Investigating the relationship between spatial skills and web mapping application performance among university students

ABSTRACT

Successfully performing a task using a web mapping application may not only depend on the application itself and how well it meets users’ needs but also on users’ spatial skills. Spatial ability is comprised of different spatial skills and high performance in one skill does not entail similar performance in another. Hence, we explored how university students’ performance in seven spatial skills (three small- and four large-scale skills) affects their performance in four tasks (such as finding the closest point of interest or finding the shortest path) using Google Maps, considering three groups of participants based on high, medium, and low spatial skills performance. Beyond exploring this relation for each spatial skill separately, we introduce a composite index for performance in spatial skills, considering them as either two separate clusters of small- and large-scale skills or as an amalgam of both. Results indicate that participants with higher small-scale, but not with higher large-scale, spatial skills, when these are separately assessed, perform better on Google Maps than the other two groups, especially the low spatial-skilled group. This also holds when small-scale skills are considered as a cluster and when all skills are considered as a whole.

KEYWORDS:

• Spatial skills
• web mapping application
• assessment
• composite index
• performance
• digitally skilled

Acknowledgments

Authors would like to thank all individuals who participated to the study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All analyses have been performed in R (R Core Team, 2021) using: “tidyverse” (Wickham et al., 2019), “descTools” (Andri et al., 2022), “dplyr” (Wickham et al., 2022) “gdata” (Warnes et al., 2022), “ggplot2” (Wickham, 2016), “rstatix” (Kassambara, 2022), “ggstatsplot” (Patil, 2021), “ltm” (Rizopoulos, 2006) and “corrplot” (Wei & Simko, 2021) packages.

The survey data that support the findings of this study are openly available in figshare at https://doi.org/10.6084/m9.figshare.21424257.v4.

Additional information

Funding

The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) (https:// www.elidek.gr/en/) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment” [Project Number: HFRI-FM17-2661]. This research was partially supported by the project that has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.