Honorable Mention
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.
https://doi.org/10.1145/3411764.3445366
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)