Clustering sequential navigation patterns in multiple-source reading tasks with dynamic time warping method

doi:10.1111/jcal.12748

2022-11-06

Qiwei He *

Francesca Borgonovi

Javier Suárez‐Álvarez

DOI：10.1111/jcal.12748

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摘要

En 中文

Background Data-driven investigations of how students transit pages in digital reading tasks and how much time they spend on each transition allow mapping sequences of navigation behaviours into students' navigation reading strategies. Objectives The purpose of this study is threefold: (1) to identify students' navigation patterns in multiple-source reading tasks using a sequence clustering approach; (2) to examine how students' navigation patterns are associated with their reading performance and socio-demographic characteristics; (3) to showcase how the navigation sequences could be clustered on the similarity measure by dynamic time warping (DTW) methods. Methods This study draws on process data from a sample of 16,957 students from 69 countries participating in the PISA 2018 study to identify how students navigate through a multiple-source reading item. Students' navigation sequences were characterized by two indicators: the page sequence that tracks the page transition path and the time sequence that records the time duration on each visited page. K-medoid partitioning clustering analyses were conducted on pairwise distance similarity measures computed by the DTW method. Results and conclusions Students' navigation patterns were found moderately associated with their reading proficiency levels. Students who visited all the pages and spent more time reading without rush transitions obtained the highest reading scores. Girls were more likely to achieve higher scores than boys when longer navigation sequences were used with shorter reading time on transited pages. Students who navigated only limited pages and spent shorter reading time were averagely at the lowest rank of socio-economic status. Implications This study provides evidence for the exploration of students' navigation patterns and the examination of associations between navigation patterns and reading scores with the use of process data.

Keyword:

dynamic time warping

multiple-source reading tasks

navigation sequences

PISA

sequence clustering