ADA: A System for Automating the Learning Data Analytics Processing Life Cycle

Learning analytics is an emerging field focusing on tracing, collecting, and analysing data through learners’ interactions with educational content. The standardisation of the data collected to supporting interoperability and reuse is one of the key open issues in this field. One of the most promising routes to data standardisation is through the xAPI: a framework for developing standard ‘statements’ as representations of learning activity. This paper presents work con- ducted within the context of the Institute of Coding.1 Additionally, we have developed a system called ADA for automating the learning analytics data processing life cycle. To our knowledge, ADA is the only system aiming to automate the turning data into xAPI statements for standardisation, sending data to and extracting data from a learning record store or mongoDB, and providing learning analytics. The Open University Learning Analytics Dataset is used in the test case. The test case study has led to the extension of the xAPI with five new methods: 1) persona attributes, 2) register, 3) unregister, 4) submit, and 5) a number of views information

Log-based Evaluation of Label Splits for Process Models

Process mining techniques aim to extract insights in processes from event logs. One of the challenges in process mining is identifying interesting and meaningful event labels that contribute to a better understanding of the process. Our application area is mining data from smart homes for elderly, where the ultimate goal is to signal deviations from usual behavior and provide timely recommendations in order to extend the period of independent living. Extracting individual process models showing user behavior is an important instrument in achieving this goal. However, the interpretation of sensor data at an appropriate abstraction level is not straightforward. For example, a motion sensor in a bedroom can be triggered by tossing and turning in bed or by getting up. We try to derive the actual activity depending on the context (time, previous events, etc.). In this paper we introduce the notion of label refinements, which links more abstract event descriptions with their more refined counterparts. We present a statistical evaluation method to determine the usefulness of a label refinement for a given event log from a process perspective. Based on data from smart homes, we show how our statistical evaluation method for label refinements can be used in practice. Our method was able to select two label refinements out of a set of candidate label refinements that both had a positive effect on model precision.Comment: Paper accepted at the 20th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, to appear in Procedia Computer Scienc