Jakub Kuzilek

I am Prague (CZ) based postdoc researcher focusing on Educational Data Mining and Learning Analytics. I am interested in machine learning, signal processing, and R. Let’s start scrolling and learn more.

Publications

(2018). Learning Analytics Dashboard Analysing First-Year Engineering Students. In EC-TEL 2018.

PDF Project Poster

(2018). Student Drop-out Modelling Using Virtual Learning Environment Behaviour Data. In EC-TEL 2018.

PDF Code Slides

(2017). Open University Learning Analytics dataset. In SciData.

PDF Dataset Project

(2012). Scoring system for 12 lead ECG quality assessment. In CinC2012.

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(2011). An automatic method for holter ECG denoising using ICA. In ISABEL2011.

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(2011). Data driven approach to ECG signal quality assessment using multistep SVM classification. In CinC2011.

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(2010). Detection of inferior myocardial infarction: a comparison of various decision systems and learning algorithms. In CinC2010.

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(2010). Processing holter ECG signal corrupted with noise: Using ICA for QRS complex detection. In ISABEL2010.

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Recent Posts

We analysed the data from OULAD dataset using Markov chains in order to model student drop-out based on their behaviour in Virtual Learning Environment.

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Finally, after days of struggling with bureaucracy, I managed to find enough time to complete my first blogdown project - my personal academic webpage! I must admit it blogdown is awesome and the way Hugo works is brilliant.

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Projects

Student Analyse

Student Analyse is project supported by Grant Agency of Czech Republic. It aims at discovery of various ways to explore the student behaviour during their path throught higher education process and using the obtainedknowledge to better predict student success. Those students who might be at risk are then supported with additional help delivered via teaching staff.

OU Analyse

OU Analyse is a project piloting machine-learning based methods for early identification of students at risk of failing. All students with their risk of failure are available weekly to the course tutors and the Student Support Teams to consider appropriate support. The overall objective is to significantly improve the retention of OU students.

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