Characterizing active learning environments in physics using latent profile analysis

Latent Structured Active Learning

In this paper we present active learning algorithms in the context of structured prediction problems. To reduce the amount of labeling necessary to learn good models, our algorithms operate with weakly labeled data and we query additional examples based on entropies of local marginals, which are a good surrogate for uncertainty. We demonstrate the effectiveness of our approach in the task of 3D...

Active Learning in Changing Environments

The Collaborative Learning in the e-Learning Environments

Introduction: The collaborative learning and interactive electronic-learning (e- learning) is one of the key factors in education system success. This study examined the collaborative e-learning in the framework of constructivism theory. Methods: This is a review article. The databases such as Scientific Information Databases, Magiran, Science Direct, and Google Scholar were reviewed. Also,...

Designing collaborative learning model in online learning environments

Introduction: Most online learning environments are challenging for the design of collaborative learning activities to achieve high-level learning skills. Therefore, the purpose of this study was to design and validate a model for collaborative learning in online learning environments. Methods: The research method used in this study was a mixed method, including qualitative content analysis and...

Active Learning in Cost - Sensitive Environments

Active learning techniques aim to reduce the amount of labeled data required for a supervised learner to achieve a certain level of performance. This can be very useful in domains where unlabeled data is easy to obtain but labelling data is costly. In this dissertation, I introduce methods of creating computationally efficient active learning techniques that handle different misclassification c...