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Optimizing fact learning gains: Using personal parameter settings to improve the learning schedule.

Koelewijn, L.S. (2010) Optimizing fact learning gains: Using personal parameter settings to improve the learning schedule. Master's Thesis / Essay, Human-Machine Communication.

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Learning a list of facts in an efficient way is not as simple as it might appear. The spacing effect and time costs of the learning trials are amongst other aspects that have to be taken into consideration. This thesis is about creating an algorithm which produces learning schedules that maximize the retention of a learned item set on a test. This has been attempted before (Pavlik & Anderson, 2008; Van Rijn, Van Maanen & Van Woudenberg, submitted for publication; Van Thiel, 2010), but none of these studies account for the large differences in individual learning abilities that exist between people. I present an adaptation of the latency-based ACT-R spacing algorithm used by Van Thiel (2010) and in addition introduce the personalization of two important parameters to account for these individual differences. A series of experiments is performed in a laboratory setup as well as in a more realistic real-world setting to test the algorithm’s performance. Analysis of the results shows no significant increase in retention on a test of the learned items when using personal parameter settings. All data do indicate however that the use of personal parameter settings does not hurt retention on a test. The analysis also shows personalization is potentially more important in a real-world setting. Including personal parameter settings thus seems to be justified.

Item Type: Thesis (Master's Thesis / Essay)
Supervisor nameSupervisor E mail
Degree programme: Human-Machine Communication
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:31
Last Modified: 02 May 2019 12:41

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