Todd Rose, the director of the Mind, Brain, and Education program at the Harvard Graduate School of Education, has emerged as a central intellectual figure behind the movement. In particular, his 2016 book, “The End of Average,” is seen as an important justification for and guide to the personalization of learning.
what Rose argues against. He holds that our culture is obsessed with measuring and finding averages—averages of human ability and averages of the human body. Sometimes the average is held to be the ideal.
The jaggedness principle means that many of the attributes we care about are multi-faceted, not of a whole. For example, human ability is not one thing, so it doesn’t make sense to talk about someone as “smart” or “dumb.” That’s unidimensional. Someone might be very good with numbers, very bad with words, about average in using space, and gifted in using of visual imagery.
Since the 1930s, psychologists have debated whether intelligence is best characterized as one thing or many.
But most psychologists stopped playing this game in the 1990s. The resolution came through the work of John Carroll, who developed a third model in which abilities form a hierarchy. We can think of abilities as separate, but nested in higher-order abilities. Hence, there is a general, all-purpose intelligence, and it influences other abilities, so they are correlated. But the abilities nested within general intelligence are independent, so the correlations are modest. Thus, Rose’s jaggedness principle is certainly not new to psychology, and it’s incomplete.
The second (Context Principle) of Rose’s principles holds that personality traits don’t exist, and there’s a similar problem with this claim: Rose describes a concept with limited predictive power as having none at all. The most commonly accepted theory holds that personality can be described by variation on five dimensions
Rose’s third principle (pathways principle) suggests that there are multiple ways to reach a goal like walking or reading, and that there is not a fixed set of stages through which each of us passes.
Rose thinks students should earn credentials, not diplomas. In other words, a school would not certify that you’re “educated in computer science” but that you have specific knowledge and skills—that you can program games on handheld devices, for example. He think grades should be replaced by testaments of competency (my note: badges); the school affirms that you’ve mastered the skills and knowledge, period. Finally, Rose argues that students should have more flexibility in choosing their educational pathways.
(1) the circumstances under which personalized learning can help students and
(2) the best way to evaluate the real educational value for products that are marketed under the personalized learning banner.
The most descriptive label we could come up with for the practices that the two of us have observed in our school visits might be undepersonalized teaching.
The most stereotypical depersonalized teaching experience is the large lecture class, but there are many other situations in which teachers do not connect with individual students and/or meet the students’ specific needs. For example, even a small class might contain students with a wide-enough range of skills, aptitudes, and needs that the teacher cannot possibly serve them all equally well. Or a student may have needs (or aptitudes) that the teacher simply doesn’t get an opportunity to see within the amount of contact time that the class allows. The truth is that students fall through the cracks all the time, even in the best classes taught by the best teachers. Failing a course is the most visible evidence, but more often students drift through the class and earn a passing grade—maybe even a good grade—without getting any lasting educational benefit.
personalized learning as a practice rather than a product
Technology then becomes an enabler for increasing meaningful personal contact. In our observations, we have seen three main technology-enabled strategies for lowering classroom barriers to one-on-one teacher/student (and student/student) interactions:
Moving content broadcast out of the classroom: Even in relatively small classes, a lot of class time can be taken up with content broadcast such as lectures and announcements. Personalized learning strategies often try to move as much broadcast out of class time as possible in order to make room for more conversation. This strategy is sometimes called “flipping” because it is commonly accomplished by having the teacher record the lectures they would normally give in class and assign the lecture videos as homework,
Turning homework time into contact time: In a traditional class, much of the work that the students do is invisible to the teacher. For some aspects, such as homework problems, teachers can observe the results but are often severely limited by time constraints.Personalized learning approaches often allow the teacher to observe the students’ work in digital products, so that there is more opportunity to coach students.
Providing tutoring: Sometimes students get stuck in problem areas that don’t require help from a skilled human instructor. Although software isn’t good at teaching everything, it can be good at teaching some things. Personalized learning approaches can offload the tutoring for those topics to adaptive learning software that gives students interactive feedback while also turning the students’ work into contact time by making it observable to the teacher at a glance through analytics.
In the business world, an analogous initiative might be called “business process redesign.” Emphasis is on process. The primary question being asked is, “What is the most effective way to accomplish the goal?” The redesigned process may well need software, but it is the process itself that matters. In personalized learning, the process we are redesigning is that of teaching individual students what they need to learn from a class as effectively as possible (though we can easily imagine applying the same kind of exercise to improving advising, course registration, or any other important function).
