:

    • Who’s logging in: this is a simple but powerful predictor of student success. Instructors see how long it’s been since students have logged in to the course and how many have been away for five days or more. And not by fishing through student profiles or reports but in an automatic view complete with red flags where they’re needed.
    • Whether they’re engaged: which students have had low levels of course activity, at 20 percent or below the average in the last week.
    • Whose grades are suffering: which students are currently trending at 25 percent or more below the course average so they can target extra help to where it’s most needed – even when it isn’t asked for.
    • Who has missed deadlines: instructors might know this anecdotally or on a case-by-case basis, but now they can get a real-time view of all students that have missed one or more deadline.

    Eerily similar, no? A number of years back, when I pressed Course Signals inventor John Campbell on which factors in the LMS are most highly predictive of student success across different courses, he named exactly these four. The only surprise here is that this isn’t a common analytics feature of every LMS and courseware platform on the market yet. Purdue proved that their value in helping at-risk students is high. I’m glad Blackboard is stepping up.

    The one piece that’s missing is a simple standard where an SIS or other longitudinal data system could pass an at-risk “credit score” to the early warning system to modify its sensitivity. If a student on the honor roll drops off the radar for a week, it’s less of a cause for concern that a student on academic probation (for example). I tried to push this idea for a standard at the IMS a few years back but got nowhere with it at the time. I hope that Blackboard will push for something like it now that they have a system to take the data.