When text datasets are very large, manually coding line by line becomes impractical. As a result, researchers sometimes try to use machine learning algorithms to automatically code text data. One of the most popular algorithms is topic modeling. For a given text dataset, a topic model provides probability distributions of words for a set of…

In this paper, the probability content in senior secondary mathematics Victorian curriculum between 1978 and 2016 was classified and compared, by content, context, procedural complexity, the SOLO thinking frameworks and use of technology. While probability continues to form an important and increasing component of the curriculum, it has moved from…

Policymakers are increasingly interested in the extent to which experimental results generalize from a sample to a population of inference. When the sample is not randomly selected, propensity score methods are used to reweight the sample. Subclassification by propensity score is commonly used in which the population is partitioned into strata…

Understanding large-scale patterns in student course enrollment is a problem of great interest to university administrators and educational researchers. Yet important decisions are often made without a good quantitative framework of the process underlying student choices. We propose a probabilistic approach to modelling course enrollment…

How to identify at-risk students in open online courses has received increasing attention, since the dropout rate is unexpectedly high. Most prior studies have focused on using machine learning techniques to predict student dropout based on features extracted from students' learning activity logs. However, little work has viewed the dropout…

In this paper, we propose a visual saliency algorithm for automatically finding the topic transition points in an educational video. First, we propose a method for assigning a saliency score to each word extracted from an educational video. We design several mid-level features that are indicative of visual saliency. The optimal feature combination…

This paper reports the results of a research about the strategies and difficulties developed by university students in the process of modeling and simulating of random phenomena in an environment of a spreadsheet. The results indicate that students had difficulties to identify key components of the problems, which are crucial to formulate a…

The Monte Carlo study compared the performance of predictive discriminant analysis (PDA) and that of logistic regression (LR) for the two-group classification problem. Prior probabilities were used for classification, but the cost of misclassification was assumed to be equal. The study used a fully crossed three-factor experimental design (with…

Cross-validated classification accuracies were compared under assumptions of equal and varying degrees of unequal prior probabilities of group membership for 24 bootstrap and 48 simulated data sets. The data sets varied in sample size, number of predictors, relative group size, and degree of group separation. Total-group hit rates were used to…

Classical test theory is based on the concept of a true score for each examinee, defined as the expected or average score across an infinite number of repeated parallel tests. In most cases, there is only a score from a single administration of the test in question. The difference between this single observed score and the underlying true score is…

Methodological issues of using a class of neural networks called Mixture Density Networks (MDN) for discriminant analysis are discussed. MDN models have the advantage of having a rigorous probabilistic interpretation, and they have proven to be a viable alternative as a classification procedure in discrete domains. Both classification and…

Educational assessment is a process of collecting evidence and interpreting it to provide instructors with information regarding students' learning. However, the current design and scoring of most standardized educational tests are insufficient to serve this purpose. The limitation exists primarily due to the lack of cognitive information…

A Monte Carlo approach is proposed, using the Statistical Analysis System (SAS) programming language, for estimating reliability coefficients in generalizability theory studies. Test scores are generated by a probabilistic model that considers the probability for a person with a given ability score to answer an item with a given difficulty…

The comments presented here were made after the presentation of four papers and commentary by two other symposium participants. They address issues in language comparison and classification. First, comments are made on the papers ("An African Test Case in Comparative Methodology,""The Mathematics of Multilateral Comparison,""Testing a Basic…

This paper describes a study, now in progress, of new methods for representing the sampling variability of Mantel-Haenszel differential item functioning (DIF) results, based on the system for categorizing the severity of DIF that is now in place at the Educational Testing Service. The methods, which involve a Bayesian elaboration of procedures…