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, learner’s 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 Hedges’g 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.