Thomas Bolster’s research while affiliated with Carnegie Mellon University and other places

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Publications (1)


Fig. 1. Graph of the efficiency function for optimized practice 
Fig. 2. Average learning curves across the first 5 practices for items in either condition 
Using Optimally Selected Drill Practice to Train Basic Facts
  • Conference Paper
  • Full-text available

June 2008

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139 Reads

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28 Citations

Lecture Notes in Computer Science

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Thomas Bolster

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How to best sequence instruction in a collection of basic facts is a problem often faced by intelligent tutoring systems. To solve this problem, the following work details two tests of a system to provide drill practice (test trials with feedback) for foreign language vocabulary learning using a practice schedule determined to be optimal according to a cognitive model. In the first test, students chose between an optimized version and a version that merely cycled the vocabulary items. Examination of the time on task data revealed a preference for practice based on the decisions of the cognitive model. In the second test, the system was used to train the component parts of Chinese characters and measure the transfer of knowledge to subsequent learning of Chinese characters. Chinese character learning was improved for students with the relevant optimized training.

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Citations (1)


... In tutors such as the Pinyin Tutor, this data can be used by the program to customize the course of learning for each student to avoid repetition of easy items and to focus on problem areas. This training follows rules for graduated interval recall , as computed by a model for each student (Pavlik, Bolster, Wu, Koedinger, & MacWhinney, 2008). The tutors can also be configured to run online experimental comparisons between different learning conditions, using within-subject designs. ...

Reference:

Language Teaching Research Quarterly Brian MacWhinney's 55 Years Research into Language Education and Psychology: A Systematic Review and Brian MacWhinney's Personal Reflection
Using Optimally Selected Drill Practice to Train Basic Facts

Lecture Notes in Computer Science