The Effects of Situational Teaching on Preschool Children’s Originality Development

Preschool education is a critical stage in a child's life. Several studies have confirmed that the pivotal moment for the development of creativity is between the ages of three and five. Through cultivation, training, and learning development ability, preschool children's creativity can be amplified through the presence of nature and the imagination of instructional strategies. The purpose of this research is to look into the effect of situational learning in physical education on preschool children's creativity. This study's methodology begins with a pre-test of the child's creativity performance, followed by a six-week program of situational physical learning lessons. The data is compared to show how children's learning abilities have changed and how effective situational learning training has been in increasing creativity.

Modeling the Interaction Between Perception-Based and Production-Based Learning in Children's Early Acquisition of Semantic Knowledge

Children learn the meaning of words and sentences in their native language at an impressive speed and from highly ambiguous input. To account for this learning, previous computational modeling has focused mainly on the study of perception-based mechanisms like cross-situational learning. However, children do not learn only by exposure to the input. As soon as they start to talk, they practice their knowledge in social interactions and they receive feedback from their caregivers. In this work, we propose a model integrating both perception- and production-based learning using artificial neural networks which we train on a large corpus of crowd-sourced images with corresponding descriptions. We found that production-based learning improves performance above and beyond perception-based learning across a wide range of semantic tasks including both word- and sentence-level semantics. In addition, we documented a synergy between these two mechanisms, where their alternation allows the model to converge on more balanced semantic knowledge. The broader impact of this work is to highlight the importance of modeling language learning in the context of social interactions where children are not only understood as passively absorbing the input, but also as actively participating in the construction of their linguistic knowledge.

Competence development and learning assistance systems for the data-driven future

The continuous acquisition of new digital competences and the development of situational learning assistance systems will become more important than ever in the coming years, because the world of work is becoming more complex, more informative and all above more data-driven. Jobs are changing due to increasing digitalisation, whereby the use of modern technologies must be designed in a way, that employees can continue to work productively in the company despite these changes and benefit purposefully from digital solutions. The research results presented under the main topic „Competence development and learning assistance systems for the data-driven future“ address this problem of state of the art technologies in the workplace and their effects on workers. The members of the Scientific Society for Work and Business Organisation (WGAB) present innovative concepts and research results for practitioners and scientists and thus provide valuable input for current challenges.

Evaluating the Acquisition of Semantic Knowledge from Cross-situational Learning in Artificial Neural Networks

When learning their native language, children acquire the meanings of words and sentences from highly ambiguous input without much explicit supervision. One possible learning mechanism is cross-situational learning, which has been successfully tested in laboratory experiments with children. Here we use Artificial Neural Networks to test if this mechanism scales up to more natural language and visual scenes using a large dataset of crowd-sourced images with corresponding descriptions. We evaluate learning using a series of tasks inspired by methods commonly used in laboratory studies of language acquisition. We show that the model acquires rich semantic knowledge both at the word- and sentence-level, mirroring the patterns and trajectory of learning in early childhood. Our work highlights the usefulness of low-level co-occurrence statistics across modalities in facilitating the early acquisition of higher-level semantic knowledge.

Unsupervised Online Grounding for Social Robots

Robots that incorporate social norms in their behaviors are seen as more supportive, friendly, and understanding. Since it is impossible to manually specify the most appropriate behavior for all possible situations, robots need to be able to learn it through trial and error, by observing interactions between humans, or by utilizing theoretical knowledge available in natural language. In contrast to the former two approaches, the latter has not received much attention because understanding natural language is non-trivial and requires proper grounding mechanisms to link words to corresponding perceptual information. Previous grounding studies have mostly focused on grounding of concepts relevant to object manipulation, while grounding of more abstract concepts relevant to the learning of social norms has so far not been investigated. Therefore, this paper presents an unsupervised cross-situational learning based online grounding framework to ground emotion types, emotion intensities and genders. The proposed framework is evaluated through a simulated human–agent interaction scenario and compared to an existing unsupervised Bayesian grounding framework. The obtained results show that the proposed framework is able to ground words, including synonyms, through their corresponding perceptual features in an unsupervised and open-ended manner, while outperfoming the baseline in terms of grounding accuracy, transparency, and deployability.

Cocreating Change Through Sport-Based Social Entrepreneurship

This article describes the design of an undergraduate course in which students learn how to cocreate change using social entrepreneurship. This approach is presented as a way of broadening sport management students’ awareness of nontraditional career opportunities and facilitating an understanding of the skills and knowledge necessary to succeed as a social entrepreneur. Drawing on situational learning theory and cognitive learning theory, the course facilitates learning through student engagement in a community of practice and weekly workshops.

Multiple components of statistical word learning are resource dependent: Evidence from a dual-task learning paradigm

It is increasingly understood that people may learn new word/object mappings in part via a form of statistical learning in which they track co-occurrences between words and objects across situations (cross-situational learning). Multiple learning processes contribute to this, thought to reflect the simultaneous influence of real-time hypothesis testing and graduate learning. It is unclear how these processes interact, and if any require explicit cognitive resources. To manipulate the availability of working memory resources for explicit processing, participants completed a dual-task paradigm in which a cross-situational word-learning task was interleaved with a short-term memory task. We then used trial-by-trial analyses to estimate how different learning processes that play out simultaneously are impacted by resource availability. Critically, we found that the effect of hypothesis testing and gradual learning effects showed a small reduction under limited resources, and that the effect of memory load was not fully mediated by these processes. This suggests that neither is purely explicit, and there may be additional resource-dependent processes at play. Consistent with a hybrid account, these findings suggest that these two aspects of learning may reflect different aspects of a single system gated by attention, rather than competing learning systems.