The difference between IT and Ed Tech

In a recent interview with John Jantsch for the Duct Tape Marketing podcast, Danny Iny argued that the difference between information and education essentially comes down to responsibility. Information is simply about presentation. Here are some things you might want to know. Whether and the extent to which you come to know them is entirely up to you.

In contrast, education implies that the one presenting information also takes on a degree of responsibility for ensuring that it is learned. Education is a relationship in which teachers and learners agree to share in the responsibility for the success of the learning experience.

This distinction, argues Iny, accounts for why books are so cheep and university is so expensive. Books merely present information, while universities take on an non-trivial amount of responsibility for what is learned, and how well.

(It is a shame that many teachers don’t appreciate this distinction, and their role as educators. I will admit that, when I was teaching, I didn’t fully grasp the extent of my responsibility for the success of my students. I wish I could go back and reteach those courses as an educator instead of as a mere informer.)

If we accept Iny’s distinction between information and education, what are the implications for what we today call educational technologies, or ‘Ed Tech’? As we look to the future of technology designed to meet specific needs of teachers and learners, is educational technology something that we wish to aspire to, or avoid?

Accepting Iny’s definition, I would contend that what we call educational technologies today are not really educational technologies at all. The reason is that neither they nor the vendors that maintain them take specific responsibility for the success or failure of the individual students they touch. Although vendors are quick to take credit for increased rates of student success, taking credit is not the same as taking responsibility. In higher education, the contract is between the student and the institution. If the student does not succeed, responsibility is shared between the two. No technology or ed tech vendor wants to be held accountable for the success of an individual student. In the absence of such a willingness or desire to accept a significant degree of responsibility for the success of particular individuals, what we have are not educational technologies, but rather information technologies designed for use in educational contexts. Like books…but more expensive.

With the advent of AI, however, we are beginning to see an increasing shift as technologies appear to take more and more responsibility for the learning process itself. Adaptive tutoring. Automated nudging. These approaches are designed to do more than present information. Instead, they are designed to promote learning itself. Should we consider these educational technologies? I think so. And yet they are not treated as such, because vendors in these areas are still unwilling (accountability is tricky) or unable (because of resistance from government and institutions) to accept responsibility for individual student outcomes. There is no culpability. That’s what teachers are for. In the absence of a willingness to carry the burden of responsibility for a student’s success, even these sophisticated approaches are still treated as information technologies, when they should actually be considered far more seriously.

As we look to the future, it does seem possible that the information technology platforms deployed in the context of education will, indeed, increasingly become and be considered full educational technologies. But this can only happen if vendors are willing to accept the kind of responsibility that comes with such a designation, and teachers are willing to share responsibility with technologies capable of automating them out of a job. This possible future state of educational technology may or may not be inevitable. It also may or may not be desirable.


RESOURCES

This Week in Learning Analytics (November 1 – 7, 2014)

The Report to the European Commission on New Modes of Learning and Teaching in Higher Education recommends the fill and informed consent of all students who lend their data for the sake of educational purposes. (Image Source: Report to the European Commission on New Modes of Learning in Higher Education)

The Report to the European Commission on New Modes of Learning and Teaching in Higher Education recommends the fill and informed consent of all students who lend their data for the sake of educational purposes. (Image Source: Report to the European Commission on New Modes of Learning in Higher Education)

Latest News

7 November 2014
Microsoft and Other Firms Pledge to Protect Student Data
Fourteen companies, including Microsoft and Mifflin Harcourt, Amplify, and Edmodo, have pledged to adopt nationwide policies that will restrict and protect data collected from K-12 students. The group in pledging not to (1) sell student information, (2) target students with advertisements, or (3) compile personal student profiles unless authorized by parents or schools. The pledge, which is not legally binding, was developed by the Future of Privacy Forum.

