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Open AccessArticle

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

1
Department of Information Systems and Technologies, Samara National Research University, Moskovskoe Shosse 34, 443086 Samara, Russia
2
Division of Electronics, Ruđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, Croatia
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(8), 906; https://doi.org/10.3390/e22080906
Received: 22 July 2020 / Revised: 15 August 2020 / Accepted: 16 August 2020 / Published: 18 August 2020
(This article belongs to the Special Issue Human-Centric AI: The Symbiosis of Human and Artificial Intelligence)
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. View Full-Text
Keywords: item information; pool-based sampling; multiple-choice testing; item response theory; active learning; deep learning item information; pool-based sampling; multiple-choice testing; item response theory; active learning; deep learning
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MDPI and ACS Style

Kulikovskikh, I.; Lipic, T.; Šmuc, T. From Knowledge Transmission to Knowledge Construction: A Step towards Human-Like Active Learning. Entropy 2020, 22, 906. https://doi.org/10.3390/e22080906

AMA Style

Kulikovskikh I, Lipic T, Šmuc T. From Knowledge Transmission to Knowledge Construction: A Step towards Human-Like Active Learning. Entropy. 2020; 22(8):906. https://doi.org/10.3390/e22080906

Chicago/Turabian Style

Kulikovskikh, Ilona; Lipic, Tomislav; Šmuc, Tomislav. 2020. "From Knowledge Transmission to Knowledge Construction: A Step towards Human-Like Active Learning" Entropy 22, no. 8: 906. https://doi.org/10.3390/e22080906

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