Three Big Changes Ahead for Higher Education

The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking.”  Albert Einstein

A change of thinking is almost obligatory when considering the future of Higher Education. Last week was the final week of the MOOC Current/Future State of Higher Education (CHFE12); its overarching objective was to explore the influences and pressures facing universities today and to identify where higher education is headed. Numerous esteemed educators [including author and Georgia Tech’s  Richard Demillo and Vice Provost Joel Hartman of University of Central Florida] shared their knowledge, expertise, research, and in some cases predictions via webinars, to shed light on the conundrums within higher ed. The results are surprising, encouraging and telling of what educators need to do to adapt and be prepared.

In this post I’ll share my synopsis of the course by focusing on three areas of change. My aim is to share with readers the areas that will be most helpful and maybe even instructive in how-to adapt thinking and/or teaching. The three areas are: 1) the drivers of change in higher education, 2) change in pedagogical methods based upon progressive educators, and, 3) changes in educational models that are on the horizon for higher education. The latter point, is my prediction of where higher education is going based upon research and findings during this MOOC.  I’ve also included a list of resources at the end of this post, links to the recorded webinars and resources available from CHFE12.

1. Drivers of Change in Higher Ed
Change is constant, yet the rate of change in higher education is accelerating. The theme of ‘change’ was pervasive throughout the course; each webinar guest spoke of ‘change’ in some context. Though it was during the weeks’ topic of Leadership in Education that explored change in-depth. James Hilton, Chief information officer at the University of Virginia during the webinar he led, Thriving in Times of Disruption, described change as profound, and creating chaos and conflict. Yet at the same time, Hilton described change as an opportunity for institutions to find its North Star, meaning its strengths —to energize the institution by focusing on those identified.

The first step in adapting and thriving in a period of chaos is to objectively and carefully examine the drivers of change that are affecting a given sector. In our case education, we can narrow it down to three primary forces driving change.

  1. The abundance of quality content available on the web. Students have access to more content than ever before, which changes the paradigm of the student/teacher relationship. The professor teaching in a classroom is not the primary method for accessing scholarly expertise; content is no longer bound by cost, location or time.
  2. Interactive applications and platforms on the web accessible due to cloud technology. Blogs, Slideshare and Learning Management Platforms such as Moodle, are just a few examples of tools that are changing how education (content) is delivered and accessed. Online learning, through MOOCs, either Coursera or CHFE12 (via Desire2Learn) are examples of learning that was not available ten, or even five years ago.
  3. Mobile devices with Internet connectivity capability.  Millions of people around the globe, even in third world countries are owners of mobile devices that connect to the Internet. This capability drives not just educational change, but cultural and societal change as well.

2. Changes in Pedagogy
Education is becoming student focused, which means pedagogical methods are adapting and transforming accordingly.  During a webinar in week two of CFHE, Joel Hartman spoke of the new pedagogical models implemented at UCF. The new approach puts the student in the center, and provides a choice of five learning modalities. This approach is highly successful. A critical component to its success is educating faculty on the effective use of pedagogical methods in the various modalities. I wrote about these methods in-depth in a previous post.  Competency based learning, another approach uses a different pedagogy, one that focuses on the application of skill and knowledge through demonstration. Each are described in-depth below.

  1. Competency based learning either in face-to-face or online. Competency learning challenges the notion of ‘seat-time’ based upon the Carnegie Unit (time-based reference for measuring student achievement that is used by American colleges). Western Governors and Southern New Hampshire’s Innovation U (which I wrote about here) and University Now — all examples of schools using competencies as a model for assessment and learning. Businesses in the community are supportive of this model, often partnering with colleges in the process.

 Another form of competency learning are adaptive learning platforms. Where student learning is customized through online programs that adapt and respond to students strengths and weaknesses. Knewton learning is an example of a company that delivers adaptive learning programs.

2. Blended and Online learning that are based upon models of constructivism or  connectivism.  Students are involved and active participants within learning, either constructing their learning with content and student peers (constructivism), or connecting with nodes within a network, making connections and acquiring knowledge as a result. Learning is moving towards treating students as part of the learning, not as passive ‘vessels’.

3. Changing Models of Higher Ed
Below are the three directions that may emerge based upon the advancements and developments as discussed in the CFHE12 MOOC.

