The Alberta Teachers’ Association

“At School in the Year 2000,” a futuristic image of learning
depicted on a postcard from the World’s Fair in Paris, circa 1900
Image Source: Wikimedia Commons

This Time It's Personal

Mechanized teaching machines captured people’s imagination in the late 19th and 20th century. Today, yet again, a new generation of technology platforms promise to deliver “personalized learning” for each and every student. This rebirth of the teaching machine centres on digital software tutors (known as adaptive learning systems, orALS) and their grand claims to individualize learning by controlling the pace, place and content of learning for every student. This time around, however, it’s personal.

Personal choice, with centralized control, in an increasingly data-driven, standardized and mechanized learning system, has long been the fantasy of technocrats wanting to (re)shape K–12 teaching and learning with technology. In this alternative reality, class sizes no longer matter and new staffing patterns emerge. The time students spend in schools becomes irrelevant because brick and mortar structures will fade away. Yet, this fantasy disregards the parental desire (and societal expectation) that children will gather together to learn.

Technologies have amplified our desire for choice, flexibility and individualization in North America; it’s easy to be seduced by a vision of computers delivering only what we want, when we want it and how we want it (customized). The marketing mantra, from media conglomerates to banks, is to offer services 24/7, in any place and at any pace. Many governments have adopted this language in their eagerness to reduce costs with businesslike customization and streamlined workforce productivity—all with the expectation that a flexible public education system will also be more efficient and (cost) effective.

The ALS crusade is organized and well funded by venture capitalists and corporations, and its power is growing. Many companies hope to profit from student and teacher data that can be easily collected, stored, processed, customized, analyzed and ultimately sold, but our children and youth are not automated teller machines or retail loyalty cards from which to extract data.

ALS—the new teaching machines—do not build more resilient, creative, entrepreneurial or empathetic citizens through individualized, linear and mechanical software algorithms. Nor do they balance the desire for greater choice with the equity needed for a society to flourish. Computer adaptive learning systems are reductionist in nature and primarily focus on those things that can be easily digitized and tested (e.g., math, science, reading). They fail to recognize that high-quality learning environments are deeply relational, humanistic, creative, socially constructed, active and inquiry oriented.

This article discusses how old notions of teaching machines are being reborn through the seduction of data analytics and competency-based personalization (individualization). The article is also a declaration against the fatalistic notion that ALS are the next evolutionary stage for K–12 education in the 21st century.

History of teaching machines

Over the years various devices to mechanically teach students have been patented. In 1926, Sidney Pressey invented a machine that had two modes of operation: teach and test. After reading through material in the teach mode, a student flicked the control to the test mode and proceeded to answer by pulling down one of four response keys. To give the illusion of progress, the machine scored the response and recorded the number of correct answers. A reward dial could be added so that when a student got a certain number of correct responses, a piece of candy would drop into a small dish for the student (think Pavlov’s dog). It was simply a multiple-choice test in a mechanical box.

In the 1950s, psychologist B. F. Skinner (1954a) made the bold claim that the dawn of the machine age of education had finally arrived. With his particular brand of teaching machines and programmed learning, Skinner vowed that “students could learn twice as much in the same time and with the same effort as in a standard classroom” (Oppenheimer 1997). Skinner said his machine had an important advantage over others because with this machine a student was “free to move at his own pace [and] … only moves on when he has completely mastered all the preceding material … to a final stage in which he is competent” (Skinner 1954b). For Skinner, learning was about measurability, uniformity and control of the student. This view of learning dismissed the larger social, cultural and emotional situations in which knowledge is created.

Now, in 2013, Dreambox Learning, a U.S. technology company, claims its proprietary intelligent adaptive learning (IAL) system has the “effectiveness comparable to human tutoring [and] accelerates math teaching and learning” (www.dreambox.com). The company’s contracted research white paper (Lemke, Dreambox Learning 2013) goes on to say, “The level of sophistication of today’s IAL systems is far superior to similar technologies of the past.” This particular brand of teaching machine individualizes learning by adjusting “path and pace to stay within the child’s zone of optimized learning to accelerate understanding and critical thinking” (www.dreambox.com).

