Temat: Collaborative Learning - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Effect of robot utterances using onomatopoeia on collaborative learning
Autorzy:: Jimenez, F.
Kanoh, M.
Yoshikawa, T.
Furuhashi, T.
Nakamura, T.
Powiązania:: https://bibliotekanauki.pl/articles/91733.pdf
Data publikacji:: 2014
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: robot
onomatopoeia
collaborative learning
Opis:: We investigated the effect of robot’s utterances using onomatopoeia in collaborative learning. The robot was designed to provide encouragement using onomatopoeia when students are given problems to be solved issued by a learning system. Eight college students used a mathematics learning system with a robot for three weeks and then took exams. The results indicated that the robot using utterances with onomatopoeia could comfort learners more than the robot without onomatopoeia. It suggests that the robot that praises or comforts using onomatopoeia helps learners maintain their motivation in collaborative learning.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 2; 125-131
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Tytuł:: Impact of learners’ quality and diversity in collaborative clustering
Autorzy:: Rastin, Parisa
Matei, Basarab
Cabanes, Guénaël
Grozavu, Nistor
Bennani, Younés
Powiązania:: https://bibliotekanauki.pl/articles/91600.pdf
Data publikacji:: 2019
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: collaborative clustering
topological neural networks
unsupervised learning
diversity
quality
Opis:: Collaborative Clustering is a data mining task the aim of which is to use several clustering algorithms to analyze different aspects of the same data. The aim of collaborative clustering is to reveal the common underlying structure of data spread across multiple data sites by applying clustering techniques. The idea of collaborative clustering is that each collaborator shares some information about the segmentation (structure) of its local data and improve its own clustering with the information provided by the other learners. This paper analyses the impact of the quality and the diversity of the potential learners to the quality of the collaboration for topological collaborative clustering algorithms based on the learning of a Self-Organizing Map (SOM). Experimental analysis on real data-sets showed that the diversity between learners impact the quality of the collaboration. We also showed that some internal indexes of quality are a good estimator of the increase of quality due to the collaboration.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 2; 149-165
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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