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Analysis of Museum Tourism Elements for the Elderly Based on Statistical Experiments

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HCI International 2024 Posters (HCII 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2115))

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Abstract

This study, set against the backdrop of elderly museum tourism, employs three experimental designs: elderly cognitive strategy experiments, elderly cognitive offloading efficiency experiments, and elderly social willingness experiments. It quantitatively analyzes the internal and external interactive experiences of the elderly demographic during their travel process, aiming to enhance their mental and physical health and quality of life. The cognitive strategy experiment results reveal that within the 55–75 age group, there’s a need to improve lives through external devices or digital technology, with the age demarcation for assistive technology support at 56.7 years. This need increases with age; with external technological support, offloading efficiency experiments show that the elderly’s memory capacity is 69%, significantly higher than the comparison group’s recall ability at 38%, with statistical expectations, variance, coefficient of variation, and skewness distributions being more favorable. Multivariate linear regression statistics indicate a high correlation between the elderly’s life satisfaction and their willingness to engage socially, with social influence playing a leading role in life satisfaction. The results of this paper provide a theoretical basis for research related to the travel of the elderly.

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Acknowledgment

This study was supported by the National Social Science Fund of China (22BSH122).

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Correspondence to Lu Zhang .

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Xue, Y., Zhang, L. (2024). Analysis of Museum Tourism Elements for the Elderly Based on Statistical Experiments. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2024 Posters. HCII 2024. Communications in Computer and Information Science, vol 2115. Springer, Cham. https://doi.org/10.1007/978-3-031-61947-2_31

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  • DOI: https://doi.org/10.1007/978-3-031-61947-2_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-61946-5

  • Online ISBN: 978-3-031-61947-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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