6 November 2014
Lecturer Calls for Clarity in Use of Learning Analytics
Sharon Slade (Open University) talks about her university’s effort to develop and ethical policy on the use of student data, that attempts to carefully address conflicting student concerns: (1) concerns about institutional ‘snooping’ on the one hand, and (2) an interest in personalized modes of communication. The Ethical Use of Student Data for Learning Analytics Policy produced at the Open University is the first of its kind, and the result of an exemplary effort that should be repeated widely.

6 November 2014
Echo360 Appoints Dr. Bradley S. Fordham as Global Chief Technology Officer
Echo360, an active learning and lecture capture platform, has appointed Dr. Fordham as Global Technology Officer. With a wealth of industry and scholarly experience, Dr. Fordham will add significant expertise, legitimacy, and exposure to the platform. The this is the latest in a series of recent investments in developing the platform’s real-time analytics capabilities, which until recently, have been rather limited and unsophisticated.

6 November 2014
Harvard Researchers Used Secret Cameras to Study Attendance. Was That Unethical?
In the spring of 2013, cameras in 10 Harvard classrooms recorded one image per minute, and the photographs were scanned to determine which seats were filled. The study rankled computer-science professor, Harry R. Lewis, who viewed the exercise as an obvious intrusion into student privacy. George Siemens notes that attendance data is the ‘lowest of the low,’ and notes that the level of surveillance taking place in online courses far exceeds what was collected as part of the attendance-tracking exercise. Since Lewis raised his concerns, Harvard has committed itself to reaching out to every faculty member and student whose image may have been captured to inform them of the research, a not-so-easy effort, as images were captured anonymously and have subsequently been destroyed as part of the research methodology.

5 November 2014
Disadvantages Students in Georgia District Get Home Internet Service
Fayette County Schools in Georgia have partnered with Kajeet to give Title 1 students a Kajeet SmartSpot so that they can access online textbooks, apps, email, documents, sites, and their teachers while outside of school. The mobile hotspot works with the Kajeet cloud service and allows districts and schools to restrict access according to site- and time- base rules. The service also monitors student activity and provides teachers and administrators with learning analytics reports.

1 November 2014
Track Your Child’s Development Easily
In May 2011, Jayashankar Balaraman — a serial entrepreneur with a background in advertising and marketing — moved into the education space with the launch of KNEWCLEUS, which in just three years has grown to become India’s largest parent-school engagement platform. The platform’s success is a result of the ease with which it makes parent-teacher communication, and the analytics engine that monitors student performance, identifies areas in need of remediation, and recommends relevant content.

Latest Blogs

Does Exercise (and Learning) Count If Not Counted? by Joshua Kim
Kim asks the age-old question, “If I exercise and my fitness app does not record my steps, did my exercise ever happen?” He wonders about how the ability to track certain forms of activity, including learning activity, ends up altering behavior and shifting values on the basis of ‘trackability.’ The danger here, cautions Kim, is that we may come to conflate good teaching with digital practices that are more amenable to datafication.

Report on Modernisation of Higher Education: Focus on Open Access and Learning Analytics by Brian Kelly
A brief summary and review of the Report to the European Commission on New Modes of LEarning and Teaching in Higher Education, delivered in October 2014 by the High Level Group on the Modernisation of Higher Education. The report makes explicit mention of learning analytics, recommending collaboration over competition, and an increase in personalized learning informed by better data. The report’s advocacy of ‘better data’ includes strong ethical considerations, including the full and informed consent of students and the ability to ‘opt-out.’

10 Hottest Technologies in Higher Education by Vala Afshar
Afshar summarizes the hottest technologies discussed by CIOs at the 2014 Annual EDUCUASE conference last month. Included in the list are wifi, social media, badges, analytics, wearables, drones, 3D printing, digital courseware, Small Private Online Courses (SPOCs), and virtual reality. Although analytics is included as one of many trends, it of course is also a major driver for each of these technologies as well.

