Understanding paired associate transfer effects based on shared stimulus components
Applying optimal scheduling of practice in the Chinese Learnlab study
Over the course of approximately 448 practice trials of Chinese vocabulary practice, certain scedules of practice were delivered to test several transfer hypotheses implied (but not implmemented) by the model of practice used for in-vivo experiment optimal training schedules.
These schedules made use of the fact that there are 6 ways to test (Pinyin->English, Sound->English, Hanzi->English, English->Pinyin, Sound->Pinyin, Hanzi->Pinyin) vocabulary knowledge given current tutor capability. There were 21 total within-subjects conditions each with a particular sequence of the 6 types of trials. For instance, to look for unit learning we compared a condition where subjects practiced a Hanzi-Pinyin pair with a study (a presentation of the entire pair) followed by a drill 4 trials later with a condition where the preparation was identical expect that prior to Hanzi->Pinyin practice subjects had 2 trials of English->Pinyin practice for the same word. In this case, if the Hanzi->Pinyin practice benefits from prior English->Pinyin practice it seems to imply that the Pinyin response is being learned as a unit somewhat independently of any particualr association.
There were 3 main hypotheses described below, which we call unit knowledge component learning, resonant learning, and stimulus mapping.
In all three cases the hypothesis was confirmed.
- Optimal Spacing Interval
- Expanding Spacing Interval
- Wide Spacing Interval
- Narrow Spacing Interval
How can the optimal sequence of learning be computed?
Background and significance
Measures of normal and robust learning.
Alternative structures of instructional schedule for knowledge component training based on the predictions of an ACT-R based cognitive model. Further independent variables include how the material is presented for each learning event and the assumptions of the model used to presents schedule the learning. The assumptions of the model include alternative analyses of task demands, the structure of relevant knowledge components, and learner background.
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.