Outcome vs. Process: Different Incarnations of Personalization

This was written by Yong Zhao who is the author writes and speaks about education reform. He blogs here and tweets here. This post was found here.


by Yong Zhao

There are different views of personalized learning. My advocacy for personalization has been occasionally misunderstood as supporting the narrow view of personalized learning driven by big data and learning analytics with technology or online learning in general. Below is an excerpt of a chapter from a book I coauthored with a group of teachers and school leaders: World Class Learners Bundle to be published by Corwin. Hope it helps clarify my take on personalized learning.–Yong

To personalize is to design or produce something to meet individual requirements. In education, personalization is often used in the forms of “personalized learning,” “personalized education,” or “personalized instruction.” The term personalization is often used interchangeably with individualization, and sometimes with customization. The general idea is to enable individual students to have an educational experience that meets their individual needs.

Although it is has long been recognized that individual students have different needs and high-quality education cannot be “one size fits all,” personalization in education has different meanings and realizations in practice because education has many components that can be personalized, individualized, or customized. For example, personalization can happen at the pace of learning by allowing students to learn at their own speed. Personalization can also be employed to enable students to choose when and where they learn. It can also be used in ways that allow students to have a choice of work assignments in the classroom. Furthermore, personalization is a strategy that enables students to demonstrate their learning by creating a product of their own choosing.

Generally speaking, personalization can be put into two categories: process personalization and outcome personalization. Process personalization enables students to enjoy choice in the learning process, whereas outcome personalization allows students to define the end results of their learning. Process personalization is by far the most prominent version in education today because the current education paradigm has a predetermined outcome for all students. That is, no matter how one gets there, we want everyone to get to the same place: mastery of the knowledge and skills prescribed in the authoritative curriculum or standards.

Personalization of the Learning Process

Although the outcome remains the same, the journey to the destination can be personalized to accommodate different needs, abilities, learning styles, and interests of students. Some of the most common aspects of individualization or personalization that have taken place (or should take place) include pace, content, product, learning environments, and assessment.

Personalization of pace: For all sorts of reasons, students come to school with different abilities and thus will acquire the same content at different speeds. To accommodate different abilities in students, schools have been encouraged to allow students to progress at their individual pace. One of the earliest experiments for self-paced learning is programmed instruction advocated by behaviorist psychologists such as B. F. Skinner in the 1960s (Skinner, 1968). Skinner and like-minded individuals relied on technology to enable students to pace their own learning and receive feedback. With the advent of modern computer technologies, individualization of learning pace became more prominent with computer-based learning. Today, the tradition continues in the form of personalized learning with the support of Big Data and learning analytics technology. Personalization of pace can also happen in the classroom by permitting students to work at their own speed. At the school level, one form of personalization is ability grouping or tracking, which puts students into different classes that move at different paces.

Personalization of content: Content can also be personalized to meet individual needs. Although all students in the traditional educational paradigm need to master the same content as prescribed by curriculum standards, they can be exposed to different content that best suits them. For example, following the principles of differentiated instruction (Tomlinson, 2001), students can be given different tasks based on their level of understanding of the content to be covered using Bloom’s Taxonomy. To accommodate different interests and learning styles, students can also choose different genres of content. For instance, different kinds of texts, novels, or short stories can be used to meet the needs of individual students at different reading levels. The media used to present the content can also be individualized. Some may prefer reading, others listening. Some may learn best from audio, others visual, and still others physical manipulation.

Personalization of product: Students often need to produce some sort of product (e.g., papers, exhibits, or exams) to demonstrate their mastery of the intended content. To accommodate different levels and styles of learning, the type of products expected of students can be personalized. Some students may prefer to write a paper, others may choose to compose a song. Some may demonstrate their learning by constructing a product such as a poster, others may create a multimedia interactive book. Some students may choose to take a traditional test, while others may design a video game.

Personalization of the learning environment: Where and how learning occurs can also be individualized. Although the same standard and content is expected of all students in the traditional paradigm, students may choose to learn in different places, from different sources, and with different arrangements. With the wide accessibility to online resources, students do not need to learn the content from the classroom alone nor do they need to learn from the teacher only. They could also learn from field trips and extended trips. Moreover, students could choose to take courses online from other institutions. In terms of how students can learn, they could learn by themselves, or in collaboration with others. In the classroom, a teacher can create different learning environments to support personalized learning. Teachers may use different grouping strategies to accommodate student working styles and preferences or they can create different physical arrangements in the classroom for different learning purposes.

Personalization of Outcome

Personalization of the learning process has tremendous value in improving student learning. It is undoubtedly a major improvement over the traditional one-size-fits-all teaching practices. Thus, personalization has been advocated for decades as an effective approach in the traditional education paradigm to meet the needs of individual students, especially students who have disabilities or are judged to be less ready for certain school tasks. However, it is not enough for cultivating the creative and entrepreneurial talents we need in the new world, as discussed in World Class Learners: Educating Creative and Entrepreneurial Students (Zhao, 2012). A different level of personalization is needed: personalization of learning outcomes.

Personalization of learning outcomes takes personalization to a different level by allowing students to pursue their strengths and interests. It does not accept a prescribed curriculum or set of standards as common to all students, as in the traditional paradigm. Thus, the goal of education is not to fix students’ deficits measured by external standards. Rather, this level of personalization assumes that all talents, skills, and knowledge are of equal value and thus all learning outcomes are valuable. As a result, instead of forcing or luring all students to master the same knowledge and skills, this approach asks for personalized educational experiences that support the development of individual talent. Recent developments in technology also enable students to have access to global educational resources, hence providing opportunities for students to construct a learning environment that meets their diverse needs.

Strength-based personalization: Allowing students to personalize their outcomes is to enhance their strengths. Thus, strength-based personalization requires teachers to not focus on what the students cannot do. Instead, the teacher looks hard at what each student can do and uses that as a starting point to build an individualized pathway for the student. In other words, rather than having students follow a predetermined curriculum, schools follow students and work with them to co-create the curriculum, which is highly individualized. The curriculum emerges as student learning progresses. To do so, schools need to offer a broad range of courses or other learning activities for students to explore their strengths. In this model, the school becomes a museum of learning opportunities. Students can choose to take advantage of any of these opportunities, as museum visitors would any of the exhibits. Teachers become curators of learning opportunities and also “tour guides” for students. They do not impose but can certainly mentor, motivate, and challenge.

Passion-driven personalization: Personalization can also be driven by students’ passions, which can be different from their strengths. That is, what a student may be good at can be different what he or she is passionate about. Students’ interests should be considered as legitimate sources of motivation; what students are passionate about has intrinsic value, although it may or may not coincide with the prescribed curriculum. To support personalization driven by students’ interests and passions, schools need to develop mechanisms to identify students’ interests. Schools must treat these interests seriously once they are identified, and schools must develop courses and learning activities accordingly.

In summary, personalization of learning outcomes is not mutually exclusive with personalization of process. In fact, it requires all of the different strategies of process personalization. But it goes beyond process personalization by extending personalization beyond a predefined curriculum. Curriculum standards may still be valuable as a guide for specific subjects and domains, should students choose to master that subject or domain. However, students are not forced to learn what has been prescribed, particularly at a prescribed time, location, and pace.

MYTH: Blended Learning is the Next Ed Tech Revolution - Hype, Harm and Hope

This was written by Dr. Phil McRae who is an executive staff officer with the Alberta Teachers` Association and adjunct professor within the faculty of education at the University of Alberta. Dr. Phil McRae’s Biography, Research, Writing, Scholarship and Presentations can be found at www.philmcrae.com, and you can follow him on Twitter here. This post first appeared here.

by Dr. Phil McRae

“The great enemy of the truth is very often not the lie — deliberate, contrived, and dishonest — but the myth — persistent, persuasive, and unrealistic." 

