Active Learning

Deep vs. Surface Learning highlights a divergence of opinion around what it means to know something. Deep Learning is associated with intrinsically motivated forms of engagement, characterized by making meaningful connections between new and previous understandings. Surface Learning tends to be associated with extrinsic motivations and is focused on the memorization and recall of information for formulaic responses.

Embeddedness Discourses comprise perspectives on learning that refuse separations of self from other and individual from collective. Perceived boundaries among persons and peoples are understood as heuristic conveniences, as collective phenomena are recognized to unfold from and to be enfolded in individual phenomena. Phrased differently, collective forms are understood as learning bodies.

Inquiry-Based Learning was designed to interrupt assumptions of context-free knowledge, passive learning, and smooth paths to understanding. It focuses on pursuing authentic interests, posing researchable questions, and participating in knowledge production. Several types of Inquiry-Based Learning have been described, varying on matters of teacher role, learner autonomy, and flexibility of focus.

Experiential Learning is, in essence, formalized teaching advice based on the ancient realization that humans learn by doing. Focusing on the individual, it recommends firsthand experiences that should occur in real-world environments, affording the learner concrete experience, opportunity to reflect on that experience, opportunity to develop abstract concepts based on that reflection, and opportunities to experiment to continue the cycle of learning.

Embodiment Discourses comprise perspectives on learning that refuse a separation of mental and physical. Mental and physical are understood as integrated and inseparable aspects of the body. Phrased differently, the body is not seen as something that a learner learns through, but as the learner. Correspondingly, behaviors are not seen as goals or indications of learning, but as integral elements of learning.

Coherence Discourses regard distinctions and descriptions as useful devices to make sense of the complex dynamics of learning, but they caution that such devices are mere heuristic conveniences. Coherence Discourses suggest that truths do not exist independently or outside of a system – which is a commentary on humans’ understanding of reality, not a commentary on reality. Most Coherence Discourses employ biological and ecological metaphors, with dynamics framed in evolutionary terms.

Machine Learning encompasses all efforts and strategies to structure and program machines to improve their performances on specific tasks. In these applications, “learning” is usually defined in operational rather than cognitive terms – that is, in terms of measurable improvements in performance that are due to changes in the machine’s structure, programs, or data. Many strategies are under development, employing different computer architectures and a range of logics.

Activity Theory simultaneously considers the individual’s cognition and the social reality by focusing on “activity systems” – that is, taking into account the knower’s history, motivations, mediating artefacts (language, tools, etc.), community, situation, and culture. More broadly, it looks at systems comprising multiple actors, such as teams and organizations, with particular given attention to rules and divisions of labor.

Action Learning is a team-based approach to solving real-life problems, intended for businesses, non-profits, and other organizations. Key components of Action Learning are becoming aware of lack of relevant knowledge and, when the problem at hand is solved, reflecting on the process and results. Most forms of Action Learning include a coach, although the team is encouraged to be self-managing.