The long-term effects of cooperative learning on children's behaviour and interactions

This study investigates the long-term effects of training in small-group and interpersonal behaviours on children's behaviours and interactions as they worked in small groups two years after they were initially trained. Forty-eight third grade children, who had been trained two years previously in cooperative group behaviours, were assigned to the Trained condition and 44 third grade children who had not previously been trained were assigned to the Untrained condition. The children in the trained and untrained groups were reconstituted from the pool of students who had participated previously in either trained or untrained group activities. The results showed that there was a long-term training effect with the children in the Trained groups demonstrating more cooperative behaviour and providing more explanations in response to requests for help than their untrained peers.

The residual effects of cooperative-learning experiences: A two-year follow-up

The author investigated how training in small-group and interpersonal behaviors affected children's behavior and interactions as they worked in small groups 2 years later. The authors assigned 52 fifth graders, who had been trained 2 years previously in cooperative group behaviors, to the trained condition and 36 fifth graders, who had not previously been trained, to the untrained condition. Both were reconstituted from the pool of students who had participated previously in group activities. The results showed a residual training effect, with the children in the trained groups being more cooperative and helpful than their untrained peers.

On learning context-free and context-sensitive languages

The long short-term memory (LSTM) is not the only neural network which learns a context sensitive language. Second-order sequential cascaded networks (SCNs) are able to induce means from a finite fragment of a context-sensitive language for processing strings outside the training set. The dynamical behavior of the SCN is qualitatively distinct from that observed in LSTM networks. Differences in performance and dynamics are discussed.

Communicating vegetation management science to land managers and other stakeholders

Science communication. including extension services. plays a key role in achieving sustainable native vegetation management. One of the pivotal aspects of the debate on sustainable vegetation management is the scientific information underpinning policy-making. In recent years. extension services have Shifted their focus from top-down technology transfer to bottom-up participation and empowerment. I here has also been a broadening of communication strategies to recognise the range of stakeholders involved in native vegetation management and to encompass environmental concerns. This paper examines the differences between government approaches to extension services to deliver policy and the need for effective communication to address broader science issues that underpin native vegetation management. The importance of knowing the learning styles of the stakeholders involved in native vegetation management is discussed at a time of increasing reliance on mass communication for information exchange and the importance of personal communication to achieve on-ground sustainable management. Critical factors for effective science-management communication are identified Such as: (i) undertaking scientific studies (research) with community involvement, acceptance and agreed understanding of project objectives (ii) realistic community consultation periods: (iii) matching communication channels with stakeholder needs; (iv) combining scientific with local knowledge in in holistic (biophysical and social) approach to understanding in issued and (v) regional partnerships. These communication factors are considered to be essential to implementing on-ground natural resource management strategics and actions, including those concerned with native vegetation management.

Input-driven Language Learning

Input-driven models provide an explicit and readily testable account of language learning. Although we share Ellis's view that the statistical structure of the linguistic environment is a crucial and, until recently, relatively neglected variable in language learning, we also recognize that the approach makes three assumptions about cognition and language learning that are not universally shared. The three assumptions concern (a) the language learner as an intuitive statistician, (b) the constraints on what constitute relevant surface cues, and (c) the redescription problem faced by any system that seeks to derive abstract grammatical relations from the frequency of co-occurring surface forms and functions. These are significant assumptions that must be established if input-driven models are to gain wider acceptance. We comment on these issues and briefly describe a distributed, instance-based approach that retains the key features of the input-driven account advocated by Ellis but that also addresses shortcomings of the current approaches.

Extending Learning Opportunities through a virtual faculty

This paper presents the notion of a virtual faculty as a viable alternative to extending and maintaining learner opportunities for students in regional universities or at universities where specialisations in which they are interested may not be offered. Staff from a number of Australian Universities participated in a CUTSD project to explore the viability of establishing a virtual faculty using videoconferencing as the medium of delivery. The success of this project was the result of close collaboration at a number of levels within the participating institutions and a willingness to explore effective approaches to teaching and learning for a videoconference environment.