~ John F. Kennedy

Blended learning, where students’ face-to-face education is blended with Internet resources or online courses, has been gaining considerable attention in education reform circles. It has become entangled with the ambiguous notion of personalized learning and is being positioned as the new way to individualize learning in competency-based education systems.

Michael Horn, co-founder of the Clayton Christensen Institute for Disruptive Innovation, and a key proponent of blended learning, claims that it is the “new model that is student-centric, highly personalized for each learner, and more productive, as it delivers dramatically better results at the same or lower cost” (Horn and Staker 2011, 13).

To what extent is this a new model of learning in a digital age? How are private corporations employing old rhetoric to advance new avenues into public education? Most importantly, is blended learning becoming yet another overhyped myth on the crowded road of technology-as-education-reform panacea?

ORIGINS OF A MYTH

Students blending the use of technology with face-to-face instruction as a means of collaborating and extending their learning experiences is not unusual, revolutionary or foreign to the average Canadian classroom. As a concept, blended learning is now almost two decades old, having been imported into K–12 education in the late 1990s from corporate education, business training firms and the post-secondary education sector. Although the precise origin is unclear, it has been suggested that an Atlanta-based computer training business coined the term in 1999 (Friesen 2012), as it announced the release of a new generation of online courses for adults that were to be blended with live instruction.

Many blended learning practices already fit well with a vast array of hybrid face-to-face and digital experiences that students encounter in K–12 schools, including distributed learning, distance learning, or e-learning. Dr. Norm Friesen, a key academic in this area, suggests that blended learning “designates the range of possibilities presented by combining Internet and digital media with established classroom forms that require the physical co-presence of teacher and students” (Friesen 2012). As this broad definition illustrates, it would be difficult to find any use of technology in education that does not easily fit into this boundary.

Despite this fluidity of meaning, different models of blended learning have taken shape. In particular, Staker and Horn (2012) have attempted to classify blended learning environments into four models: rotation, flex, self-blend and enriched virtual. These four combinations range from those that are more connected to people and brick-and-mortar buildings (rotation, flex) to contexts in which the students are primarily self-directed through online courses or platforms that “deliver” the curriculum (self-blend and enriched virtual). In the more self-directed models, teachers or non-certificated facilitators are conditional and only scheduled for support as deemed necessary.

Although many models have been implemented over the last 20 years, there is scant evidence of the success of blended learning. Out of 46 robust research studies conducted between 1996 and 2008, only five have focused on results for students in K–12 settings (Murphy et al. 2014). As a recent article in Education Week illustrates, when looking for strong evidence of success around this strategy for K–12 students, very little “definitive evidence” or few significant results can be directly attributed to blended learning (Sparks 2015).

HYPE

The current hype around blended learning models, especially in the United States, is that they bring to life personalized learning for each and every child. Personalized learning, as promoted under a new canopy of blended learning, is neither a pedagogic theory nor a coherent set of learning approaches, regardless of the proposed models. In fact, personalized learning is an idea struggling for an identity (McRae 2014, 2010). A description of personalization that’s tightly linked to technology-mediated individualization “anywhere, anytime” is premised on archaic ideas of teaching machines imagined early in the 20th century (McRae 2013).

Some blended learning rhetoric suggests that personalization is to be achieved through individualized self-paced computer programs (known as adaptive learning systems), combined with small-group instruction for students who have the most pressing academic needs. For those looking to specifically advance blended learning in times of severe economic constraints, a certificated teacher is optional.

Software companies selling their adaptive learning products boldly state that the “best personalized learning programs will give students millions of potential pathways to follow through curricula and end up with the desired result — true comprehension” (Green 2013). This is part of the myth of blended learning and is marketed using superficial math and reading software programs (adaptive learning systems) that make dubious claims of driving up scores on high-stakes tests. Corporate attempts to “standardize personalization” in this way are both ironic and absurd.

These adaptive learning systems (the new teaching machines) do not build more resilient, creative, entrepreneurial or empathetic citizens through their individualized, standardized, linear and mechanical software algorithms. On the contrary, they diminish the many opportunities for human relationships to flourish, which is a hallmark of high-quality learning environments.

One of the blended learning examples that has received perhaps the greatest attention is the “flipped classroom.” It is so named because it inverts classroom instruction during the day, so that students watch online video of lectures at home at their own pace, perhaps communicating with peers and teachers via online discussions in the evening, and spend their days doing homework in the classroom. Think of the popular media hype and mythical cure for math challenges sold to the public by the Khan Academy. There is nothing revolutionary or deeply engaging about pure lecture as a pedagogy, yet apparently adding hours of digitally distributed video each evening to a child’s life makes it so. In fact, research suggests that the use of this type of lecture recorded technology, as a primary approach to learning, can result in students falling behind in the curriculum (Gosper et al. 2008).

Many myths, when viewed up close, provide deep reflections of ourselves and society. Technologies in particular have amplified our North American desires for choice, flexibility and individualization, so it’s easy to be seduced by a vision of blended learning environments delivering only what we want, when and how we want it customized.

The marketing mantra from corporations as diverse as media conglomerates to banks is that of services at any time, in any place or at any pace. Many governments have in turn adopted this in an eagerness to reduce costs with businesslike customization and streamlined workforce productivity, all with the expectation that a flexible and blended education system will be more efficient and (cost) effective.

In the mythical space of blended learning, class sizes apparently no longer matter and new staffing patterns begin to emerge. The amount of time students spend in schools becomes irrelevant as brick-and-mortar structures fade away. However, this myth disregards the overwhelming parental desire and societal expectation that children and youth will gather together to learn in highly relational settings with knowledgeable and mindful professionals (teachers) who understand both the art and science of learning. As John F. Kennedy (1962) so eloquently stated: “The great enemy of the truth is very often not the lie — deliberate, contrived, and dishonest — but the myth — persistent, persuasive, and unrealistic.”

The U.S. Department of Education (2013) has clearly articulated a commitment to making blended learning come to life through nebulous ideas of competency-based systems and personalized learning.

“Transitioning away from seat time, in favor of a structure that creates flexibility, allows students to progress as they demonstrate mastery of academic content, regardless of time, place, or pace of learning. By enabling students to master skills at their own pace, competency-based learning systems help to save both time and money … make better use of technology, support new staffing patterns that utilize teacher skills and interests differently … Each of these presents an opportunity to achieve greater efficiency and increase productivity.”

The cost efficiency and effectiveness rhetoric must be given special attention as part of the myth of blended learning in competency based systems.

HARM

Schools and classrooms across North America are being subjected to economic volatility and severe constraints by reduced public education funding. Blended learning can be positioned as the vehicle to bring in third-party education providers to wipe out the expectations of small class sizes and certificated teachers in traditional classrooms. This idea is gaining momentum through a variety of U.S. virtual and charter schools that are radically reducing the numbers of teachers and executing increased class sizes under the banner of blended learning. As Michael Horn states when asked to give expert advice on blended learning models, “budget cuts and teacher shortages are an opportunity, not a threat” (Horn et al. 2014).

As school jurisdictions across the U.S. turn to online learning and blended models as a way to reallocate resources, the private providers are also advocating for “eradicating rules that restrict class size and student-teacher ratios” (Horn and Staker 2011, 13). To achieve this means lifting the rules around teacher certification so that schools can replace teachers at will with para-professionals or noncertificated individual learning specialists. As Christensen and Horn (2008) suggest, “Computer-based learning on a large scale is also less expensive than the current labor intensive system and could solve the financial dilemmas facing public schools” (13).

To enable this in an education system, several policies must be enshrined by governments that would allow private schools, virtual cyber-charter schools or educational technology companies direct access to students outside of a protected public system. The first is to open up multiple pathways of learning, which are more flexible in terms of time and space, and designed around technology solutions that only the company can deliver.

