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
What sorts of complexity needs to be accounted for to make the ACT-R delcarative memory model adequately represent the various common types of transfer in vocabulary learning?
Background and significance
There is a venerable literature (Soloman & Asch, 1962) on effects like those described in this work, but there appears to be no unified modeling of such phenomena in the context of a practice scheduling algorithm. The assumption is that increased accuracy of the model will increase the accuracy of the predictions it makes when calcualting schedules during in-vivo learning.
For example, based on the idea that there is desireable difficulty for practice, one might assume that it is better to start by giving Foreign->English drill and then reverse to English->Foreign drill after an initial proficiency is reached. Unfortuantely, determining this initial proficiency necessary befire tranistioning to a more difficut task must be done on a case by case basis unless there is a detailed model that encompasses both tasks. This study helps to provide such a model.
Naive subjects were screened for no prior Chinese experience. Effects of conditions were assessed approximately one minute after treatments.
Transfer was measured in the unit knowledge component learning conditions. Accelerated future learnign was measure in the resonance learning conditions.
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.