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.

Identification and minisequencing-based discrimination of SHV beta-lactamases in nosocomial infection-associated Klebsiella pneumoniae in Brisbane, Australia

Extended-spectrum beta-lactamases (ESBLs) are active against oxyimino cephalosporins and monobactams. Twenty-one Klebsiella pneumoniae isolates obtained between 1991 and 1995 at the Princess Alexandra Hospital in Brisbane, Australia, were subject to amplification and sequencing of the SHV beta-lactamase-encoding genes. Thirteen strains were phenotypically ESBL positive. Of these, six strains carried the bla(SHV-2a) gene and seven strains carried the bla(SHV-12) gene. Eight strains were phenotypically ESBL negative. Of these, seven strains carried the non-ESBL bla(SHV-11) gene and one strain carried the non-ESBL bla(SHV-1) gene. There was complete correspondence between the ESBL phenotype and the presence or absence of an ESBL-encoding gene(s). In addition, it was determined that of the 13 ESBL-positive strains, at least 4 carried copies of a non-ESBL-encoding gene in addition to the bla(SHV-2a) or bla(SHV12) gene. A minisequencing-based assay was developed to discriminate the different SHV classes. This technique, termed first-nucleotide change, involves the identification of the base added to a primer in a single-nucleotide extension reaction. The assay targeted polymorphisms at the first bases of codons 238 and 240 and reliably discriminated ESBL-positive strains from ESBL-negative strains and also distinguished strains carrying bla(SHV-2a) from strains carrying bla(SHV-12). In addition, this method was used to demonstrated an association between the relative copy numbers of bla(SHV) genes in individual strains and the levels of antibiotic resistance.

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.