Refinement and Fluency
The PSLC Refinement and Fluency cluster
The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, refinement, and fluent control of critical knowledge components. The core concepts developed in this cluster include:
- task analysis: to design effective instruction, we must analyze learning tasks into their simplest components,
- basic skills: for true fluency, higher level skills must be grounded on well-practiced lower level skills,
- scheduling of practice: the optimal scheduling of practice uses principles of memory consolidation to maximize robust learning,
- resonance: the acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems,
- explicit instruction: explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple,
- implicit instruction: on the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns,
- cue validity: in both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components,
- focusing: instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues, and
- transfer: a learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.
The general hypothesis is that the structure of instructional activities affects learning. These activities can include practice, scheduling, recall, explicit instruction, implicit presentation, focusing, combining, resonance, and other activities.
This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that robust learning occurs when the learning event space is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.
See material provided by Phil Pavlik for the general glossary.
How can analyses of task and learner’s knowledge lead to a structuring of instructional events that lead to robust learning?
Alternative structures of instructional events based on alternative analyses of task demands, relevant knowledge components, and learner background. Assessing the learner’s background is essentially part of the learning task analysis.
Measures of normal and robust learning.
Robust learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task.
Attention to features of the task domain as a knowledge component is processed leads to associating those features with the knowledge component. If the features are valid, then forming or strengthening such associations facilitates retrieval during subsequent assessment or instruction, and thus leads to more robust learning.
- Using syntactic priming to increase robust learning (de Jong, Perfetti, DeKeyser)
- French dictation training (MacWhinney)
- French gender cues (MacWhinney)
- Chinese pinyin dictation (MacWhinney)
- First language effects on second language grammar acquisition (Mitamura)
- Optimizing the practice schedule (Pavlik)
- Semantic grouping during vocabulary training (Tokowicz)
- Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren) [Was in Coordinative Learning and in Interactive Communication]
- Mental rotations during vocabulary training (Tokowicz)