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.

Dinoflagellate symbiosis: strategies and adaptations for the aquisition and fixation of inorganic carbon

Dinoflagellates exist in symbiosis with a number of marine invertebrates including giant clams, which are the largest of these symbiotic organisms. The dinoflagellates (Symbiodinium sp.) live intercellularly within tubules in the mantle of the host clam. The transport of inorganic carbon (Ci) from seawater to Symbiodinium (=zooxanthellae) is an essential function of hosts that derive the majority of their respiratory energy from the photosynthate exported by the zooxanthellae. Immunolocalisation studies show that the host has adapted its physiology to acquire, rather than remove CO2, from the haemolymph and clam tissues. Two carbonic anhydrase (CA) isoforms (32 and 70 kDa) play an essential part in this process. These have been localised to the mantle and gill tissues where they catalyse the interconversion of HCO3- to CO2, which then diffuses into the host tissues. The zooxanthellae exhibit a number of strategies to maximise Ci acquisition and utilisation. This is necessary as they express a form II Rubisco that has poor discrimination between CO2 and O-2. Evidence is presented for a carbon concentrating mechanism (CCM) to overcome. this disadvantage. The CCM incorporates the presence of a light-activated CA activity, a capacity to take up both HCO3- and CO2, an ability to accumulate an elevated concentration of Ci within the algal cell, and localisation of Rubisco to the pyrenoid. These algae also express both external and intracellular CAs, with the intracellular isoforms being localised to the thylakoid lumen and pyrenoid. These results have been incorporated into a model that explains the transport of Ci from seawater through the clam to the zooxanthellae.

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.