Google Drive copy at https://docs.google.com/document/d/18-8B40oqkeCo7-SXzwwYsyNMBozZGC29kxcNw2VFnXE/pub
Recent History and Charges of This Very Present
In 1971 this day, the U.S. Congress designated August 26 as Women’s Equality Day, the anniversary of the 1920 passage of the 19th Amendment to the Constitution, by which women were granted the right to vote.
40 years ago women couldn’t open a bank account without their husbands’ signatures.
In 1960 it was unheard of American space women.
Today’s 113th Congress (2013-2015) has 19% women.
This year there are 25 women CEOs of the Fortune 1000, which represents 4.6% of Fortune 1000 CEO positions.
Although overall more women than men graduate from college with a bachelor’s degree, women’s participation in engineering and computer science undergraduate programs remains below 20%.
Nature’s science writer and editor M. Mitchell Waldrop recently contributed a MOOCs article to the Scientific American . There are many intriguing aspects, lurking uncertainties, and heatedly debated controversies about the MOOCs phenomenon. What I care about here and now is the parallel that Chris Dede, Harvard professor, draws in that article between the “university business” and “learning business” in higher ed.
The university business is about solving the affordability problem, with a primary concern about increasing financial pressure to reduce tuition and other costs at times when student debt is sky rocketing. The learning business is about solving the accountability problem, with a primary concern about the quality of education, meaning qualified and talented teachers and an increasingly complex learning environment with many social, and situational, and time constraints.
To provide high quality while reducing costs (more with less, so to speak) is a productivity question. The university business answer to it is to adopt MOOCs and other online technologies “to do things more cheaply”, within “existing structures and practices.” The earlier innovations of personal computing, Internet, and course management systems show the exact limit in materializing real gains in productivity, that is, quality education at reduced costs. Dede’s claim is that we won’t see real gains in productivity and effectiveness of learning, or a thriving learning business, until “universities radically reshape [their existing] structures and practices to take full advantage of the technology.”
How do we do it is the sticky question. Since we’ve seen MOOCs in action, do we know more about the changes we need to make? Stanford’s take on this challenge is to “embed digital learning into the fabric of the entire University”. Ambitious, abstract statement. Here there are some concrete pointers.
A private summit held on March 4, 2013 in Cambridge and sponsored by MIT and Harvard apparently arrived at a “strong consensus that [a] blended model combining online lectures with a teacher led classroom experience [would be] the ideal” . Waldrop concludes his article with a similarly compelling argument for the importance of direct human interaction. Ironically, MOOCs’ innovation with flipping the classroom is testimony that “online technology’s most profound effect on education may be to make human interaction more important than ever.”
Student mentoring? Mean it!
As it stands, student participation in MOOCs lacks diversity . Undergraduate computing education is predominantly white or Asian and male, and percentages of women has been on a steady decline from 30% in 1991 to 17% in 2010. Mark Guzdial points to the demographic survey results of the first Coursera MOOC from Georgia Tech, which show that MOOC completers (enrollees who finish the course) were 88.6% white or Asian and 91% male. The results suggest, says Guzdial, that “MOOC-based computing education would be even more exclusive than what we currently have.” His research on broadening participation in computing and other findings suggest that “one-on-one encouragement is the most effective way of engaging and retaining students from underrepresented groups.”
Full circle competencies
Cognitive skills (reasoning, memory, and problem solving) is one of the three competency categories that compose deeper learning – process through which we become capable of taking what’s learned in one situation and transferring or applying to new situations and problems . The other two categories are:
- intra-personal skills by which we manage our behavior and emotions to achieve our goals (including learning goals), such as work ethic, metacognition, appreciation for diversity, flexibility, self-direction, self-monitoring, and responsibility.
- inter-personal skills by which we express ideas and interpret and respond to others ideas, such as teamwork, collaboration, and leadership.
In disciplines with stronger communal endeavors as science, says Dede, “education is more than knowledge.” It is about “abilities like leadership and collaboration and traits like tenacity”, which are best learned face to face. David Krakauer, a biologist who directs the Institute for Discovery at the University of Wisconsin–Madison, makes the same point when he agrees that very large lecture halls can be replaced by lectures watched on iPads. At the same time, he points out, “there is no substitute for a conversation.”
Ultimate goal of a college degree is to educate graduates to achieve success in the workplace, further education, and other areas of adult responsibility and life, e.g., civic engagement and personal fulfillment, health, and relationships. And it takes the cultivation of both cognitive and noncognitive skills to learn on the job or transfer what we learn across jobs.
