engleski

Evaluating the potential of Learning Analytics and Educational Data Mining concepts and tools in higher education in Croatia

Widespread mobile and web technology is used frequently to (i) present learning content, (ii) facilitate communication / interaction between learners and educators and (iii) assess learning outcomes, regardless of the topic or the context of learning. Data generated in the process is commonly stored and available for subsequent analysis. Increasing volumes of data about learning and teaching processes generated in different educational contexts (whether formal or informal, higher education or lifelong learning) led to advent of concepts such as learning analytics (LA) and educational data mining (EDM). LA and EDM develop rapidly due to advances in data management, especially in terms of managing large data sets since it enables storing and organizing educational data in an easy way. In addition to modern day tools for data management, a large number of tools for (big) data analysis are available on the market. In higher education, one of the key questions is how to increase student engagement and, in the process, achieve transformative learning outcomes. LA and EDM are crucial tools for answering such a complex question. Approaches behind LA and EDM are very similar. The key difference is that LA relies on human interpretation of the data, data visualization and social network analysis, whereas EDM is based on automated machine learning / data mining methods. Drawing on number of success stories and research reports, we explore and assess appropriateness of LA and EDM concepts and tools in one higher education institution (HEI) in Croatia. Analysis that goes beyond basic reports provided within centrally managed academic information systems is advocated and potential benefits are investigated and explained. As a proof of concept, a data mart that combines data from couple of data sources (Moodle LMS and custom academic information system) is devised and presented as a basis for systemic, real-time analysis of educational data in HEIs.

learning analytics, educational data mining, higher education

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