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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.


Diagram of a fully connected (ergodic) 3-state Hidden Markov Model. S1, S2, and S3 represent the hidden states, O1, O2, and O3 are observations emitted by the hidden states, and aij represents the probability of transitioning from state i to state j.
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Figure 3: Diagram of a fully connected (ergodic) 3-state Hidden Markov Model. S1, S2, and S3 represent the hidden states, O1, O2, and O3 are observations emitted by the hidden states, and aij represents the probability of transitioning from state i to state j.

Mentions: Figure 3 graphically depicts the structure of a 3-state, fully connected HMM. S1, S2, and S3 represent the three hidden states, and O1, O2, and O3 are random variables representing the observations emitted by each state according to its emission probability distribution.


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)

Diagram of a fully connected (ergodic) 3-state Hidden Markov Model. S1, S2, and S3 represent the hidden states, O1, O2, and O3 are observations emitted by the hidden states, and aij represents the probability of transitioning from state i to state j.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 3: Diagram of a fully connected (ergodic) 3-state Hidden Markov Model. S1, S2, and S3 represent the hidden states, O1, O2, and O3 are observations emitted by the hidden states, and aij represents the probability of transitioning from state i to state j.
Mentions: Figure 3 graphically depicts the structure of a 3-state, fully connected HMM. S1, S2, and S3 represent the three hidden states, and O1, O2, and O3 are random variables representing the observations emitted by each state according to its emission probability distribution.

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.