We are caught in an ever-renewing cycle of promises. ALS continue to promote the notion of the isolated individual, in front of a technology platform, receiving concrete and sequential content for mastery. However, the rebranding is that of personalization (individual), flexible and customized (technology platform), delivering 21st-century competencies (content).

At their most innocent, ALS are a renewed attempt to reintroduce behaviourism and operant conditioning to make learning more efficient; at their most sinister, they attempt to make children into measurable commodities to be catalogued, and the information harvested is exploited and monetized by corporations. The rise of ALS could be the tsunami that finally and systematically privatizes public education systems.

Seduction

Why are ALS so seductive? First, they offer greater access to data that can be used to hyperindividualize learning and, in turn, diagnose the challenges facing entire school systems. Second, the modern teaching machines and the growing reach and power of technologies promise to (re)shape students into powerful knowledge workers of the 21st century.

For publishers and educational technology companies, ALS are a means to atomize students and their data away from the public education systems. They allow companies to create long-term “personal” relationships with students, so that they, the companies, can market their products over a student’s lifetime. ALS prevent materials from being shared or transferred over time (because the materials are all digitized and copyright protected) and allow for direct marketing of products and services at any time, place or pace to students and families.

For teachers, ALS are pitched as easy ways to bump students’ test scores, while generating detailed individualized student reports through the software’s surveillance structures. Companies market their algorithms as improving teaching, and freeing up teachers’ time by relieving them of their burdens in a world of test-based accountability. Pressey stated in 1926 that the teaching machine will “make [the teacher] free for those inspirational and thought-stimulating activities which are, presumably, the real function of the teacher” (p. 374).

For parents, this is an extension of the growth of the tutoring movement. It’s estimated that one-third of Alberta parents pay for private tutors (Alberta Teachers’ Association 2011) and the Canadian Council on Learning found in its national survey (2007) that “most parents who hire tutors (73 per cent) estimate that their children’s overall academic performance is in the A or B range.” This is a global obsession; in 2010, 74 per cent of all South Korean students were engaged in some form of private after-school instruction, at an average cost of $2,600 per student for the year (Ripley 2011).

Adaptive learning systems are seductive to a North American society reeling from economic volatility and decline. The middle class is shrinking. Parents are enrolling their children in after-school programs or tutoring in the belief they are giving them a competitive edge over the pack. Hyperparents invest more time, money and energy in their offspring than parents in previous generations ever did, and ALS are seen as one more tool on the treadmill to Harvard.

All of this has dramatic consequences for childhood. Since the late 1970s, children have lost 12 hours per week of free time, including a 25 per cent decrease in play time and a 50 per cent decrease in unstructured outdoor activities (Juster, Ono and Stafford 2004). As well, today’s parents work longer hours and families spend less time with their children (Parkland Institute 2012).

For students frustrated with working in a group setting or having to negotiate the diversity of a public school, the teaching machine provides relief. The new teaching machine becomes a panacea for students who are struggling academically or who are irritated by the pace of learning in schools. Yet, as Hargreaves and Shirley (2009) say, “Customized learning is pleasurable and instantly gratifying. Nevertheless it … ultimately becomes just one more process of business-driven training delivered to satisfy individual consumer tastes and desires” (p. 84).

Setting

Big Data

In the first quarter of the 21st century, people have become deeply (inter)connected with machines. These connections have blurred the boundaries between online and offline behaviours. All online data can be tracked, from cellphones, information from credit card purchases and retail loyalty card transactions to our medical records and our online social media connections. Essentially, we are leaving digital breadcrumbs around our increasingly connected lives, and data about our existence is growing at an exponential rate.

