Limits...
Modeling strategic use of human computer interfaces with novel hidden Markov models.

Mariano LJ, Poore JC, Krum DM, Schwartz JL, Coskren WD, Jones EM - Front Psychol (2015)

Bottom Line: We further report the results of a preliminary study designed to establish the validity of our modeling approach.Pre- and post-task questionnaires probed for self-reported styles of problem solving, as well as task engagement, difficulty, and workload.Overall, we find that this novel approach to decomposing unstructured behavioral data within software environments provides a sensible means for understanding how users learn to integrate software functionality for strategic task pursuit.

View Article: PubMed Central - PubMed

Affiliation: The Charles Stark Draper Laboratory, Inc. Cambridge, MA, USA.

ABSTRACT
Immersive software tools are virtual environments designed to give their users an augmented view of real-world data and ways of manipulating that data. As virtual environments, every action users make while interacting with these tools can be carefully logged, as can the state of the software and the information it presents to the user, giving these actions context. This data provides a high-resolution lens through which dynamic cognitive and behavioral processes can be viewed. In this report, we describe new methods for the analysis and interpretation of such data, utilizing a novel implementation of the Beta Process Hidden Markov Model (BP-HMM) for analysis of software activity logs. We further report the results of a preliminary study designed to establish the validity of our modeling approach. A group of 20 participants were asked to play a simple computer game, instrumented to log every interaction with the interface. Participants had no previous experience with the game's functionality or rules, so the activity logs collected during their naïve interactions capture patterns of exploratory behavior and skill acquisition as they attempted to learn the rules of the game. Pre- and post-task questionnaires probed for self-reported styles of problem solving, as well as task engagement, difficulty, and workload. We jointly modeled the activity log sequences collected from all participants using the BP-HMM approach, identifying a global library of activity patterns representative of the collective behavior of all the participants. Analyses show systematic relationships between both pre- and post-task questionnaires, self-reported approaches to analytic problem solving, and metrics extracted from the BP-HMM decomposition. Overall, we find that this novel approach to decomposing unstructured behavioral data within software environments provides a sensible means for understanding how users learn to integrate software functionality for strategic task pursuit.

No MeSH data available.


Observation probability distributions for states 5 and 7. Each of these states has been identified as a “sequential search” state. They represent behavior of participants who haven't yet discovered how to score points in the game, as demonstrated by the low probabilities assigned to swap activities. We believe these states are capturing a specific type of search strategy wherein participants systematically, sequentially selected adjacent tiles moving across rows and down columns.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4490801&req=5

Figure 7: Observation probability distributions for states 5 and 7. Each of these states has been identified as a “sequential search” state. They represent behavior of participants who haven't yet discovered how to score points in the game, as demonstrated by the low probabilities assigned to swap activities. We believe these states are capturing a specific type of search strategy wherein participants systematically, sequentially selected adjacent tiles moving across rows and down columns.

Mentions: Figure 7 depicts the activity distributions of states 5 and 7 which are dominated by reswap activities in static state 5, and reswap + wiggle activities for wiggle state 7. As in the random search states, the negligible probability assigned to swap activities in these states indicates that they capture behavior of individuals who haven't yet discovered the rule. However, the probabilities associated with select symbols are also very low, indicating that new tile selections were limited to the region directly adjacent to the currently highlighted tile. The properties of this state appear to capture a behavior pattern we observed, which can qualitatively be described as a “sequential search” strategy. Participants exhibiting this behavior systematically, sequentially selected adjacent blocks horizontally across rows, and vertically down columns. Participants with the wiggle function enabled also exhibited this behavior, interleaved with periods of wiggle activity.


Modeling strategic use of human computer interfaces with novel hidden Markov models.

Mariano LJ, Poore JC, Krum DM, Schwartz JL, Coskren WD, Jones EM - Front Psychol (2015)

Observation probability distributions for states 5 and 7. Each of these states has been identified as a “sequential search” state. They represent behavior of participants who haven't yet discovered how to score points in the game, as demonstrated by the low probabilities assigned to swap activities. We believe these states are capturing a specific type of search strategy wherein participants systematically, sequentially selected adjacent tiles moving across rows and down columns.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4490801&req=5

Figure 7: Observation probability distributions for states 5 and 7. Each of these states has been identified as a “sequential search” state. They represent behavior of participants who haven't yet discovered how to score points in the game, as demonstrated by the low probabilities assigned to swap activities. We believe these states are capturing a specific type of search strategy wherein participants systematically, sequentially selected adjacent tiles moving across rows and down columns.
Mentions: Figure 7 depicts the activity distributions of states 5 and 7 which are dominated by reswap activities in static state 5, and reswap + wiggle activities for wiggle state 7. As in the random search states, the negligible probability assigned to swap activities in these states indicates that they capture behavior of individuals who haven't yet discovered the rule. However, the probabilities associated with select symbols are also very low, indicating that new tile selections were limited to the region directly adjacent to the currently highlighted tile. The properties of this state appear to capture a behavior pattern we observed, which can qualitatively be described as a “sequential search” strategy. Participants exhibiting this behavior systematically, sequentially selected adjacent blocks horizontally across rows, and vertically down columns. Participants with the wiggle function enabled also exhibited this behavior, interleaved with periods of wiggle activity.

Bottom Line: We further report the results of a preliminary study designed to establish the validity of our modeling approach.Pre- and post-task questionnaires probed for self-reported styles of problem solving, as well as task engagement, difficulty, and workload.Overall, we find that this novel approach to decomposing unstructured behavioral data within software environments provides a sensible means for understanding how users learn to integrate software functionality for strategic task pursuit.

View Article: PubMed Central - PubMed

Affiliation: The Charles Stark Draper Laboratory, Inc. Cambridge, MA, USA.

ABSTRACT
Immersive software tools are virtual environments designed to give their users an augmented view of real-world data and ways of manipulating that data. As virtual environments, every action users make while interacting with these tools can be carefully logged, as can the state of the software and the information it presents to the user, giving these actions context. This data provides a high-resolution lens through which dynamic cognitive and behavioral processes can be viewed. In this report, we describe new methods for the analysis and interpretation of such data, utilizing a novel implementation of the Beta Process Hidden Markov Model (BP-HMM) for analysis of software activity logs. We further report the results of a preliminary study designed to establish the validity of our modeling approach. A group of 20 participants were asked to play a simple computer game, instrumented to log every interaction with the interface. Participants had no previous experience with the game's functionality or rules, so the activity logs collected during their naïve interactions capture patterns of exploratory behavior and skill acquisition as they attempted to learn the rules of the game. Pre- and post-task questionnaires probed for self-reported styles of problem solving, as well as task engagement, difficulty, and workload. We jointly modeled the activity log sequences collected from all participants using the BP-HMM approach, identifying a global library of activity patterns representative of the collective behavior of all the participants. Analyses show systematic relationships between both pre- and post-task questionnaires, self-reported approaches to analytic problem solving, and metrics extracted from the BP-HMM decomposition. Overall, we find that this novel approach to decomposing unstructured behavioral data within software environments provides a sensible means for understanding how users learn to integrate software functionality for strategic task pursuit.

No MeSH data available.