The Effects of Input Enhancement Techniques on ESP Learners' Microgenetic Development of Conjunction Production

Microgenetic design or microgenetic method is a scientific method in which the same setting is studied repeatedly in order toobserve possible changes in details. Reviewing the current literature, one can conclude that microgenetic methods havepositive effects of learning in general and language learning in particular. The main objective of the current study was toinvestigate the possible effects of implementing input enhancement techniques using microgenetic methods, on Iranian ESPlearners' conjunction production. To this end, a number of 40 Iranian ESP learners were participated in the study. During thefour-week period of the current study, the participants received the instructions and treatment, two sessions a week and eachsession lasted for an hour. After administering the pre-test before the instruction, using input enhancement techniques, anumber of the conjunctions were presented and taught to the learners in the first session. The data were collected afterinstructional sessions during the first, third, fifth, and seventh weeks through paragraphs focusing on conjunction productionwritten by the participants. The results indicated that, in the course of time, input enhancement techniques using microgeneticmethods, significantly affect Iranian ESP learners' conjunction production.

Effects of Three Interventions on Children's Spatial Structuring and Coordination of Area Units

We examine the effects of 3 interventions designed to support Grades 2–5 children's growth in measuring rectangular regions in different ways. We employed the microgenetic method to observe and describe conceptual transitions and investigate how they may have been prompted by the interventions. We compared the interventions with respect to children's learning and then examined patterns in observable behaviors before and after transitions to more sophisticated levels of thinking according to a learning trajectory for area measurement. Our findings indicate that creating a complete record of the structure of the 2-dimensional array—by drawing organized rows and columns of equal-sized unit squares—best supported children in conceptualizing how units were built, organized, and coordinated, leading to improved performance.

Win-Stay, Lose-Sample: A Simple Sequential Algorithm for Approximating Bayesian Inference

People can behave in a way that is consistent with Bayesian models of cognition, despite the fact that performing exact Bayesian inference is computationally challenging. What algorithms could people be using to make this possible? We show that a simple sequential algorithm “Win-Stay, Lose-Sample”, inspired by the Win-Stay, Lose-Shift (WSLS) principle, can be used to approximate Bayesian inference. We investigate the behavior of adults and preschoolers on two causal learning tasks to test whether people might use a similar algorithm. These studies use a “mini-microgenetic method”, investigating how people sequentially update their beliefs as they encounter new evidence. Experiment 1 investigates a deterministic causal learning scenario and Experiments 2 and 3 examine how people make inferences in a stochastic scenario. The behavior of adults and preschoolers in these experiments is consistent with our Bayesian version of the WSLS principle. This algorithm provides both a practical method for performing Bayesian inference and a new way to understand people’s judgments.