As the amount of our online personal data increases, so, too, does the desire to harvest it for patterns. Out of the ability to track social connections and economic habits down to the individual level, micropatterns emerge. People (and their data) become atomized; behaviours are tracked in real time and compared with the behaviours of millions of other people. With more powerful computing technologies, large data sets may hold the power of prediction (think Amazon book recommendations). This is known as the “big data phenomenon.”

Big data is about finding the seemingly hidden connections in a population or even in our own (learning) behaviours. Companies—and some governments—are beginning to see these big data insights as holding the potential to provide new products, redesign systems and personalize services.

As data gathering increases across society, and we crank out even more information about ourselves, companies look to one of the last frontiers to privatize: student and teacher data. With access to big data on student populations, companies would have limitless opportunities to increase profits and growth. However, in public systems, with democratic governance, it is difficult to get access to intimate data on students and teachers. Public school jurisdictions often frustrate businesses that try to market directly (and to hyperpersonalize) their products to students, parents and teachers.

This may all change with inBloom, a $100 million dollar K–12 education data-sharing initiative launched in 2013 by the Bill & Melinda Gates Foundation and the Carnegie Corporation of New York. InBloom is a database that will reportedly share students’ personal information with 21 for-profit companies. As Simon reports (2013), “In operation just three months, the [inBloom Inc.] database already holds files on millions of children identified by name, address and sometimes social security number. Learning disabilities are documented, test scores recorded, attendance noted. In some cases, the database tracks student hobbies, career goals, attitudes toward school—even homework completion. Local education officials retain legal control over their students’ information. But federal law allows them to share files in their portion of the database with private companies selling educational products and services.”

Two concerns have arisen from this big-data development in education. The first is that Amplify Education, a for-profit division of Rupert Murdoch’s News Corp., built the database infrastructure for inBloom. (In July 2011, the Australian media mogul Murdoch faced allegations that some of his News Corp companies had been regularly hacking the phones of celebrities, royalty and public citizens, and Murdoch was investigated for bribery and corruption in the U.K. and the U.S.) Murdoch has openly articulated his interests in profiting from K–12 education: “When it comes to K through 12 education we see a $500 billion sector in the U.S. alone that is waiting desperately to be transformed by big breakthroughs ... [News Corp company Wireless Generation] is at the forefront of individualized, technology-based learning that is poised to revolutionize public education for a new generation of students” (Murdoch 2010).

The second concern is that parents were not aware that their children’s personal information could be shared with for-profit private technology companies without their consent. Moreover, inBloom “cannot guarantee the security of the information stored … or that the information will not be intercepted when it is being transmitted” (Simon 2013). The Electronic Privacy Information Center has subsequently filed a lawsuit against the U.S. Department of Education, charging it with violating student privacy rights and undermining parental consent (Strauss 2013a). In Louisiana, State Superintendent John White announced he would recall all confidential student data from inBloom (Leader 2013).

Issues of privacy, data access, and who actually owns student and teacher data will intensify in the next few months. There can be value in having big data analyzed to discover new patterns, but not at the expense of removing privacy protections for students in a public education system.

Personalized Learning

Personalized learning is neither a pedagogical theory nor a coherent set of teaching approaches—it is an idea struggling for an identity (McRae 2010). Personalized learning linked to technology-mediated individualization anywhere, anytime, is based on old ideas from the assembly line. It’s a model advanced by private corporations, virtual schools and charter schools in the U.S.

Personalizing learning as an act of differentiating learning in a highly relational environment is not new to teaching. Legions of teachers enter classrooms to engage diverse minds through multiple activities and to support each student as he or she inquires into problems. These same teachers, who are keenly aware of their students’ particular learning styles and passions, are also simultaneously contending with poverty, lack of parental involvement (or the opposite—helicopter parents), large classes, familial and community influences, student effort, and numerous digital and popular culture distractions that add to the complexity of their professional practice.