Schools keep track of students’ online behavior, but do parents even know? by Taylor Armerding
A truly exceptional review of literature and debates surrounding the collection and use of data from K-12 students. What kinds of data are a school’s ‘business’ to collect? How does an institution ensure informed consent, when privacy policies are often so complex as to be inaccessible by many parents? What is a school’s responsibility if it discovers something with implications for student success? Are schools ‘grooming kids for a lifetime of surveillance?’

Should Google Be a Signatory to Student Privacy Pledge? by Tracy Mitrano
Mitrano asks why the K-12 School Service Provider Pledge to Safeguard Student Privacy is not being more strongly considered in higher education, and asks why Google and Amazon have not publicly committed themselves to the pledge alongside Microsoft, Houghton Mifflin Harcourt, Knewton, and others.

Why I’m Voting ‘Yes’ on the Smart Schools Bond Act, Proposition 3 by Leonie Haimson
New York Proposition 3 (also known as the Smart Schools Bond Act) would allow the sale of bonds top generate $2 billion statewide for capital funding. In spite of her resistance to using bond revenue to purchase electronic devices in schools (one of the key ways in which the bond revenues are meant to be spent), Haimson notes the urgent need that many schools have for an injection of funding, and notes that the finds may be spent in a wide variety of ways. She raises a concern about the proliferation of technolgies driven by companies interested in educational data mining, but notes that, thanks to the Children’s Online Privacy Protection Act, all parents have the right to opt out of any online data-mining instructional or testing program that collects personal data, whether their children participate in this program at school or home.

Featured Videos, Presentations & Webinars

Carolyn Rosé: Learning analytics and educational data mining in learning discourses
Talk delivered to the International Society of the learning Sciences Network of Academic Programs in the LEarning Sciences (NAPLES). Click HERE for full Webinar Recording.

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Recent Publications

Embracing Big Data in Complex Educational Systems: The Learning Analytics Imperative and the Policy Challenge
Leah MacFadyen, Shane Dawson, Abelardo Pardo, Dragan Gašević

In the new era of big educational data, learning analytics (LA) offer the possibility of implementing real–time assessment and feedback systems and processes at scale that are focused on improvement of learning, development of self–regulated learning skills, and student success. However, to realize this promise, the necessary shifts in the culture, technological infrastructure, and teaching practices of higher education, from assessment–for–accountability to assessment–for–learning, cannot be achieved through piecemeal implementation of new tools. We propose here that the challenge of successful institutional change for learning analytics implementation is a wicked problem that calls for new adaptive forms of leadership, collaboration, policy development and strategic planning. Higher education institutions are best viewed as complex systems underpinned by policy, and we introduce two policy and planning frameworks developed for complex systems that may offer institutional teams practical guidance in their project of optimizing their educational systems with learning analytics.

Learning Analytics: Challenges and Future Research Directions
Mohamed Amine Chatti, Vlatko Lukarov, Hendrik Thüs, Arham Muslim, Ahmed Mohamed Fahmy Yousef, Usman Wahid, Christoph Greven, Arnab Chakrabarti, Ulrik Schroeder

In recent years, learning analytics (LA) has attracted a great deal of attention in technology-enhanced learning (TEL) research as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future TEL landscape. Generally, LA deals with the development of methods that harness educational data sets to support the learning process. This paper provides a foundation for future research in LA. It provides a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). It further identifies various challenges and research opportunities in the area of LA in relation to each dimension.

