The Effects of System Interpretation Errors on Learning New Input Mechanisms
| Autor: | Andy Cockburn, Carl Gutwin, Madison Klarkowski, Kevin C. Lam |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Transition (fiction) Interpretation (philosophy) 05 social sciences 020207 software engineering 02 engineering and technology Expertise development Memory retention Skill development Affect (psychology) User Error Task (project management) government.politician 0202 electrical engineering electronic engineering information engineering government 0501 psychology and cognitive sciences 050107 human factors Cognitive psychology |
| Zdroj: | CHI |
| DOI: | 10.1145/3411764.3445366 |
| Popis: | Input mechanisms can produce noisy signals that computers must interpret, and this interpretation can misconstrue the user’s intention. Researchers have studied how interpretation errors can affect users’ task performance, but little is known about how these errors affect learning, and whether they help or hinder the transition to expertise. Previous findings suggest that increasing the user’s attention can facilitate learning, so frequent interpretation errors may increase attention and learning; alternatively, however, interpretation errors may negatively interfere with skill development. To explore these potentially important effects, we conducted studies where participants learned commands with various rates of artificially injected interpretation errors. Our results showed that higher rates of interpretation error led to worse memory retention, higher completion times, higher occurrences of user error (beyond those injected by the system), and greater perceived effort. These findings indicate that when input mechanisms must interpret the user’s input, interpretation errors cause problems for user learning. |
| Databáze: | OpenAIRE |
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