人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

1. Introduction to Learning and Instruction. SECTION I: LEARNING. 2. Learning to Read Fluently. 3. Learning to Read for Comprehension. 4. Learning to Write. 5. Learning Mathematics. 6. Learning Science. SECTION II: INSTRUCTION. 7. Teaching by Giving Productive Feedback. 8. Teaching by Providing Concreteness, Activity, and Familiarity. 9. Teaching by Explaining Examples. 10. Teaching by Guiding Cognitive Processes during Learning. 11. Teaching by Fostering Learning Strategies. 12. Teaching by Fostering Problem-Solving Strategies. 13. Teaching by Creating Cognitive Apprenticeship in Classrooms. 14. Teaching by Priming Students' Motivation to Learn. References. Author Index. Subject Index.

Learning and Instruction

http://library.um.ac.id/free-contents/downloadpdf.php/buku/classroom-management-the-successful-use-of-behavior-modification-k-daniel-oleary-susan-g-oleary-23149.pdf

Classroom management : the successful use of behavior modification / K. Daniel O'leary, Susan G. O'leary

The present paper analyzes the self-generated explanations (from talk-aloud protocols) that “Good” and “Poor” students produce while studying worked-out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self-explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such learning results in example-independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self-explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self-explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation-based learning to account for these psychological findings.

/pdf/self-explanations-how-students-study-and-use-examples-in-2ogla1l0fu.pdf

Self-Explanations: How Students Study and Use Examples in Learning To Solve Problems. Technical Report No. 9.

Children use non-verbal cues to learn new words from robots as well as people

Interactive learning technologies, such as robots, increasingly find their way into schools. However, more research is needed to see how children might work with such systems in the future. This paper presents the unsupervised, four month deployment of a Robot-Extended Computer Assisted Learning (RECAL) system with 61 children working in their own classroom. Using automatically collected quantitative data we discuss how their usage patterns and self-regulated learning process developed throughout the study. CCS CONCEPTS • Applied computing → Interactive learning environments; • Computer systems organization → Robotics; • Human-centered computing → Field studies; • Social and professional topics → Children; • Hardware → Sensors and actuators. Acm Reference Format: Daniel P. Davison, Frances M. Wijnen, Vicky Charisi, Jan van der Meij, Vanessa Evers, and Dennis Reidsma. 2020. Working with a Social Robot in School: A Long-Term Real-World Unsupervised Deployment. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI’20), March 23-26, 2020, Cambridge, United Kingdom.. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3319502.3374803

https://dl.acm.org/doi/pdf/10.1145/3319502.3374803

Working with a Social Robot in School: A Long-Term Real-World Unsupervised Deployment

This review examines recent methodological approaches for the evaluation of child-robot interaction in learning settings. The main aims are to map existing work from a user-centered perspective, to identify possible trends related to evaluation methods for child-robot interaction, and to discuss potential future directions. We present a systematic review of existing studies, which have been thematically organized based on their research objectives. We then examine the evaluation methods that were used in these studies and we propose a conceptual framework based on the one hand on the themes that emerged, namely the social interaction between the child and the robot, the social acceptance, possible emotional interactions, the learning process and the learning outcome, and on the other hand on the corresponding measures. These methods have been considered in relation with the age ranges of the children, because of the relationship of their cognitive level to the choice of a developmentally appropriate evaluation method. We use this framework to highlight current trends and needs for the field and to contextualize the methodological directions for child-robot interaction. Finally, we discuss the challenges and limitations of the current methodological approaches as well as possible future directions for the evaluation methods of child-robot interaction in learning settings.

Evaluation methods for user-centered child-robot interaction

We present anew dialogue engine called Flipper 2.0 (Flipper) which aims to help developers of embodied conversational agents (ECAs) to quickly and flexibly create dialogues. Flipper provides a technically stable and robust dialogue management system to integrate with other components of ECAs such as behaviour realisers. We compare Flipper with state-of-the-art dialogue design systems. We describe the details of our dialogue engine, how it handles dialogue management and how it supports the authoring of dialogues. We demonstrate the use of the dialogue engine with examples of design patterns and discuss practical applications. Finally we give recommendations on the cases in which it is beneficial to use Flipper.

https://dl.acm.org/doi/pdf/10.1145/3267851.3267882

Flipper 2.0: A Pragmatic Dialogue Engine for Embodied Conversational Agents

One of the main characteristics for an effective learning is the possibility for learners to choose their own ways and pace of learning, according to their personal previous experiences and needs. Social interaction during the learning process has a crucial role to the skills that learners may develop. In this paper, we present a theoretical approach, which considers relevant theories of child’s development in order to proceed from a child-child collaborative learning approach to a child- robot symbiotic co-development. In this symbiotic interaction, the robot is able to interact with the learner and adapt its behaviours according to the child’s behaviour and development. This sets some theoretical foundations for an on-going research project that develops technologies for a social robot that facilitates learning through symbiotic interaction.

/pdf/towards-a-child-robot-symbiotic-co-development-a-theoretical-4usyiq2f6k.pdf

Towards a child-robot symbiotic co-development: a theoretical approach

Robots are gradually but steadily being introduced in our daily lives. A paramount application is that of education, where robots can assume the role of a tutor, a peer or simply a tool to help learners in a specific knowledge domain. Such endeavor posits specific challenges: affective social behavior, proper modelling of the learner’s progress, discrimination of the learner’s utterances, expressions and mental states, which, in turn, require an integrated architecture combining perception, cognition and action. In this paper we present an attempt to improve the current state of robots in the educational domain by introducing the EASEL EU project. Specifically, we introduce the EASEL’s unified robot architecture, an innovative Synthetic Tutor Assistant (STA) whose goal is to interactively guide learners in a science-based learning paradigm, allowing us to achieve such rich multimodal interactions.

/pdf/towards-a-synthetic-tutor-assistant-the-easel-project-and-1fb5fhw39h.pdf

Daniel Davison

Papers

Working with a Social Robot in School: A Long-Term Real-World Unsupervised Deployment

Evaluation methods for user-centered child-robot interaction

Flipper 2.0: A Pragmatic Dialogue Engine for Embodied Conversational Agents

Towards a child-robot symbiotic co-development: a theoretical approach

Towards a Synthetic Tutor Assistant: The EASEL Project and its Architecture