Calls for Papers / Participation

Conferences

2015 Southeast Educational Data Symposium (SEEDS) Emory University (Atlanta, GA) | 20 Feb 2015 (APPLICATION DEADLINE: 14 November 2014)

11th International Conference on Computer Supported Collaborative
Learning: “Exploring the material conditions of learning: Opportunities and
challenges for CSCL”
 
University of Gothenburg, Sweden | 7 – 11 June 2015 (SUBMISSION DEADLINE: 17 November 2014)

28th annual Florida AI Research Symposium (FLAIRS-28) on Intelligent Learning Technologies Hollywood, Florida, USA (SUBMISSION DEADLINE: 17 November 2014)

EDM 2015: 8th International Conference on Education Data Mining Madrid, Spain | 26 – 29 June, 2015 (SUBMISSION DEADLINE: 12 January 2014)

Journals / Book Chapters

Universities and Knowledge Society Journal (RUSC): Special Section on Learning Analytics (SUBMISSION DEADLINE: 20 January 2015)

Employment Opportunities

Simon Fraser University (Victoria, BC, Canada)
Tenure Track Position In Educational Technology And Learning Design – The Faculty of Education, Simon Fraser University (http://www.sfu.ca/education.html) seeks applications for a tenure-track position in Educational Technology and Learning Design at the Assistant Professor rank beginning September 1, 2015, or earlier. The successful candidate will join an existing complement of faculty engaged in Educational Technology and Learning Design, and will contribute to teaching and graduate student supervision in our vibrant Masters program at our Surrey campus and PhD program at our Burnaby campus. DEADLINE FOR APPLICATION: December 1, 2014

NEW! University at Buffalo (Buffalo, NY, USA)
Associate for Institutional Research/Research Scientist: Online Learning Analytics – The University at Buffalo (UB), State University of New York seeks a scholar in online learning analytics to join its newly formed Center for Educational Innovation. Reporting to the Senior Vice-Provost for Academic Affairs, the Center for Educational Innovation has a mission to support and guide the campus on issues related to teaching, learning and assessment, and at the same time serves as a nexus for campus-wide efforts to further elevate the scholarship of and research support for pedagogical advancement and improved learning. The Research Scientist in online learning analytics will work in the area of Online Learning within the department and join a campus-wide network of faculty and researchers working on “big data”. DEADLINE FOR APPLICATION: December 6, 2014

NEW! University of Boulder Colorado (Boulder, Colorado, USA)
Multiple Tenure Track Positions in Computer Science – The openings are targeted at the level of Assistant Professor, although exceptional candidates at higher ranks may be considered. Research areas of particular interest include secure and reliable software systems, numerical optimization and high-performance scientific computing, and network science and machine learning. DEADLINE FOR APPLICATION: Posted Until Filled

University of Technology, Sydney (Sydney, AUS)
Postdoctoral Research Fellow: Academic Writing Analytics – Postdoctoral research position specialising in the use of language technologies to provide learning analytics on the quality of student writing, across diverse levels, genres and domains DEADLINE FOR APPLICATION: Posted Until Filled

University of Michigan (Ann Arbor, MI)
Senior Digital Media Specialist – The University of Michigan is seeking a qualified Senior Digital Media Specialist to create digital content in support of online and residential educational experiences for the Office of Digital Education & Innovation (DEI). DEADLINE FOR APPLICATION: Posted Until Filled

NYU Steinhardt School of Culture, Education,and Human Developments Center for Research on Higher Education Outcomes (USA)
12-month postdoctoral position – available for a qualified and creative individual with interests in postsecondary assessment, learning analytics, data management, and institutional research.The Postdoctoral Fellow will be responsible for promoting the use of institutional data sources and data systems for the purpose of developing institutional assessment tools that can inform decision making and contribute to institutional improvement across New York University (NYU). DEADLINE FOR APPLICATION: Open Until Filled

Echo360 Analytics: Issues and Limitations

I have recently been working with the classroom technologies group at Emory University to understand and deliver information about about Echo360 Active Learning Platform use, in order to improve service delivery and support best practices on campus. In the course of my work to date, however, several issues and limitations have come to light which I think are valuable to share with the Echo360 user base, and also with the learning analytics community in general.

