We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation techniques, inference methods, and query selection strategies to assist a user charged with choosing a conf...

We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation techniques, inference methods, and query selection strategies to assist a user charged with choosing a conf...

How to achieve shared meaning is a significant issue when more than one intelligent agent is involved in the same domain. We define the task of concept convergence, by which intelligent agents can achieve a shared, agreed-upon meaning of a concept (restricted to empirical domains). For this purpose we present a framework that, integrating computational argumentation and inductive concept learni...

This paper describes UAIC’s system built for participating in the Scalable Concept Image Annotation challenge 2015. We submitted runs both for Subtask 1 (Image Concept detection and localisation) and for Subtask 2 (Generation of Textual Descriptions of Images). For the first subtask we created an ontology with relations between concepts and their synonyms, hyponyms and hypernyms and also with r...

Five eighth grade science classes of students in at a middle school were assigned to three treatment groups: those who individually concept mapped, those who collaboratively concept mapped, and those who independently used their study time. The findings revealed that individually generating concept maps on computers during study time positively influenced science concept learning above and beyo...

The purpose of this study was to examine shifts in young children’s learning progression levels while they interacted with virtual manipulative mathematics apps on touch-screen devices. A total of 100 children participated in six mathematics learning sequences while using 18 virtual manipulative mathematics touch-screen apps during clinical interviews. Researchers developed a micro-scoring tool...

This paper focuses attention on psychological evidence of relevance to advanced mathematical thinking. It considers Edelman’s theory of neural Darwinism and Paivio’s theory of dual coding and contemplates how they reveal different perspectives in mathematics education. The former is consonant with the notion of concept image with both supportive and conflicting mental resonances, the latter is ...