Open University Learning Analytics dataset

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

AI in Education: learner choice and fundamental rights

This article examines benefits and risks of Artificial Intelligence (AI) in education in relation to fundamental human rights. The article is based on an EU scoping study [Berendt, B., A. Littlejohn, P. Kern, P. Mitros, X. Shacklock, and M. Blakemore. 2017. Big Data for Monitoring Educational Systems. Luxembourg: Publications Office of the European Union. https://publications.europa.eu/en/publication-detail/-/publication/94cb5fc8-473e-11e7-aea8-01aa75ed71a1/]. The study takes into account the potential for AI and ‘Big Data’ to provide more effective monitoring of the education system in real-time, but also considers the implications for fundamental human rights and freedoms of both teachers and learners. The analysis highlights a need to balance the benefits and risks as AI tools are developed, marketed and deployed. We conclude with a call to embed consideration of the benefits and risks of AI in education as technology tools into the development, marketing and deployment of these tools. There are questions around who – which body or organisation – should take responsibility for regulating AI in education, particularly since AI impacts not only data protection and privacy, but on fundamental rights in general. Given AI’s global impact, it should be regulated at a trans-national level, with a global organisation such as the UN taking on this role

Erfassung und Beurteilung der Belastung der Elbe mit Schadstoffen. Teilprojekt: Tschechische Elbenebenfluesse Abschlussbericht

While the German investigations covered the Elbe river itself from the source to the mouth, the Czech investigations focused on the main tributaries of the Elbe in the Czech Republic. This way, apart from an assessment of the river status, one also gets a picture of potential transfrontier pollution. The methods of investigation applied in this research project will also be employed in future routine measuring programmes on behalf of the Czech government as well as in the development of further water protection strategies and measures. The investigations were carried out in close cooperation with the GKSS Research Centre at Geesthacht in the period between October 1994 and May 1997Die Untersuchungen und Ergebnisse im vorliegenden Abschlussbericht bauen auf dem deutschen F und E-Vorhaben 'Erfassung und Beurteilung der Belastung der Elbe mit Schadstoffen', Teilprojekt 2.1 'Schwermetalle und Schwermetallspecies' des GKSS-Forschungszentrums Geesthacht (BMBF Forschungsvorhaben 02-WTP 355/4) auf. Waehrend die deutschen Untersuchungen den gesamten Elbe-Hauptstrom von der Muendung bis zur Quelle umfassen, behandeln die tschechischen Arbeiten die wichtigsten Nebenfluesse der Elbe in der CR und bilden daher eine konsequente Fortfuehrung des deutschen Untersuchungsprogramms auf tschechischem Gebiet. Das Ziel des vorliegenden Forschungsvorhabens besteht nicht nur in einer eingehenden Bestandsaufnahme der wichtigsten Elbenebenfluesse mit ihren Auswirkungen auf die Vorbelastung der Elbe auf dem Gebiet der CR, sondern auch in der Moeglichkeit eine grenzueberschreitende Belastungsbewertung des gesamten Elbeeinzugsgebietes unter einheitlichen Rahmenbedingungen zu gewaehrleisten. Die Ergebnisse und die im Rahmen dieses Forschungsvorhabens angewandte Untersuchungsmethodik sollten sowohl bei zukuenftigen tschechischen behoerdlichen Routinemessprogrammen als auch bei der Erarbeitung von weiteren Gewaesserschutzstrategien und Gewaesserschutzmassnahmen behilflich sein. Die Arbeiten wurden in enger Zusammenarbeit mit dem GKSS-Forschungszentrum Geesthacht durchgefuehrt und im Zeitraum Oktober 1994 - Mai 1997 realisiert. (orig.)SIGLEAvailable from TIB Hannover: F98B1822+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Bildung, Wissenschaft, Forschung und Technologie, Bonn (Germany)DEGerman

Refinement and augmentation for data in micro open learning activities with an evolutionary rule generator

Improving both the quantity and quality of existing data are placed at the center of research for adaptive micro open learning. To cover this research gap, our work targets on the current scarcity of both data and rules that represent open learning activities. An evolutionary rule generator is constructed, which consists of an outer loop and an inner loop. The outer loop runs a genetic algorithm (GA) to produce association rules that can be effective in the micro open learning scenario from a small amount of available data sources; while the inner loop optimizes generated candidates by taking into account both rare and negative association rules (NARs). These optimized rules are further applied in refining and augmenting data denoting learners' behaviors in open learning into a low-dimensional, descriptive and interpretable form. The performance of rule discovery and data processing have been empirically evaluated using genuine open learning data

Explainable Artificial Intelligence for Human-Centric Data Analysis in Virtual Learning Environments

The amount of data to analyze in virtual learning environments (VLEs) grows exponentially everyday. The daily interaction of students with VLE platforms represents a digital foot print of the students' engagement with the learning materials and activities. This big and worth source of information needs to be managed and processed to be useful. Educational Data Mining and Learning Analytics are two research branches that have been recently emerged to analyze educational data. Artificial Intelligence techniques are commonly used to extract hidden knowledge from data and to construct models that could be used, for example, to predict students' outcomes. However, in the educational field, where the interaction between humans and AI systems is a main concern, there is a need of developing new Explainable AI (XAI) systems, that are able to communicate, in a human understandable way, the data analysis results. In this paper, we use an XAI tool, called ExpliClas, with the aim of facilitating data analysis in the context of the decision-making processes to be carried out by all the stakeholders involved in the educational process. The Open University Learning Analytics Dataset (OULAD) has been used to predict students' outcome, and both graphical and textual explanations of the predictions have shown the need and the effectiveness of using XAI in the educational field

Exploiting Time in Adaptive Learning from Educational Data

Virtual Learning Environments (VLEs) are web platforms where educational content is delivered, along with tools to support individual study. Logs that record how students interact with the platform are collected daily, so automated methods can be used to extract useful knowledge from these data. All stakeholders involved in the learning activities of the VLEs, especially students and teachers, can benefit from the insights derived from the educational data and valuable information can be extracted using machine learning algorithms. Usually, educational data are examined as stationary data using conventional batch methods. However, these data are non-stationary by nature and could be better treated as data streams. This paper reports the results of a classification study in which Random Forests, applied in both batch and adaptive mode, are used to build a model for predicting student exam failure/success. In addition, an analysis of the most important features is performed to detect the most discriminating attributes related to the student’s result. Experiments conducted on a subset of the Open University Learning Analytics (OULAD) dataset demonstrate the reliability of the adaptive version of Random Forest in accurately classifying the evolving educational data