Looking at the future of Online Learning： a rejoinder to Contact North 2016 Report

Stephen Downes

Although we have entered the era of online learning， the era of change in learning has not ended. Indeed， most of the structures and institutions of the pre-internet age of learning remain. It is unlikely that this will continue to be the case. In this paper， I argue in general that the development of online learning will increase engagement and empowerment， allowing people to manage their own learning， rather than propelling us to a new enterprise driven education marketplace in which this valuable social support system is placed under the control of private industry. This discussion is addressed specifically toward a Contact North article looking at the future of online learning in Canada in 2016. However， each of the points made by the Contact North author reflects a widespread and commonly held point of view. The intent therefore is not only to offer a second look at one particular article， but to offer a greater perspective on an array of presuppositions.

Keywords： learning； internet； future； education； technology

A survey of learning needs of learners at the Open University of China

Ying Wang， Zhiguo Sun and Shu Liu

This article reports on a survey of the learning needs of learners at the Open University of China （OUC）， which is part of the second investigation of OUC learner characteristics since its establishment. Drawing on the research report of the project Research and Practice of the Instructional Quality Assurance System of Chinese Open Universities （2014）， a questionnaire survey was launched at the OUC portal site. Participants were from 32 provincial branches of OUC， three experimental colleges of OUC and the OUC headquarters. Altogether， 2746 completed questionnaires were collected but only 1095 were valid. SPSS21.0 was used for quantitative analysis of the data， in particular relevance between dimensions （or factors）. It is found that demand was higher for subject matter knowledge than for foundational knowledge with the greatest learning needs coming from learners in secondary industries. Essential professional competence received the highest rating， which， however， significantly varied with age. Participants were found to favor blended learning. They also expressed their desire for more training in career planning and employability and more effective communication channels and suggested that assessment content be more practical and assessment mode more flexible. Implications of these findings are discussed and further research is suggested.

Keywords： the Open University of China； open education； learners； learning needs； characteristics； quantitative analysis

Using data mining to study university students online behaviors

Zuhui Hu and Quan Shi

Using QFD model and two-way clustering method to analyze e-commerce student competencies

Ying Wei， Xilong Liu， Mingxuan Zhao and Jian Zhou

The growth of e-commerce has benefited from the development of the Internet but is being restrained by shortage of qualified practitioners. Studies on the competencies of e-commerce students mostly focus on course developments， instructional model construction and theoretical analyses of demands for student competencies in the job markets with very few qualitative analyses of e-commerce competencies. Based on data from an online learning platform for e-commerce students at a higher vocational education college， this study used the QFD model to analyze students business performances of their online store and assess the competencies needed by the students. It then used the two-way clustering method to extract features from the resulting competencies and explored what caused students differences in their e-commerce competencies from the perspective of the courses taught. Results from this study can help student identify their personal competencies and give teachers a profile of their students competencies so that students can be better guided in terms of course learning and career planning.

Keywords： e-commerce； online education； educational data mining； QFD model； two way clustering

Interactivity of distance learning resources

Zhijun Wang， Li Chen， Min Chen， and Tongtong Li

Distance instruction takes place chiefly via learner-content interaction， which is deemed to be the foundation of distance learning. Interactivity of learning resources represents their potential to support interaction in distance instruction with a direct impact on actual learner-content interaction， hence a key indicator of learning resources quality evaluation. Informed by previous research into this topic， this study set out to analyze the interactivity of distance learning resources in terms of learner-content interaction performances. It examined this interactivity from eight dimensions： options， controllability， editability， assessability， simulated dialogue， automatic feedback， learning guide， and situation representation. The secondary indicators and specific learner performances of each dimension were also identified and defined. Arguably， this study can provide theoretical underpinning to the development of interaction in distance instruction. Furthermore， it can complement current evaluation frameworks for distance learning resources which usually do not cover learning resources interactivity to an adequate extent， rendering such evaluation more operable and facilitating analysis and evaluation.

Keywords： distance learning； learning resources； interactivity； evaluation； interaction in distance instruction

An association analysis of context-aware learning resources： towards a recommendation model for learning resources

Di Wu and Baoqiang Li

Recommendation of context-sensitive learning resources and learning paths can reduce online learners time cost， enhance learning efficiency， strengthen learning interest and maximize learning effectiveness. Nevertheless， learning resources are complex in organizational structure and usually independent from each other. It is therefore an issue of utmost importance to be able to make use of context-aware technology and the association analysis method to recommend personalized learning content fit for the specific learning context. This study focuses on the use of context-aware technology and multilevel， multi-relational association algorithm to personalize recommendation of learning resources， aiming to identify characteristics of context-aware behaviors and explore the mechanism involved. It is hoped that findings from this study will be able to improve the quality and effect of personalized learning resources recommendation.

Keywords： context-awareness； association analysis； data mining； resource recommendation