 Waldrop, M. Mitchell 2013. Massive Open Online Courses, aka MOOCs, Transform Higher Education and Science. Scientific American (March 13, 2013). Available at http://www.scientificamerican.com/article.cfm?id=massive-open-online-courses-transform-higher-education-and-science.
 Friedman, Thomas L. 2013. The Professor’s Big Stage. New York Times, March 5, 2013. Available at http://www.nytimes.com/2013/03/06/opinion/friedman-the-professors-big-stage.html?ref=thomaslfriedman&_r=1&.
 Guzdial, Mark. 2013. Research Questions About MOOCs. Communications of the ACM, Blog@CACM (February 20, 3013). Available at http://cacm.acm.org/blogs/blog-cacm/161153-research-questions-about-moocs/fulltext.
 National Research Council. 2013. Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century. Committee on Defining Deeper Learning and 21st Century Skills. Pellegrino, J.W. and Hilton, M.L. (Eds.). Board on Washington, DC: The National Academies Press.
Students, teachers, researchers, entrepreneurs, patients, caregivers, and other U.S. taxpayers deserve open access to scientific journal articles arising from U.S. taxpayer-funded research.
This is my belief, and I’m hopeful that enough of us share it and are willing to advocate for open access policies for all federal agencies that fund scientific research.
I just signed a petition to require free access over the Internet to scientific journal articles arising from taxpayer-funded research, and I invite readers of this post to show support.
It takes a minute to sign it! If implemented, open access policies for federally funded research will touch everybody’s education, formal and informal, for life-time.
Five graduate students in the MS IT program made poster presentations at the UNH Manchester Graduate Research Conference on April 25, 2012.
The posters featured projects in CIS 805 Web Application Development (instructor Mihaela Sabin), CIS 810 Object Oriented Software Development (instructor Michael Jonas), and CIS 825 Networking Technologies (instructor Don Cochrane) courses:
- Network Modeling Software Use in Research and Education, by Alex Scripcenco and Casey Eyring
- SpEAK: Speech Experiment Accessible Knowledge System for Capstone Speech Project, by Jackie Tims and Justin Thibeault
- What Version Are you On? An Investigation into Software Configuration Management, by Jackie Tims
- CivicCRM Framework to Manage Donors for Nonprofits: From Design to Deployment, by Imran Shahzad
From left to right: Alex, Casey, Jackie, Justin, and Imran.
Next Generation Learning Challenges is a grant program that provides investment capital through waves of funding (third wave currently runs through June 2012) to boost educational success for all students, as measured by college readiness, persistence, and completion, through technology-enabled solutions. The program’s idea is that technology transforms not only industries, but people: how we organize, conduct business, form communities, discover, create, and … learn. The program’s requests for proposals draw a clear picture of the number one priority that should shake the core of our current education system, affordability for high quality education, or designing effective learning environments of scale.
Innovation at the edge is doing the pull from the core
How do we get about achieving this priority when constrained by the Baumol’s effect (Snir, 2011), saying that no or little gain in productivity can be obtained in highly skilled labor intensive fields such as education or nursing. After all, it takes teachers the same amount of time to grade an essay today as it took them 50 years ago. A model to consider is John Seeley Brown’s theory (Brown, 2010) of inverting a 20th century push economy into a 21st century pull economy. A push economy is characterized by big firms with large capital and labor that mass build a lot of standardized products and push them into the market using a centralized, hierarchical, and tightly controlled distribution infrastructure. A pull economy is meant for agile, flexible firms that apply rapid, on-the-fly customization to pull the best and new ideas through loosely coupled networks, open commons, and spikes of capabilities around the world. Cloud computing and social media are the ingredients of the sociotechnical systems that populate the infrastructure of the 21st century pull economy.
Contrary to the conventional wisdom of the company’s core that has been pulling or absorbing innovation from the edges, The Power of Pull book (Hagel III, Brown, and Davidson, 2010) introduces the idea of a dominant edge that will pull the core to innovate the core’s business. A telling example in Brown’s presentation (Brown, 2010) is the social development network of one of the biggest software firms, SAP. The SAP’s social development network is currently involving 1.4 million developers from around the world with access to an open platform that socializes the construction of the next generation SAP products. My example is that of the edge developed by the Fedora Project around the Red Hat and Ubuntu cores. On the core-edge spike spectrum, at the extreme of a core-less edge lies Wikipedia and other commons-based peer production systems (Benkler, 2006), but this makes the topic for another discussion.