Personalizing learning can be a progressive stance to education reform and is in line with many new forms of assessment, differentiated learning and instruction, and the redesign of high schools beyond age cohorts and classes. More-flexible approaches to education are undeniably necessary, and finding ways to personalize learning will be important if students are to adequately develop the skills and knowledge that will help them creatively navigate an uncertain future. However, personalized learning defined as an isolated child sitting in front of a computer screen for hours on end is folly.

Enablers

To enable ALS to materialize in an education system, governments and school districts will have to agree to give publishers and educational technology companies direct access to students. Doing this would create multiple pathways of learning that are more flexible with respect to time and space and are based on technology solutions that only the company can deliver. On the surface this flexibility sounds promising, as teachers and school leaders certainly recognize that the industrial model of command and control does not fit with our hyperconnected world. Unfortunately, the flexibility of any-time, any-pace learning is manifesting itself in the U.S. in adaptive learning software programs or mandatory online learning courses delivered by private companies.

Challenges facing teachers and public education

1. Commodification of Student Data

Public schools must be the guardians of students’ personal data. Teachers, as children’s guardians, can’t collect big data without parental consent and then inadvertently allow it to be passed to companies looking for a new marketplace. With ALS, companies can market directly to all students or their parents without the obstructions (or guidance) of a robust public education system. The data analytics crusade in schools and issues about who owns and controls the big data of children and youth must be challenged.

2. Reductionist Thinking

ALS can direct teacher and student attention to math and reading only. DreamBox Learning states in its direct e-mails to parents: “Research has shown that mastery of early math skills is the single best predictor of future academic success—more important even than early reading!” (McRae, personal communication, January 28, 2013).

3. Learning Is Socially Constructed

Research finds that learning is successful when it’s socially constructed and occurs in an active and inquiry-oriented process that engages people in social, emotional, cultural and deeply intrapersonal experiences. This research will likely hold true, whether or not future learning environments are face to face, online or in blended learning online/offline settings.

4. Adaptation

Much good can come from giving students more personalized experiences with learning, but the world does not adapt to people—people must adapt to the world. In order for them to adapt and bounce back, we must build resilience in our children and youth.

Zolli and Healy (2012) define resilience as the ability of a “system, enterprise, or a person to maintain its core purpose and integrity in the face of dramatically changed circumstances” and see resilience as “preserving adaptive capacity” (p. 8) and “the ability to adapt to changed circumstances while fulfilling one’s core purpose, which is an essential skill in an age of unforeseeable disruption and volatility” (p. 9). Resilience not only encourages adaptability, it also strengthens 21st-century collaborative skills, connectivity and an appreciation of diversity in the world. Resilience is not shaped through teaching machines but through highly relational learning environments, and it will be especially important in a global world defined by increased volatility, ambiguity, uncertainty and complexity.

5. Echo-Chamber Effect

We’re entering a digital age of mobility in which students access information they want at any time, place or pace using a variety of devices. This will have a profound effect on critical thinking as students are increasingly fed only the exact type of information (specific political views, topical book themes, local environmental conditions) and sources (individual blogs, Twitter feeds, Facebook updates or websites) to which they digitally subscribe. In many ways, hyperpersonalized (customized) digital spaces have the potential to limit students to only the content that they want to see, hear and read. A condition can then arise in online communities where participants find their own opinions constantly echoed back to them (the echo-chamber effect), thus reinforcing a truth that resonates with their individual belief systems (McRae 2006). Diversity of talents, free will and personal choice take on new meanings in digital echo chambers. In considering personalization and technology, we must be thoughtful about the role of critical thinking, diversity and chance.

