1,354,533 research outputs found
Processing and Understanding Moodle Log Data and Their Temporal Dimension
No full textThe increased adoption of online learning environments has resulted in the availability of vast amounts of educational log data, which raises questions that could be answered by a thorough and accurate examination of students’ online learning behaviours. Event logs describe something that occurred on a platform and provide multiple dimensions that help to characterize what actions students take, when, and where (in which course and in which part of the course). Temporal analysis has been shown to be relevant in learning analytics (LA) research, and capturing time-on-task as a proxy to model learning behaviour, predict performance, and prevent drop-out has been the subject of several studies. In Moodle, one of the most used learning management systems, while most events are logged at their beginning, other events are recorded at their end. The duration of an event is usually calculated as the difference between two consecutive records assuming that a log records the action’s starting time. Therefore, when an event is logged at its end, the difference between the starting and the ending event identifies their sum, not the duration of the first. Moreover, in the pursuit of a better user experience, increasingly more online learning platforms’ functions are shifted to the client, with the unintended effect of reducing significant logs and conceivably misinterpreting student behaviour. The purpose of this study is to present Moodle’s logging system to illustrate where the temporal dimension of Moodle log data can be difficult to interpret and how this knowledge can be used to improve data processing. Starting from the correct extraction of Moodle logs, we focus on factors to consider when preparing data for temporal dimensional analysis. Considering the significance of the correct interpretation of log data to the LA community, we intend to initiate a discussion on this domain understanding to prevent the loss of data-related knowledge
Time-on-Task Estimation by data-driven Outlier Detection based on Learning Activities
No full textTemporal analysis has been demonstrated to be relevant in Learning Analytics research, and capturing time-on-task, i.e., the amount of time spent by students in quality learning, as a proxy to model learning behaviour, predict performance, and avoid drop-out has been the focus of a number of investigations. Nonetheless, most studies do not provide enough information on how their data were prepared for their findings to be easily replicated, even though data pre-processing decisions have an impact on the analysis' outcomes and can lead to inaccurate predictions. One of the key aspects in the preparation of learning data for temporal analysis is the detection of anomalous values of temporal duration of students' activities. Most of the works in the literature address this problem without taking into account the fact that different activities can have very different typical execution times. In this paper, we propose a methodology for estimating time-on-task that starts with a well-defined data consolidation and then applies an outlier detection strategy to the data based on a distinct study of each learning activity and its peculiarities. Our real-world data experiments show that the proposed methodology outperforms the current state of the art, providing more accurate time estimations for students' learning tasks
Above barrier potential diffusion
No full textThe stationary phase method is applied to diffusion by a potential barrier for an incoming
wave packet with energies greater than the height of the barrier. It is observed that
a direct application leads to paradoxical results. The correct solution, con_rmed by
numerical calculations is the creation of multiple peaks as a consequence of multiple
reections. Lessons concerning the use of the stationary phase method are drawn
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