Guidance in storytelling tables supports emotional development in kindergartners

Promoting the social-emotional development of kindergartners is of special relevance as will lay the foundations for emotion regulation in later childhood and adulthood stages. Considering that tangible storytelling tables are already used for language and literacy skills in kindergarten, we addressed the problem of designing a storytelling intervention aimed at social-emotional development suitable in such a context by using an emotional laden story as content and embedding a guidance method that can be implemented with either a human or robot guide to enhance the learning setting. The study considered two guided storytelling activities (one traditional guided by the teacher, and one in which guidance was provided by a robot) and a control condition without additional guidance. The three conditions were compared in terms of kindergartners’ enactment process, an emotion recognition test and a story recall test. The results show that the guidance method properly supported emotion naming, children involvement and goal completion during the storytelling activity whereas the intervention supported the learning gain on emotion recognition. The study revealed that both robot and human guidance did not differ significantly in the performance tests but did outperform the control. In view of the results, this research is helpful for researchers and teachers to create in an informed way a range of environments in the kindergarten class based on storytelling tables, either with or without guidance, and with or without robot support. Future work may further investigate how specific interaction issues concerning robot embodiment (e.g., voice and behavioral cues to direct children’s attention) might enhance or not the children’s performanceOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has partially been funded by the Spanish Ministry of Science, Innovation and Universities under Juan de la Cierva programme (IJC2018–037522-I). The writing of this work has received financial support from the Consellería de Educación, Universidade e Formación Profesional (accreditation 2019–2022 ED431G-2019/04, reference ED431C2022/19) and the European Regional Development Fund (ERDF)S

An empirical study on how humans appreciate automated counterfactual explanations which embrace imprecise information

The explanatory capacity of interpretable fuzzy rule-based classifiers is usually limited to offering explanations for the predicted class only. A lack of potentially useful explanations for non-predicted alternatives can be overcome by designing methods for the so-called counterfactual reasoning. Nevertheless, state-of-the-art methods for counterfactual explanation generation require special attention to human evaluation aspects, as the final decision upon the classification under consideration is left for the end user. In this paper, we first introduce novel methods for qualitative and quantitative counterfactual explanation generation. Then, we carry out a comparative analysis of qualitative explanation generation methods operating on (combinations of) linguistic terms as well as a quantitative method suggesting precise changes in feature values. Then, we propose a new metric for assessing the perceived complexity of the generated explanations. Further, we design and carry out two human evaluation experiments to assess the explanatory power of the aforementioned methods. As a major result, we show that the estimated explanation complexity correlates well with the informativeness, relevance, and readability of explanations perceived by the targeted study participants. This fact opens the door to using the new automatic complexity metric for guiding multi-objective evolutionary explainable fuzzy modeling in the near futureIlia Stepin is an FPI researcher (grant PRE2019-090153). Jose M. Alonso-Moral is a Ramon y Cajal researcher (grant RYC-2016–19802). This work was supported by the Spanish Ministry of Science and Innovation (grants RTI2018-099646-B-I00, PID2021-123152OB-C21, and TED2021-130295B-C33) and the Galician Ministry of Culture, Education, Professional Training and University (grants ED431F2018/02, ED431G2019/04, and ED431C2022/19). All the grants were co-funded by the European Regional Development Fund (ERDF/FEDER program).S