Learning Analytics focuses on the collection and analysis of learners' data to improve their learning experience by providing informed guidance and to optimise learning materials. To support the research in this area we have developed a dataset, containing data from courses presented at the Open University (OU). What makes the dataset unique is the fact that it contains demographic data together with aggregated clickstream data of students' interactions in the Virtual Learning Environment (VLE). This enables the analysis of student behaviour, represented by their actions. The dataset contains the information about 22 courses, 32,593 students, their assessment results, and logs of their interactions with the VLE represented by daily summaries of student clicks (10,655,280 entries

We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA). This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medical data storage were evaluated. Evaluation criteria was root mean square error (RMSE) between original ECG and filtered data contaminated with artificial noise. Proposed algorithm achieved comparable result in terms of standard noises (power line interference, base line wander, EMG), but noticeably significantly better results were achieved when uncommon noise (electrode cable movement artefact) were compared.

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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leading to drop-out or passive withdrawal i.e. the state when a student is not studying, but he has not actively withdrawn from studies.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 .