The Software & Information Industry Association, the principal trade association for the software and digital content industries in America, is a clear backer of redefining and expanding the role of the teacher, and advocates that “teacher contracts and other regulatory constraints may also need to be addressed to provide the flexibility in a teacher’s role needed to make this dramatic shift in instruction” (Wolf 2010, 15).

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 hyper-connected world. Yet the flexibility of any-time, any-place learning is manifesting itself in the U.S. around adaptive learning software programs or mandatory online learning courses that are being delivered by private companies. New course access legislation (as found in Wisconsin, Texas, Utah, Florida, Michigan and Minnesota) now allows anyone to teach online courses to students regardless of jurisdiction, certification or geographic location (Dwinal 2015). In other words, every course, for every student, anywhere, anytime — and now — taught by anyone. Half the teachers, but sold as twice the fun?

In the case of K12 Inc., the United States’ largest private for-profit provider of online education for grades K–12, student-teacher ratios are as high as one teacher to 275 students (Aaronson and O’Connor 2012). As the president and CEO at McGraw-Hill Education affirms: “With this new method and capability, all of a sudden you could see a teacher handling many more students ... the productivity could double or triple” (Olster 2013).

The harsh reality, however, is that private online schooling is not about new blended learning models, flexibility or choice, it is about profit through the constant cycle of enrolment and withdrawal of students known as the “churn rate” (Gibson and Clements 2013). In contrast, our current publically funded and publically delivered online schools across Alberta reinforce the important role of certificated teachers as compassionate and empathetic architects of learning who work relentlessly to reduce the drop-out rates and increase student engagement in virtual learning environments.

Rocketship Education, one of the many rapidly growing charter schools out of the U.S., has adopted a rotation model of blended learning known as the Rocketship Hybrid School Model for kindergarten to Grade 5 students. It combines online learning on campus with traditional classroom-based activities in order to save $500,000 per charter school per year in teacher salary costs (Danner 2010).

To accomplish this, Rocketship Education has cut half its teachers, changed its scope of practice and hired low-paid adults to supervise and monitor students in computer labs. The new staffing patterns within this rotation blended learning model place the schools in a one to 100-plus student/teacher ratio, with one or two low-wage computer lab monitors. These support personnel are endowed with titles like “individual learning specialists,” “coaches” or “facilitators” (Public Broadcasting Service 2012).

Without certificated teachers present, there is a need to gather data on student performance, so the children spend a great deal of time in a computer lab with an adaptive learning program monitoring their every interaction. John Danner, former CEO of Rocketship Charter Schools and a board member of DreamBox Learning Inc., promotes increased screen time during the day for children. He thinks that as the quality of software improves, “‘Rocketeers’ could spend as much as 50 per cent of the school day with computers” (Strauss 2013). How many hours of development, in the minds and bodies of children and youth, are we willing to sacrifice for more individualized computer-human interactions under the guise of blended learning?

If blended learning through the rotation model is to be defined by reducing the number of certificated teachers in schools and placing students in computer labs to spend half of their day in front of math and reading software programs, then education in the 21st century is indeed heading down an antiquated and very dangerous path. This is not historically the way blended learning has come alive in Alberta classrooms, nor should it be our preferred future.

HOPE

The growth of digital media and the Internet has led to an explosion of resources and opportunities for teachers, students and learning communities. A constant shift is occurring with different mobile apps, blogs, video podcasts, social media tools, e-learning courses, or learning management systems in schools that all promise to help teachers create and organize student work, provide (real-time) feedback or communicate more efficiently.

With the proliferation of digital tools in our lives, many K–12 students now experience learning through a blend of face-to-face and digital or online media and are able to access new ideas and resources where student attitudes and engagement towards their education can be positively supported. If blended learning is to lead to positive outcomes for students, then it must be highly relational, active and inquiry oriented (both online and offline), and commit to empowering students with digital tools.

If done right, blended learning can be used to support more equitable access to learning resources and discipline-specific expertise. It may also engage students (and teachers) in a variety of online and offline learning activities that differentiate instruction and bring greater diversity to the learning context. Improving communication between teachers, students and parents and extending relationships across boundaries and time may also be an outcome of blended learning. It may also hold value by employing certain technologies that help teachers and students to formatively assess learning.

To make this truly hopeful, school-based technology infrastructure must be robust and up-to-date, with equitable access, and the necessary resources (human and technology) must be made available to pedagogically support the blending. It is not tenable if Internet connectivity is unreliable or limited, or if there exists inequitable access to bandwidth or technology infrastructure in the school and home. Finally, if technical glitches are pervasive, or if dependable technical support is not available for students and teachers, then it is unlikely that blended learning will be a sustainable concept.

CONCLUSION

Blended learning is not a new term nor a revolutionary concept for classrooms in this second decade of the 21st century. However, the way it is being (re)interpreted could be hopeful or harmful depending on how it is implemented. It is an increasingly ambiguous and vague notion that is growing in popularity as many groups try to claim the space and establish the models, despite a lack of evidence and research. We should therefore be skeptical around the mythos of blended learning before endorsing or lauding it as the next great reform.

Blended learning has occupied a place in discourses of educational change for well over a decade, but it cannot be co-opted into a movement that displaces the human dimension of learning with an economic imperative to reduce labour costs by cutting the teaching population in half. Of particular concern in times of severe economic restraint is that high schools may become the testing ground for policymakers looking at ways to redesign by cutting certificated teachers in favour of massive online cohorts of students tutored by “facilitators” or “individual learning specialists.”

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

For the vast majority of students within Alberta’s K–12 public education system, we must achieve a more nuanced balance that combines both digital technologies and the physical presence of a caring, knowledgeable and pedagogically thoughtful teacher. This is not an optional “nice to have,” but a “must have” if children and youth are to build resilience for the future. Blended learning may be (re)shaped by privatization myths, with adaptive learning systems as their voice, but in Alberta, our teachers still remain the quintessence of the human enterprise of paying it forward for our next generation. It is time for Alberta teachers to claim the space of blended learning and push back at the myths and questionable rhetoric.

Citation:
McRae, P. (2015). Myth: Blended learning is the next ed-tech revolution – hype, harm and hope. Alberta Teachers' Association Magazine 4 (95). Edmonton, AB: Barnett House Press p. 19-27.


REFERENCES

Aaronson , T., and J. O'Connor. 2012. “In K12 courses, 275 students to a single teacher.” Miami Herald, September 16. http://www.miamiherald.com/2012/09/16/3005122/in-k12-courses-275-students-to.html.

Christensen, C. M., and M.B. Horn. 2008. “How Do We Transform Our Schools?”Education Next 8, no. 3 (Summer), 13–19.

Danner, J. 2010. “Rocketship Hybrid School Model — Half The Teachers and Twice the Pay.” Donnell-Kay Foundation website. http://dkfoundation.org/news/rocketship-hybrid-school-model-half-teachers-and-twice-pay (accessed May 4, 2015).

Dwinal, M. 2015. “Solving the Nation's Teacher Shortage: How online leanrning can fix the broken teacher labor market.” Clayton Christensen Institute website. http://www.christenseninstitute.org/wp-content/uploads/2015/03/Solving-the-nations-teacher-shortage.pdf (accessed May 4, 2015).

Friesen, N. 2012. “Defining Blended Learning.” Learning Spaces, August. http://learningspaces.org/papers/Defining_Blended_Learning_NF.pdf (accessed May 4, 2015).

Gibson, D., and J. Clements. 2013. Delivery Matters: Cyber Charter Schools and K–12 Education in Alberta. Edmonton, AB: Parkland Institute.

Gosper, M., D. Green, M. McNeill, R. Phillips, G. Preston and K. Woo. 2008. Final Report: The Impact of Web-Based Lecture Technologies on Current and Future Practices in Learning and Teaching. Sydney: Macquarie University.

