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.
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.
I got the news today, from my good friend Bryan Higgs, that Pearson and Google have teamed up to launch OpenClass, a free content management system for educational use (see news in the Chronicle, October 14, 2011). This is exciting!
I’ve been using Google Sites for the students’ online portfolios and Google Groups for the class forum (now, I’ve switched to Piazza.com), and WordPress for the web sites of the courses I teach. The idea I like the most about the Google+Pearson’s solution is that teaching and learning, when it comes to a supportive system, cannot be unilateral, with the instructor in charge of creating and managing content and activities. Instead, all participants should be engaged in what and how content is created and shared, students and instructor alike.
I probably miss some fundamental understanding at the infrastructure level about ‘forum’ and ‘mailing list’. This is also caused by various technologies used in higher education, whether proprietary, open, or free, each with its own nomenclature for online communication means. I cannot emphasize enough the extent of confusion faculty and students alike have (myself included), when it comes to mailing list, forum, listserv, groupserv, discussion board, message board, thread, post, group email, and so on. Many times there is also a formal and sometimes lengthy process in setting up online communication that’s course specific and does not quite fall under the tools of the course management system the institution uses. This is especially aggravated when communication occurs across courses or with course outsiders (experts, invited speakers, community partners, etc.), who cannot apply for accounts with that college or university.
My way of providing some kind of asynchronous online group communication (when I don’t have staff or computing resources to build and maintain a supporting infrastructure) is to use Google Sites, Google Groups, or WordPress (thank goodness they offer free hosting!). Google Groups, for example, is my means of setting up a course mailing list (haven’t thought of calling it forum), to which all students who register for that course subscribe. The purpose of it is to keep all outside-class conversation in one place. Class participants use either email or the mailing list site to ask and reply; they use the site to search, check on members, and share work to some extent (by uploading files or setting up pages). I deliberately stay away from imposing any rules about how to use the tool. The focus is on the activity itself rather than what’s convenient and in which situation and for whom.
So here is my basic question, which comes before ‘compare and contrast’ the two: what’s a mailing list and what’s a forum? I’m interested in understanding the concepts rather than software package, implementation, or administration of these services. I won’t be surprised to find out that in fact current technologies permit a mailing list to offer forum features and allow a forum to do mailing list jobs.
In the end (which is relative, of course, like all things :-), we might find ourselves in the situation of clarifying a bit the taxonomy of concepts that describe asynchronous online group communication.
Student participation in and outside class is wonderful. How do teachers make it happen? Persuasion alone does not do it. “If it’s not graded, it does not count” is the mantra on which teachers and students alike fully agree. Carving out a percentage slice of the final grade and calling it “participation” does not do it either. Who’s measuring it? Based on what criteria? When and how does it happen? How is it observed and by whom? If a measuring stick is waved at students every class, how genuinely do they participate? As a social science colleague and friend put it, “welcome to the hard questions of what others call soft sciences!”
So the question boils down to “to grade or not to grade.” I’ve been of the principle that beliefs, attitudes, and personally held values are not quite grade-able. And not in the scope of my expertise. Therefore, I have been tweaking the syllabus and course requirements (what students are asked to do) with the hope that even if I don’t measure participation the lack of it is clearly affecting learning outcomes measured by other activities, such as doing assignments, working in teams, or presenting projects. I haven’t seen, however, a real improvement in student engagement with the course material and with peers for the purpose of learning.
The decision to quantify participation this semester is based on several observations.
- Most of my students use self-evaluation and self-reflection responsibly to share with me beliefs and experiences they had with doing assigned work (that’s graded).
- Most of my students find pair programming very useful.
- Some of my students make important contributions to the mailing list.
- A few students volunteer important questions and answers in class.
- A few students come prepared every single class: solid grasp of the reading assignment and high quality homework assignment submitted on time.
These observations have helped me craft the following strategy to assess student participation:
- Students use a rubric to evaluate their partner’s collaboration and include that score in the the self-evaluation that accompanies their assignment submissions.
- Students are asked to contribute at least two posts to the mailing list: asking an important question and formulating an important answer.
- I am very explicit about instances of student participation I see in class. For example, “Laura, this is a complete and correct answer, and an important one. Your participation counts!”. Or, “Chris, this is a very important question. In a couple of weeks we’ll revisit it, because we’ll have the knowledge and skills to tackle it. Your participation counts!”
- I am very explicit about instances of student participation outside class (that is, traffic on the mailing list). For example, “Aaron, this is the most influential post made this week. Your participation counts!”
Data from these various sources is then converted into a weekly score I attach to each student’s self-evaluation when I validate how they score the quality of their work and the collaboration with their partner.
My experience is that learning objectives are the centerpiece of program accreditation and review. Although the intention is to be explicit about our student-oriented approach when we design a course and, therefore, always start with stating learning objectives, the reality has shown that students pay no attention to them and teachers kick and scream when they are asked to craft them. Learning sciences and education research have been trying to convince us of the contrary.
One thing I learned though is that learning objectives are of limited help by themselves. The key is to align them with two other indispensable components: (1) assessments to verify that students learn what the objectives claim and (2) pedagogies and interventions that prepare students to learn what the objectives claim. An important ingredient to this alignment is that learning objectives are measurable. I recommend that we add a bullet number #3 where the S-K-A formula is described in Teaching Open Source: How to Write learning Objectives; and list another useful resource, Carnegie Mellon Enhancing Education along with MIT Teaching and Learning Laboratory.
My take is that the TOS book (as we think of it being used in a course) should have around 5 learning objectives, and each chapter should refine the granularity of some of these top-level learning objectives for the purpose of validating the kind of assessments included in each chapter. I don’t think it’s useful to have learning objectives for each section. Or, we should replace those section-level learning objectives with assessments that measure how much students have learned according to the initial learning plan (i.e. learning objectives). For example, we probably agree that ‘apply’ or ‘demonstrate’ are very suitable action verbs for TOS learning objectives. However, to reach this cognitive level, it’s useful to expect students to ‘identify’ and ‘illustrate’.
What I’m trying to say is that scaffolding the learning process needs support from instructional means and assessment means, always in line with our mantra-like learning objectives – we got so far :-). These means are the essence of the book anyway. We simply need to tie them back to what learning objectives they serve.