Automatic detection of users’ skill levels using high-frequency user interface events

Ghazarian, Arin; Noorhosseini, Seyed Majid

doi:10.1007/s11257-010-9073-5

Cited by 39 publications

(23 citation statements)

References 44 publications

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“…Ghazarian and Noorhosseini (2010) defined task skill to indicate the skill level of a user in performing a specific task in an application. Masarakal (2010) defined expertise as ''the ability of a user to complete a task'' (p. 2).…”

Section: Task Skillmentioning

confidence: 99%

“…Masarakal (2010) used similar features to classify expertise in Microsoft Word. Ghazarian and Noorhosseini (2010) built automatic skill classifiers for desktop applications. Common among these projects is that they focused on repetitions with specific tasks and UI components, where users could transition from novice to skilled behaviors in the span of minutes.…”

Section: In Situ Identification Of Expertisementioning

confidence: 99%

“…Initial attempts to automatically detect expertise have been made in recent research literature (Ghazarian & Noorhosseini, 2010;Hurst et al, 2007;Masarakal, 2010). However, such work is limited to tasks where a user can transition from novice to expert within minutes, such as the speed at which users make selections from a menu.…”

Section: Introductionmentioning

confidence: 99%

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An Investigation of Metrics for the In Situ Detection of Software Expertise

Grossman

Fitzmaurice

2014

Human–Computer Interaction

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Task-based analysis is a common and effective way to measure expertise levels of software users. However, such assessments typically require inperson laboratory studies and inherently require knowledge of the user's task. Today, there is no accepted method for assessing a user's expertise levels outside of a lab, during a user's own home or work environment activities. In this article, we explore the feasibility of software applications automatically inferring a user's expertise levels, based on the user's in situ usage patterns. We outline the potential usage metrics that may be indicative of expertise levels and then perform a study, where we capture such metrics, by installing logging software in the participants' own workplace environments. We then invite those participants into a laboratory study and perform a more traditional task-based assessment of expertise. Our analysis of the study examines if metrics captured in situ, without any task knowledge, can be indicative of user expertise levels. The results show the existence of significant correlations between metrics calculated from in situ usage logs, and task-based user expertise assessments from our laboratory study. We discuss the implications of the results and how future software applications may be able to measure and leverage knowledge of the expertise of its users.

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Section: Task Skillmentioning

confidence: 99%

Section: In Situ Identification Of Expertisementioning

confidence: 99%

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An Investigation of Metrics for the In Situ Detection of Software Expertise

Grossman

Fitzmaurice

2014

Human–Computer Interaction

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“…Ghazarian and Noorhosseini (2010) created automatic skill classifi ers for desktop applications. They used machine learning algorithms to build statistical predictive models of skill.…”

Section: Introductionmentioning

confidence: 99%

Pauses in man-machine interactions: a clue to users' skill levels and their user interface requirements

Ghazarian¹,

Ghazarian²

2013

IJCPS

Self Cite

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Abstract:Computer users have motionless periods of time while performing computer-based tasks. Do these pauses relate to the mental and perceptual actions required to perform tasks? Do users essentially pause while they think, wait to retrieve the next step to perform, or search the location of something on the screen? How do the pauses change as the users gain experience and progress from novice to skilled? To answer these questions, we conducted user studies to investigate the link between the pauses observed in users' interactions with computer-based applications and their skill levels. In this paper, we introduce a set of pause-related attributes that can distinguish among different levels of skills in performing Graphical User Interface (GUI) tasks. We employ machine learning algorithms to build skill classifi ers based on these attributes. These skill classifi ers can be used to create skill-adaptive applications.

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“…Each computer users have different levels of system skills, different behavior as well as different perspective view on the same displayed visualization (Ghazarian and Noorhosseini, 2010). The purpose of this study was constructing an effective algorithm in order to learn the users' feedbacks on their displayed visualization.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Learning Method on Users' Feedbacks

Wong¹

2011

Journal of Computer Science

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Problem statement: The purpose of this study was constructing an effective algorithm in order to learn the users' feedbacks from their displayed visualization. This is due to existing visualization tools typically involve presenting network data regardless of considering level of network data knowledge among different levels of computer users. The machine learning algorithm has been applied in order to find the most effective statistical analysis and learning algorithm in learning users' feedbacks. Approach: The objectives of this study were to conduct statistical analysis and learning algorithm model for different levels of computer users' feedbacks and procedure to test the classifier. Results: WEKA the machine learning workbench that supports many activities of machine learning practitioners will be used to implement the proposed algorithm. The implemented program will work as training testing model. Conclusion: We can produce an adaptive visualization to the different levels of computer users as we have learnt their feedbacks (behavior) and update the classifier model.

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Automatic detection of users’ skill levels using high-frequency user interface events

Cited by 39 publications

References 44 publications

An Investigation of Metrics for the In Situ Detection of Software Expertise

An Investigation of Metrics for the In Situ Detection of Software Expertise

Pauses in man-machine interactions: a clue to users' skill levels and their user interface requirements

Statistical Analysis and Learning Method on Users' Feedbacks

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