ABSTRACT

This resource links Mayes (Chapter 1) with Beetham (Chapter 2). It shows how different theoretical commitments can be translated into design principles, as well as what current learning theories have in common. It can be used to apply broad design principles to consideration of learning outcomes, student progression, assessment and feedback, and learning environments. https://www.niso.org/standards/z39-96/ns/oasis-exchange/table"> Associative Constructive (individual) Constructive (social) Situative The theory People learn by association, initially through stimulus-response conditioning, later by acquiring concepts in a chain of reasoning, or steps in a sequence of actions. Learning is successful when instruction leads to accurate or smooth performance, for example when factual material is committed to memory or when skilled performance is compiled in an errorless sequence. Associative theories are less concerned with how concepts or skills are represented or transferred, but more with how different instruction regimes support acquisition and reproduction. Routines of structured activity are key to learning.

People learn by actively investigating the world around them, receiving feedback and drawing conclusions. New learning must be integrated into the individual’s existing conceptual or competency structures. These learned structures or rubrics can be applied to new contexts and expressed in new ways (transferred).

Learning tasks should be devised to encourage the progressive achievement of understanding, or the progressive development of skill – with the learner taking control over how the task is approached.

Attention and motivation are key to constructivist learning.

Individual exploration is scaffolded by social interactions. Peer learners and teachers play a key role in developing a shared understanding of the task, and providing feedback on the learner’s activities and concepts. Social constructive theories are concerned with how emerging concepts and skills are supported by others, allowing learners to achieve beyond their individual capabilities. Learning depends on prior social resources such as language(s), tools, designed environments. People learn by participating in communities of practice, progressing from novice to expert through observation, reflection, being mentored, and ‘legitimate peripheral participation’ in shared activities. Like social constructivism, situativity emphasizes the social context of learning, but this context is likely to be close – or identical – to the real-world setting in which the learner will eventually practice. Participation is not only the route to individual learning but the end goal. The social environment provides motivation and identity rewards. Recent updates (see, Mayes, Chapter 1) Neural networks and other machine learning technologies seem to show that learning can be implicit or emergent in a highly inter-connected system capable of detecting and responding to patterns of input. Human learners can acquire complex concepts or behaviours by responding to underlying patterns in the environment without necessarily consciously formalising those patterns as conceptual structures. These forms of learning are rapid and do not apparently increase cognitive load. Cognitive neuroscience confirms the modularity of many brain functions involved in learning. Discovery learning and direct instruction may activate different regions. There is evidence to support integration and consolidation as specific (complex, multi-modular) brain activities, separate from the discovery phase. This form of learning is slower, more uncertain, and requires active attention. As for constructivism. ‘Connectivism’ as a description of learning in open networks, characterized by complexity, emergence, self-organisation, the location of ‘learning’ in ‘the network’ itself rather than in the individual participant, and (in some versions) the merging of human and non-human agency. Alternatively, the blurring of boundaries between declarative knowledge and knowledge-sharing practices, and between texts and tools. The virtual network as a real-world context for professional and scholarly participation. In some versions e.g. Actor Network Theory, participation is considered in terms of interactions among human and non-human agents. Relationship to learning outcome taxonomies

Maps to Laurillard’s (2012) learning by acquisition (concepts), and learning by practice (skills)

Bloom, Anderson and Krathwohl’s (2001) ‘remembering’ action verbs are particularly relevant to associative learning goals.

Maps to Laurillard’s learning by investigation

Bloom’s ‘understanding’, ‘applying’ and ‘analysing’ (and higher order) action verbs are particularly relevant to constructivist learning goals.

Maps to Laurillard’s learning by investigation + learning by collaboration.

Also maps to ‘learning with others’ in Resource 2, this volume.

Maps to Laurillard’s learning by acquisition/practice + learning by collaboration.

Also maps to ‘learning with others’ in Resource 2, this volume.