Limitations

Echo360’s reporting and analytic capabilities (as of Echo System Server version 5.4.15.14.41133) are weak. Currently, administrative reporting functionality is limited to one of five pdf reports, filtered either by date or course / section:

  • Views by Presentation
  • Views by Product
  • Quota by Section
  • Views by Time of Day
  • Views by Week of Year

It is unfortunate that administrators are not given the option to export data in tabulated formats (i.e. csv or excel), as this inability makes trending rather difficult. The greatest issue for Echo360 administrators at Emory University, however, is the fact that, while view counts by presentation, product, section, time of day, and week of year are interesting in a way (our current service model doesn’t involve using section quotas), they are not particularly helpful or actionable. Information that is necessary from both the perspective of day-to-day operations and that of continual service improvement, however, is the following:

  • Total Capture Uploads by Month (presentation counts and storage use)
  • Cumulative Uploads by Month (presentation counts and storage use)
  • Number of Active Captures by Month
  • Total Capture Accesses by Month
  • Cumulative Monthly Captures by Organization
  • Cumulative Monthly Storage by Organization

In order to deliver this information to stakeholders, I have written queries which I run against our production database in order to generate custom reports which are then delivered to stakeholders on a monthly basis (I am happy to share these queries with whomever might be interested). A lack of schema documentation made this task somewhat challenging at first, but some timely assistance from Michael Fardon at Curtain University quickly shed some very appreciated light on how to effectively navigate the ess database. I recently met with a group of university stakeholders, who expressed gratitude at the level of intelligence that I have been able to provide outside of the standard level of reporting that is otherwise available to Echo360 administrators.

Issues

Echo Center Statistics: Usage Heat Map

Analytics are actionable only to the extent that they are meaningful. In documentation prepared both by Echo360 and at other institutions, the Usage Heat Map feature is praised for its ability to “show specific points in a presentation or lecture that get the most student reviews, so you can easily identify and address these areas of confusion well before exam time” (http://echo360.com/how-it-works/analytics). Unfortunately, it is impossible to find any documentation describing exactly how presentation activity levels are determined. How much activity is necessary to produce a ‘hot spot’ versus a ‘warm spot’? What does ‘Minimal’ activity mean, exactly? In the absence of specifics about how presentation ‘heat’ is calculated, the heat map may be used heuristically, but should also be interpreted with extreme caution, particularly in cases where the total number of unique viewers is low.

Echo360 Statistics - Usage Heat Map

Echo Center Reports: Student Aggregate

In addition to their ability to view high-level presentation activity parameters, instructors also have the ability to generate a couple of different reports. The Student Aggregate report provides a list of all students with access to presentations in a particular course. For each student, an instructor is provided with the following information:

Echo 360 Reports - Student Aggregate

  • Unique Views – the number of different Echoes the student has viewed
  • Cumulative Views – the total number of Echoes the student has viewed
  • Completion – average completion rate for all Echoes viewed (a proxy for engagement)
  • Bookmarks – number of bookmarks posted for all Echoes
  • Discussions – number of discussion topics posted for all Echoes
  • Downloads – shows if student downloads an Echo rather than streaming it (interactions are not collected for downloaded Echo content)
  • Live Views – number of times a student attended a live webcast rather than attend in class
  • Last Viewed – most recent Echo viewed by the student
  • Date Viewed – date a student last viewed any Echo

Student-level analytics are here meant to provide an instructor with information about student activity (presumably as a proxy for engagement which, in turn, is tacitly assumed to be a predictor of success) in a way that allows that instructor to make comparisons and intervene where low levels of engagement are identified. A major issue in this report’s design, however, is that it reports on aggregate activity for all presentations in all courses in which the student is, or has ever been, enrolled. Since student course enrollments differ radically, and since viewing requirements between courses differ wildly, cross-student comparisons are actually impossible. At best, therefore, this report is useless and, at worst, misleading. It would be better to either include activity that only pertains to the specific course, or else jettison this report entirely.