6. Children and Screen Time

To what extent do we want children and youth spending even more time immersed in adaptive learning software programs during the school day? Research finds that children between the ages of 8 and 18 already spend an average of 7.5 hours a day in front of screens: televisions, computers, video games and phones (Kaiser Family Foundation 2010). To gather data through ALS, children will need to spend time allowing the machine to monitor their interactions. John Danner, former CEO of Rocketship Charter Schools and a member of the board of directors of DreamBox Learning, envisions even more screen time during the day for children. “As the quality of software improves, Danner thinks ‘Rocketeers’ could spend as much as 50 percent of the school day with computers” (Strauss 2013b).

Those who work with children, families, schools and communities are asking serious questions about the effects of online digital activities on health and mental well-being. A particular concern is late-night screen time, especially if children are spending hours at home in front of the screen with the virtual computer tutor. A growing body of new research shows that late-night screen time decreases sleep quality and quantity and negatively affects children’s readiness to learn (Howard-Jones 2012; Rich 2012). Are we really willing to sacrifice developing the healthy minds and bodies of our children and youth for the sake of data analytics?

A Better Path

No simple computerized solutions exist for the complex and diverse challenges of poverty and inequity, or lack of parental engagement (or, conversely, hyperparenting). How, then, can we improve educational practices and create great schools for all students? What is a better path than the seduction of adaptive learning systems?

One better path is the establishment of conditions of professional practice where high-quality teachers and principals with a sense of efficacy differentiate instruction and advance new forms of assessment for learning with and without technology. Teachers could be engaged in a conversation, earlier rather than later, about using data (big or small) to enhance student learning.

Technologies could be employed to help students become empowered citizens rather than passive consumers. Educational innovations are needed that will create a society where people flourish within culturally rich, informed, democratic, digitally connected and diverse communities. We should not descend into a culture of individualism through technology, where people are fragmented by a continuous partial attention.

The education of our next generations should not be about machines but, rather, about a community of learners whose physical, intellectual and social well-being is sacred. This point of view is driven by the human desire to connect, maintain friendships, tell stories, share thoughts and inquire into the nature of the world. It’s a perspective that flows naturally together with research on learning that suggests education is not about just content or physical place—it is a collective and highly relational set of experiences within a community of learners.

Emerging technologies and smart data have a place in educational transformation, but they must be used to enhance what research in the learning sciences continues to reinforce as the foundation of learning: the pedagogical relationships between students, teachers, parents and community. Attempts to displace this human dimension of learning with the teaching machine (whatever you imagine it to be) is a distraction to the most important support great schools can offer students each and every day: relationships, relationships, relationships.

Bibliography

Alberta Teachers’ Association. 2011. Changing Landscapes for Learning Our Way to the Next Alberta. Edmonton, Alta.: Alberta Teachers’ Association. Available at http://bit.ly/12bjum3 (accessed May 8, 2013).

Dewey, J. 1938. Experience and Education. New York: MacMillan.

Canadian Council on Learning. 2007. Survey of Canadian Attitudes Toward Learning: Canadian Attitudes Toward Tutoring. Vancouver, B.C.: Canadian Council on Learning. Available at http://bit.ly/YsGPyS (accessed May 8, 2013).

Eisner, E. 1994. The Educational Imagination: On the Design and Evaluation of School Programs. 3rd ed. New York: Macmillan.

Hargreaves, A., and D. Shirley. 2009. The Fourth Way: The Inspiring Future for Educational Change. Thousand Oaks, Calif.: Corwin.

Howard-Jones, P. 2012. The Impact of Digital Technologies on Human Wellbeing. Invitational Research Colloquium on Promise and Peril: The Impact of Technology on Children, Schools and Communities, Alberta Teachers’ Association, Edmonton, Alberta, April 25. Available at www.teachers.ab.ca/SiteCollectionDocuments/ATA/Teaching-in-Alberta/Paul%20Howard%20Jones.pdf or http://tinyurl.com/cdh5t9a (accessed May 9, 2013).

