Distinguishing easily confusable categories requires learners to detect their predictive differences. Interleaved sequences -- switching between categories -- help learners to detect such differences. Nonetheless, learners prefer to block -- switching within a category -- to detect commonalities. Across two 2 × 2-factorial experiments, we…

The benefits of incorporating process information in a large-scale assessment with the complex micro-level evidence from the examinees (i.e., process log data) are well documented in the research across large-scale assessments and learning analytics. This study introduces a deep-learning-based approach to predictive modeling of the examinee's…

This paper explores magical realism as a theoretical lens for analysing mathematics classrooms. Traditional frameworks often overlook the complexities of teaching and learning, focusing solely on measurable aspects. Inspired by magical realism -- a literary genre blending the fantastical and the everyday -- I propose a new perspective on how…

Traditional mathematics instruction often fails to address the diverse learning needs of students, leading to frustration and disengagement. This study proposes personalized learning, emphasizing sequential learning, diagnostic assessments, and technology to tailor instruction. This research examines the potential of personalized learning to…

This design case presents the conceptualization, design, and development of Early Science Matters (ESM), an online teacher professional development (oTPD) program for early childhood teachers. The program design consisted of five primary features that were expected to promote participant learning, including (1) visually oriented content, (2)…