This paper introduces AMMORE, a new dataset of 53,000 math open-response question-answer pairs from Rori, a mathematics learning platform used by middle and high school students in several African countries. Using this dataset, we conducted two experiments to evaluate the use of large language models (LLM) for grading particularly challenging…

Learning data analytics improves the learning field in higher education using educational data for extracting useful patterns and making better decisions. Identifying potential at-risk students may help instructors and academic guidance to improve the students' performance and the achievement of learning outcomes. The aim of this research study is…

We show an approach to semi-automatic grading of answers given by students to open ended questions (open answers). We use both peer-evaluation and teacher evaluation. A learner is modeled by her Knowledge and her assessments quality (Judgment). The data generated by the peer- and teacher-evaluations, and by the learner models is represented by a…

Markov chain Monte Carlo (MCMC) methods enable a fully Bayesian approach to parameter estimation of item response models. In this simulation study, the authors compared the recovery of graded response model parameters using marginal maximum likelihood (MML) and Gibbs sampling (MCMC) under various latent trait distributions, test lengths, and…

A universal criterion scale is applied to the direct estimation of validity for various groups of law school applicants and to the testing of the assumptions underlying the more familiar extrapolation methods. Data from American law schools are used to illustrate the procedures. (Author/BW)