EdFacts Community Video

Here is a video that I produced with QIP and AEM teams...

Technology-Enabled Personalized Learning

Last week I had the privilege of representing QIP at the Technology-Enabled Personalized Learning Summit. The invitation-only event was hosted by the Friday Institute for Educational Innovation at NC State University and included the right mix of education practitioners, service providers, nonprofit leaders, researchers, and education technology thought leaders to tackle some of the big questions around the future of personalized learning.

Here are some take-aways:

• Personalized learning is more than differentiated instruction -- it is learner-centered vs. teacher/group-centered mass-customization.
• Personalized learning requires a different set of practices, tools, roles, and resources.
• Research-based evidence about effective personalized learning has not made it into mainstream practice.
• Noncognitive factors, such as grit, tenacity, and perseverance, become important as learners develop habits of success and take on greater autonomy in personalized models.
• Personalized learning uses different data -- metrics for "growth" and "grit" continuously measured for feedback to the learner rather than less frequent measurement of achievement for benchmarking and accountability.
• Technical standards for interoperability of personalized learning data exist, but there are issues preventing scaled adoption/implementation...data integration barriers are slowing down progress toward personalized learning.
• Personalized learning at scale can only work by leveraging technology and "big data" -- privacy concerns must be addressed.
• We can compel students to attend school but we can't compel them to learn...or can we -- the human-centered design of personalized learning must include motivational design.
• "Engagement isn't necessarily enjoyment. If you're drowning, you're engaged in the experienced, but it's not enjoyable" - Chris Dede
• Human to human relationships matter (student-to-student, educator-to-student) 
• Emerging models leverage non-instructional roles and technology to free up teacher time to work more with individuals and smaller groups.
• Personalized learning opens new career pathways for education professionals and opportunities for both traditional schools of education and other organizations to develop/support new professions.  A new set of professional competencies are needed and those competencies need to be defined as new professional roles and delivery models emerge. 
• Personalized competency-based professional learning is important to optimize professional development and as a model for personalized student learning. 
• Personalized learning at scale can benefit by new kinds of collaboration between learning science research and practice. 

Technology-enabled personalized learning calls for human-focused designs and delivery systems that are fundamentally different from traditional classroom models.  Summit participants identified some potential solutions to current barriers and important next steps to make scaled personalized learning a reality.  Stay tuned...

"2-Sigma" Learning at Scale (Part 2): What research tells us.

Under what conditions can MOST STUDENTS learn as effectively as the top 20% of students under the conventional classroom condition?  This 11 minute video explores what research tells us.

 

What does it take for students under the classroom model of group instruction to learn as effectively as students under good tutoring conditions?

In this seven minute video I share some ideas about the effectiveness of one-on-one tutoring and the path to "2-Sigma" learning at scale.

Scaffolding and Feedback

I'm learning more about the power of scaffolding and feedback for optimized student learning.  I've learned about it from the learning sciences perspective by studying research papers on the subject, but I've recently gained some new practical and tangible insights as I tutor one of my sons.

Effective tutoring uses both scaffolding and feedback to help students bridge gaps in understanding and develop new skills.  The techniques are conceptually similar but different in that scaffolding is more proactive and feedback is more reactive.  An example of scaffolding is when a tutor is helping a student learn how to solve a multi-step mathematics problem and prompts the student, "What do you do first?"

Scaffolding provides a safe structure within which the learner continues to build on and reinforce existing knowledge and skills.  It provides guidance and motivation in the process of learning while keeping the learner at the center of the learning experience.  It does not let the learner off-the-hook by providing answers.  If the student says, "I don't know."   The tutor might rephrase the question as a multiple choice or ask a more leading question such as "What kind of problem is this?" or "What is the problem asking you to figure out?"  Scaffolding is not the same as instruction. A good tutor resists the urge to say, "The first thing you need to do is…"   Only when it is clear that the learner has a knowledge gap does the tutor step in…and that brings us to the role of feedback.

Formative feedback in tutoring is the tutor's response to a knowledge gap, misunderstanding, or area for improvement aimed at correcting/improving the cognitive imperfection.  Feedback is reactive in that it's driven by formative assessment or observation data.  Formative feedback is descriptive and specific, shedding light on the nature of the misunderstanding or procedural error and guiding the learner to correct it.  Feedback can be instructive like, "No, you skipped a step; the first thing you need to do is…,"  or it can be scaffolding-like, helping the learner self-correct, such as, "Check your signs."