Dancing in the Classroom (or, What Teachers can Learn from Jack White)

White Stripes Dancing

In a recent interview with Rolling Stone Magazine, Musician Jack White showed his cranky side while commenting about the current state of live music:

“People can’t clap anymore, because they’ve got a fucking texting thing in their fucking hand, and probably a drink, too!” he says. “Some musicians don’t care about this stuff, but I let the crowd tell me what to do. There’s no set list. I’m not just saying the same things I said in Cleveland last night. If they can’t give me that energy back? Maybe I’m wasting my time.”

If concert-goers who voluntarily part with $300 for prime tickets for one of the most engaging musicians/showmen touring today, an artist who makes an active effort at every show to actively engage their audience, is it any wonder to find students voluntarily parting with tens of thousands of dollars a year only to text and facebook their way through classes taken with even the most elite and engaging of university professors?

When it comes to university teaching, I am most often inclined to say that student engagement is the teacher’s responsibility. Students don’t know any better. They are the product of socialization processes driven by media experience and smart phone notifications. Viewed as an orator, it is the instructor’s duty to take the student where they are, to understand their knowledge-state, values, and interests, and entice them to enter into an experience of knowledge that is otherwise foreign, and even ‘boring.’ I hesitate to blame students for not taking responsibility for their learning, since it is exactly this kind of responsibility that is a key outcome of higher education. There is a sense, however, in which students ARE to blame for their lack of engagement. A lack of attention in the classroom is not necessarily a function of an unengaging teacher, or even of an unengaged student, but rather of the fact that students are making the choice to be engaged by media, content, and interests that are familiar and elsewhere rather than unfamiliar and present.

What’s the solution? According to Ryan Bort, the key to becoming truly engaged in and by the concert experience is to dance:

But say we are willing. What’s is the best way to stave off this inevitable boredom and really engage with what we’ve dedicated our night to come see? How do we reclaim the live experience for what it’s worth? It’s really simple, actually: Dance. Give in to that impulse. Don’t be scared. Go ahead and channel a little Bowie. I’m looking at you, stoic guy with the blank expression and girl who can’t see over the person in front of you. If you dance — and I’m not talking about timid, mindless knee bobbing — all of the encumbrances of the structured venue show will rescind into the periphery and you will enjoy yourself and the music in the realest way possible. A new world will reveal itself and you will be free. So next time dance and dance like you mean it, or keep dancing if that may be the case. In fact, it even works particularly well when the music is live and you’re surrounded by other people in a confined space.

What would dancing in the classroom look like? How can we, as educators, encourage learners to “go ahead and channel a little Bowie”? Are there inhibitions that we need to work with our students to overcome, inhibitions that ‘smart’ technologies serve to foster? How do we move learners to become fully embodied within a learning environment, to be fully present and, in so doing, to abandon themselves within the confined space of the classroom?

Educational Technology is not a Rotisserie Oven

Ed-Tech is not a rotisserie oven

An important and fruitful area of discussion in learning analytics involves the use of embedded student dashboards, which are most commonly sold and promoted as tools for leveraging peer pressure to increase student success (like UMBC’s Check My Activity Tool). In my experience with a similar tool over the last year however, it has become abundantly clear that not all students respond to analytics in the same way. In fact, in two separate classes, instructors who piloted the tool found otherwise high-performing students see decreases in academic performance as a consequence of a kind of ‘gaming’ behavior (not intentional, but a consequence of confusing proxies — ie. Course accesses, minutes in course, interactions, etc — with learning outcomes). Others have observed similar negative results on the part of poor performers, who see a decrease in motivation following an ‘objective’ display of their performance relative to peers. This doesn’t involve learning styles, but does point to the fact that students differ and in such a way that we can’t expect them all to react the same in common learning environments. The task of the teacher, then, would seem to involve communicative strategies that would mitigate damaging effects while enhancing positive ones. The worst thing an instructor can do with any educational technology is to “set it and forget it,” expecting that it will achieve some glorious effect without the need for support from good pedagogy and good teaching.

In other words, Educational technology is not a rotisserie oven.