From Knowledge Transmission to Knowledge Construction: A Step towards Human-Like Active Learning

Abstract

Machines usually employ a guess-and-check strategy to analyze data: they take the data, make a guess, check the answer, adjust it with regard to the correct one if necessary, and try again on a new data set. An active learning environment guarantees better performance while training on less, but carefully chosen, data which reduces the costs of both annotating and analyzing large data sets. This issue becomes even more critical for deep learning applications. Human-like active learning integrates a variety of strategies and instructional models chosen by a teacher to contribute to learners’ knowledge, while machine active learning strategies lack versatile tools for shifting the focus of instruction away from knowledge transmission to learners’ knowledge construction. We approach this gap by considering an active learning environment in an educational setting. We propose a new strategy that measures the information capacity of data using the information function from the four-parameter logistic item response theory (4PL IRT). We compared the proposed strategy with the most common active learning strategies—Least Confidence and Entropy Sampling. The results of computational experiments showed that the Information Capacity strategy shares similar behavior but provides a more flexible framework for building transparent knowledge models in deep learning.

Human-Centric AI: The Symbiosis of Human and Artificial Intelligence

“…The contribution by Kulikovskikh et al [ 9 ], “From Knowledge Transmission to Knowledge Construction: A Step towards Human-Like Active Learning,” explains how deep learning models to decisions by imitating human-like reasoning in multiple-choice testing. The authors proposed a new strategy that measures the information capacity of data using the information function from the four-parameter logistic item response theory (4PL IRT) and compared the proposed strategy (termed Information Capacity) with the most common active learning strategies—Least Confidence and Entropy Sampling.…”

Using active learning and an agent-based system to perform interactive knowledge extraction based on the COVID-19 corpus

“…Such customized user-validated features can more efficiently represent the expertise under different contexts, increasing the re-usability of extant knowledge. The knowledge transfer from experts will significantly reduce the labeling and training workload and enhance the efficiency of pattern recognition (Settles, 2010; Springer, 2016; Kulikovskikh et al ., 2020). Finally, the separated and concreted concepts of different datasets will be extracted by multiple individual agents and annotated with a particular context that is inherent in the corresponding data, whereby the system can integrate knowledge into a global knowledge base.…”