Student engagement and mastery learning should not be constrained by seat time
Back into the education realm, it is certainly time to change the factory model of education, in which schools, textbook publishers, and instructional technology vendors predict what learners need from kindergarten to graduate education, build and protect (copyright, that is) massive stocks of knowledge assets, and push them through traditional classrooms in which, eventually, teachers push their teaching, one-size-fits-all core at students.
Technology is a productivity engine. What challenges technological applications to learning is the craft of weaving within technology people, those who directly participate in an educational setting – students and teachers alike; and processes, how participation of students and teachers forms and manifests when designing, using, and evaluating educational technologies. Learning sciences have discovered that educational processes, to effectively impact learning, should be designed and structured with the learner’s needs in mind.
Actively engaging students in learning experiences, or active learning, is as old as the Socratic method and has been pondered upon by constructivists (Jean Piaget, Lev Vygotsky, Herbert Simon, among the most influential) and put in practice through constructionist (Seymour Paper’s model of learning) and other experiential and inquiry-based learning approaches. Another innovation that informs learner-centered educational environments is Bloom’s student mastery, personalized learning (Bloom, 1984), as opposed to learning constrained by seat time. His striking finding, known as the 2-sigma effect, is that one-on-one tutoring produces two standard deviations improvements in student learning compared to the traditional classroom lecture model. Bloom’s studies have shown that the average tutored student performed better than 98% of the students in the traditional class.
Student pull versus lecture push
Disrupting the class (Christensen, Horn, and Johnson, 2008), inverting the classroom (Lage, Platt, and Treglia, 2000), and the viral success of the Khan Academy open platform and video lessons create exciting opportunities for students to pull the learning that bring them success.
Technology and innovative sociotechnical systems that are possible with cloud computing and social media do not scale teacher and student productivity as measured by student-to-teacher ratio. Instead, individual and group self-directed experiences get more productive when students pull and personalize learning from a dynamic, social network of resources. The shift from lecture-push to student-pull refines the student-to-teacher ratio metric into “student-to-valuable-human-time-with-the-teacher” ratio (in the words of Sal Khan).
Stanford and MIT examples
The blending of personalized learning with highly effective interpersonal interactions dramatically changes teaching and learning as we know it. Stanford professor Daphne Koller tells the story of three free and online computer science courses that were offered in fall 2011, Intro to AI, Machine Learning, and Databases. The courses give students access to lecture videos, assignments, and exams, provide students with regular feedback on progress, and have them participate in a forum in which they vote on questions and answers . In the first four weeks, 300,000 students registered for theses courses with millions of video views and thousands of submitted assignments. More interactive formats are in works, such as real-time group discussions, affordably and at large scale (Koller, 2011).
MIT has just announced the launch of the MIT online learning initiative of a portfolio of MIT courses through an online interactive learning platform, MITx. An experimental prototype version of MITx will be launched in spring 2012 timeframe and, once the open learning infrastructure is in stable form, MIT will release the open-source software infrastructure and determine ways for other institutions to join MIT in improving the technology.
Next Generation Learning Challenges: Transforming education through technology. nextgenlearning.org.
Snir, M. (2011). “Computing and Information Science and Engineering: One Discipline, Many Specialties”. Communications of the ACM, 54:3(38-41).
Brown, J.S. (2010). “Collaborative Innovation and a Pull Economy”. Video: JSB at Stanford, April 17, 2010. edgeperspectives.com.
Hagel III, J., Brown, J.S., and Davidson, L. (2010). The Power of Pull: How Small Moves, Smartly Made, Can Set Big Things in Motion. New York: Basic Books.
Benkler, Y. (2006). The Wealth of Networks: How Social Production Transforms Markets and Freedom. New Haven: Yale University Press.
Christensen, C.M., Horn, M.B., and Johnson, C.W. (2008). Disrupting Class: How Disruptive Innovation will Change the Way the World Learns. New York: McGraw-Hill.
Lage, M.J., Platt, G.J., and Treglia, M.(2000). “Inverting the Classroom: A Gateway to Creating an Inclusive Learning Environment“. Journal of Economic Education, 30:1(30-43).
Bloom, B. (1984). “The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring”, Educational Researcher, 13:6(4-16).
Koller, D. “Death Knell for the Lecturer: Technology as a Passport to Personalized Education”. New York Times, December 5, 2011.