1) The traditional college experience where students attend college or universities will change; will be augmented with online learning, flipped classrooms, and blended learning. The community will become part of the education experience, where students work within the community to solve problems (project based learning) or work as part of their education. Four years of attendance will be the exception, less will be the norm.

2) Do-it-yourself college:  Students will adopt the ‘build-your-own-degree plan’. Colleges are becoming unbundled, which means students will be able to choose preferred courses, MOOCs and assessment of skill for credit. This model will grow as costs for college continue to be out-of-reach for the average family.

3) Life long learning (competency based): Students will become learners for life, where learning is one long, continuous process. It will not stop after high school, or even after college, but will continue as learners need new skills for career change, job training or even personal interests. MOOCs will play a role, as will courses offered through other venues, perhaps face-to-face, online or through iTunes U.  This model has much potential.

Conclusion
The future is bright for higher education. Resistance to change can be a significant barrier to the developments needed within education, but those that resist will eventually be forced to get on board or be left behind. Those that do change and adapt will have the opportunity to reach students as never before – educate people of all ages to be productive, educated and contributing members of society. It all begins with embracing change, and changing our thinking.

Resources:
CFHE12: Content Home Page through Desire2learn
Blended Learning Toolkit, by Thomas Cavanaugh,  EDUCAUSE Review
Strengthening the Pathway to Higher Education, by Dr. Brian Mitchell, Huffington Post
Why Online Education Works, by Alex Tabarrok, CATO Unbound

Photo Credit: Constant Change, by mermaid99, Flickr

Dream or Nightmare? The Ethics of Learning Analytics

Just because it [academic data] is accessible doesn’t make it ethical (Boyd & Crawford, 2012).

What are the implications of higher education institutions collecting student data and compiling a multitude of reports based upon students’ online clicks, page views, time logged on, and electronic notes? Do educators have a responsibility to tell students what they are doing? These are interesting questions that education institutions should be wrestling with in this era of Big Data.

I’ve written several posts recently about learning analytics, emphasizing the need for meaningful analysis, student-centered reporting, and transparency. However, I admit to having reservations about analytics and its role in education. Not only is there potential for abuse and manipulation of data, but I am concerned about privacy and student rights. Surprisingly, there has been little written about the moral implications and the potential nightmares that analytics could create. Simon Buckingham Shum a leading scholar in learning analytics research, led a talk Learning Analytics: Dream or Nightmare? for EDUCUASE’s online spring focus session, (webinar recording available here). Shum discusses Big Data in education focusing on analytics in K-12 and higher education settings. Shums’ talk promotes deep thought as he highlights  the positive aspects of academic analysis but also its dark side. In this post I highlight some of the concerns put forth by Shum, the ethical considerations we as educators should be concerned about, and the questions we should be asking.

Simon Buckingham Shum, EDUCAUSE Webinar, 2012

Questions to Ask
If we look at where the idea of using Big Data to improve productivity and growth came from, we need look no further than to the business sector. Businesses thrive on analyzing customer data, sales, market performance and inventory logistics. Can we apply the same principles to education?  In his talk, Shum asks a rhetorical question about analytics using the slide at the right to emphasize his point. BusinessAnayltics.edu or LearningAnalytics.com? Can we treat academic data the same way as business’ treat data?  IBM thinks so. I attended a webinar several months ago where IBM was sharing a case study about a college where its analytics platforms were implemented.  I was uneasy throughout the  webinar, I heard over again and again words such as  strategy, performance, achievement, strategic planning. Rarely did I hear the words, student, learning or development. I am not suggesting that IBM is incapable of providing valuable expertise, I am only using this sliver of insight I gained through a brief webinar to highlight the bigger issue, which is the need to ask questions that include, ‘how  should we approach educational data?’, ‘who should have access?’ and ‘how does academic data differ from other types of data?’.

What do students think?
Speaking of breadth, do students know the depth and breadth of data that is collected about them within the academic platform (LMS such as Moodle or Blackboard) they use consistently? And if they do, what are their responses? Some students will not care, yet others may be vehemently opposed. However, when students are involved in the discussion of how to use the data, and are part of the conversation, which Shum suggests, the concern of privacy and ethics becomes clearer. Transparency is essential. Grand Rapids Community College blog features an excellent article, Obligation of knowing: Ethics of Data Collection and Analytics, which suggests using transparency to create trust. Letting students know how data will be used, and how they will benefit is a good place to start.

