منابع مشابه
Theory of self-learning Q-matrix
Cognitive assessment is a growing area in psychological and educational measurement, where tests are given to assess mastery/deficiency of attributes or skills. A key issue is the correct identification of attributes associated with items in a test. In this paper, we set up a mathematical framework under which theoretical properties may be discussed. We establish sufficient conditions to ensure...
متن کاملData-Driven Learning of Q-Matrix.
The recent surge of interests in cognitive assessment has led to developments of novel statistical models for diagnostic classification. Central to many such models is the well-known Q-matrix, which specifies the item-attribute relationships. This article proposes a data-driven approach to identification of the Q-matrix and estimation of related model parameters. A key ingredient is a flexible ...
متن کاملAlternating Recursive Method for Q-matrix Learning
The key issue affecting Cognitive Diagnostic Models (CDMs) is how to specify attributes and the Q-matrix. In this paper, we first attempt to use the Boolean Matrix Factorization (BMF) method to express conjunctive models in CDMs. Because BMF is an NPhard problem [2], we propose a recursive method that updates the attribute matrix (its rank equals to one) in each step. As Boolean algebra is irre...
متن کاملlearning style, self efficacy and intrinsic motivation as predictors of iranian ielts reading comprehension
this thesis attempts to measure learning styles, self efficacy and intrinsic motivation as predictors of iranian ielts reading comprehension. in order to address this issue, a quantitative study was conducted on some randomly selected intact students at ferdowsi university. these two groups were assigned as they were undergraduate (ba=91) and graduate (ma =74) students; they were all aged betwe...
Nonlinear random matrix theory for deep learning Nonlinear random matrix theory for deep learning
Neural network configurations with random weights play an important role in the analysis of deep learning. They define the initial loss landscape and are closely related to kernel and random feature methods. Despite the fact that these networks are built out of random matrices, the vast and powerful machinery of random matrix theory has so far found limited success in studying them. A main obst...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bernoulli
سال: 2013
ISSN: 1350-7265
DOI: 10.3150/12-bej430