Green, N. 2013. “What to look for in a personalized learning plan.” DreamBox Learning website. http://www.dreambox.com/blog/personalized-learning-plan#sthash.ubJ00yA3.dpuf (accessed May 5, 2015).

Horn, M. B., and H. Staker. 2011. “The Rise of K–12 Blended Learning.” Clayton Christensen Institute website. http://www.christenseninstitute.org/wp-content/uploads/2013/04/The-rise-of-K-12-blended-learning.pdf (accessed May 5, 2015).

Horn, M. B., C. Christensen and C.W. Johnson. 2010. Disrupting Class: How Disruptive Innovation Will Change the Way the World Learns. New York: McGraw-Hill.

Horn, M. B., T. Hudson and J. Everly. 2014. “Blended Learning in K8 Schools: Expert Advice from Michael Horn.” DreamBox Learning website: http://www.dreambox.com/webinar/blended-learning-k8-schools-expert-advice-michael-horn (accessed May 5, 2015).

Kennedy, J. F. 1962. “Yale University Commencement Address.” Transcript of speech given at Yale University, New Haven, CT, June 11, 1962. Miller Center, University of Virginia website. http://millercenter.org/president/speeches/speech-3370 (accessed May 5, 2015).

McRae, P. A. 2010. “The Politics of Personalization in the 21st Century.” Alberta Teachers' Association Magazine 91, no. 1: 8–11.

McRae, P. A. 2013. “Rebirth of the Teaching Machine through the Seduction of Data Analytics.” Alberta Teachers' Association Magazine 93, no. 4. Also available at http://philmcrae.com/2/post/2013/04/rebirth-of-the-teaching-maching-through-the-seduction-of-data-analytics-this-time-its-personal1.html (accessed May 5, 2015).

McRae, P. A. 2014. “[Debate] Challenging the Promise of Personalized Learning — WISE 2014.” World Innovation Summit for Education (WISE). https://www.youtube.com/watch?v=qwI4oC_A0IM (accessed May 5, 2015).

Murphy, R., E. Snow, J. Mislevy, L. Gallagher, A. Krumm and X. Wei. 2014. Blended Learning Report. Austin, TX: Michael and Susan Dell Foundation.

Olster, S. 2013. “Better Technology and More Productive Teachers are Just Around the Corner.” Fortune website. http://tech.fortune.cnn.com/2013/01/10/the-future-of-the-classroom (accessed May 5, 2015).

Public Broadcasting Service (PBS). 2012. “Can 'Rocketship' Launch a Fleet of Successful, Mass-Produced Schools?” PBS Newshour, December 28. http://www.pbs.org/newshour/bb/education-july-dec12-rocket_12-28/ (accessed May 5, 2015).

Sparks, S. D. 2015. “Blended Learning Research Yields Limited Results.” Education Week, April 13. http://www.edweek.org/ew/articles/2015/04/15/blended-learning-research-yields-limited-results.html (accessed May 5, 2015).

Staker, H., and M.B. Horn. 2012. “Classifying K-12 Blended Learning.” Clayton Christensen Institute website. http://www.christenseninstitute.org/wp-content/uploads/2013/04/Classifying-K-12-blended-learning.pdf (accessed May 5, 2015).

Strauss, V. 2013. “Rocketship Charter Schools Revamping Signature ‘Learning Lab’.”The Answer Sheet blog, Washington Post, January 25. http://www.washingtonpost.com/blogs/answer-sheet/wp/2013/01/25/rocketship-charter-schools-revamping-signature-learning-lab (accessed May 5, 2025).

U.S. Department of Education. 2013. “Competency-Based Learning or Personalized Learning.” U.S. Department of Education website. http://www.ed.gov/oii-news/competency-based-learning-or-personalized-learning (accessed May 5, 2015).

Wolf, M. A. 2010. Innovate to Educate: System [Re]design for Personalized Learning, A Report from the 2010 Symposium. Software & Information Industry Association. http://net.educause.edu/ir/library/pdf/CSD6181.pdf (accessed May 5, 2015).

Four Reasons to Worry About "Personalized Learning"

This was written by Alfie Kohn who writes and speaks on parenting and education. Kohn tweets here and his website is here. This post was originally found here.

by Alfie Kohn

Tocqueville’s observations about the curious version of democracy that Americans were cultivating in the 1830s have served as a touchstone for social scientists ever since. One sociologist writes about the continued relevance of what Tocqueville noticed way back then, particularly the odd fact that we cherish our commitment to individualism yet experience a “relentless pressure to conform.” Each of us can do what he likes as long as he ends up fundamentally similar to everyone else: You’re “free to expand as a standardized individual.”[1]

A couple of decades ago, that last phrase reminded me of how our pitiful individuality was screwed to the backs of our cars in the form of customized license plates. Today it brings to mind what goes by the name “personalized learning.”

A suffix can change everything. When you attach -ality to sentiment, for example, you end up with what Wallace Stevens called a failure of feeling. When -ized is added topersonal, again, the original idea has been not merely changed but corrupted — and even worse is something we might call Personalized Learning, Inc. (PLI), in which companies sell us digital products to monitor students while purporting to respond to the differences among them.

Personal learning entails working with each child to create projects of intellectual discovery that reflect his or her unique needs and interests. It requires the presence of a caring teacher who knows each child well.

Personalized learning entails adjusting the difficulty level of prefabricated skills-based exercises based on students’ test scores. It requires the purchase of software from one of those companies that can afford full-page ads in Education Week.

For some time, corporations have sold mass-produced commodities of questionable value and then permitted us to customize peripheral details to suit our “preferences.” In the 1970s, Burger King rolled out its “Have it your way!” campaign, announcing that we were now empowered to request a recently thawed slab of factory-produced ground meat without the usual pickle — or even with extra lettuce! In America, I can be me!

A couple of decades later, the production company that created Barney, the alarmingly friendly purple dinosaur, sold personalized videos called “My Party with Barney.” You mailed them a photo of your kid’s face and they digitally attached it to a generic animated child’s body that “plays” with Barney in the video. Your kid’s name is also inserted into the soundtrack every so often to complete the customization, with Barney enthusing: “Have a balloon … Abigail!”[2] The result may have delighted, or even fooled, some three year olds. But why in god’s name are adult educators buying the equivalent of My Party with Barney in order to boost their students’ reading scores?

*

How can we tell when the lovely idea of personal learning has been co-opted[3] and then twisted into PLI? Here are four warning signs:

1. The tasks have been personalized for kids, not created by them. With PLI, the center of gravity is outside the students (as Dewey once put it), and their choices arelimited to when — or maybe, if they’re lucky, how – they’ll master a set of skills mandated by people who have never met them. In the words of education author Will Richardson, “’Personalized’ learning is something that we do to kids; ‘personal’ learning is something they do for themselves.”[4]

Sometimes one of the corporate folks will let slip an acknowledgment of just how student-centered their programs aren’t. “In education,” a publishing executive explained to a reporter, personalization is “not about giving students what they want, it’s about a recommended learning path just for them.”[5] A term like “mass customized learning,” meanwhile, may sound Orwellian but it’s not really an oxymoron because what’s customized is mass-produced – which is to say, standardized. Authentic personal learning isn’t.[6]

2. Education is about the transmission of bits of information, not the construction of meaning. Closely related to the pseudochoice provided to students is the underlying model of learning. Behaviorism, the beast that just won’t die, lurks at the core of PLI just as it animates “competency-based progression,” “mastery learning,” and programs that tweak the “delivery of instruction.” (Hint: Unless someone is sending out for pizza at a faculty meeting, the word delivery is always troubling in the context of schooling.)

