With the rapid advancement of Virtual Learning Environments (VLE) in higher education, the amount of available student data grows. Universities collect the information about students, their demographics, their study results and their behaviour in the online environment. By applying modelling and predictive analysis methods it is possible to predict student outcome or detect bottlenecks in course design. Our work aims at statistical simulation of student behaviour in the VLE in order to identify behavioural patterns leading to drop-out or passive withdrawal i.e. the state when a student is not studying, but he has not actively withdrawn from studies. For that purpose, the method called Markov chain modelling has been used. Recorded student activities in VLE (VLE logs) has been used for constructing of probabilistic representation that students will perform some activity in the next week based on their activities in the current week. The result is an instance of the family of absorbing Markov chains, which can be analysed using the property called time to absorption. The preliminary results show that interesting patterns in student VLE behaviour can be uncovered, especially when combined with the information about submission of the first assessment. Our analysis has been performed using Open University Learning Analytics dataset (OULAD) and research notes are available online 1 .
Student drop-out is one of the most critical issues that higher educational institutions face nowadays. The problem is significant for first-year students. These freshmen are especially at risk of failing due to the transition from different educational settings at high school. Thanks to the massive boom of Information and Communication Technologies, universities have started to collect a vast amount of study- and student-related data. Teachers can use the collected information to support students at risk of failing their studies. At the Faculty of Mechanical Engineering, Czech Technical University in Prague, the situation is no different, and first-year students are a vulnerable group similar to other institutions. The most critical part of the first year is the first exam period. One of the essential skills the student needs to develop is planning for exams. The presented research aims to explore the exam-taking patterns of first-year students. Data of 361 first-year students have been analysed and used to construct “layered” Markov chain probabilistic graphs. The graphs have revealed interesting behavioural patterns within the groups of successful and unsuccessful students.
Nowadays, the higher education institutions experience the problem with the student drop-out. In response to this problem, universities started employing analytical dashboards and educational data mining methods such as machine learning, to detect students at risk of failing their studies. In this paper, we present interactive web-based Learning Analytics dashboard -Analyst, which has been successfully deployed at Faculty of Mechanical Engineering (FME), Czech Technical University in Prague. The dashboard provides academic teaching staff with the opportunity to analyse student-related data from various sources in multiple ways to identify those, who might have difficulties to complete their degree. For this purpose, multiple analytical dashboard views have been implemented; this includes summary statistic, study progression graph, and credit completion probabilities graph. In addition, users have the option to export all analysis related graphs for the future use. Using outcomes provided by the Analyst, the university successfully ran the interventions on the selected at-risk students and increased the retention rate in the first study year.
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