The new name has triggered healthy discussions around the word “computing” and the many names that the academic degrees in IT have across the nation and around the world. Why is the IT discipline taught in degree programs of study, undergraduate or graduate, that may be called IT, but also something else? How do we know that a degree program educates students in the IT discipline and not something else? Is there a broader definition of what IT and other computing disciplines have in common?
Some of these questions found answers in the Computing Curricula 2005 Overview Report. Others keep the dialog about computing education compelling and engaged (read about the 2009 Future of Computing Education Summitt).
Naming the place that houses BS CIS and MS IT at UNH Manchester with Computing Technology simply says that the two degree programs belong together, without having to undo the particular history of the BS CIS and MS IT making. Collateral musing on the merit of naming things and how names stand the test of time is left for future posts.
Why Computing Technology?
Computing technology has transformed and is driving innovation in all economic sectors. Occupations in IT are fast growing and address vastly diverse needs to harness information with computing technology means, whether tablets, network sensors, clouds of virtual servers, smart phones, or personal computers – legendary now, by information age standards.
Computing is a purposeful activity that is computer bound: requires, benefits, and creates computer-powered devices and environments.
Technology is about the tools, systems, and techniques that equip the practice in any particular domain, including computing.
From an academic stand point, computing encompasses five disciplines: computer science (CS), computer engineering (CE), information systems (IS), software engineering (SE), and information technology (IT).
Association for Computing Machinery (ACM), the world’s largest educational and scientific society in computing, is the ultimate authority regarding the resources that advance computing as a science and a profession, including curriculum recommendations for computing disciplines.
The IT 2008 Computing Curricula IT Volume marks the birthday of information technology as an academic discipline – the youngest among its siblings.
What Is the Information Technology Discipline?
The IT discipline studies the mapping of the computing needs of organizations and users to adequate computing technology solutions. The IT discipline overarching goal of advocating for users and meeting their needs within an organizational and societal context is accomplished through five interrelated IT objectives:
of computing technology processes and artifacts, prototypes, products, and services.
Graduates of an IT degree program should be the first to take responsibility for solving a computing need and devising a solution in an organizational context.
A visually smart description of the relationships among the key curricular components of the IT discipline depicts the five pillars of programming, networking, human-computer interaction, databases, and web systems, built on a foundation of knowledge of the fundamentals of IT. Overarching the entire foundation and pillars are information assurance and security, and professionalism (IT 2008 Computing Curricula IT Volume, pg. 18).
How Does the IT Discipline Relate to Other Computing Disciplines?
Two dimensions model the curricular domain space of a computing discipline (Computing Curricula 2005, 16-21):
- Theory/practice spectrum, ranging from theory and principles to application, deployment, and configuration.
- Technology/organization spectrum, ranging from hardware and infrastructure to software, application technologies, and organizational issues.
In this domain space, the IT discipline distinguishes from other computing discipline by being more applied than theoretical and primarily concerned with infrastructure systems and application technologies.
The other computing disciplines, CE, IS, CS, and SE, exhibit different commonalities and differences.
Side by side, these views make it clearer that:
- CE and IS show the highest degree of complementarity along the technology/organization axis.
- CS and IT do the same, but along the theory/practice axis.
- SE maximizes the overlap between CS and IT.
What is the common core of computing disciplines?
There are concerns that computing education will suffer from increasing balkanization if more specialized computing disciplines will continue to spin off. The exercise of assembling the CE, IS, CS, SE, and IT views of computing together draws attention not only to differences but also to commonalities. Marc Snir (Snir, 2011) raises the question of what should define a common core of computing and information education. His answer says it all:
“It is about educating students in ways of thinking and problem solving that characterize our community and differentiate us from other communities:
- A system view of the world
- A focus on mathematical and computational representations of systems
- Information representation and transformation.
A common core defines the computing canon in which students’ entire computing education is firmly grounded, such that it passes the test of Einstein’s definition of education:
“Education is what remains after one has forgotten everything he learned in school.”
The ways of thinking and problem solving that are computing specific have earned a widely accepted name, computational thinking (Wing, 2006). Essential to computational thinking are problem solving thought processes (formulating problems and their solutions) with integral support from computing tools to effectively represent and transform information.
Marc Snir’s viewpoint on computing and information science and engineering as a use-inspired research discipline and educational computing program raises poignant and timely issues for higher education. This post, although marginally related, is the result of having read his article.
Snir, M. 2011. Computing and Information Science and Engineering: One Discipline, Many Specialties. Communications of the ACM. 54, 3 (March 2011), 38-43.
Wing, J.M. 2006 Computational Thinking. Communications of the ACM. 49, 3 (March 2006), 33-35.