In fact, the perceived need to personalize probably comes from this way of thinking about education in the first place. If the point is to dump a load of facts into children, then it may be necessary to adjust the style and rate of dumping – and to help teachers become more efficient at it. But if the point is to help kids understand ideas from the inside out and answer their own questions about the world, then what they’re doing is already personal (and varied). It doesn’t have to be artificially personalized.

3. The main objective is just to raise test scores. This explains PLI’s constant use of instruments that resemble standardized tests. When we hear a phrase like “monitor students’ progress,” we should immediately ask, “What do you mean by progress?” That word, like achievement, often refers to nothing more than results on dreadful tests. And here’s the next logical question when something is described as a way of “personalizing” instruction: What’s the effect of this on kids’ interest in reading or math or writing – or in school itself? Personal learning tends to nourish kids’ curiosity and deepen their enthusiasm. “Personalized” or “customized” learning – not so much.

But the red light flashes here not just because of the focus on standardized tests but because of the larger preoccupation with data data data data data. Elsewhere, I’ve written about the folly of believing that everything can and should be reduced to numbers.[7] PLI shamelessly clings to this myopic and outdated worldview. One of those ads in Education Week not long ago featured a comically enthusiastic cartoon owl in a tuxedo wearing an “I [heart] Data” button. This drawing was followed by boasts about the company’s “computer-adaptive assessments and instruction” that “constantly generate data to personalize learning.” (Honest — it appeared in Ed. Week, not in The Onion.)

The assumption here is that curriculum can be broken into little pieces, that skills are acquired sequentially and can be assessed with discrete, contrived tests and reductive rubrics. Tracking kids’ “progress” with digital profiles and predictive algorithms paints a 21st-century gloss on a very-early-20th-century theory of learning. It not only assumes but perpetuates a bunch-o’-facts approach because it counts only what lends itself to being counted – namely, the number of facts and skills memorized or the percentage of coursework completed.

4. It’s all about the tech. Two overlapping groups of educators seem particularly enamored of PLI: (1) those who are awed by anything that emanates from the private sector, including books about leadership whose examples are drawn from Fortune 500 companies and filled with declarations about the need to “leverage strategic cultures for transformational disruption”[8]; and (2) those who experience excitement that borders on sexual arousal from anything involving technology – even though much of what falls under the heading “ed tech” is, to put it charitably, of scant educational value.[9]

“Follow the money” is apt advice in many sectors of education — for example, in language arts, where millions are made selling leveled “guided reading” systems, skills-based literacy workbooks, and the like. Simpler strategies, such as having kids choose, read, and discuss real books from the library may be more effective, but, as reading expert Dick Allington asks drily, “Who promotes a research-based practice that seems an unlikely profit center? No one.”[10] Personalization is an even more disturbing example of this phenomenon because the word has come to be equated with technology – perhaps because it’s far more profitable for the purveyors that way and, at the same time, “It’s so much cheaper to buy a new computer than to pay a teacher’s salary year after year.”[10]

This version of “personalized learning” actually began 60 years ago when B.F. Skinner proposed setting each child before a teaching machine, an idea rooted in “measurability, uniformity, and control of the student,” according to Canadian educator Philip McRae. Today’s adaptive learning systems still promote the notion of the isolated individual. . .being delivered concrete and sequential content for mastery. However, the re-branding is that of personalization. . . [with a] customized technology platform delivering 21st century competencies. . . .At its most innocent, it is a renewed attempt at bringing back behaviourism and operant conditioning to make learning more efficient. At its most sinister, it establishes children as measurable commodities to be cataloged and capitalized upon by corporations.[11]

Certain forms of technology can be used to support progressive education, but meaningful (and truly personal) learning never requires technology. Therefore, if an idea like personalization is presented from the start as entailing software or a screen, we ought to be extremely skeptical about who really benefits.

One final caveat: in the best student-centered, project-based education, kids spend much of their time learning with and from one another. Thus, while making sense of ideas is surely personal, it is not exclusively individual because it involves collaboration and takes place in a community. Even proponents of personal learning may sometimes forget that fact, but it’s a fact that was never learned by supporters of personalized learning.

NOTES

1. John W. Meyer, “Myths of Socialization and of Personality,” in Reconstructing Individualism, ed. by T. C. Heller et al. (Stanford University Press, 1986), p. 211.

2. www.youtube.com/watch?v=dYVzRjWvalA. Recommended only for those with strong stomachs.

3. I wrote about this general phenomenon in “Progressive Labels for Regressive Practices,” blog post, January 31, 2015.

4. Will Richardson, “Personalizing Flipped Engagement,” blog post, July 2, 2012.

5. Vikram Savkar, a senior vice president at the Nature Publishing Group, is quoted in Michelle R. Davis, “Moving Beyond One-Size-Fits-All,” Education Week Technology Counts, March 17, 2011, pp. 10-11. This special insert was devoted to the theme of “individualized digital learning.”

6. See Maja Wilson, “Personalization: It’s Anything But Personal,” Educational Leadership, March 2014: 73-77.

7. Alfie Kohn, “Schooling Beyond Measure,” Education Week, September 19, 2012; and“Turning Children into Data,” Education Week, August 25, 2010.

8. Or is it “disrupt leveraged strategies for cultural transformation”? I may have nodded off there for a few minutes.

9. See under: “SMART Boards, dumb curriculum.” Similarly, “innovation” in some districts consists of taking the usual menu of forgettable facts, isolated skills, grades, tests, textbooks, and homework — and slapping it onto an iPad. Other educators, meanwhile, radiate self-satisfaction because they assign their students to watch online lectures at home, as if flipping the place and time in which dubious pedagogical practices take place – while continuing to make students work a second shift after they get home from school – constituted a daring pedagogical advance. For a thoughtful discussion of useful and useless uses of technology, see Sylvia Libow Martinez and Gary Stager, Invent to Learn (Constructing Modern Knowledge Press, 2013).

10. Richard L. Allington, “Proven Programs, Profits, and Practice,” in Reading for Profit:How the Bottom Line Leaves Kids Behind, ed. by Bess Altwerger (Heinemann, 2005), p. 226.

11. Lizanne Foster, “Personalized Learning Means Kids with Computers, not Teachers,”Huffington Post, November 28, 2014.

12. Philip McRae, “Rebirth of the Teaching Machine through the Seduction of Data Analytics: This Time It’s Personal,” blog post, April 14, 2013. Italics omitted.

Classroom Technology: Nightmare or Dream?

Technological advances in our schools in the last 10 years have been remarkable, and there is no doubt that technology will continue to disrupt our schools in both helpful and harmful ways. To be clear, I love technology and use it every single day. I teach with it and learn with it. It's important to remember, however, that technology cannot be allowed to have a monopoly on innovation in our schools. If public education is to survive the next 10 years, we need to see how technology and personalization can be read as either a dream or a nightmare, depending on who is writing the story.

If Bill Gates, Rupert Murdoch, Arne Duncan, and Michelle Rhee are writing the plot, then personalization in learning is about using technology for union busting, test score analytics and the marketization of our children's minds. In this story, the rich get a computer and a teacher but the poor get just a computer. Herein, technology and personalization isn't about learning – it’s about money. In this story’s final chapter technology functions as a Trojan horse, sneakily shouldering an army of economists and shadow industries that have been stalking public education for a very long time, waiting for an in.

If Sir Ken Robinson, Pasi Sahlberg, Alfie Kohn, Yong Zhao, Linda Darling-Hammond, Will Richardson and Diane Ravitch are writing the plot, then personalization is about student excitement, creativity, intrinsic motivation, curiosity and citizenship. In this story, all children are given computers and teachers, even when it’s cheaper to deny some students the latter. Herein, personalization and technology is used for the purposes of universal education not subordinated to the interests of big business.

