Effects of Two Teaching Strategies on Preschoolers' Oral Language Skills: Repeated Read-Aloud With Question and Answer Teaching Embedded and Repeated Read-Aloud With Executive Function Activities Embedded

The purpose of this study was to investigate the impact of two teaching strategies on preschoolers' oral language skills: repeated read-aloud with question and answer teaching embedded, and repeated read-aloud with executive function (EF) activities embedded. A quasi-experimental pretest-posttest design was employed. Children ranging in age from 4 years 6 months to 6 years and 4 months participated in the study (n = 53). They were recruited from preschools in Taitung, Taiwan, randomly assigned to the three study groups. 36 children were divided and assigned to the two experimental groups (question and answer teaching group and EF group), while the remaining 17, to the control group. The participating preservice teachers attended 32 h of training which included the theory, practice, and outcome evaluation measure for the teaching strategies implemented. The intervention spanned 2 months. Oral language tests (curriculum-based receptive vocabulary, inferential comprehension, and oral comprehension) were administered before and after the intervention. The findings revealed that both experimental groups positively impacted participants' receptive vocabulary and oral comprehension when compared with the control group, although the performances between the two experimental groups did not differ significantly. For inferential comprehension, there was no statistically significant difference across the three groups. Implications of the study findings are discussed and potential topics for future research proposed.

How the Brain Represents Language and Answers Questions? Using an AI System to Understand the Underlying Neurobiological Mechanisms

To understand the computations that underlie high-level cognitive processes we propose a framework of mechanisms that could in principle implement START, an AI program that answers questions using natural language. START organizes a sentence into a series of triplets, each containing three elements (subject, verb, object). We propose that the brain similarly defines triplets and then chunks the three elements into a spatial pattern. A complete sentence can be represented using up to 7 triplets in a working memory buffer organized by theta and gamma oscillations. This buffer can transfer information into long-term memory networks where a second chunking operation converts the serial triplets into a single spatial pattern in a network, with each triplet (with corresponding elements) represented in specialized subregions. The triplets that define a sentence become synaptically linked, thereby encoding the sentence in synaptic weights. When a question is posed, there is a search for the closest stored memory (having the greatest number of shared triplets). We have devised a search process that does not require that the question and the stored memory have the same number of triplets or have triplets in the same order. Once the most similar memory is recalled and undergoes 2-level dechunking, the sought for information can be obtained by element-by-element comparison of the key triplet in the question to the corresponding triplet in the retrieved memory. This search may require a reordering to align corresponding triplets, the use of pointers that link different triplets, or the use of semantic memory. Our framework uses 12 network processes; existing models can implement many of these, but in other cases we can only suggest neural implementations. Overall, our scheme provides the first view of how language-based question answering could be implemented by the brain.