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      Effects of Interpretation Error on User Learning in Novel Input Mechanisms

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      LAM-THESIS-2021.pdf (6.134Mb)
      Date
      2021-09-24
      Author
      Lam, Kevin Ca-Wai
      ORCID
      0000-0003-4604-3703
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      Novel input mechanisms generate signals that are interpreted as commands in computer systems. Sometimes noise from various sources can cause the system to produce errors when attempting to interpret the signal, causing a misrepresentation of the user's intention. While research has been done in understanding how these interpretation errors affect the performance of users of novel signal-based input mechanisms, such as a brain-computer interface (BCI), there is a lack of knowledge in how user learning is affected. Previous literature in command-based selection tasks has suggested that errors will have a negative impact on expertise development; however, the presence of errors could conversely improve a user's learning by demanding more attention from the user. This thesis begins by studying people's ability to use a novel input mechanism with a noisy input signal: a motor imagery BCI. By converting a user's brain signals into computer commands, a user could complete selection tasks using imagined movement. However, the high degree of interpretation errors caused by noise in the input signals made it difficult to differentiate the user's intent from the noise. As such, the results of the BCI study served as motivation to test the effects of interpretation errors on user learning. Two studies were conducted to determine how user performance and learning were affected by different rates of interpretation error in a novel input mechanism. The results from these two studies showed that interpretation errors led to slower task completion times, lower accuracy in memory recall, greater rates of user errors, and increased frustration. This new knowledge about the effects of interpretation errors can contribute to better design of input mechanisms and training programs for novel input systems.
      Degree
      Master of Science (M.Sc.)
      Department
      Computer Science
      Program
      Computer Science
      Supervisor
      Gutwin, Carl; Klarkowski, Madison
      Committee
      Eramian, Mark; McQuillan, Ian; Loehr, Janeen
      Copyright Date
      October 2021
      URI
      https://hdl.handle.net/10388/13609
      Subject
      Input techniques
      Expertise development
      Memory-based retrieval
      User learning
      Input error
      Interpretation error
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