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.

The Limits of Rational Choice: New Institutionalism in the Test Bed of Central Banking Politics in Australia

This paper tests the explanatory capacities of different versions of new institutionalism by examining the Australian case of a general transition in central banking practice and monetary politics: namely, the increased emphasis on low inflation and central bank independence. Standard versions of rational choice institutionalism largely dominate the literature on the politics of central banking, but this approach (here termed RC1) fails to account for Australian empirics. RC1 has a tendency to establish actor preferences exogenously to the analysis; actors' motives are also assumed a priori; actor's preferences are depicted in relatively static, ahistorical terms. And there is the tendency, even a methodological requirement, to assume relatively simple motives and preference sets among actors, in part because of the game theoretic nature of RC1 reasoning. It is possible to build a more accurate rational choice model by re-specifying and essentially updating the context, incentives and choice sets that have driven rational choice in this case. Enter RC2. However, this move subtly introduces methodological shifts and new theoretical challenges. By contrast, historical institutionalism uses an inductive methodology. Compared with deduction, it is arguably better able to deal with complexity and nuance. It also utilises a dynamic, historical approach, and specifies (dynamically) endogenous preference formation by interpretive actors. Historical institutionalism is also able to more easily incorporate a wider set of key explanatory variables and incorporate wider social aggregates. Hence, it is argued that historical institutionalism is the preferred explanatory theory and methodology in this case.

Spatial and temporal patterns in Australian wheat yield and their relationship with ENSO

Spatial and temporal variability in wheat production in Australia is dominated by rainfall occurrence. The length of historical production records is inadequate, however, to analyse spatial and temporal patterns conclusively. In this study we used modelling and simulation to identify key spatial patterns in Australian wheat yield, identify groups of years in the historical record in which spatial patterns were similar, and examine association of those wheat yield year groups with indicators of the El Nino Southern Oscillation (ENSO). A simple stress index model was trained on 19 years of Australian Bureau of Statistics shire yield data (1975-93). The model was then used to simulate shire yield from 1901 to 1999 for all wheat-producing shires. Principal components analysis was used to determine the dominating spatial relationships in wheat yield among shires. Six major components of spatial variability were found. Five of these represented near spatially independent zones across the Australian wheatbelt that demonstrated coherent temporal (annual) variability in wheat yield. A second orthogonal component was required to explain the temporal variation in New South Wales. The principal component scores were used to identify high- and low-yielding years in each zone. Year type groupings identified in this way were tested for association with indicators of ENSO. Significant associations were found for all zones in the Australian wheatbelt. Associations were as strong or stronger when ENSO indicators preceding the wheat season (April-May phases of the Southern Oscillation Index) were used rather than indicators based on classification during the wheat season. Although this association suggests an obvious role for seasonal climate forecasting in national wheat crop forecasting, the discriminatory power of the ENSO indicators, although significant, was not strong. By examining the historical years forming the wheat yield analog sets within each zone, it may be possible to identify novel climate system or ocean-atmosphere features that may be causal and, hence, most useful in improving seasonal forecasting schemes.

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.