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Full-Text Articles in Physical Sciences and Mathematics
Freedom Of Choice As A Motivational Factor In Active Learning, Atanas Radenski
Freedom Of Choice As A Motivational Factor In Active Learning, Atanas Radenski
Mathematics, Physics, and Computer Science Faculty Articles and Research
Freedom to choose what, when, and how to contribute in a learning process can motivate students to actively engage and achieve more in their studies. However, freedom of choice complicates course management and may deter instructors from allowing such freedom. Our approach is to utilize existing functionality of course management systems such as Moodle to automatically facilitate and coordinate free student choices and provide much needed relief for instructors at the same time. Using Moodle we have developed novel digital study packs that blend freedom of choice with guidance and control. Our survey shows that assisted freedom of choice is …
Active Learning For Causal Bayesian Network Structure With Non-Symmetrical Entropy, Li G., Tze-Yun Leong
Active Learning For Causal Bayesian Network Structure With Non-Symmetrical Entropy, Li G., Tze-Yun Leong
Research Collection School Of Computing and Information Systems
Causal knowledge is crucial for facilitating comprehension, diagnosis, prediction, and control in automated reasoning. Active learning in causal Bayesian networks involves interventions by manipulating specific variables, and observing the patterns of change over other variables to derive causal knowledge. In this paper, we propose a new active learning approach that supports interventions with node selection. Our method admits a node selection criterion based on non-symmetrical entropy from the current data and a stop criterion based on structure entropy of the resulting networks. We examine the technical challenges and practical issues involved. Experimental results on a set of benchmark Bayesian networks …
Semisupervised Svm Batch Mode Active Learning With Applications To Image Retrieval, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Semisupervised Svm Batch Mode Active Learning With Applications To Image Retrieval, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Active learning has been shown as a key technique for improving content-based image retrieval (CBIR) performance. Among various methods, support vector machine (SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. First, SVM often suffers from learning with a small number of labeled examples, which is the case in relevance feedback. Second, SVM active learning usually does not take into account the redundancy among examples, and therefore could select multiple examples in relevance feedback that are similar (or even identical) to …
Empirical Usage Metadata In Learning Objects, Gwen Nugent, Kevin Kupzyk, S. A. Riley, L.D. Miller, Jesse Hostetler, Leen-Kiat Soh, Ashok Samal
Empirical Usage Metadata In Learning Objects, Gwen Nugent, Kevin Kupzyk, S. A. Riley, L.D. Miller, Jesse Hostetler, Leen-Kiat Soh, Ashok Samal
CSE Conference and Workshop Papers
The iLOG Project (Intelligent Learning Object Guide) is designed to augment multimedia learning objects with information about (1) how a learning object has been used, (2) how it has impacted instruction and learning, and (3) how it should be used. The goal of the project is to generate metadata tags from data collected while students interact with learning objects; these metadata tags can then be used to help teachers identify learning objects that match the educational and experiential backgrounds of their students. The project involves the development of an agent-based intelligent system for tracking student interaction with learning objects, in …