Simon Shum, Webinar

Solutions
Simon Shum closes his webinar with two slides, the first with an image of a man holding a magnifying glass, asking ‘who gets to hold magnifying glass’, implying that educators should be considering not only who should be analyzing and viewing student data but why. The final slide, an image of a student holding a mirror, suggests that analytics should be used as a mirror for learners to become more reflective, and less dependent. Yet it is up to the institution to determine how data will be used which will determine the result— either a nightmare scenario where analytics breed resentment and myopia, or a dream scenario. In the dream scenario, analytics can create a generation of tools that support and develop learners, where students become self-directed, responsive and armed with the skills needed for the 21st century.

Resources:

Photo Credit: Personal Data, by Charlie Collis (highwaycharilie), Flickr

Learning Analytics for Instructors Need to be Loud and Clear

Learning Analytics…less data more insight. Analytics primary task is not to report the past, but to help find the optimal path to the desired future. (Shum, 2012)

Learning analytics, [analyzing students’ online behaviour patterns to support learning improvement] is not about data collection, but helping learners and instructors make connections with the data. I attended a webinar this past week with Campus Technology, Grand Canyon University: How we are improving student outcomes using LoudAnalytics on the LoudCloud Ecosystem. Grand Canyon University of Arizona shared results from their learning analytics pilot project using LoudAnalytics from LoudCloud, a company which presents themselves as a learning ecosystem, the next-generation of learning management systems. In this post I identify what kind of analytic reports are essential and the most useful to course instructors, which are not, and why this is so. The findings in this post I gathered from the webinar and content from week four of the course Current/Future State of Higher Education.

Meaningful’ Data for the Instructor
I wrote a post last week that addressed how student data gathered from online behaviours from a school’s platform, can put the learner in the ‘driver’s seat’, essentially in control of his or her own learning. A dashboard which gives real-time info on a student’s ‘stats’, can be a visual tool to help learners reach their goals, identify problems and contribute to  motivation. However, what about the course instructor? What analytic tools are available through the LMS platform that can provide meaningful data, data that is consumable – in a usable form that encourages instructors to take action in real-time?

Grand Canyon University Webinar,  Slide #14

To the left is an example of a report from LoudAnalytics that displays data about students’ progress in a visual format. Students are represented by circles; the size of the circle representative of the hours spent on the course home page (interacting with course content, etc.) and the colour of each circle representing a letter grade. I see this as a ‘snapshot’ view of  students progress holistically, but don’t see this report on its own as providing ‘actionable’ data. Time spent within the LMS does not translate always to grades and engagement level, but is just one metric.

Grand Canyon University Webinar, Slide #47

The report to the right however, does appear to provide constructive data for the course instructor. When instructors consider the previous report and the one here, the instructor is able to do something with it. For example upon review, the instructor might want to reach out to student #2 (and potentially one or two others) with an email to the student that might read like this:

Dear [name of student], it appears that you have an assignment outstanding, and have not participated in a forum recently. I am concerned about your progress in the class. There are several resources available for support, …..”

There are limitations to this scenario I’ve described here, it is one-dimensional given we don’t have complete information, but the idea is that the indicators provided in this report are specific about student actions, or non-actions that give the instructor something to work with.

What Data is NOT Helpful
It is information about student actions, i.e. missing assignments, non-participation in discussion forums, low test grades, that is valuable for instructors, what I call ‘actionable’ data. Other data, such as number of times logged on to the course  home page, or the number of minutes spent within the platform, is not meaningful or of much practical use. I suggest that platform providers (i.e. Moodle LoudCloud etc.) consider generating reports that are focused and specific to the users needs (users defined within three groups: student, instructor and administrator). However, making too many reports available will detract from the value of the analytics. For example, the report below shows the time in minutes a student spent within the LoudCloud system, which gives a snapshot of student behaviour, but, I don’t see how this information is useful for the instructor. Perhaps it might be, if considered in conjunction with other reports, but then we get into data overload.

Grand Canyon University Webinar, Slide #48

Furthermore, just because we can measure something, doesn’t mean it is valuable or even useful. Another example is the program that Course Smart, the e-textbook provider is launching to give course instructors reports on student engagement. I wrote about this last week, yet I use this again as an example to show how reports are created from data that end up being inconsequential.