Students in the course spend part of their class time in a computer lab, working at their own pace through an adaptive learning math program. Students who already know much of the content can move through it quickly, giving them more time to master the concepts that they have yet to learn. Students who have more to learn can take their time and get tutoring and reinforcement from the software. Teachers, now freed from the task of lecturing, roam the room and give individual attention to those students who need it. They can also see how students are doing, individually and as a class, through the software’s analytics. But the course has another critical component that takes place outside the computer lab, separate from the technology. Every week, the teachers meet with the students to discuss learning goals and strategies. Students review the goals they set the previous week, discuss their progress toward those goals, evaluate whether the strategies they used helped them, and develop new goals for the next week.
U.S. Education Secretary spared no words in her critique of education reform efforts during the Bush and Obama administrations. “I don’t think there is much we can hold onto, from a federal level, that we can say was a real success,”
Her vision of personalized learning has plenty of detractors. Educators and administrators have already begun to voice their reservations about personalized learning in schools. At a gathering of educators in Oakland last October speakers decried what they described as the privatization of public education through the introduction of technology initiatives such as personalized learning. More recently, former AltSchool educator Paul Emerich wrote a blog post titled, “Why I Left Silicon Valley, EdTech, and ‘Personalized’ Learning,” where he offered critiques of the personalized learning movement in his school. The post touched on concerns about his workload and interactions with students.
Parents are raising pressure too. In at least two states, their concerns over screen-time and digital content used in online educational platform has forced districts to suspend the implementation of technology-enabled personalized learning programs such as Summit Learning.
De Vos pointed to previous federal-led education funding programs as a “carrot” that made little or no impact. Her critique is not unfounded: A report published last year by the Education Department’s research division found that the $7 billion School Improvement Grants program made “no significant impacts” on test scores, high school graduation rates or college enrollment.
One of the largest online charter schools in the country closed this week amid a financial and legal dispute with the state of Ohio.
Education Secretary Betsy DeVos in a keynote address this week to the American Enterprise Institute.
She also cited a survey by the American Federation of Teachers that 60 percent of its teachers reported having moderate to no influence over the content and skills taught in their own classrooms. That same survey also noted that 86 percent of teachers said they do not feel respected by DeVos.
Personalization is often used in the ed-tech community to describe a student moving through a prescribed set of activities at his own pace. The only choice a student gets is what box to check on the screen and how quickly to move through the exercises. For many educators that’s not the true meaning of “personalized learning.”
Diana Laufenberg, director of Inquiry Schools and a former teacher at Science Leadership Academy in Philadelphia: personalization only comes when students have authentic choice over how to tackle a problem.
“We often say we want creativity and innovation – personalization – but every mechanism we use to measure it is through control and compliance,” Laufenberg said. “Those things never come together as long as that is the overriding moment.”
building relationships with students so I can better connect lessons to their interests, hopes and dreams; providing them with many opportunities for organizational and cognitive choice; and creating situations where they can get positive, as well as critical, feedback in a supportive way from me, their classmates and themselves.
Response: Personalized Learning Is ‘Based On Relationships, Not Algorithms’
Too often, the notion of “personalized learning” means choice-based programmed rather than truly personalized. This comes from the tech world, where “personalization” is synonymous with user choice. It’s the idea of giving a thumbs up or a thumbs down on Pandora. It’s the idea of having adaptive programs that change based upon one’s personal preferences. It’s the Facebook algorithm that tells you what information is the most relevant to you. It’s about content delivery rather than user creation.
While tech companies promise personalization, they often promote independent, isolated learning. True personalization is interdependent rather than isolated. True personalization is based upon a horizontal relationship rather than a top-down customization. True personalization is based upon a deeply human relationship rather than a program or an algorithm or a set of scripts. True personalization is a mix between personal autonomy and group belonging. It’s a mix between what someone wants and what someone needs. It’s a chance to make, rather than simply a chance to consume.
The EDUCAUSE Learning Initiative has just launched its 2018 Key Issues in Teaching and Learning Survey, so vote today: http://www.tinyurl.com/ki2018.
Each year, the ELI surveys the teaching and learning community in order to discover the key issues and themes in teaching and learning. These top issues provide the thematic foundation or basis for all of our conversations, courses, and publications for the coming year. Longitudinally they also provide the way to track the evolving discourse in the teaching and learning space. More information about this annual survey can be found at https://www.educause.edu/eli/initiatives/key-issues-in-teaching-and-learning.
