Committee-Based Sample Selection for Probabilistic Classifiers

In many real-world learning tasks, it is expensive to acquire a sufficient number of labeled examples for training. This paper investigates methods for reducing annotation cost by `sample selection'. In this approach, during training the learning program examines many unlabeled examples and selects for labeling only those that are most informative at each stage. This avoids redundantly labeling examples that contribute little new information. Our work follows on previous research on Query By Committee, extending the committee-based paradigm to the context of probabilistic classification. We describe a family of empirical methods for committee-based sample selection in probabilistic classification models, which evaluate the informativeness of an example by measuring the degree of disagreement between several model variants. These variants (the committee) are drawn randomly from a probability distribution conditioned by the training set labeled so far. The method was applied to the real-world natural language processing task of stochastic part-of-speech tagging. We find that all variants of the method achieve a significant reduction in annotation cost, although their computational efficiency differs. In particular, the simplest variant, a two member committee with no parameters to tune, gives excellent results. We also show that sample selection yields a significant reduction in the size of the model used by the tagger

A learning community approach to identifying interventions in health systems to reduce colorectal cancer screening disparities.

Although colorectal cancer (CRC) screening in the United States has been increasing, screening rates are not optimal, and there are persistent disparities in CRC screening and mortality, particularly among minority patients. As most CRC screening takes place in primary care, health systems are well-positioned to address this important population health problem. However, most health systems have not actively engaged in identifying and implementing effective evidence-based intervention strategies that can raise CRC screening rates and reduce disparities. Drawing on the Collective Impact Model and the Interactive Systems Framework for Dissemination and Implementation, our project team applied a learning community strategy to help two health systems in southeastern Pennsylvania identify evidence-based CRC screening interventions for primary care patients. Initially, this approach involved activating a coordinating team, steering committee (health system leadership and stakeholder organizations), and patient and stakeholder advisory committee to identify candidate CRC screening intervention strategies. The coordinating team guided the steering committee through a scoping review to identify seven randomized trials that identified interventions that addressed CRC screening disparities. Subsequently, the coordinating team and steering committee applied a screening intervention classification typology to select an intervention strategy that involved using an outreach strategy to provide minority patients with access to both stool blood test and colonoscopy screening. Finally, the coordinating team and steering committee engaged the health system patient and stakeholder advisory committee in planning for intervention implementation, thus taking up the challenge of reducing and important health disparity in patient populations served by the two health systems

Costing the lifecycle of networked learning: Documenting the costs from conception to evaluation

This paper reports the development of a course development lifecycle model which is intended to inform the identification of ‘hidden’ costs associated with network‐based learning. The development of this model formed part of a six‐month research study funded by the Joint Information Systems Committee of the UK Funding Councils. The study aimed to produce a planning document and financial schema through which the full costs of networked learning could be documented A discussion is given of the initial five‐stage model, the testing and development of this model and the evolution of a final three‐phase model. Hypothetical examples are given of the use of the three‐phase model

How might learning technology impact on the modern delivery of learning in Scotland?

This document has been prepared following a meeting on 23 June 2010 between Michael Russell, Scottish Cabinet Secretary for Education and Lifelong Learning, and the Association for Learning Technology (ALT), represented by Seb Schmoller, Chief Executive and Dr Linda Creanor, ALT Trustee. The purpose of the document is to highlight areas which are of particular relevance to education in Scotland and to respond to specific questions raised at the meeting in Edinburgh. It has been written by members of the ALT-Scotland group, consisting of institutional ALT representatives from Scottish colleges and universities as well as Scottish-based ALT committee members whose backgrounds encompass all sectors of Scottish education

Handbook for Learning-centred evaluation of Computer-facilitated learning projects in higher education

This handbook supports a project funded by the Australian Government Committee for University Teaching and Staff Development (CUTSD). The amended project title is “Staff Development in Evaluation of Technology-based Teaching Development Projects: An Action Inquiry Approach”. The project is hosted by Murdoch University on behalf of the Australasian Society for Computers in Learning in Tertiary Education (ASCILITE), as a consortium of 11 universities. The rationale of the project is to guide a group of university staff through the evaluation of a Computer-facilitated Learning (CFL1) project by a process of action inquiry and mentoring, supported by the practical and theoretical material contained in this handbook

Optimising Selective Sampling for Bootstrapping Named Entity Recognition

Training a statistical named entity recognition system in a new domain requires costly manual annotation of large quantities of in-domain data. Active learning promises to reduce the annotation cost by selecting only highly informative data points. This paper is concerned with a real active learning experiment to bootstrap a named entity recognition system for a new domain of radio astronomical abstracts. We evaluate several committee-based metrics for quantifying the disagreement between classifiers built using multiple views, and demonstrate that the choice of metric can be optimised in simulation experiments with existing annotated data from different domains. A final evaluation shows that we gained substantial savings compared to a randomly sampled baseline. 1

JointZone: users' view of an adaptive online learning resource for rheumatology

This paper describes an online learning resource for rheumatology that was designed for a wide constituency of users including primarily undergraduate medical students and health professionals. Although the online resources afford an informal learning environment, the site was pedagogically designed to comply with the general recommendations of the Standing Committee on Training and Education of EULAR (European League Against Rheumatism) for a rheumatology core curriculum. Any Internet user may freely browse the site content with optional registration providing access to adaptive features that personalize the user’s view, for example, providing a reading history and targeted support based on scores from completed case studies. The site has now been available since early 2003, and an online survey of site registrants indicates that well structured pedagogical materials that reflect a learners’ dominant ‘community of practice’ appear to be a successful aid to informal learning

アクティブ・ラーニングに関する群馬高専の現状と取り組み

Due to the accelerated progress of knowledge and information technology, as typified by artificial intelligence, education is required to acquire knowledge and skills; yet concurrently, cultivating the abilities of thought, judgment, and expression is necessary as well, and "deep learning" and "active learning" have attracted much attention in school education. The National Institute of Technology clearly stated the promotion of active learning using information and communication technology in the plan of 2014 of the 3rd Medium Term Plan. In response to this, the Education and Research Committee at the National Institute of Technology, Gunma College commenced efforts toward introducing active learning in 2014. In this review, we will compile the results of initiatives to introduce active learning in the past five years, hoping to present future guidelines. In Chapter 2, we reviewed the policy on active learning promoted by the Ministry of Education, Culture, Sports, Science and Technology, and described the status of active learning introduction at elementary and junior high school, high school, and college and university levels. In Chapters 3 and 4, based on a questionnaire survey, we reported on the current situation of our school about the educational method in which students actively learn