It [Course Smarts’ program] will track students’ behavior: how much time they spend reading, how many pages they view, and how many notes and highlights they make. That data will get crunched into an engagement score for each student. The idea is that faculty members can reach out to students showing low engagement (Parry, 2012).

I have a hard time imaging how instructors will use this information. The problem from the get-go is that Course Smart assumes that student engagement is defined by the number of electronic ‘notes’ made in the e-book and how long the student spends ‘reading’ the textbook. Not only is this logic flawed, but as one of my readers pointed out, it has a ‘big brother’ feel about it. I do agree, and I will be writing about the ethics of learning analytics next week.

Closing Thoughts
Learning analytics can be a powerful tool for instructors, yet only when meaningful data is compiled in such a way that it is user-friendly, relevant and actionable, in other words reports must be loud and clear.  LoudCloud is onto something here, I very much like their visual presentation. Yet LoudCloud and other LMS providers need to narrow down the number of analytic reports made available, customizing what they offer to the users needs. Make it clear, specific and meaningful.

Next post: Dream or Nightmare: The Ethics of Learning Analytics, Online Learning Insights

Resources:
Grand Canyon University: How we are improving student outcomes using Loud Analytics on the Loud Cloud Ecosystem. (November 13, 2012) Campus Technology Webinar (now on demand)

LT-C2012 Learning Analytics Symposium, (2012),  Simon Buckingham Shum, Slideshare
Introduction to Learning and Knowledge Analytics Syllabus, (2011), An Open Course
Putting Learners in the Driver’s Seat, Online Learning Insights

Putting Learners in the Driver’s Seat With Learning Analytics

Steering WheelI read something disturbing this week from Inside Higher Ed, Measuring Engagement with Books

“The big buzz in higher ed is analytics,” said senior vice-president of marketing Cindy Clarke, of the e-textbook provider Course Smart. “Based on the issues there are with institutions around improving the return they’re getting on their investment in course materials, we realized we had a valuable data set that we could package up [emphasis added].” (Tilsley, 2012)

Coincidently, last week’s topic in the course I am taking Current/Future State of Higher Education (#CFHE12) was Learning Analytics, the same topic this article refers to. It’s a promising area of study and is a ‘hot’ topic in higher education right now. Data, in the form of students’ online behaviours obtained by measuring clicks, keystrokes, time logged-on, number of ‘hits’ [visits] on web pages, is collected and then compiled into ‘meaningful’ information.

Yet Course Smart’s [in my opinion] program is an example of learning analytics gone awry. The ‘packaging up’ as mentioned by Ms. Clarke refers to the program Course Smart developed with data on students’ reading patterns. The program looks at how students interact with the e-textbooks, the number of times a student views a page and for how long, highlights made, etc. Course Smart compiles this ‘data’ and sends a Student Engagement Report to professors.  Are these metrics a true measure of a student’s level of engagement?  It seems that student engagement covers a far broader scope than time spent reading a textbook.  Even if the report did provide meaningful indicators, how would an instructor actually use it to teach more effectively?

Analytics in higher education is considered by some to be a panacea to its woes. Yet it’s  complex, sensitive, and almost onerous given the abundance of student data that is collected by institutions. In this post I’ll give an overview of the three areas of analytics, micro, meso and macro to provide clarity and context as explained by a guest presenter in a recent CFHE12 webinar, Simon Buckingham Shum, Associate Director of Knowledge Media Institute, Open University, UK. I’ll also share how learning analytics is used to help students learn as discussed by educator Erik Duval, Professor at Katholieke Universiteit Leuven, Belgium, during another #CFFE12 webinar, and finally how educators can use analytics to help students take charge and ownership of their learning, essentially put them in the driver’s seat.

The Big Picture of Student Data
When we speak of learning analytics, we are at the ground floor of Big [academic] Data. Data analytics in academia has the potential to support decision makers, academic researchers, policy makers, instructors and students. Simon Shum described the layers of analytics this way:

What Questions Should Data Answer?
At the macro level, institutions share data with others, compare and determine what is useful to influence and support decisions on educational funding models, policies and for International comparisons. At the institutional level [meso level], analysis includes examining student progress and related data to make programming decisions, identifying at-risk students by predicting student performance, and making curricular decisions. Shum’s approach to analysis is holistic, he puts forth questions that educators should be asking when making decisions about how to use data effectively at the meso and micro level:

  • What are we measuring and why?
  • What problem are we trying to solve with the data?
  • What level of results should we share with the learner?
  • What are the ethical considerations?
  • How can we create a functioning ecosystem that uses data effectively and responsibly?
Dashboard of student progress displayed throughout a course. Designed by students in a research project with Duval.

