Continued from the emailed newsletter

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To briefly summarize, an adaptive learning system consists of algorithms that optimize content and presentation in order to adjust for a learner's goals, needs, and current level. Whereas in a traditional e-learning or classroom course, the student linearly follows the curriculum that an instructor selects or creates, an adaptive learning system uses a data-driven approach to adjust and customize the learning path, pace, and content format for each student according to his/her learning needs.

As a learner develops knowledge and skills in any area, the learner’s progress is minutely tracked in real-time by the adaptive learning system. At each milestone, the learner’s data-driven algorithmic “tutor” updates the optimum sequence (or a selection of relevant choices) of subsequent learning activities and content to engage the learner, emphasizing the skills that need focus. The activities are intended to be challenging but not too difficult for the learner to master. The “tutor” and the student decide everything about the learning experience together (though it is in reality the “tutor” that has final say), enabling learners to track what they actually know vs. their goals.

Best-of-breed intelligent adaptive learning systems are being integrated with learning management systems, which provide school systems with comprehensive e-learning, documentation, and administrative reporting capabilities. The U.S. Department of Education has a national technology plan (NETP) that endorses the use of adaptive learning integrated with learning management systems. The use of adaptive learning has been closely aligned with the Race to the Top early-learning reform initiative, Common Core state standards, and the NETP, all of which aim to teach 21st-century skills to increase U.S. student competitive advantage.

The impact: Personalized learning
Adaptive learning systems provide a new, scalable way to roll out a personalized learning educational approach on a massive scale from K through 12. Fundamentally, the approach consists of leveraging AI-powered adaptive learning systems to tailor schooling to each K–12 student’s strengths, needs, skills and interests. Each child is tracked via a “learner’s profile” that is continuously updated using AI to create a “personalized learning path” (PLP). The PLP leverages all relevant learning methods: individual and small-group project-based learning; independent work; and one-on-one tutoring with a teacher or parent/caretaker, factoring their effectiveness and the needs of each student.

A PLP enables each student to work on different skills at an individual pace, monitored by a teacher/tutor/mentor in real-time who can provide needed resources and support (including emotional support). Using AI-powered machine learning, the system automatically adjusts the student’s PLP in real-time as the student’s strengths, needs, skills and interests evolve, and leveraging the sum total of available educational and cognitive research material (ie:Big Data).

This may strike some as a way to mass-produce education by leaving students in the hands of the same bots, but counter-intuitively, this has the potential to foster a great deal of individuality thanks to “project-based learning space” (PLS)—a virtual concept enabled by PLPs. A “project-based learning space” essentially provides each child with their own “classroom,” which can serve as a workshop, a lab, or a conference room. The student owns the PLS, can share it with teachers,  peers, and others by invitation—whatever the need is. 

Leveraged correctly, the PLS concept can promote and nurture self-expression while also remedying (if only partially) the  profound disparities in learning resources and support inside and outside of today’s educational institutions by providing each student a virtual space that many may not have physically. Further, each student’s PLS will function as a data “lab” in which AI can simulate the possibilities for not just subject-matter learning but for general child development, incorporating the latest research studies from around the world. One of the functions of this AI-modelling is the moment-to-moment (not semester-to-semester) adjusting of the student’s PLP in real time. This could be a significant aid to overcome social/learning inequities, and also provide continuous, meaningful guidance for parents/caretakers who want to engage in their child’s educational experiences. 

As an added benefit, anonymized data from each child’s learning experience can be aggregated into a continuous child development and learning research megaproject. This results in a positive feedback loop in which each school can then have access to supercomputer AI-enabled data modeling to feed insights gained from all students nationwide into each student’s personalized learning plan, in turn generating further data to feed the research. Deep learning technology can be brought in to detect and recognize even the slightest beginnings of promising learning and performance capabilities.
Personalized learning will result in a profound technology-assisted paradigm shift in education akin to what we’re seeing in telehealth. Like in telehealth, cloud-based educational innovation will skip several generations of development in the process of reimagining education. A major focus of the PLP effort will be enabling software and content developers in the U.S. and around the world to creatively participate in developing tools and other resources to facilitate the transformation of education at all levels.

COVID-19
The COVID-19 pandemic has profoundly affected entire commu­nities, states and the world. Though older people have been especially hard-hit, younger generations will have their lives—and education—transformed and disrupted for years to come. No one knows exactly what kind of “revolution” is coming in response to pandemic shock waves (perhaps multiple waves), but we do know that the impacts of the ubiquitous virus are uneven in severity toward different ages, races, ethnicities, social classes, living conditions, and educational attainment. In what might be the quiet before a worsening storm, the economic and educational divide in America is further deepening. Worse: in contrast to hurricanes like Katrina that come and go and leave their trail of destruction, the impacts of pandemics lurk indefinitely in the societal anatomy.

In the midst of this challenging journey, which none of us expected in our lifetimes, communities and schools are preparing to reopen and face unprecedented educational, fiscal, and other challenges. In the past few months, schools and families have done a remarkable job of swiftly adopting distance learning. But the educational journey ahead will require even greater dedication and innovation to bridge the digital divide and finally remedy the vast inequities among students.

In part this article is intended to provide some guidance, inspiration, and support for coping with these complex challenges. What we know—and tried to respond to creatively—is that, in addition to adopting all necessary health and safety protocols, educational agencies, teachers, families, and other community stakeholders are committed to: collaboratively planning and implementing data-driven instructional strategies; providing enrichment opportunities and timely social-emotional support for students; and ensuring community engagement in all phases of the process. 

Best wishes to all from the St. James Faith Lab team!
 

The Rev. Canon Cindy Evans Voorhees

Executive Director

St. James Faith Lab

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