Fast mapping is a phenomenon by which children learn the meanings of novel adjectives after a very small number of exposures when the new word is contrasted with a known word. The present study was a preliminary test of whether machine learners could use such contrasts in unconstrained speech to learn adjective meanings and categories. Six decision tree-based learning methods were evaluated that use contrasting examples in order to work toward an adjective fast-mapping system for machine learners. Subjects tended to compare objects using adjectives of the same category, implying that such contrasts may be a useful source of data about adjective meaning, though none of the learning algorithms showed strong advantages over any other.
Boys and girls perform equally at maths, according to a study looking to dispel gender myths in education. Analysis of over 20,000 students from primary and secondary schools across the UK suggested that differences in maths attainment between girls and boys are almost negligible. It also indicated that regular and high-quality maths practice improves outcomes across the board and that primary pupils outperformed secondary students with better attainment scores. The study, carried out by Professor Keith Topping at the University of Dundee and the education assessment company Renaissance has led to calls for a cultural change in schools. Professor Topping believes his findings challenge many prevailing stereotypes around gender and the study of maths.
If you're a visual person, do you always need pictures in order to learn best, even if the thing you're learning is a musical instrument? And what about aural learners who like to hear their information in order to remember it - do they need to listen to learn? What about if they're learning to drive a car? It's a popular belief that people have different styles of learning - visual, aural, reading and writing or kinaesthetic (carrying out physical activities). But as hundreds of thousands of pupils around the UK revise for exams, is that really how learning works?
A learner driver in south west London stopped to give two police officers running to an arrest a lift. The officers from Chessington Safer Neighbourhood team were sprinting to the aid of colleagues who were pursuing a suspect. They are now trying to trace the female learner to thank her for her help. At around 20:00 BST on Thursday two officers were on foot patrol in Merrett Gardens when they spotted a man acting suspiciously. As they approached he decided to run and a lengthy foot chase began.
In real-world applications of education, an effective teacher adaptively chooses the next example to teach based on the learner's current state. However, most existing work in algorithmic machine teaching focuses on the batch setting, where adaptivity plays no role. In this paper, we study the case of teaching consistent, version space learners in an interactive setting. At any time step, the teacher provides an example, the learner performs an update, and the teacher observes the learner's new state. We highlight that adaptivity does not speed up the teaching process when considering existing models of version space learners, such as the "worst-case" model (the learner picks the next hypothesis randomly from the version space) and the "preference-based" model (the learner picks hypothesis according to some global preference).