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Familiarity Instrument Validation

This project aims to validate the familiarity instrument I developed for evaluating players' perceived familiarity with exergames. The user study incorporated 20 participants who played exergames and completed surveys. Additionally, I also involved using electroencephalogram (EEG) data to objectively track brain activity during familiarity induction. This instrument serves as a valuable tool for both exergame designers and players seeking optimized game experiences.

Role: Researcher

Method: Usability Test + Survey + EEG

Research Type: PhD Research / Exploratory UXR

Company: Nanyang Technological University

Timeline: 3 months

Background

My PhD research proposed the familiarity design as a solution to enhance user engagement, particularly for older adults. Familiarity with exergame interfaces and tasks significantly boosts older adults' motivation and ability to play. However, there's a critical gap: the lack of a validated instrument to measure perceived familiarity with specific exergames. This project introduces a psychometric familiarity instrument that evaluates users' familiarity with exergame interfaces and tasks. The instrument underwent validation through EEG data and survey responses from 20 participants.

Goals

  1. Instrument Development: Develop and validate a psychometric familiarity instrument tailored to exergames, specifically focusing on users' familiarity with key stimuli: interface and task.

  2. Validation and Reliability: Validate the instrument's effectiveness and reliability by assessing its correlation with electroencephalography (EEG) data and evaluating psychometric measures, including criterion-related validity, convergent validity, sensitivity, and internal consistency.

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Method

​Usability Test

  • 3 Exergames (15 minutes play for each exergame)

  • 20 Participants (10 Younger Adults + 10 Older Adults)

Data Collection

  • EEG Data Collection: Measure brain activity in response to exergame stimuli

  • Questionnaire (rate perceived familiarity and satisfaction)

Data Analysis​

  • Correlation Analysis

  • Validation Analysis (psychometric measures)

Insights

  1. Instrument validated from four different psychometric measures

    • Criterion-related validity: Correlation between EEG data and instrument/questionnaire results​.

    • Convergent validity: Correlations between the familiarity instrument results and participants’ rated overall familiarity.

    • Sensitivity: distinguish between low and high familiarity conditions (different exergames).

    • Internal consistency: Reliability analysis, Cronbach’s α

  2. Comparison between the two age groups: The influence of familiarity design on users’ satisfaction with the exergames is more salient to older adults than younger adults.

  3. Please find more results and details of the familiarity instrument in the below publication

    • Zhang, H., Wang, D., Wang, Y., Chi, Y. and Miao, C., Development and validation of a practical instrument for evaluating players’ familiarity with exergames. International Journal of Human-Computer Studies, Vol. 145, 102521, 2021.

Research Impact

Strategical Impact​

Efficient Familiarity Assessment: By introducing an effective and efficient familiarity assessment tool, the research contributes to streamlining the evaluation of users' familiarity levels with different exergames. This strategic impact empowers designers and researchers to efficiently gauge users' familiarity, facilitating more informed design decisions and enhancing user experiences.

Stakeholder Collaboration Impact

Cross-Functional Collaboration: The validation of the familiarity instrument fosters collaboration between researchers, designers, and developers. It encourages cross-functional teams to work together to integrate familiarity design principles effectively into exergames, ensuring a more engaging and satisfying experience for users of all ages.

Product Impact

Improved Exergame Design: The research's findings influence the design of exergames by highlighting the significance of familiarity design elements. This impact guides developers in creating exergames that are more engaging, enjoyable, and accessible to users of different age groups, particularly older adults.

My Learning

  1. Collecting noise-free EEG data during exergames is challenging. Simulating brain activity with screenshots and videos, while imperfect, offers a viable solution. Future studies should explore more data collection methods.

  2. Familiarity design extends beyond exergames and applies to various technologies, including other commercial games and digital systems. 

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