Learnatric is being designed to leverage an innovative pedagogical approach combined with state of the art technology to deliver value to students through continuously adaptive and optimized learning processes. The student’s progress is monitored by the platform which will utilize machine learning building to a true Artificial Intelligence (AI) system to transparently and dynamically adjust the learning process to optimize the student’s learning experience. The overarching pedagogy is based on the concept of Threshold Concepts and Troublesome Knowledge, where we seek to help each student achieve his/her individual breakthrough as quickly as possible. This approach allows us to focus on the learner in a way unlike any existing EdTech platform.
“A threshold concept can be considered as akin to a portal, opening up a new and previously inaccessible way of thinking about something. It represents a transformed way of understanding, or interpreting, or viewing something without which the learner cannot progress. As a consequence of comprehending a threshold concept there may thus be a transformed internal view of subject matter, subject landscape, or even worldview. This transformation may be sudden or it may be protracted over a considerable period of time, with the transition to understanding proving troublesome. Such a transformed view or landscape may represent how people ‘think’ in a particular discipline, or how they perceive, apprehend, or experience particular phenomena within that discipline (or more generally)” (Meyer & Land, 2003).
So, what makes a Threshold Concept different from, say, a ‘key concept’? Fundamentally, Threshold Concepts are commonly seen as the areas of a subject at which students have to put in concerted effort to push through conceptually. Further, more advanced ideas depend on the understanding of certain important fundamentals. In all subject domains and disciplines there are points which lead us into “previously inaccessible ways of thinking”. If a concept is a way of organizing and making sense of what is known in a particular field, a Threshold Concept organizes the knowledge and experience, making an epiphany or ‘eureka moment’ possible.
Using Threshold Concepts as the guideposts within Learnatric, we distinguish our system from most online educational programs today as Learnatric does more than just use linear assessments to create branching pathways within a system of static, sequential lessons. Learnatric uses a complex tracking algorithm that guides the student through a unique sequence of activities and introduce Threshold Concept (TC) lessons as a student demonstrates the skills and knowledge needed to advance. These TC lessons are modified to meet the learning needs and predilections of the student.
Functionally, the learning attribute tags that are embedded in the gamified exploration activities of the platform serve as the primary data input for the designed algorithm. These tags provide an evaluation score within content domains, learning styles, and attitude skills in a way similar to chess ELO ratings in each sub-domain area. The learning analytics feed into the Learnatric learning engine, which continually updates each student’s profile and provides information to adjust content delivery, keeping each student on their optimal learning pathway.
What do we mean by “machine learning” in the design of the Learnatric Learning Engine? In essence, machine learning is the process for programming systems to take in massive amounts of data, analyze it, and make increasingly better decisions based on the outcomes produced. In the same way that machine learning has led to breakthroughs in autonomous cars by tracking the data of actual drivers in Google Maps, we are now able to take the collective experience of effective teachers to create the content and then allow the Learnatric learning engine to learn when to present content and the right sequence of modifications to optimize student outcomes. The more data points that are tracked, the more accurately the system will be able to adapt to students using the system. This is the same way that a professional teacher takes years to form best practices in the classroom based on the experience of teaching. By providing data reporting to the classroom teacher and parent, we have created a loop that helps identify and remediate or challenge more effectively.