Analytics in the Real Time – Helping Students
Duval, not only a professor, is chair of the research unit on human-computer interaction analytics, and his research focuses on how data can provide valuable feedback to the learner. He gathers and analyzes student behaviour patterns through online behaviours as it relates to their learning, which he shares with his students, there is 100% transparency. Information for the learner comes in several forms, one of which is a dashboard that provides a snapshot view of how the student is doing at a given point in the class. This view is designed to trigger self-reflection where learners can view their progress, compare their performance with others in the class. When asked how students respond to viewing others’ performance, Duval says he spends considerable time at the beginning of the class explaining, discussing and reviewing the purpose of the reports, how to use them and what they mean to students’ overall learning.

This approach is also in ‘real time’, it is actionable — students [and instructors] have access to feedback as the course progresses. Students can adjust, make decisions, and take action as needed. Instructors can also reach out to struggling students, ‘intervene’ with resources and support. Duval describes this as putting the learner in the driver’s seat, which is the name of the conference held in Belgium recently on Learner Analytics.

How can Instructors use Learning Analytics at the Ground Level?
There are questions and concerns about how much data students should have access to, with FERPA guidelines and privacy issues, educators must tread carefully. That being said, we can begin by asking the right questions: what do we want students to achieve, and how can data help them? What do we need to do to educate students about the data, how they can use it?

What can Educators do?

  • Identify what tools are available within your learning management system for data analysis. I’ve included in previous posts YouTube videos that provide instruction on how to use LMS tools, and other strategies for analytics. See the resources section for the links.
  • Be part of the discussion with faculty and administrators about learner analytics – ask questions, focus on ‘why’.
  • If presented with analytical tools or reports, determine how they can be used to support learners through instruction or intervention.
  • Become familiar with how analytics can help instructors be more effective in helping students learn.
  • Review  programs based upon analytics that are used at other institutions: Purdue’s Course Signals ProgramUniversity of Michigan Academics Analysis Program and Community College at Rhode Island program,Connectedu.

Closing thoughts
Learning analytics has tremendous potential for education, though I am cautiously optimistic about its use in higher education. I am far from an expert, but I see the value in giving students ownership of their learning through tools provided by analytics,  dashboards for example, similar to Duval’s. We need to involve the student in this conversation – it’s not the data that’s the solution to the challenges that higher education is facing, it’s the students. Let’s put them in charge, give students the tools to make the decisions to make learning meaningful, put them in the driver’s seat.

Resources:

Conference, Learners in the Drivers Seat, Belgium
How Instructors Can Use Analytics to Support Student Engagement, Online Learning Insights
SoLAR, Society for Learning and Analytics Research
Learning and Knowledge Analytics, Resources
Engage: Test by ACT to predict student success in college
Erik Duval’s Slideshare, Presentations
Understanding Learning Academics and Student Data, MindShift

Abelard to Apple and the Future of Higher Ed

University leadership in the United States for the most part is unaware that the crossroads is ahead.  […] The obvious question is how so many smart people could miss what seems to be an inevitable crisis?  Richard Demillo, Abelard to Apple: The Fate of American Colleges and Universities.

A wake-up call is the best way to describe the book I just finished, Abelard to Apple: The Fate of American Colleges and Universities by Richard Demillo professor at Georgia Tech and Director of its Center for 21st Century Universities. It’s a must-read for any stakeholder involved with higher education institutions or for those with a keen interest in the direction that higher education is going. The book should come with a warning though, it may cause elevated blood pressure, anxiety or even feelings of distress for anyone working for, or with a middle-tier college. The ‘middle’, as defined by Demillo are schools that make up the majority of higher education institutions in the United States; not the elites with large endowment funds, or the proprietary for profits, but all the rest.

Its universities in the Middle, according to Demillo who are in peril and will face challenges over the next few years given the influx of new learning models that technological innovations are providing. This message was similar yet more emphatic in a webinar featuring Demillo in week two of the MOOC the Current /Future State of Higher Ed [#CFHE12]. It was this webinar that motivated me to read his book. I share below where colleges are headed as per Demillo, and the three principles he recommends colleges carry out— essential, according to Demillo, for survival.

