Active learning seeks to train the best classifier at the lowest annotation cost by intelligently picking the best examples to label. Traditional algorithms assume there is a single annotator and disregard the possibility of requesting additional independent annotations for a previously labeled example. However, relabeling examples is important, because all annotators make mistakes — especially...

Can children of all age groups engage in self-directed learning? While active learning has been widely advocated in education, it remains unclear whether its benefits apply to children at all developmental levels. In the present study, we demonstrate that 19-month-old toddlers acquire higher-order generalizations in the causal domain only when their play is facilitated by an adult experimenter ...

We present an active learning method for placing the event mentions in an operative note into a pre-specified event structure. Event mentions are first classified into action, peripheral action, observation, and report events. The actions are further classified into their appropriate location within the event structure. We examine how utilizing active learning significantly reduces the time nee...

We propose a two-step active learning method for supervised speaker adaptation. In the first step, the initial adaptation data is collected to obtain a phone error distribution. In the second step, those sentences whose phone distributions are close to the error distribution are selected, and their utterances are collected as the additional adaptation data. We evaluated the method using a Japan...

We explore how adults learn counterintuitive causal relationships, and whether they discover hypotheses by revising their beliefs incrementally. We examined how adults learned a novel and unusual causal rule when presented with data that initially appeared to conform to a simpler, more salient rule. Adults watched a video of several blocks placed sequentially on a blicket detector, and were the...

When learning by observing an expert, cases can be automatically generated in an inexpensive manner. However, since this is a passive method of learning the observer has no control over which problems are solved and this can result in case bases that do not contain a representative distribution of the problem space. In order to overcome this we present a method to incorporate active learning wi...

Kanazawa Institute of Technology (KIT) has conducted educational reforms since 1995 and joined the CDIO initiative at June, 2011. In the present paper, first, an overview of the KIT engineering educational program is presented, including the 2012 curriculum which was developed after joining the CDIO initiative. Second, an active learning system based on a “Comprehensive Learning Initiative Proc...

Introduction: In Pharmacy Diploma Program, mathematics is known as pharmaceutical mathematics. Due to the importance of pharmaceutical mathematics in practice, it is important to have a basic mathematical skill as a basis in calculations in pharmaceutical science. Therefore, it is necessary to create a lecturing condition that enables students more active in understanding the lessons. This rese...

Learning of classification models from real-world data often requires additional human expert effort to annotate the data. However, this process can be rather costly and finding ways of reducing the human annotation effort is critical for this task. The objective of this paper is to develop and study new ways of providing human feedback for efficient learning of classification models by labelin...