2026-10-15T08:51:50Z https://datacatalogue.cessda.eu/oai-pmh/v0/oai

ea668cd3c6f9fb8685e88cae5824eab0631a2612ef8583d85f1d480dc041ad52 2026-06-02T13:09:04Z language:enopenaire_data

doi:10.17026/DANS-XGS-BDQTeasy-dataset:111344 https://doi.org/10.17026/DANS-XGS-BDQT Log-based behavoral and self-reported personal qualities in educational assessments J He <p>Personal qualities and skills such as perseverance and deep learning are important to assess and monitor for educational purposes, but their measurement in cross-cultural assessments is challenging. Conventional self-reported measures suffer from biases which can produce paradoxical results and pose difficulties to draw meaningful comparative inferences. We propose a new source of behavioral measures constructed unobtrusively from computer-generated logs in addition to self-reported measures. Using data from two large-scale educational surveys, we tested the convergent and predictive validity of self-reported and behavioral measures, and found that the behavioral measures are resistant to the self-presentation style or reference group effect, hence can compensate for the limitations of self-reports in cross-cultural analysis. We also illustrate the complementary nature of both measures, and encourage researchers to apply them integratively in educational evaluations.</p> Social Sciences en 2018-09-22 Dataset https://ssh.datastations.nl/oai doi:10.17026/DANS-XGS-BDQT 2025-12-03T01:00:28Z ddi:codebook:2_5