Enhanced Prediction Models for Predicting Spatial Visualization (VZ) in Address Verification Task

10 pages•Published: March 13, 2019

Thitivatr Patanasakpinyo, Georgi Batinov, Kofi Whitney, Adel Sulaiman and Les Miller

Abstract

In the field of Human Computer Interaction and Psychology, it is accepted that spatial visualization (VZ) is one ability that can indicate individual’s performance on computer applications. Since users with different levels of VZ seem to prefer different types of user interfaces (UI), knowing a user’s level of VZ provides a great opportunity for application developers to design software with higher satisfaction and usability. In this paper, we proposed three models to predict a participant’s level of VZ based on the participant’s actions (taps) on the tablet screen while doing an address verification task in the neighbor- hood using the tablet. After applying the proposed prediction models with data of thirty participants, they yielded an optimal accuracy of 93.33%.

Keyphrases: address verification, human computer interaction, prediction, spatial ability, spatial visualization, user interface

In: Gordon Lee and Ying Jin (editors). Proceedings of 34th International Conference on Computers and Their Applications, vol 58, pages 247-256.

Links:	https://easychair.org/publications/paper/st5w
	https://doi.org/10.29007/v9g3

BibTeX entry

@inproceedings{CATA2019:Enhanced_Prediction_Models_Predicting,
  author    = {Thitivatr Patanasakpinyo and Georgi Batinov and Kofi Whitney and Adel Sulaiman and Les Miller},
  title     = {Enhanced Prediction Models for Predicting Spatial Visualization (VZ) in Address Verification Task},
  booktitle = {Proceedings of 34th International Conference on Computers and Their Applications},
  editor    = {Gordon Lee and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {58},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/st5w},
  doi       = {10.29007/v9g3},
  pages     = {247-256},
  year      = {2019}}

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