11.1 Proposal In analysing the Tongan data, we proposed the following hypothesis. First, based on the fact that ergativity is manifested also at the level of syntax, we argue that ERG is a structural case. Second, following Bobaljik (1993), we assume that the difference between ergative case marking and accusative case marking is fundamentally the choice of active Agr. If a language chooses Agr...

Reliable student models are vital for the correct functioning of Intelligent Tutoring Systems. This means that diagnosis tools used to update the student models must be also reliable. Through adaptive testing, student knowledge can be inferred. The tests are based on a psychometric theory, the Item Response Theory. In this theory, each question has a function assigned that is essential for dete...

Computer adaptive testing (CAT) is theoretically sound and efficient, and is commonly seen in larger testing programs. It is, however, rarely seen in a smaller-scale scenario, such as in classrooms or business daily routines, because of the complexity of most adopted Item Response Theory (IRT) models. While the Sequential Probability Ratio Test (SPRT) model is less complicated, it only provides...

Writing on the board is an important part of a lecture. Lecturers’ handwriting is not always perfect. Usually, a lecturer can write slower and more legibly, this will increase understandability but slow down the lecture. In this paper, we analyze an optimal trade-off between speed and legibility. c ⃝2012 World Academic Press, UK. All rights reserved.

As many states are slated to soon use scores derived from classroom observation instruments in high-stakes decisions, developers must cultivate methods for improving the functioning of these instruments. We show how multidimensional, multilevel item response theory models can yield information critical for improving the performance of observational instruments.

We develop T-SKIRT: a temporal, structuredknowledge, IRT-based method for predicting student responses online. By explicitly accounting for student learning and employing a structured, multidimensional representation of student proficiencies, the model outperforms standard IRTbased methods on an online response prediction task when applied to real responses collected from students interacting w...