3 research outputs found
Human Active Learning
Active machine learning (AML) is a popular research area in machine learning. It allows selection of the most informative instances in training data of the domain for manual labeling. AML aims to produce a highly accurate classifier using as few labeled instances as possible, thereby minimizing the cost of obtaining labeled data. As machines can learn from experience like humans do, using AML for human category learning may help human learning become more efficient and hence reduce the cost of teaching. This chapter is a review of recent research literature concerning the use of AML technique to enhance human learning and teaching. There are a few studies on the applications of AML to the human category learning domain. The most interesting study was by Castro et al., which showed that humans learn faster with better performance when they can actively select the informative instances from a pool of unlabeled data instead of random sampling. Although AML can facilitate object categorization for humans, there are still many challenges and questions that need to be addressed in the use of AML for modeling human categorization. In this chapter, we will discuss some of these challenges
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Children Learn Better When They Select Their Own Data
Human learners ask questions, manipulate objects, and
perform interventions on their environment. These behaviors
are true of adults, but even more so for young children.
Recent studies have demonstrated that adults learn better
under conditions of selection learning, where they can make
decisions about the information they wish to acquire, as
compared to reception learning, where they merely observe
data that happens to be available to them. Yet to date, it
remains unclear whether this advantage is available to
children, and if so, does it arise because children can gather
data in a non-random way? In the current study, we show that
7-year-old children show superior learning under conditions
of selection in a category-learning task, and that their
information gathering is systematically driven by uncertaint
The Theoretical and Methodological Opportunities Afforded by Guided Play With Young Children
For infants and young children, learning takes place all the time and everywhere. How children learn best both in and out of school has been a long-standing topic of debate in education, cognitive development, and cognitive science. Recently, guided play has been proposed as an integrative approach for thinking about learning as a child-led, adult-assisted playful activity. The interactive and dynamic nature of guided play presents theoretical and methodological challenges and opportunities. Drawing upon research from multiple disciplines, we discuss the integration of cutting-edge computational modeling and data science tools to address some of these challenges, and highlight avenues toward an empirically grounded, computationally precise and ecologically valid framework of guided play in early education