In a range of settings, human operators make decisions with the assistance of automation, the reliability of which can vary depending upon context. Currently, the processes by which humans track the level of reliability of automation are unclear. In the current study, we test cognitive models of learning that could potentially explain how humans…

To explore the differential impact of job automation for different groups of primary and secondary school students, an analysis of variance was conducted using survey data on the occupational aspirations of British school students (aged 7-18) and probability statistics derived from a model of job automation. Results indicated that students aged…

We have used an unsupervised machine learning method called latent Dirichlet allocation (LDA) to thematically analyze all papers published in the Physics Education Research Conference Proceedings between 2001 and 2018. By looking at co-occurrences of words across the data corpus, this technique has allowed us to identify ten distinct themes or…

Network meta-analysis (NMA) is gaining popularity for comparing multiple treatments in a single analysis. Generalized linear mixed models provide a unifying framework for NMA, allow us to analyze datasets with dichotomous, continuous or count endpoints, and take into account multiarm trials, potential heterogeneity between trials and network…

One of the most prevalent features of digital mathematics textbooks, compared to traditional ones, is the provision of automated feedback on students' solutions. Since feedback is regarded as an important factor that influences learning, this is often seen as an affordance of digital mathematics textbooks. While there is a large body of mainly…

Successful detection of unusual responses is critical for using machine scoring in the assessment context. This study evaluated the utility of approaches to detecting unusual responses in automated essay scoring. Two research questions were pursued. One question concerned the performance of various prescreening advisory flags, and the other…

Binary cueing systems assist in many tasks, often alerting people about potential hazards (such as alarms and alerts). We investigate whether cues, besides possibly improving decision accuracy, also affect the effort users invest in tasks and whether the required effort in tasks affects the responses to cues. We developed a novel experimental tool…

Considering learning and how to improve students' performances, an adaptive educational system must know how an individual learns best. In this context, this work presents an innovative approach for student modeling through probabilistic learning styles combination. Experiments have shown that our approach is able to automatically detect and…

With the rapid development of Internet technologies, distance education has become a popular educational mode. In this paper, the authors propose an online image automatic annotation distance education system, which could effectively help children learn interrelations between image content and corresponding keywords. Image automatic annotation is…

Mathematical word problems represent a common item format for assessing student competencies. Automatic item generation (AIG) is an effective way of constructing many items with predictable difficulties, based on a set of predefined task parameters. The current study presents a framework for the automatic generation of probability word problems…

Describes a bottom-up procedure for discovering multivalued dependencies in observed data without knowing a priori the relationships among the attributes. The proposed algorithm is an application of technique designed for learning conditional independencies in probabilistic reasoning; a prototype system for automated database schema design has…

This paper describes the development, implementation, and evaluation of an automated system for predicting the acceptability status of candidate reading-comprehension stimuli extracted from a database of journal and magazine articles. The system uses a combination of classification and regression techniques to predict the probability that a given…