Personalization and technology can be about collaborating to discover our passions (the dream) but it can also be about competing over profits (the nightmare). Worse still, personalization can turn into a kind of hyper-personalization, where computers are given to students with zero facilitation from real life teachers. This is akin to pilotless flying and surgeonless surgery and yet this is precisely the vision of many in power, a vision where technology uses the learner, instead of the learner using the technology. However, this can only become a reality if good people remain silent. Classroom innovators and public educators must speak out against the nightmare narrative of technological implementation (of Gates and Murdoch) so that technology and personalization can assist the dream of learning for all.

Rebirth of the Teaching Machine through the Seduction of Data Analytics: This Time It's Personal

This was written by Phil McRae who is an executive staff officer with the Alberta Teachers` Association. Dr. Phil McRae’s Biography, Research, Writing, Scholarship and Presentations can be found at www.philmcrae.com, and you can follow him on Twitter here. This post first appeared here.

by Phil McRae

Postcard from the World's Fair in Pairs -- Circa 1899 A Futuristic Image of Learning
"At School in the Year 2000' Image Source via Wikimedia Commons

Notions of mechanized teaching machines captured the imagination of many 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 centers around digital software tutors (known as adaptive learning systems) and their grand claims to individualize learning by controlling the pace, place and content for each and every student. This time around it is personal.

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

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

The adaptive learning system crusade in schools is organized, growing in power and well-funded by venture capitalists and corporations. Many companies are looking to profit from student (and teacher) data that can be easily collected, stored, processed, customized, analyzed, and then ultimately (re)sold. Children and youth should not be treated like automated teller machines or retail loyalty cards from which companies can extract valuable data.

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

This article paints a picture of how old notions of teaching machines are being reborn through a seduction of data analytics and competency-based personalization (think individualization). It is also intended to be a declaration against the fatalism of adaptive learning systems as the next evolutionary stage for K-12 education in the 21st Century.

The History

For generations various devices have been patented to mechanically teach students. The first popular attempt was in the 1920s when Sidney Pressey (1926) invented a machine that would run on two modes of operation: ‘teach’ and ‘test’. After reading through material in the teach mode, a student would flick the control to test and proceed to pull down one of four response keys. To give the illusion of progress, the machine would score the response and wondrously record the total number of correct answers. A ‘reward dial’ could also be added so that when a correct number of responses were achieved, 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.

Pressey’s machine was born in an age where managerial approaches to controlling and sequencing learning were popular. It was a time of efficiency where the industrial assembly line had introduced innovative technologies, increased competition, and inspired new efforts to (re)organize companies. The industrialist Fredrick Taylor (1911) was especially influential to the teaching machine movement. His concepts of scientific management drew on studies of assembly line workers and proposed new methods for managers to speed up efficiency and productivity through a process of measurement and control. It was an era that privileged behaviourism (i.e., stimulus and response). At this time Edward Thorndike’s (1921) popular book on Principles of Learning stressed that people all learned in the same basic way through individual practice and reinforcement.

However, it was not until the 1950s, that psychologist B. F. Skinner 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 he 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 would go on to say that his machine had an important advantage over past attempts because 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, 1954). For Skinner, learning was about measurability, uniformity, and control of the student. This view of learning dismissed the larger social, cultural and emotional contexts in which knowledge is created.



The next big lurch forward came from the artificial intelligence movement of the 1970s. This era reinforced behaviourist notions while introducing research in the unfolding field of computer science. This gave rise to Computer-Assisted Instruction (CAI) projects like PLATO (Programmed Logic for Automated Teaching Operations).

CAI treated students like patients who once diagnosed through computer testing and task analysis could be prescribed individual remediation by the software. But, the software development costs for CAI were high, and computers (both personal and school-based) were rare and expensive. Ultimately, the artificial intelligence of the computers was never really that intelligent. Once again the teaching machines receded into the storage room.

PLATO (Programmed Logic for Automated Teaching Operations)

In 2013, Dreambox Learning Inc., a technology company out of the United States, claims that their proprietary intelligent adaptive learning (IAL) system, has the “effectiveness comparable to human tutoring [and] accelerates math teaching and learning” (Dreambox Learning Inc., 2013). The company’s contracted research white paper unflinchingly states, “the level of sophistication of today’s IAL systems is far superior to similar technologies of the past” (Lemke, 2013, p. 13). 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” (Dreambox Learning Inc., 2013).

It is as if we are caught in an ever renewing cycle of promises, or as Yogi Berra once observed, “It’s déjà vu all over again” (Berra, 2004). Adaptive learning systems still promote the notion of the isolated individual, in front of a technology platform, being delivered concrete and sequential content for mastery. However, the re-branding is that of personalization (individual), flexible and customized (technology platform) delivering 21st century competencies (content).

At its most innocent it is a renewed attempt at bringing back behaviourism and operant conditioning to make learning more efficient. At its most sinister; it establishes children as measurable commodities to be cataloged and capitalized upon by corporations. It is a movement that could be the last tsunami that systematically privatizes public education systems.

The Seduction

So why is this movement so seductive? First, it is seen as opening up possibilities for greater access to data that can be used to hyper-individualize learning and in turn diagnose the challenges facing entire school systems. Second, the modern teaching machines, and the growing reach and power of technologies, promises to (re)shape students into powerful knowledge workers of the 21st Century.

For publishers and educational technology companies, the adaptive learning systems are a means to ‘atomize’ students (and their data) away from the shelter and protection of public education systems. It allows them to create long-term ‘personal’ relationships with students, so they can market their products over the student’s lifetime. It prevents materials from being shared or transferred over time as the materials are all digitized and copyright protected. It allows for direct marketing of products and services at any time, place or pace to students or their families.

For teachers, adaptive learning systems are sold as providing easy ways to bump test scores for each and every student, while generating detailed individual student reports through the software’s surveillance structures. Companies market their algorithms as not only teaching better, but also freeing up teachers’ time and relieving their burdens in a world of test-based accountability. Just as Pressey (1926) stated almost a century ago, the machine will “make her [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 in the tutoring movement. It is estimated that one third of Alberta parents now pay for private tutors (Alberta Teachers’ Association, 2011). As the Canadian Council on Learning (2007) found in their national survey, “most parents who hire tutors (73%) estimate that their children's overall academic performance is in the A or B range”. This is a global obsession, and in 2010 74% 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 America society reeling from economic volatility and decline. It is a time where the middle class is rapidly shrinking. Parents are obsessively enrolling their children in after-school programs or tutoring with a fanatic devotion to giving their offspring a competitive edge over the pack. Hyper-parents are investing more time, money and energy in their offspring than in previous generations, and adaptive learning systems may be seen as one more tool on the treadmill to Harvard. As Carl Honore (2008) says, “It is not just kids who are under pressure now; it’s parents too. We feel we have to push, polish and protect our offspring with superhuman zeal - or else we’re somehow falling down on the job. We start from the noble and natural instinct to do the best for our kids but end up going too far. Social and cultural pressure drives a lot of this”.

This has resulted in some dramatic consequences for childhood. Since the late 1970s, children have lost 12 hours per week of free time, including a 25% decrease in play and a 50% decrease in unstructured outdoor activities. (Juster et al., 2004). Parents are working longer hours and families are spending less time with their children (Parkland Institute, 2012). The adaptive learning algorithm, wondrously sold as virtual tutor, could also become a convenient digital baby rattle.

For students frustrated with working in a group setting, or having to negotiate the diversity of a public school setting, the teaching machine provides relief. The new teaching machine becomes the panacea for students who are struggling academically or irritated by the pace of learning in schools. Yet, as Hargreaves and Shirley (2009) suggest: “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).

There are no quick fixes to learning and teaching. Excellence in life, and with all complex activities, takes time and patience. This time is what Malcom Gladwell (2008) calls the ten thousand hour rule, where “researchers have settled on what they believe is the magic number for true expertise: ten thousand hours” (pp. 40). Although seductive, data analytics and algorithms of the software that magically determine the pacing, path, or content for the learner, do not reinforce this type of dedication for true expertise.

