Flip your classroom: Reach every student in every class every day

TL;DR: The purposes of this report were to illustrate how to implement the flipped classroom and to describe students' perceptions of this approach within 2 undergraduate nutrition courses.

TL;DR: A structured attempt to integrate flip teaching into language classrooms using a WebQuest active learning strategy indicates that the structured and semi-structured flip lessons were more effective instructional designs than the non-flip lessons.

TL;DR: It is found that gamified learning interventions have a positive impact on student learning and the impact of intrinsic and extrinsic motivation varies depending on whether the student is motivated intrinsically or extrinsically.

TL;DR: The findings of this study indicate that integrating the self-regulated strategy into flipped learning can improve students' self-efficacy as well as their strategies of planning and using study time, and hence they can learn effectively and have better learning achievements.

TL;DR: Observations from a flipped classroom experience of teaching an HCI bachelor course at the University of Trento (Italy) in Fall 2015 show students’ initial reluctance and lack of familiarity with using asynchronous communicationcollaboration tools for better managing their group work and learning in blended education programs.

TL;DR: In this paper, the authors describe and compare how, through a contextualized team-based learning (TBL) design, varied the knowledge about the economic analysis of inequality (conceptually and at a knowledge application level) among students of the Bachelor's degree in Business Administration and Management at the University of Barcelona.

TL;DR: In this article, the authors present a critical look at an L2 autonomous learning pathway undertaken online by Chinese exchange students in a "virtual self-access" environment, which proved to be engaging and motivating and facilitated the students' first approach to autonomous learning.

TL;DR: The aim of this paper is to extract the applications of machine learning and deep learning in E-learning systems using a machine learning technique known as Latent Dirichlet Allocation (LDA).