Titlebook: Investigating Chinese HE EFL Classrooms; Using Collaborative Lin Lin Book 2015 Springer-Verlag Berlin Heidelberg 2015 Attitude Measurement

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Tenets and Practices of Collaborative Learning in Classrooms: Empirical Supports, is applied in the classrooms where more empirical findings regarding its benefits as well as demerits are described. It also makes an account concerning how CL is used and researched in the Chinese EFL classrooms. A critique of the methods used in the area of research serves as the final part of th

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,Evaluating the Use and Effectiveness of Collaborative Learning: Students’ Interactional Behaviors a-q1), concerning “on task”, “contribute ideas”, “help peers” and “ask for help” behaviors (see Sect. ., Appendix 6), which was mainly answered using the classroom observation data and was analyzed using the mixed ANOVA. More specifically, the results were presented in numerical form from descriptive

FEIGN

,Collaboration as Conducive to Learning: Students’ Preferences for Learning and Attainments,dents’ perceptions of the learning approaches used are reported first. Attention then turns to the effect of CL on participants’ learning preferences, usingCantwell and Andrews’s (.) instrument for investigation. Finally, the reported positive attainments are further presented. All of these results

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Collaborative Learning in Practice: Toward a Pedagogy,s going to deepen our understanding of CL as used in practice. Findings of this chapter are derived mainly from students’ learning journals and interviews, and some excerpts are extracted from answers to the open-ended questions in the pre-CL questionnaire. The supporting numerical data was gathered

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Attitudes and Motivation,ed samples T-tests. Data were also run on a correlation analysis of important variables in this study to measure any linear correlation. Through a discussion of the results as presented in the previous four chapters, findings of the study cover the effect of CL on students’ learning, social, and aff

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Lin Linolutional neural network. A trained agent can then predict an action to perform based on the scene it finds itself in. This method is generic, and the network is trained without knowledge of the task, targets or environment in which it is acting. Another common challenge in imitation learning is gen

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heterogeneous