580 California St., Suite 400
San Francisco, CA, 94104
Key research themes
1. How can competency development in mathematical modelling be effectively supported in student learning trajectories?
This research theme explores the strategies, challenges, and structures necessary to develop and sustain modelling competencies in students across secondary education, with an emphasis on realistic, technology-rich environments and progressive curricular integration. It matters because mathematical modelling competency is crucial for students to solve real-world problems meaningfully and independently, yet its sustained implementation and student mastery remain challenging.
Key finding: This study presents a technology-rich teaching approach implemented in Year 9 Australian classrooms that integrates real-world problem contexts and graphing calculator technologies to scaffold students' transitions through... Read more
Key finding: Through case studies of junior and senior secondary modelling programs in Australia, this work identifies critical elements that support sustained modelling presence in curricula, emphasizing that modelling as... Read more
Key finding: Investigating prospective teachers’ modelling processes reveals that participants tend to follow a linear, result-oriented single cycle through modelling phases without iterative refinement or validation, highlighting key... Read more
Key finding: This longitudinal study documents that after a three-year modelling program starting in primary education, seventh graders demonstrate the ability to mathematize complex, authentic situations using diverse operations and... Read more
Key finding: Analysis of secondary mathematics student teachers' perspectives reveals that they view modelling as making mathematics concrete, linking math to real-life problems, and developing materials, motivating their course choice by... Read more
2. What is the impact of open and social student modelling interfaces on learner engagement and knowledge reflection?
This research stream investigates how exposing learners to transparent representations of their own and peers' knowledge states through Open Student Modeling (OSM) and Open Social Student Modeling (OSSM) can improve engagement, motivation, and learning outcomes. It matters because adaptive educational technologies rely on student models, but transparency and social comparison may catalyze deeper reflection and self-regulation, enhancing effectiveness.
Key finding: Employing MasteryGrids, an open social student modelling interface, in a large-scale classroom study demonstrated that allowing students to compare their topic-wise knowledge progress with peers and class averages... Read more
Key finding: This work evaluates a coarse-grained, topic-based student modelling approach implemented in QuizGuide and shows that while topic-level models may be less precise than fine-grained concept models, they provide effective... Read more
3. How can probabilistic and Bayesian approaches enhance student modelling for knowledge assessment in adaptive learning systems?
This theme revolves around the application of probabilistic modelling, particularly Bayesian networks, to dynamically assess and update student knowledge states in intelligent tutoring systems, allowing for adaptive assessment and improved prediction of student performance. It is important because probabilistic student models can represent uncertainty and misconceptions, enabling more accurate and efficient personalization of learning experiences.
Key finding: The study introduces a question generation method based on student misconceptions and information gain maximization within Bayesian networks to adaptively assess and update student knowledge models in probabilistic domains.... Read more
Key finding: Evaluating a generic Bayesian student model integrated into computerized testing revealed high agreement between expert grading and model-based knowledge diagnosis on written exams in the domain of first-degree equations.... Read more