The Title
The title, Abelard to Apple, is an analogy for teaching.  Peter Abelard (1079 – 1142) a French monk of the Middle Ages, a gifted and charismatic lecturer, was one of first scholars to conduct formal lectures for small groups of students. Apple refers to iTunes University, where  accomplished professors can create lectures and upload them to the sophisticated and slick platform iTunes U. Technology has transformed the traditional lecture model;  thousands of students can now view lecture content delivered by expert scholars in their field [like Abelard] to anyone, anywhere and anytime, free of charge [unlike Abelard]. What does this mean for the traditional model of higher education?

The Message
Demillo’s message is compelling. The messenger has a list of credentials and experience that suggest he knows what he’s talking about. Demillo has hefty credentials. He’s currently a professor of computing and management at Georgia Tech, and has worked in government and business (at one time the Chief Technology Officer at Hewlett-Packard).  Demillo’s experience makes his arguments credible.  Below are some highlights from his book and webinar that summarize the key points.

  • Traditional universities are no longer the gatekeepers. With the recent technological innovations that have created numerous learning options, combined with the higher cost of education, the value of a college education is in question.
  • Only the elite have ‘global’ brands that will hold their value, the remaining – the middle, have misjudged their value. Customers (families) will no longer be willing to, or able to pay for a college education.
  • As faculty-centered institutions turn their attention inward toward the needs of their profession, they become protective, rigid and inevitably irrelevant.
  • The middle-tier has many stakeholders making the system multi-sided and complex, which makes change cumbersome and onerous unless there is a strong leadership presence with a sound strategy.

Key Takeaways
Though Demillo highlights all that is wrong with the current system, he does provide three principles that successful institutions will need to follow to thrive.

  1. Focus on value. Schools need to focus on charging a reasonable cost while offering quality education if they are to be competitive. And schools that offer a unique learning experience that is differentiates itself from others will position themselves well.
  2. Focus on costs. Cutting costs to survive is essential through better use of facilities, materials and other [more details in the book about this area].
  3. Establish reputation.  Build a reputation on school’s strengths and focus on the student. Schools each have a unique niche, focus on what sets the school apart from the others. No longer can schools fall into a pattern of trying to be like the elites or another bigger and better schools. (Demillo, pp. 36 – 38).

I’ve barely covered the depth that Demillo goes into in his book. He provides excellent discussion and narrative throughout on how the ‘middle’ can adapt to the tumultuous environment of higher education.

There’s Hope
The book is not all doom and gloom, there are glimmers of hope for those institutions that are able to re-focus and adapt. The book published in 2011 just before the MOOC phenomenon, seems somewhat prophetic.  Rereading the book in two or three years will be even more interesting. I am curious though, what it will look like for those colleges that won’t be successful in adapting. Will they just close their doors and become a landmark? How long will it take? Though change is rapid, there are already several alternative options for higher education that didn’t exist seven or eight months ago. Time will tell.

ResourcesNew Developments in Higher Ed

Three Good Reasons Why Educators Should Embrace Ed Tech Entrepreneurs

Udacity nabs $15M to make online education less boring“, Venture Beat
Start-ups try to Crack Education Market“, Wall Street Journal
A Boom Time for Education Start-Ups“, The Chronicle

These headlines are enough to make some educators cringe, given that many in higher education and K-12 environments feel strongly that for-profit companies have no place in academia. Entrepreneurship and commercial activity in education was  the topic last week in the course I’m participating in, the Current/Future State of Higher Education (CFHE12). We examined a number of resources on the topic including several from GSV Advisors, a company which advises and helps secure funding for educational start-up companies. In this post I give three reasons why educators in higher education should support, if not embrace educational technology start-ups. I was a fence-sitter prior to this past week’s topic in CFHE12, but have since made up my mind. My aim here is to convince readers as I was convinced this week, how ed-tech companies can be part of the solution to the current education crisis.

Three Reasons to Support Educational Start-ups:

  1. Educational start-ups have the potential to transform higher education by providing solutions to problems that can’t be solved with existing methods.
  2. Ed tech innovators provide products and platforms that give access to educational opportunities to students and instructors around the globe.
  3. Start-ups provide tools that have the potential to improve learning outcomes, increase learner engagement and create life-long learners.