Research shows that scaffolding and feedback are most effective when addressed at at-least "step-based" granularity.  For example, on a multi-step mathematics problem, effective feedback goes beyond telling the learner whether or not the answer to a problem is right or wrong.  Expert mathematicians know how to solve for x, because they have mastered the process-skills, but also because they have deep understanding of the concepts that make the process work.  On competencies that don't involve process steps the scaffolding might involve questions that prompt metacognition (i.e. when the learner thinks about what they are thinking) and explore new connections leading toward deeper understanding.  For example, building connections between historical events.

Scaffolding and feedback are some of the most powerful catalysts for learning.  I know this from the research and from practical experience.  Learning takes place much more effectively and efficiently under good tutoring conditions that include scaffolding, feedback, high expectations, and competency-based advancement.

Digital Infrastructure for Personalized Learning at Scale

One of the ideas I'm working with for my upcoming presentation at the iNACOL Blended and Online Learning Symposium is the role that various data initiatives can play in the infrastructure to support personalized learning at scale. This graphic shows some of the key players and concepts in that infrastructure:

Note the learner is in the center, and a continuous measurement-feedback loop is central. Technology is not a complete solution, only an enabler. The data infrastructure is especially important for providing the right kind of immediate feedback to the learner and with educators adjusting learning experiences to learner needs. We know from learning sciences research what works, we just can't do it to scale without the help of technology.

A complete scaled solution must also include innovations in professional roles, practices, organizing principles, and fiscal models. I plan to share thoughts on these other innovation in my session at iNACOL and later in this blog.

I'm also leading a workshop in which participants will design a competency-based "the-learner-is-the-school" model using some resources I've developed.  Here is the session information:

Blending Learning Science, Learning Standards, Process Optimization, and Data Standards for Competency-Based Learning at Scale
Monday, October 28 from 3:30-4:30

Workshop: Design a Competency-Based System/Organization/Culture to Maximize Student Learning Wednesday, October 30 from 10:00-12:15

"Learning Styles" vs. Personally Optimized Learning Experiences

I've been following a debate on a LinkedIn group on the value considering "learning styles" when designing online learning experiences.  The Wikipedia definition of learning styles is: "an individual's natural or habitual pattern of acquiring and processing information in learning situations."  One of the most commonly used learning style categories are visual, auditory, and kinesthetic.  The following is an adaptation/expansion of a comment I posted to the thread...

Like, Howard Gardner's theory of multiple intelligences "learning styles" are ways of trying to classify or make sense of the idea that each human being is unique, and therefore the factors to optimize learning for each person, within each learning experience, are unique.  The theories are attempts at moving away from one-size-fits-all "factory" models of education to more individualized approaches.  Many of the comments on the LinkedIn thread express the opinion that these theories  fall short, have not been proven, and can sometimes be a distraction from the more important factors that affect learning. 




When considering validity of a learning sciences theory, the context and application are important.  Under the constraints of teacher-centric, resource constrained, classroom models, there is no evidence that trying to deliver instruction to each learner's perceived "learning style" is a good approach.  However, design of online/interactive content can include multiple paths, experiences,  and modes of delivery that the learner can self-select. This seems to be a more valid application of the concept. It doesn't require a teacher or artificial intelligence engine to make a judgement about segregating learners by "styles." Instead it provides the learner with the ability to try multiple 'styles' of information delivery and/or different kinds of learning experience until the learning goal is reached.   (I don't know if there is research that compares the effect size of online content that provides multiple delivery "styles" vs. single delivery "styles," but I suspect there is value in providing the learner with alternative delivery options.)

In my opinion, the role of the learning sciences is to better understand those factors that may impact an individual's learning, to test methods designed to optimize factors in delivery of a learning experience, and to determine if those methods impact learner outcomes, generally or for specific populations/conditions. 

Today we think of Vygotsky's ZDP theory as "just good teaching practice," (quote from the LinkedIn discussion), but there was a time when the concept was not considered, and unfortunately is still widely ignored in the practice of group-centric fixed-pace instructional models.  This is why we can't rest on what we know (and do).  What the education and training communities have to offer is not working well enough for many learners.  There is much more research to be done, more learning innovations to be discovered, and a great need for improvement of methodologies toward personally-optimized learning experiences.