When it comes to learning, no one size fits all. Each student brings their own knowledge, skills, experiences and dispositions to learning. The Learning Navigator, or Navigator, using AI techniques that curate a customized learning experience, makes individualized learning at scale possible. Gooru's Engineers for Education in collaboration with our Science of Learning Research Partners began with the science of learning, or how learning occurs and its practical implications for teaching. Based on the findings the principals of Navigated Learning were developed and the Navigator designed around them to assure learning outcomes. Navigator, continuously interacts with the learning map and the learner to perform the following:
Based on data about a student's activities and outcomes from formal assessments, the “Locate” module of Navigator embeds learners in the space, and continuously updates their location. Unlike a geographical space, a learner may have acquired several competencies in the competency space. Thus, their location is not identified by a point, but by a data structure called a Skyline.
Once a learner’s location is known, based on their stated goals or recently acquired competencies, a set of further candidate competencies are identified. Curating is based on competency modeling principles, that identifies complementary, supplementary and
Navigator mediates the learning experience by making learning suggestions. Mediation is based on computing an underlying “Narrative Arc” that computes a semantically coherent and meaningful learning sequence individualized for each learner. Mediation also involves suggesting connections with other learners as well as group learning activities.
Facilitate refers to the set of interactions, classroom practices, and activities that teachers realize in association with the data and resources within the Navigator system to monitor progress and personalize suggestions. In Facilitate, the teacher plays a critical role in differentiating instruction using data offered by the system. For example, Suggest in Facilitate provides real-time data to the teacher who then uses it offer suggestions to the student.
The Navigator, is made up of three technology enablers
The Navigator Competency Model (NCM) is the conceptual model and framework for the Navigator system. The term Competency is synonymous with a cognitive learning standard or a non-cognitive attribute, such as critical thinking or creative thinking. Competencies are linked by metadata tags organized by domain, depth of learning, and tags associated with the other vectors of learning into a three dimensional dependency graph. The X-axis provides tags for the domain, the Y-axis provides tags for the pedagogy level and depth of learning (e.g., 5th grade, analyze patterns and relationships), and the Z-axis provides tags associated with other vectors, such as the non-cognitive information and skills.
Extend or refine the learning space in any area of learning
Competencies are often associated with existing structures, such as the Common Core Mathematics standards (National Governors Association Center for Best Practices, & Council of Chief State School Officers, 2010). Standards from different states, countries and organizations are crosswalked to identify overlap and to develop a robust and inclusive model. Competencies also have metadata and learning activities associated with it and information regarding proficiency (e.g. student struggles or depth of knowledge of the competency). The collective set of resources and tags organized by the framework become central components of the (Learning) Navigator system.
The Learning Data Backbone is the rich and complex set of data collected when students and teachers interact with the system resulting in continuous updates on the student, teacher and catalog information. As mentioned, all learning resources and assessments in the Navigator are aligned to competencies with many metadata tags. Each interaction within the system results in newly created links between user information and resources. Metadata are computed from the activity stream data and the efficacy of the learning activities are measured against the competencies mastered using information from the catalog. Once the system has complete activity stream data from a set of learner’s interactions, the system computes learner vectors to continuously update a more precise location of the learner. A similar process exists to operationalize the learning principles. The interaction of the learner with the system creates a data activity stream. This then informs the action suggested to the learner based on the learning principles.
The learning data backbone underlies the Learning Apps for students, teachers and administrators (collectively referred to as Users). The Apps focus on providing the stakeholders data, analysis and suggestions using open educational resources. Users can then make decisions toward achieving learning outcome gains on the competencies that the student needs to learn. Navigated Learning enables a variety of providers the ability to bring content, tools and implementation services to benefit a number of users including their own prioritized students, teachers, parents and/or administrators.