The purpose of this study is to predict the mathematical literacy levels of the students participating in the research through the data obtained from PISA 2015 exam organized by OECD using data mining and to determine the variables that affect mathematics literacy. For this purpose, students' mathematics literacy levels and the variables that…

One major challenge of longitudinal data analysis is to find an appropriate statistical model that corresponds to the theory of change and the research questions at hand. In the present article, we argue that "continuous-time models" are well suited to study the continuously developing constructs of primary interest in the education…

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…

The demand for in-depth studies on educational data presupposes the application of technologies that allow data analysis of vast quantities, and subsequently, drawing relevant information and knowledge. The research objective herein is to employ data mining techniques on PISA databases to identify potential patterns that may explain the…

Previous research has demonstrated the potential of examining log-file data from computer-based assessments to understand student interactions with complex inquiry tasks. Rather than solely providing information about what has been achieved or the accuracy of student responses ("product data"), students' log files offer additional…

This paper briefly discusses four measurement challenges of data science or "big data" in educational assessments that are enabled by technology: 1. Dealing with change over time via time-based data. 2. How a digital performance space's relationships interact with learner actions, communications and products. 3. How layers of…