Prediction of academic performance and risk： a review of literature on predicative indicators in learning analytics

Yizhou Fan and Qiong Wang

As a research area to construct meaning from data， learning analytics has drawn great attention from academics in its development. One of the key issues of learning analytics， researched both domestically and abroad in comprehensive empirical studies and with profound research result， is how to predict learners learning success or failure. However， there have been limitations in the literature review of studies of predicative indicators neglecting the applicable situations or contexts， blurring the task types and suitable participants and leaving out representative researchers， their studies and practice. Through systematic literature retrieval and review， focusing on learning contexts and task types， this study analyzes three types of commonly used predictive indicators， namely， dispositional indicators， human-machine interaction indicators， and human-human interaction indicators. It gives a detailed account of crucial predictive indicators proven to be effective， namely， past academic performance， initial knowledge， learning motivation， positive or negative learning behaviors， learners emotional status， knowledge representation events， human-human interaction frequency， sense of community， etc. This study also analyzes one typical learning analysis system in each of the four quadrants developed based on the two dimensions of “at school or in the workplace” and “individual learning or group learning”. Finally， future development and research trends are proposed.

Keywords： education big data； learning analytics； predictive analytics； predictive indicator； academic performance； academic risk； literature review

The effect of mind mapping on student academic performance： A meta-analysis of 10 years international mind mapping practice

Yu Li， Yangli Chai and Hanbing Yan

There have been controversial theories among international scholars about the effectiveness of mind mapping， as an important learning approach， in improving academic performance， the basic characteristics of effective mind mapping-facilitated learning or the factors influencing this mode of learning. Selecting samples from international empirical studies about mind mapping in the last 10 years （2007-2016） from renowned databases like China National Knowledge Infrastructure and Web of Science， this study analyzes 60 sample references conforming to the standards of meta-analysis （The total sample sizes are 6，225， including 132 effect sizes）. The analysis is done through standardized coding， descriptive data collection， main effect， heterogeneity， moderator effect and publication bias， etc. The result shows the average effect size is 0.763 in terms of how much mind mapping contributes to students academic performance improvement. According to Hedgesg standard， Mind Map is conducive to the improvement of academic performance for students. The effects also depend on students characteristics and what they study. In particular， different students and study content would result in distinctive magnitude of the effects. Finally， the study points out the limitations and potential areas to explore in this field.

Keywords： mind mapping； academic performance； effect； educational application； meta-analysis； students characteristics； learning content

How goes the revolution？： Three themes in the shifting MOOC landscape

Jeremy Knox

Since the rise to prominence of the MOOC platform organisations in 2012， over 4500 courses have been offered to date （Online Course report， 2016）. However， despite the claims of innovation， disruption and revolution that continue to drive MOOC hyperbole， the general understanding of learning in MOOCs remains somewhat conventional， and certainly undertheorised. Assumptions about MOOC learning remain differentiated around the ‘xMOOC and ‘cMOOC terms， supposedly defining a centralised platform model， and a more distributed networked arrangement respectively. This paper will outline a number of critical perspectives through which different understandings of MOOC learning （and teaching） can be approached. Drawing on specific examples from current MOOC offerings and organisational developments， this paper will discuss： the trends for particular subject disciplines in MOOCs to date， alongside a continued promotional stance that claims broad sector disruption； a shift from ‘massive class enrolments towards ‘small and ‘private groupings， alongside more automated course delivery； and the developing relationship between MOOCs and learning analytics， indicative of an imminent and potent mainstreaming of predictive and interventionist data science in education （Williamson， 2015）. Moving across these three themes， this paper will discuss the emerging figure of the ‘MOOC learner， the function and responsibilities of teaching and the teacher， as well as the influence of technology on these roles and practices.

Keywords： MOOC learning； learning analytics； data science

Rethinking maker education in China in the perspective of learning sciences

Xudong Zheng and Hongchao Peng

As a mode of education linking learning and innovative creation， maker education connects the old and the new in terms of its theoretical foundation， technological support， learning activities and value proposition， which， nevertheless， is easily neglected by current maker education practitioners. There exist four types of outstanding issues in the maker education movement in domestic institutions， namely， partial theoretical understanding of maker education and creative talents， extremist approach of maker environments and technological application， teacher incapacity and role transformation， and evaluation of the movement. The compatibility of the development of learning sciences and the practice of maker education has provided a new perspective， a new approach， as well as future reference to re-examining maker education and to launching maker education programs.

Keywords： learning sciences； maker education； innovation capacity； learning assessment； course evaluation； formal learning； non-formal learning

（英文目錄、摘要譯者：劉占榮）