We build a virtual agent for learning language in a 2D maze-like world. The agent sees images of the surrounding environment, listens to a virtual teacher, and takes actions to receive rewards. It interactively learns the teacher's language from scratch based on two language use cases: sentence-directed navigation and question answering. It learns simultaneously the visual representations of the world, the language, and the action control. By disentangling language grounding from other computational routines and sharing a concept detection function between language grounding and prediction, the agent reliably interpolates and extrapolates to interpret sentences that contain new word combinations or new words missing from training sentences. The new words are transferred from the answers of language prediction. Such a language ability is trained and evaluated on a population of over 1.6 million distinct sentences consisting of 119 object words, 8 color words, 9 spatial-relation words, and 50 grammatical words. The proposed model significantly outperforms five comparison methods for interpreting zero-shot sentences. In addition, we demonstrate human-interpretable intermediate outputs of the model in the appendix.
Any instructional practice must be derived from a teacher's knowledge base for teaching, which can be acquired by training, study, or practice. While much attention has been paid to teachers' practical content knowledge in real educational settings, comprehensive syntheses of expert knowledge on a particular teaching task for a specific group of teachers are still scarce. This paper tends to synthesize ESL/EFL teachers' pedagogical content knowledge of reading strategy instruction through learning the expertise conveyed in literature. Drawing on related studies in the field of reading strategy instruction either in general or in ESL/EFL contexts, this argumentative article first proposes a synthesized reading strategy instruction model which consists of one key component and two general principles, all of which create and are created by a safe and risk-free environment where students learn to use strategies actively and consciously with motivation and assistance. This article then elaborates on eight instructional strategies using summarizing strategy instruction as an example in terms of three types of knowledge: declarative, procedural, and conditional. With the enrichment of the pedagogical content knowledge on strategy instruction, ESL/EFL teachers might teach reading strategies effectively both with metacognition, i.e., consciously planning, monitoring, and evaluating their teaching, and for metacognition, namely, to affect their students' metacognitive awareness of strategy use in reading.
This article reports on the first phase of a case study done by a Chinese post-secondary EFL reading teacher on her exploratory inquiry into the metacognitive teaching knowledge needed by EFL Reading teachers to teach summarizing strategies with expository text to EFL undergraduates. Guided by a formalized model of instructional materials development, Phase I was an exploring process, starting from constructing a general metacognitive knowledge framework and proceeding to elaborate the detailed framework of the actual metacognitive knowledge needed by EFL reading teachers as to summarizing strategies instruction with expository text. The results of phase I were summarized in a monograph directed at teaching post-secondary EFL Reading teachers the framework and actual metacognitive knowledge they needed to know. This monograph was positively reviewed by a cross-sectional panel of 6 experts. This article concludes with a critical reflection on the methodology and value of this metacognitive knowledge exploration.
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