Educational technology companies and publishers are rushing to colonize the big data and personalized learning revolution. In the United States the trajectory of education is one of increased standardization, centralization and adaptive learning systems. Far too seldom are the conversations about fostering creativity, the arts, talent diversity, or interpersonal communicative competencies for children and youth. Big data and personalized learning is the next tsunami.

The Context

Big Data

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

As our personal data grows, so does the desire to have it harvested for patterns. With the ability to track social connections and economic habits down to the individual level, micro-patterns emerge. People (and their data) become “atomized”, behaviours are tracked in real-time, and then compared with millions of other individuals. With more powerful computing technologies large data sets may even hold the power of prediction (think Amazon book recommendations, but for personal health). This is known as the ‘big data’ phenomenon.

‘Big Data’ is about finding the seemingly hidden connections within a population or even from 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 our behaviours, 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 the intimate data on students and teachers. Public school jurisdictions often frustrate businesses as they try to direct marketing (and hyper-personalize) their products to students, parents and teachers.

This may all change with inBloom Inc., a $100 million dollar K-12 education data-sharing initiative launched in the early parts of 2013 by the Bill & Melinda Gates Foundation and the Carnegie Corporation of New York. inBloom Inc. is a database containing personal student information that will reportedly allow sharing of the data with 21 for-profit companies. As reported in Reuters (Simons, 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 K-12 education. The first is that Amplify Education Inc., a for-profit division of Rupert Murdoch’s News Corp, built the database infrastructure for inBloom Inc.. Murdoch is well known with the ongoing personal wiretapping and hacking scandal of one of his companies, and he has openly articulated his interests in profiting off 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 that extend the reach of great teaching” (Murdoch, 2010).

Second, parents in New York were not made aware that their children’s personal information could be shared with for-profit private technology companies without their consent. And as with the state of data security in our times, inBloom Inc. “cannot guarantee the security of the information stored … or that the information will not be intercepted when it is being transmitted” (Simons, 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, the State Superintendent John White recently made an announcement that he would be recalling all confidential student data from inBloom Inc. (Leader, 2013).

Issues of privacy, data access, and who actually owns student and teacher data will grow enormously 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.

Data Driven Decision Making

The professional work of teaching and learning has used data and evidence to improve educational decision making for years. Even ‘big data’ and its power can be used to help redesign a public system, as long as teachers, principals, parents give clear consent to its various ethical uses to improve student learning. Data is key to empowering and generating educational growth and insight for teachers. In fact data and evidence generated through teacher action research was a hallmark of the internationally recognized Alberta Initiative for School Improvement (AISI) for over a decade. Ironically we have more data on student assessments, and fewer opportunities for deep conversations between parents and teachers.

The right data, meaningfully and thoughtfully used, could enhance individual and collective teacher efficacy. The same data could also be used by system leaders for narrow accountability regimes and punitive action. In the United States, mandates created under the “Race to the Top” initiatives, and programs promoted by the Gates Foundation, have led to more data attempting to measure teacher effectiveness than ever before. As a society we have become obsessed with data quantity, but in many ways have fallen short on the quality of our human interactions. 

Personalized Learning

Personalized learning is neither a pedagogic theory nor a coherent set of teaching approaches; it is an idea struggling for an identity (McRae, 2010). A description of personalization of learning tightly linked to technology-mediated individualization ‘anywhere, anytime’ is premised on old ideas from the assembly line era. It is a model that is being advanced by the rapidly growing private corporations, virtual schools and charter school in the United States.

Personalizing learning, as an act of differentiating learning in a highly relational environment, is not new to the profession of teaching. Legions of teachers enter classrooms to engage diverse minds across multiple activities and to support each student as he or she inquires into problems. These same teachers, who hold a keen awareness of each of their student’s particular learning styles and passions, are also simultaneously contending with issues of poverty, lack of parental involvement (or conversely helicopter parents), large classes, familial and community influences, student effort and numerous digital and popular culture distractions that add to 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 redesigning high schools beyond age cohorts and classes. More flexible approaches to education are undeniably necessary, and findings 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 in front of a computer screen for hours on end is folly.

The Enablers

To enable this all to happen in an education system, several policies must be enshrined by governments and school districts that allow publishers or educational technology companies direct access to students. The first is to open up multiple pathways of learning, which are more flexible in terms of time and space, and designed around 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 hyper-connected world. Unfortunately, the flexibility of anytime, any pace learning is manifesting itself in the United States around adaptive learning software programs or mandatory online learning courses that are being delivered by private companies.

The U.S. Department of Education (2013) has clearly articulated a commitment to making this happen with ‘Competency-Based Learning’ or ‘Personalized Learning’: “Transitioning away from seat time, in favor of a structure that creates flexibility, allows students to progress as they demonstrate mastery of academic content, regardless of time, place, or pace of learning. By enabling students to master skills at their own pace, competency-based learning systems help to save both time and money…make better use of technology, support new staffing patterns that utilize teacher skills and interests differently…Each of these presents an opportunity to achieve greater efficiency and increase productivity.”

The notion of creating new staffing patterns has evolved in the United States to redefine and expand the role of ‘teacher’. The new staffing patterns with this model have shown to reduce the teaching force to a 1 to 150 pupil teacher ratio with the monitoring of students in computer labs, tutoring and marking supported by non-certificated staff with titles like ‘Coaches’, ‘Facilitators’ or ‘Individual Learning Specialists’. In the case of K12 Inc., the United States largest provider of online education for grades K-12, it is reported that student teacher ratios are as high as 1 teacher to 275 students (Aaronson and O’Connor, 2012). The Software & Information Industry Association, the principal trade association for the software and digital content industries in America, is a clear backer of redefining and expanding the role of the teacher, and advocates that “teacher contracts and other regulatory constraints may also need to be addressed to provide the flexibility in a teacher’s role needed to make this dramatic shift in instruction” (Wolf, 2010, p. 15).

The Challenges

1. Commodification of Student Data: 

Public schools must be the guardians of students' personal data. Teachers, as the guardians of children, cannot collect ‘big data’ without parental consent and then advertently allow it to be passed on to companies looking for a new marketplace in public education. With adaptive learning systems companies can market directly to the individual student or parents, without the obstructions (or guidance) of a robust public education system.

The data analytics crusade in schools, and issues of who owns and controls the ‘big data’ of children and youth, must be highly contested.

2. Reductionist Thinking: 

Adaptive learning systems can divert teacher and student attention to only the ‘basics’ of math and reading. In some cases even privileging just one curricular area. As DreamBox Learning Inc. forcefully states in direct emails 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).

In respecting individuality and difference, we need to move education systems towards actions that Yong Zhao (2009) says will provide “more diverse talents rather than standardized labourers, more creative individuals rather than homogenized test takers, and more entrepreneurs rather than obedient employees.” (p. 181). A narrowing of cognition through the teaching machine will not build the kind of confidence, social agility, cooperation and creativity that children growing up in post-industrial society need. As Dewey (1938) said, “Education is not preparation for life; education is life itself.”

3. Learning is Socially Constructed:

Research out of the learning science makes it clear that learning is successful when it is 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 our future learning environments are enacted face to face, online or in blended learning online/offline contexts as this carbon and silicon line begins to blur. It also holds true regardless of whether one is considered digitally literate or whether one is a member of the New Millennial Generation (Gen M).

4. Adaptation:

There is much good in providing opportunities for students to have more personalized experiences with learning, but the world does not adapt to people, we must adapt to the world. To adapt, and be able to bounce back from adversity, which is a central part of the human condition, we must build resilience in our children and youth.

Zolli and Healy (2012) define resilience as “the capacity 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)—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 builds encourages adaptability, but it also strengthens 21st Century collaborative skills, connectivity and an appreciation of diversity in the world around us. Resilience is not shaped through teaching machines, but it is through highly relational learning environments. It will be especially important in global world defined by increased volatility, ambiguity, uncertainty and complexity.

