Abstract
Many contemporary studies realized in the Learning Analytics research field provide substantial insights into the virtual learning environment stakeholders' behaviour on single-course or small-scale level. They used different knowledge discovery techniques, including frequent patterns analysis. However, there are only a few studies that have explored the stakeholders' behaviour over a more extended period of several academic years in detail. This article contributes to filling in this gap and provides a novel approach to using homogeneous groups of frequent patterns for identifying the changes in stakeholders' behaviour from the perspective of time. The novelty of this approach lies in fact, that even though the time variable is not directly involved, identification of homogeneous groups of frequent itemsets allows analysis and comparison of the stakeholders' behavioral patterns and their changes over different observed periods. Found homogeneous groups of frequent itemsets, which conform minimal threshold of selected measures, showed, that it is possible to uncover the changes in stakeholders' behaviour throughout the observed longer period. As a result, these homogenous groups of found frequent patterns allow a better understanding of the hidden changes in seasonality or trends in stakeholders' behaviour over several academic years. This article discusses the possible implications of the results and proposed approach in the context of virtual learning environment management and educational content improvement.
Highlights
The availability of different types of web-based educational systems like Learning Management Systems (LMSs) and Massive Open Online Courses (MOOCs) have significantly contributed to that the Computer-Based Education has become an integral and evitable part of the contemporary education at all levels of schools over the last two decades. These systems collect a huge amount of data about all their stakeholders, which are nowadays intensively analyzed using myriads of learning analytics and educational data mining techniques
It is quite surprising that despite the availability of data from a longer period, the studies that have explored the changes in stakeholders’ behaviour over a larger period of several academic years are still rare. This notion is in line with other researchers in the domain of learning analytics, who stated, that even though the importance of temporality in learning has been long established, it is only recently that serious attention has been paid to explore temporal concepts and data types, analyze methods for exploiting temporal data, techniques for visualizing temporal information, and practical considerations how to effectively use the outcomes of temporal analysis in particular educational contexts
RESEARCH BACKGROUND UNDERSTANDING The e-learning course used in this study dealt with the introductory topics of relational database systems. It was periodically opened during the winter term of eight consequent academic years (2010/11-2017/18) in the virtual learning environment (VLE) of the university
Summary
The availability of different types of web-based educational systems like Learning Management Systems (LMSs) and Massive Open Online Courses (MOOCs) have significantly contributed to that the Computer-Based Education has become an integral and evitable part of the contemporary education at all levels of schools over the last two decades. It is quite surprising that despite the availability of data from a longer period, the studies that have explored the changes in stakeholders’ behaviour over a larger period of several academic years are still rare This notion is in line with other researchers in the domain of learning analytics, who stated, that even though the importance of temporality in learning has been long established, it is only recently that serious attention has been paid to explore temporal concepts and data types, analyze methods for exploiting temporal data, techniques for visualizing temporal information, and practical considerations how to effectively use the outcomes of temporal analysis in particular educational contexts.
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