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…

The Elo rating system has been recognised as an effective method for modelling students and items within adaptive educational systems. The existing Elo-based models have the limiting assumption that items are only tagged with a single concept and are mainly studied in the context of adaptive testing systems. In this paper, we introduce a…

In higher education, predictive analytics can provide actionable insights to diverse stakeholders such as administrators, instructors, and students. Separate feature sets are typically used for different prediction tasks, e.g., student activity logs for predicting in-course performance and registrar data for predicting long-term college success.…

One of the questions with the longest history in educational assessment is whether it is possible to increase the reliability of a test simply by altering the way in which scores on individual test items are combined to make the overall test score. Most usually, the score available on each item is communicated to the candidate within a question…

Personalized learning environments requiring the elicitation of a student's knowledge state have inspired researchers to propose distinct models to understand that knowledge state. Recently, the spotlight has shone on comparisons between traditional, interpretable models such as Bayesian Knowledge Tracing (BKT) and complex, opaque neural network…

Language entails many nested time scales, ranging from the relatively slow scale of cultural evolution to the rapid scale of individual cognition. The nested, multiscale nature of language implies that even simple acts of text production, such as typing a sentence, entail complex interactions involving multiple concurrent processes. As such, text…

Knowledge tracing is a fundamental area of educational data modeling that aims at gaining a better understanding of the learning occurring in tutoring systems. Knowledge tracing models fit various parameters on observed student performance and are evaluated through several goodness of fit metrics. Fitted parameter values are of crucial interest in…

The Additive Factor Model (AFM) is a cognitive diagnostic model that can be used to predict student performance on items in a context that allows for student learning. Within AFM, "skills" have a learning rate, and student acquisition of a skill depends only on the number of opportunities a student has had to exercise that skill and the…

Human memory has been thoroughly studied and modeled in psychology, but mainly in laboratory setting under simplified conditions. For application in practical adaptive educational systems we need simple and robust models which can cope with aspects like varied prior knowledge or multiple-choice questions. We discuss and evaluate several models of…

With increasing diversity in classrooms, the dispositions of teachers are of primary concern with respect to meeting the needs of all students and in particular, the needs of those on the social fringe. Teacher preparation programs must ensure that graduates possess not only the requisite content and pedagogical knowledge, but also the…

The reliability of an assessment is defined as the extent to which candidates' results would remain stable if the entire assessment exercise was repeated. Whilst numerous studies have evaluated the reliability of written examinations, relatively little has been done to quantify the reliability of internal teacher assessment within schools. This is…

A great deal of research in educational data mining is geared towards predicting student performance. Bayesian Knowledge Tracing, Performance Factors Analysis, and the different variations of these have been introduced and have had some success at predicting student knowledge. It is worth noting, however, that very little has been done to…

A growing body of research suggests that accounting for student specific variability in educational data can improve modeling accuracy and may have implications for individualizing instruction. The Additive Factors Model (AFM), a logistic regression model used to fit educational data and discover/refine skill models of learning, contains a…

Spatial visualisation is a subset of spatial ability and is exemplified in predicting whether or not a net will fold to form a target solid. The researchers examined video of interviews to explore the schemes of Year 5 students for determining the validity of nets for a cube and pyramid. Findings suggest the significance of imaged actions, shown…

Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…