5. Echo-Chamber Effect: 

We are entering a digital age of mobility where students can access the information they want at any time, place or pace through a variety of devices. This will have a profound effect on critical thinking as individuals 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, hyper-personalized (customized) digital spaces have the potential to limit students to only the content that they want to see, hear and read about. A condition can then arise in online communities where participants find their own opinions constantly echoed back to them (i.e., echo-chamber effect), thus reinforcing a certain sense of truth that resonates with their individual belief systems (McRae, 2006).

This then challenges a call for a diversity of talents, and positions free will and personal choice as taking on new (and obscured) meanings in digital echo chambers. In considering personalization and technology, we need to be thoughtful about the role of critical thinking, diversity and chance (serendipity). These are all important for learning and will have long-term implications for society.

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? A growing body of research indicates children between the ages of 8 to 18 already spend an average of 7.5 hours a day in front of screens (e.g., television, computers, video-games and phones) (Kaiser Family Foundation, 2010). To gather data through adaptive learning systems, children will need to spend time allowing the machine to monitor their interactions. John Danner, former C.E.O. of Rocketship Charter Schools and a member of the Board of Directors of DreamBox Learning Inc., 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. In regards to the software as tutor at home, we should be particularly concerned with late-night screen time and research that indicates it can decrease sleep quality and quantity and negatively affect children’s readiness to learn. How many hours are we willing to sacrifice for more individualized computer-human interactions under the guise of data analytics?

A Better Path

There are no simple computerized solutions to the complex and diverse challenges of poverty and inequity, or lack of parental engagement (conversely hyper-parenting) facing schools. In an effort to continually improve educational practices and create great schools for all students, what might be a better path to the seduction of adaptive learning systems?

We can establish conditions of professional practice where high quality teachers and principals, with a sense of efficacy, can differentiate instruction and advance new forms of assessment for learning with/without technology. Teachers could be engaged in a conversation, earlier rather than later, around how they might use data (big or small) to enhance student learning.

Technologies could be employed to help students become empowered citizens rather than passive consumers. Innovations are needed in education that will help to create a society where people can 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, a community of learners whose physical, intellectual and social well-being is held 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 is a perspective that naturally flows together with the research on learning that suggests that education is not just about content or physical place but also a collective and highly relational set of experiences within a community of learners.

Emerging technologies and smart data certainly have a place in educational transformation, but they must be employed 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 this to be) is a distraction to the most important support great schools can offer students each and every day – relationships, relationships, relationships.

References

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The problem with high-tech ‘personalized’ learning tools

This was written by Sabrina Joy Stevens who is is a teacher-turned-education activist based in Washington, DC. She currently works at the American Federation of Teachers. This post was inspired by her attendance at SWSXedu and first appeared here.

by Sabrina Joy Stevens

I’ve been blessed with many great learning experiences, but one of my favorites involved the first time I read “Little Women.”

Towards the end of third grade, our class was saying goodbye to our much-loved student teacher. As part of our good byes, Miss P gave each of us a small gift. I can still remember how warmly and genuinely she smiled as she handed me a small copy of the classic book. “I know it’s pretty old,” she said, “but I really think you’ll like it. Especially Jo.”

An avid reader, I was excited and grateful. Over the next few days, as I raced through the pages and discovered that– yes, I really did like this book, and especially Jo—I remember feeling even more warm, fuzzy and grateful. I felt really special, knowing that she “knew” me enough to know how much I’d enjoy this. What was a small act for her made me feel seen and understood. I felt like I mattered.

To me, that’s personalized learning—when a person sees and recognizes in another person (or in him- or herself) what’s needed to keep learning and growing. Personalized learning occurs when a teacher and a learner know and respect each other enough to interact in meaningful ways, and when a learner begins to know herself well enough to know the next step she should take to master a new skill, or the next step on her path to becoming who she wants to be.

That’s why I couldn’t help feeling a bit disturbed at SXSWedu last week, hearing tech vendors and venture capitalists use the term “personalized learning” as though it was 1) new (what, exactly, do these people think has been going on in human brains for millennia??) and 2) a ground-breaking thing that could only be enabled through their proprietarytechnology.

Language-check: what many of these people are selling as “personalized” learning is actually digitized standardized learning. Creating tools and products that offer digital ways to deliver drill-and-kill instruction is not revolutionary. Attaching that to a large bank of flawed, standardized data merely automates and speeds the process of selecting those drill-and-kill activities and marketing more of them to teachers, students and parents. But making it easier to do more of a problematic thing does not make that thing less problematic.

What’s more, one of the few explicit justifications I heard for all of this, after questioning how, exactly, this was different than anything teachers and schools had done in the past (a past many educators are trying to run from, in favor of more participatory and empowering alternatives), was that it helped teachers manage growing class sizes, and helped everyone more easily manage the abundance of data we now have. In other words, the primary value of these tools is to help us adapt to teacher unemployment, student overcrowding, and student over-testing.

I don’t believe in adapting to dysfunctional or outright bad situations, and I hope I’m not alone in this. I believe in doing the tough work necessary to actually solve root problems, not find new ways to deal with their painful symptoms. We know from experience that adapting to problems instead of solving them only makes them worse. And I don’t think cash-strapped school districts should be paying to make their problems worse.

Now, that’s not to say that all of the new tools and apps I encountered were bad. I saw quite a few things that might be very helpful. In particular, the apps and platforms that empower teachers and students to make things, that facilitate collaboration within and across classrooms, and that help learners master distinctly 21st Century skills like coding, looked very worthwhile.

As school districts consider which new tools to adopt, the whole school community should be involved in the process of trying them out, seeing what’s empowering and what isn’t, and deciding together what they should invest in, as well as what the boundaries around student and teacher information should be.

What’s more, we should avoid succumbing to the hype surrounding these products without fully considering the implications of investing our money into them—especially if that investment comes at the expense of investing in the people and professional development that true personalized learning requires. Tech is revolutionary (in a positive way) when it empowers us to do even better what we already do well. But if we’re using tech to compensate for fewer people, we’re not replacing anything—we’re losing something.

True personalized learning comes from people knowing each other; man-made interventions can facilitate, but not fully replace, the cognitive or emotional value of meaningful in-person interaction.

And thinking beyond the school walls, we also need to consider the community’s overall well being, and whether it serves the whole community’s best interests to invest in products made by faraway corporations instead of its local people.

Teachers and other school personnel are also local consumers and tax payers who contribute to the community’s social and economic well-being through their work as well as through spending their income. In doing so, they boost local economies by supporting local business owners and workers, and they pay into the tax base that supports the people who keep all of us—regardless of our background or ability to pay—safe, healthy and educated.

Products don’t provide these additional benefits. Unless they happen to be locally based to a given community, the companies that make them don’t necessarily pay local taxes, and larger corporations often dodge as many state and federal taxes as they can, eroding the commons that sustains the rest of us.

Remember: schools are part of a community ecosystem. We have the right and responsibility to ensure public officials don’t replace the symbiotic relationships that school employees have within the community, with potentially parasitic relationships with faraway companies who would take from our public coffers without necessarily putting enough back in.

A true commitment to personalized learning requires a renewed appreciation of the personswho make it possible. Tech is great, but let’s get real: no app ever painted a classroom, or hand-sewed pillows so its students could have cozy places to learn to love reading. No smart board ever improvised a harmony with its student’s rising voice, teaching a new skill and inspiring a new song in the process. And no computer ever offered a warm smile or embrace to comfort a traumatized child, or food to a child without enough to eat. Teachers and other school employees do all of these things and more. We should be finding ways to keep them where they can protect, nurture and teach students, not finding ways to get away with fewer and fewer of them.