Correct specifications of hierarchical attribute structures in analyses using diagnostic classification models (DCMs) are pivotal because misspecifications can lead to biased parameter estimations and inaccurate classification profiles. This research is aimed to demonstrate DCM analyses with various hierarchical attribute structures via Bayesian…

This study explores the use of Large Language Models (LLMs), specifically GPT-4, in analysing classroom dialogue--a key task for teaching diagnosis and quality improvement. Traditional qualitative methods are both knowledge- and labour-intensive. This research investigates the potential of LLMs to streamline and enhance this process. Using…

Recent research seeks to develop more comprehensive learner models for adaptive learning software. For example, models of reading comprehension built using data from students' use of adaptive instructional software for mathematics have recently been developed. These models aim to deliver experiences that consider factors related to learning beyond…

This study explores using transfer learning in machine learning for natural language processing (NLP) to create generic automated essay scoring (AES) models, providing instant online scoring for statewide writing assessments in K-12 education. The goal is to develop an instant online scorer that is generalizable to any prompt, addressing the…

Modeling and simulation activities are common in secondary technology and engineering education classrooms. Virtual simulations are used to integrate engineering design into classroom instruction. The performance outcomes of a student's final virtual design usually depend on the level of knowledge application. When applying the learned content to…

In this article, we describe a software system for assessment development in online learning environments in contexts where there are robust links to cognitive modelling including domain and student modelling. BEAR Assessment System Software (BASS) establishes both a theoretical basis for the domain modelling logic, and offers tools for delivery,…

Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…

Binkley et al. (2012) contends that the economy and workplace for the 21st Century will not lie in the routine tasks of the past, instead emphasis will be put on the ability of students to communicate, share and use information to solve increasingly complex problems. This is especially true of individuals who chose to pursue careers in the…

This paper discusses a novel approach for developing more refined and accurate learner models from student data collected from Open Ended Learning Environments (OELEs). OELEs provide students choice in how they go about constructing solutions to problems, and students exhibit a variety of learning behaviors in such environments. Building accurate…

This design and development research was motivated by the rapid expansion and use of GeoGebra by mathematics educators (teachers and lecturers) in Indonesia. One of GeoGebra features is GeoGebra Applet that can be used, modified, and/or developed by educators for dynamic and interactive mathematics teaching and learning. At the time of research…

With the rise of personal desktop 3D printing, a wide spectrum of educational opportunities has become available for educators to leverage this technology in their classrooms. Until recently, the ability to create physical 3D models was well beyond the scope, skill, and budget of many schools. However, since desktop 3D printers have become readily…

The state of Wisconsin has one of the highest four year graduation rates in the nation, but deep disparities among student subgroups remain. To address this the state has created the Wisconsin Dropout Early Warning System (DEWS), a predictive model of student dropout risk for students in grades six through nine. The Wisconsin DEWS is in use…

Adolescents typically like school less after making age-graded school transitions. Stage-environment fit theory (Eccles & Midgley, 1989) attributes this to a mismatch between developmental needs and new school environments. Our in vivo study provides a basis for future quantitative designs by uncovering the most prevalent stage-environment…

Information and communication technology (ICT)-enhanced research methods such as educational data mining (EDM) have allowed researchers to effectively model a broad range of constructs pertaining to the student, moving from traditional assessments of knowledge to assessment of engagement, meta-cognition, strategy and affect. The automated…

In this article, the authors suggest a profile-likelihood approach for estimating complex models by maximum likelihood (ML) using standard software and minimal programming. The method works whenever setting some of the parameters of the model to known constants turns the model into a standard model. An important class of models that can be…