An important aspect of qualitative research is reaching saturation--loosely, a point at which observing more data will not lead to discovery of more information related to the research questions. However, there has been no validated means of objectively establishing saturation. This article proposes a novel quantitative approach to measuring…

Physical literacy is developing as a contested concept with definitional blurring across international contexts, confusing both practitioners and researchers. This paper serves the dual purpose of reporting on an interrogation of concepts associated with physical literacy in academic writing and exploring the use of a text mining data analysis…

This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text-based self-evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information…

Phenomenology as a philosophy and a method of inquiry is not limited to an approach to knowing, it is rather an intellectual engagement in interpretations and meaning making that is used to understand the lived world of human beings at a conscious level. Historically, Husserl' (1913/1962) perspective of phenomenology is a science of understanding…

Cognitive diagnosis models (CDMs) allow for the extraction of fine-grained, multidimensional diagnostic information from appropriately designed tests. In recent years, interest in such models has grown as formative assessment grows in popularity. Many dichotomous as well as several polytomous CDMs have been proposed in the last two decades, but…

Big data analytics are prevalent in fields like business, engineering, public health, and the physical sciences, but social scientists are slower than their peers in other fields in adopting this new methodology. One major reason for this is that traditional statistical procedures are typically not suitable for the analysis of large and complex…

We introduce a model which combines principles from psychometric and connectionist paradigms to allow direct Q-matrix refinement via backpropagation. We call this model dAFM, based on augmentation of the original Additive Factors Model (AFM), whose calculations and constraints we show can be exactly replicated within the framework of neural…

A common motivation for adding control variables to statistical models is to reduce the potential for spurious findings when analyzing non-experimental data and to thereby allow for more reliable causal inferences. However, as I show here, unless "all" potential confounding factors are included in an analysis (which is unlikely to be…

Learning analytics has been increasingly outlined as a powerful tool for measuring, analysing, and predicting learning experiences and behaviours. The rising use of learning analytics means that many educational researchers now require new ranges of technical analytical skills to contribute to an increasingly data-heavy field. However, it has been…

Work-integrated learning, also known as co-operative education, allows students to alternate between on-campus classes and off-campus work terms. This provides an enhanced learning experience for students and a talent pipeline for employers. We observe that co-operative job postings are a rich source of information about the required skills,…

In this paper, we describe a theoretically-grounded data mining approach to identify types of collaborative problem solvers based on students' interactions with an online simulation-based task about electronics concepts. In our approach, we developed an ontology to identify the theoretically-grounded features of collaborative problem solving…

Literature addressing missing data handling for random coefficient models is particularly scant, and the few studies to date have focused on the fully conditional specification framework and "reverse random coefficient" imputation. Although it has not received much attention in the literature, a joint modeling strategy that uses random…

In Huggins-Manley & Han (2017), it was shown that WLSMV global model fit indices used in structural equating modeling practice are sensitive to person parameter estimate RMSE and item difficulty parameter estimate RMSE that results from local dependence in 2-PL IRT models, particularly when conditioning on number of test items and sample size.…

The use of learner modelling approaches is critical for providing adaptive support in educational computer games, with predictive learner modelling being among the key approaches. While adaptive supports have been shown to improve the effectiveness of educational games, improperly customized support can have negative effects on learning outcomes.…

Cyberinfrastructure (CI) empowers the foundational computation resources underlying data analytics, spatial modeling, and many other domains serving the growing knowledge economy in the United States. In every part of these interactions with CI, questions of how to seamlessly integrate CI training into educational programs exist. In this article,…