  Using the action sequence data (i.e., log data) from the problem-solving in
technology-rich environments assessment on the 2012 Programme for the
International Assessment of Adult Competencies survey, the current study
examines the associations between adult digital problem-solving behavior and
several demographic and cognitive variables. Action sequence features extracted
using multidimensional scaling (Tang, Wang, He, Liu, & Ying, 2019) and
sequence-to-sequence autoencoders (Tang, Wang, Liu, & Ying, 2019) were used to
predict test-taker external characteristics. Features extracted from action
sequences were consistently found to contain more information on demographic
and cognitive characteristics than final scores. Partial least squares analyses
further revealed systematic associations between behavioral patterns and
demographic/cognitive characteristics.
