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). The dataset is freely available at https://analyse.kmi.open.ac.uk/open_dataset under a CC-BY 4.0 license.

Background: Formative assessments are vital for supporting learning and performance but are also considered to increase the workload of teachers. As self‐assessments in higher education are increasingly facilitated via digital learning environments allowing to offer direct feedback and tracking students' digital learning behaviour these constraints might be reduced. Yet, learning analytics do not make sufficient use of data on assessments.Aims: This exploratory case study uses learning analytics methods for investigating students' engagement with self‐assessments and how this relates to performance in the final exam and self‐reported self‐testing strategies.Materials & Methods: The research study has been conducted at a European university in a twelve‐weeks course of a Bachelor's program in Economic and Business Education including n_enroll = 159 participants. During the semester, students were offered nine self‐assessments each including three to eight tasks plus one mid‐term and one exam‐preparation self‐assessment including all prior self‐assessments tasks. The self‐assessment interaction data for each student included: the results of the last self‐assessment attempt, the number of processed self‐assessment tasks, and the time spent on the last self‐assessment attempt, the total self‐assessment attempts, and the first as well as last access of each self‐assessment. Data analytics included unsupervised machine learning and process mining approaches.Results: Findings indicate that students use the self‐assessments predominantly before summative assessments. Two distinct clusters based on engagement with self‐assessments could be identified and engagement was positively related to performance in the final exam. The findings from learning analytics data were also indicated by students' self‐reported use of self‐testing strategies.Discussion: With the help of multiple data from self‐reports, formal exams, and a learning analytics system, the findings provided multiple perspectives on the use of self‐assessments and their relationships with course performance. These findings call for applying assessment analytics and related frameworks in learning analytics as well as providing learners with related adaptive feedback.Conclusion: Future research might investigate different (self‐report) variables for clustering, other student cohorts or self‐assessment forms.

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.