ACADEMIC TRANSFORMATION (Holistic models supporting student success, leadership competencies for academic transformation, partnerships and collaborations across campus, IT transformation, academic transformation that is broad, strategic, and institutional in scope)
ACCESSIBILITY AND UNIVERSAL DESIGN FOR LEARNING (Supporting and educating the academic community in effective practice; intersections with instructional delivery modes; compliance issues)
ADAPTIVE TEACHING AND LEARNING (Digital courseware; adaptive technology; implications for course design and the instructor’s role; adaptive approaches that are not technology-based; integration with LMS; use of data to improve learner outcomes)
COMPETENCY-BASED EDUCATION AND NEW METHODS FOR THE ASSESSMENT OF STUDENT LEARNING (Developing collaborative cultures of assessment that bring together faculty, instructional designers, accreditation coordinators, and technical support personnel, real world experience credit)
DIGITAL AND INFORMATION LITERACIES (Student and faculty literacies; research skills; data discovery, management, and analysis skills; information visualization skills; partnerships for literacy programs; evaluation of student digital competencies; information evaluation)
EVALUATING TECHNOLOGY-BASED INSTRUCTIONAL INNOVATIONS (Tools and methods to gather data;data analysis techniques; qualitative vs. quantitative data; evaluation project design; using findings to change curricular practice; scholarship of teaching and learning; articulating results to stakeholders; just-in-time evaluation of innovations). here is my bibliographical overview on Big Data (scroll down to “Research literature”: http://blog.stcloudstate.edu/ims/2017/11/07/irdl-proposal/ )
EVOLUTION OF THE TEACHING AND LEARNING SUPPORT PROFESSION (Professional skills for T&L support; increasing emphasis on instructional design; delineating the skills, knowledge, business acumen, and political savvy for success; role of inter-institutional communities of practices and consortia; career-oriented professional development planning)
FACULTY DEVELOPMENT (Incentivizing faculty innovation; new roles for faculty and those who support them; evidence of impact on student learning/engagement of faculty development programs; faculty development intersections with learning analytics; engagement with student success)
GAMIFICATION OF LEARNING (Gamification designs for course activities; adaptive approaches to gamification; alternate reality games; simulations; technological implementation options for faculty)
INTEGRATED PLANNING AND ADVISING FOR STUDENT SUCCESS (Change management and campus leadership; collaboration across units; integration of technology systems and data; dashboard design; data visualization (here previous blog postings on this issue: http://blog.stcloudstate.edu/ims?s=data+visualization); counseling and coaching advising transformation; student success analytics)
LEARNING ANALYTICS (Leveraging open data standards; privacy and ethics; both faculty and student facing reports; implementing; learning analytics to transform other services; course design implications)
LEARNING SPACE DESIGNS (Makerspaces; funding; faculty development; learning designs across disciplines; supporting integrated campus planning; ROI; accessibility/UDL; rating of classroom designs)
MICRO-CREDENTIALING AND DIGITAL BADGING (Design of badging hierarchies; stackable credentials; certificates; role of open standards; ways to publish digital badges; approaches to meta-data; implications for the transcript; Personalized learning transcripts and blockchain technology (here previous blog postings on this issue: http://blog.stcloudstate.edu/ims?s=blockchain)
MOBILE LEARNING (Curricular use of mobile devices (here previous blog postings on this issue:
MULTI-DIMENSIONAL TECHNOLOGIES (Virtual, augmented, mixed, and immersive reality; video walls; integration with learning spaces; scalability, affordability, and accessibility; use of mobile devices; multi-dimensional printing and artifact creation)
NEXT-GENERATION DIGITAL LEARNING ENVIRONMENTS AND LMS SERVICES (Open standards; learning environments architectures (here previous blog postings on this issue: http://blog.stcloudstate.edu/ims/2017/03/28/digital-learning/; social learning environments; customization and personalization; OER integration; intersections with learning modalities such as adaptive, online, etc.; LMS evaluation, integration and support)
ONLINE AND BLENDED TEACHING AND LEARNING (Flipped course models; leveraging MOOCs in online learning; course development models; intersections with analytics; humanization of online courses; student engagement)
OPEN EDUCATION (Resources, textbooks, content; quality and editorial issues; faculty development; intersections with student success/access; analytics; licensing; affordability; business models; accessibility and sustainability)
PRIVACY AND SECURITY (Formulation of policies on privacy and data protection; increased sharing of data via open standards for internal and external purposes; increased use of cloud-based and third party options; education of faculty, students, and administrators)
WORKING WITH EMERGING LEARNING TECHNOLOGY (Scalability and diffusion; effective piloting practices; investments; faculty development; funding; evaluation methods and rubrics; interoperability; data-driven decision-making)