Personalized grouping strategies for mobile collaborative learning

Yousong Guo and Liang Tan

Grouping strategy is one of the important factors impacting on the learning outcomes of mobile collaborative learning （MCL）， hence top on the research agenda of the field. Nevertheless， grouping strategies for MCL developed in recent years are characterized by learners passive acceptance of these strategies， tending to ignore their desire for personalized learning as well as the features of personalized learning. In light of this situation， this study set out to develop personalized grouping strategies for MCL. These strategies are intended to cater for learners needs. Learning objectives are proposed after analysis and design， and collaborative tasks are then designed and announced on the mobile learning platform. After that， learning groups begin to take shape. This is an iterative process which ends in the formation of collaborative learning groups. Findings from the study show these strategies can cater for learners personalized needs in the learning process.

Keywords： mobile collaborative learning； mobile learning platform； personalized learning； personalized grouping strategy

Towards a model of deep learning highlighting problem-solving

Liguo Zhang， Jiarui Xie and Guohua Wang

A perpetual problem in relation to learning is that learning often occurs at the surface level. Nowadays， cultivating creative personnel has become an important goal of the whole society， particularly in terms of education. Creativity starts with problem solving skills while the enhancement of problem solving and critical thinking skills has enormous potential for deep learning. Therefore， deep learning with the aim of solving problems is an option that can be used to avoid surface learning. This article analyses the connotations， characteristics and processes of deep learning and problem solving before it proposes a deep learning model intended to develop problem solving skills. This model emphasizes real-life， sophisticated learning environments， comprising four modules： deep learning processes， resource center， evaluation and feedback， and learning interaction. Future research will focus on its use in specific courses.

Keywords： deep learning； problem solving； learning model

Flipping the classroom in higher education： A design-based research study to develop a flipped classroom framework

Helen Crompton and Judith Dunkerly-Beanendprint

Over the past 20 years some higher education instructors have increased their technology use as a way to extend and enhance students understanding and move away from the traditional lecture approach. One recent strategy is to use a flipped classroom approach and have students use technology to access the lecture and other instructional resources outside the classroom； this leaves the in-class time to engage in active learning. However， at this time there are no empirically-based flipped classroom frameworks for undergraduate students in higher education. The purpose of this study was to fill this gap in the academic literature and develop a framework. This study will provide a springboard for scholars to further examine the efficacy of the flipped classroom in higher education and practitioners to look for a flipped classroom framework for using in their own classroom.

Keywords： flipped classroom； inverted classroom； classroom flip； higher education； pedagogy

An empirical study of deep learning content and its resource representation

Hang Hu and Yuqi Dong

Adopting the research framework for deep learning and built on the experiment to explore how technology enhances learning， this study focused on learning content and resource representation. Informed by cognitive and brain science as well as the psychology of learning mathematics， the study designed two types of classroom： two control groups and two experiment groups， with four variables. The four-week experiment was carried out among Year Five pupils at a primary school in City T， one hour per day. The experiment covered subject knowledge， learning strategies corresponding to subject knowledge， cognitive structure， 4S of social skills as well as development and application of CRF digital learning resources. Based on data from academic achievement， eye tracker and ERP brainwave recordings， the study came to the following conclusions. First， learning content based on cognitive processes and the re-construction of resources have significant effects on academic achievement. Second， there exists interplay between the quality of learning and technology. Third， it is the design of technology， not technology itself， that enhances learner development. Fourth， results from the verification experiment are basically consistent with those from the exploratory experiment. Last， academic achievement， eye tracker and ERP brainwave recordings can form a solid triangulation of findings.

Keyword： learning content； digital resource； re-construction； application； empirical study

Learner support for disabled learners in distance education： the case of OUC

Guobin Yang and Quan Bao

Research on learner support for disabled learners in distance education is both of theoretical and practical relevance. This study aimed to develop a support model for disabled distance learners. It verified the model in an empirical study involving disabled learners in Inner Mongolia who enrolled on courses offered by the Open University of China （OUC）. The focus of the study was on learner support strategies for disabled learners in the blended teaching environment.

Keywords： disabled learner； distance education； learner support； academic support； non-academic support

（英文目錄、摘要譯者：肖俊洪）endprint