Juster, F. T., H. Ono, and F. Stafford. 2004. Changing Times of American Youth: 1981–2003. Ann Arbor, Mich.: Institute for Social Research, University of Michigan. Available at www.umich.edu/news/Releases/2004/Nov04/teen_time_report.pdf (accessed May 9, 2013).

Kaiser Family Foundation. 2010. Generation M²: Media in the Lives of 8- to 18-Year-Olds. Menlo Park, Calif.: Henry J. Kaiser Family Foundation.

Leader, B. 2013. “Superintendent John White Recalls Student Data Stored with Nonprofit inBloom.” The News Star, April 19. Available at http://tnsne.ws/YsHD77 (accessed May 9, 2013).

McRae, P. 2006. “Echoing Voices—Emerging Challenges for Educational Practice on the Internet.” In Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2006, ed T. Reeves and S. Yamashita, 2622–29. Chesapeake, Va.: American Association for the Advancement of Computing in Education (AACE).

———. 2010. “The Politics of Personalization in the 21st Century.” Alberta Teachers’ Association Magazine 91, no 1: 8–11. Edmonton, Alta.: Alberta Teachers’ Association.

Murdoch, R. 2010. “News Corporation to Acquire Education Technology Company Wireless Generation.” Washington, D.C.: News Corporation. Press release. Available at www.newscorp.com/news/news_464.html (accessed May 9, 2013).

Oppenheimer, T. 1997. “The Computer Delusion.” Atlantic Monthly 280: 45–62.

Parkland Institute. 2012. “Family Day on the Treadmill: Alberta Families at Risk of Too Much Stress.” Parkland Institute Fact Sheet, February 17. Available at http://bit.ly/10nw3aH (accessed May 8, 2013).

Pressey, S. L. 1926. “A Simple Apparatus Which Gives Tests and Scores—and Teaches.” School and Society 23, no 586: 373–76.

Rich, M. 2012. Finding Huck Finn: Reclaiming Childhood from a River of Electronic Screens. Invitational Research Colloquium on Promise and Peril: The Impact of Technology on Children, Schools and Communities, Alberta Teachers’ Association, Edmonton, Alberta, April 25. Available at www.learningourway.ca/images/pdf/Dr%20Michael%20Rich%20-%20ATA%20Research%20Colloquium%20Keynote%20Presentation.pdf or http://tinyurl.com/c95f9nm (accessed May 9, 2013).

Ripley, A. 2011. “Teacher, Leave Those Kids Alone.” Time, Sept. 25. Available at http://ti.me/15nNIHh (accessed May 8, 2013).

Simon, S. 2013. “K–12 Student Database Jazzes Tech Start-Ups, Spooks Parents.” Reuters (U.S. edition), March 3. Available at http://reut.rs/ZEZiGa (accessed May 8, 2013).

Skinner, B. F. 1954a. “The Science of Learning and the Art of Teaching.” Harvard Educational Review 24: 86–97.

———. (1954b). Teaching Machine and Programmed Learning. Video file. Available at http://youtu.be/jTH3ob1IRFo (accessed May 8, 2013).

Strauss, V. 2013a. “Lawsuit Charges Ed Department with Violating Student Privacy Rights.” Washington Post, March 13. Available at http://wapo.st/18Ufebt (accessed May 8, 2013).

———. 2013b. “Rocketship Charter Schools Revamping Signature ‘Learning Lab.’” Washington Post, January 25. Available at http://wapo.st/18saT1D (accessed May 8, 2013).

U.S. Department of Education. 2013. Competency-Based Learning or Personalized Learning. Washington, D.C.: United States Government. Available at http://1.usa.gov/YsInc9 (accessed May 8, 2013).

Zhao, Y. 2009. Catching Up or Leading the Way: American Education in the Age of Globalization. Alexandria, Va.: ASCD.

Zolli, A., and A. M. Healy. 2012. Resilience: Why Things Bounce Back. New York: Free Press.

____________________________________

Phil McRae is an ATA executive staff officer in the Government program area.