How Start-ups ‘Get Started
Before I explore each of these three reasons further, we’ll examine innovation and the role it plays in educational start-ups. Innovations usually begin with a personal experience that sparks a desire to solve a problem. An example is Jack Dorsey, founder of Twitter, whose story puts the debate about for-profits involvement in education into perspective. Solving a problem was what motivated Dorsey, to make something better, to meet a need that wasn’t being met. He did not set-out to make a million dollars with his idea of Twitter, or his most recent idea of Square, [a platform to accept payments with a mobile device]. Both Twitter and Square emerged from Dorsey’s drive to fix something, create a system to simplify an existing method that wasn’t working effectively. The idea for Square emerged in an effort to help an artist friend who lost a sale of $2,000 for a work of art because the buyer didn’t have cash, and the artist wasn’t able to accept credit cards. The story of Dorsey is intriguing, a “tech master mind with the soul of an artist” (Stevenson, 2012) proving that innovation begins with a passion.

Entrepreneurs, like Dorsey, start with problem that leads to an idea, that leads to the development of a solution to make something more effective. After participating in last week’s webinar with Deborah Quazzo of GSV, I could see that education entrepreneurs are no exception. Her company meticulously profiled hundreds of ed-tech start-ups, providing extensive information about each. It’s apparent after reading the backgrounds of several start-ups that each began with a passion to improve an area in education, to make it better and solve a dilemma.

1. Educational start-ups have the potential to transform higher education by providing tools and resources to solve the problems that exist in higher education today, access, cost and quality. These companies are not planning to undermine the values of education with a profit-only motive, as companies without a passion or interest in education will not be successful. Clayton Christenson, Harvard Business professor and author of the Innovator’s Dilemma, states that it is companies that put the customer [learner] first, not profits that will be successful in this economy, “In this new world, the bottom of line of business isn’t profits but rather customer delight, i.e. the provision of a continuous stream of additional value to customers and delivering it sooner.” Clayton Christenson as reported by Steve Dunning for Forbes (2011).

Below is a sampling of ed-tech start-ups that appear to be focused on the learner, that want to improve the educational experience. Each of these companies has already had a significant impact on education and are disrupting traditional methods. The company founders are not only entrepreneurs, but educators with a vision.

  • Udacity: Founded by Sebastian Thrun, Stanford professor and provider of a MOOC which drew over 100,000 students worldwide. “Sebastian and the team want their legacy to be about reinventing education and they understand the wide-reaching impact that this will have on our future.” (Peter Levine, 16z.com).
  • Canvas by Instructure: An open source LMS platform founded by two graduate students. “Canvas isn’t just a product. The people who use it aren’t just our customers. We’re partners. And we’re transforming education.”
  • Open Study: The co-founders are educators that have a passion for learning and education. Open Study is a social learning network for students. “Our mission is to make the world one large study group, regardless of school, location, or background“.

2. The tools and resources that ed tech innovators create, bring education to learners around the world, breaking down barriers of place and time. MOOCs have delivered education to thousands of learners, social learning platforms motivated thousands of struggling students – these types of innovations are considered ‘disruptive’ to the current system, yet are improving lives of learners. Education now has the potential for transformation; educators should be celebrating.

3. It will be the tools provided by the ed tech companies that will have the potential to improve learning outcomes, increase learner engagement and create life-long learners. It is up to the educators to decide how to use these tools to transform learning, the tools alone don’t transform, but are dependent upon an educator to drive the learning at some point within the learning process.

Summary
Educators might want to look at investment and entrepreneurship in the education sector from a different perspective – entrepreneurs have a desire to make education better, provide solutions to problems. The profit motive of these companies does not undermine the values of education, it is how educators to use the tools to enhance and transform education that define what education is, and how it meets the needs of learning. It is up to educators to decide how education methods need to adapt to the cultural digital transformation happening now, and use the innovations accordingly. Embrace start-ups, for without innovation, progress and improvement does not exist.

Resources:
EdSurge, Educational Technology Online Resource
Sampling of Ed Innovation Start-ups: http://gsvadvisors.com/wordpress/wp-content/uploads/2012/04/GSV-ASU-EdInnovation-Summit-2012-Company-Profiles.pdf