Mathematical modelling is endorsed as both a means and an end to learning mathematics. Despite its utility and inclusion as a curricular objective, one of many questions remaining about learners' modelling regards how modelers choose relevant situational attributes and express mathematical relationships in terms of them. Research on quantitative…

Background: This study breaks new ground by presenting a new, more sophisticated model of learning engagement that goes beyond the current state of the art embodied in the widely used Affective-Behavioral-Cognitive (ABC) model. This work synthesizes and builds upon neglected lines of research in the structure of affective engagement. It also…

Moderated multiple regression (MMR) has become a fundamental tool for applied researchers, since many effects are expected to vary based on other variables. However, the inherent complexity of MMR creates formidable challenges for adequately performing power analysis on interaction effects to ensure reliable and replicable research results. Prior…

Noticing students' mathematical thinking is foundational to responsive teaching. However, it is cognitively demanding because teachers must attend to important details in students' strategies, interpret their understandings, and decide on next steps--all in the moment. This article introduces the Big Ideas of Measurement (BIM), a research-based…

As artificial intelligence (AI) technologies are increasingly pervasive in our daily lives, the need for students to understand the working mechanisms of AI technologies has become more urgent. Data modeling is an activity that has been proposed to engage students in reasoning about the working mechanism of AI technologies. While Computational…

The phenomenon of misconception and poor conceptual understanding is a problem that often occurs in science learning, not least in physics learning. Learning models that specifically aim to improve conceptual understanding and remediate misconceptions are not widely available. This study aims to determine the effect of the CCBL model in improving…

Student performance is affected by their knowledge which changes dynamically over time. Therefore, employing recurrent neural networks (RNN), which are known to be very good in dynamic time series prediction, can be a suitable approach for student performance prediction. We propose such a neural network architecture containing two modules: (i) a…

Years and Centuries are the measurement units used to quantify a longer time duration, while subtraction is the operation required to determine the duration based on two given time points. However, subtraction of time is a difficult skill to be mastered by many elementary students. To identify the root cause of the student's failure in performing…

This paper presents results of a study aimed at describing and discussing evidence/features of advanced algebraic thinking processes. To achieve these objectives, we analysed the written production of students enrolled in engineering courses working on mathematical modelling tasks related to differential equations. Our guiding question was as…

The goal of this paper is to share an analytic framework for understanding Students? Ways of Thinking (SWoT) in STEM-rich learning environments. Before revealing our refined coding framework, we detail the nature of our collaborations and the various analytic decisions that led to its formation. These collaborations supported our collective…

While creativity has traditionally been linked to arts education, the importance of developing the ability to think creatively across the school curriculum has grown in prominence (James et al. 2019), reflected by its inclusion in OECD PISA 2022 for the first time (OECD, 2024). Creativity enables learners to thrive in a rapidly evolving workplace,…

The challenge posed by algebra story problems creates a significant hurdle for many students, transcending both the mathematical content of the problem and the specific instructional background received. This study offers a distinctive contribution to the existing literature by focusing on the cognitive conditions essential for comprehension in…

In measurement and evaluation processes, natural language responses are often avoided due to time, workload, and reliability concerns. However, the increasing popularity of automatic short-answer grading studies for natural language responses means such answers can now be measured more quickly and reliably. This study aims to build models for…

Modelling roles of mathematics in physics has proved to be a difficult task, with previous models of the interplay between the two disciplines mainly focusing on mathematical modelling and problem solving. However, to convey a realistic view of physics as a field of science to our students, we need to do more than train them to become fluent in…

Background: Blended mathematical sensemaking in science ("Math-Sci sensemaking") involves deep conceptual understanding of quantitative relationships describing scientific phenomena and has been studied in various disciplines. However, no unified characterization of blended Math-Sci sensemaking exists. Results: We developed a theoretical…