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Using Mobile Application for Word Reordering Problems to Enhance Learning Analytics

The authors have developed a mobile application for word reordering problems (WRPs) and its learning management system (LMS) based on the WRP web application in order to collect learners' study logs. Various parameters such as "total distance of movements" and "answer time" are generated by learners' trajectories to detect their hesitations in answering WRPs. The main objective of this research is to exploit the practical use of new data types provided by physical sensors embedded in mobile devices, with the belief that specific new data types possibly benefit learning analytics. The authors used acceleration data and force data to detect abnormal trajectories - a series of trajectories caused by unstable environments resulting in low accuracy of detecting hesitations. The authors conducted an experiment to determine the most suitable pair of thresholds (acceleration and force parameter values). By setting this pair, f-measure to identify abnormal trajectories has reached approximately 0.727. This means that we are able to detect abnormal trajectories at a certain rate and cut some of them off to get more accurate results to detect hesitations using mobile devices.