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Process mining is a specialised form of data analytics that aims to provide data-driven improvement recommendations, derived from event logs. These event logs contain information about the execution of real-world processes, which may be complex. Cohort identification recommends drill-down filters for process mining, based on differences in process. In this paper, we describe its integration in three process mining tools: as a stand-alone ProM plug-in, as part of the visual Miner and (planned) as part of Course Insights.dblp:conf/er/LeemansS0KSW20a fatcat:3hbh4vz6mzf7bmmndzgodlcnbe
Lecture Notes in Computer Science
Learning Analytics Dashboards (LADs) make use of rich and complex data about students and their learning activities to assist educators in understanding and making informed decisions about student learning and the design and improvement of learning processes. With the increase in the volume, velocity, variety and veracity of data on students, manual navigation and sense-making of such multi-dimensional data have become challenging. This paper proposes an analytical approach to assist LAD usersdoi:10.1007/978-3-030-52237-7_39 fatcat:wnyb4mrkvvdzta7oityc4vwria
more »... ith navigating the large set of possible drill-down actions to identify insights about learning behaviours of the sub-cohorts. A distinctive feature of the proposed approach is that it takes a process mining lens to examine and compare students' learning behaviours. The process oriented approach considers the flow and frequency of the sequences of performed learning activities, which is increasingly recognised as essential for understanding and optimising learning. We present results from an application of our approach in an existing LAD using a course with 875 students, with high demographic and educational diversity. We demonstrate the insights the approach enables, exploring how the learning behaviour of an identified sub-cohort differs from the remaining students and how the derived insights can be used by instructors.
Acknowledgements This paper unites the techniques previously introduced by Shabaninejad, Khosravi, Indulska, and colleagues (2020); Shabaninejad, Khosravi, Leemans, and colleagues (2020); and Leemans and ... , Khosravi, Indulska, Bakharia, and Isaias (2020) and Shabaninejad, Khosravi, Leemans, Sadiq, and Indulska (2020) . ... Shabaninejad, Khosravi, Leemans, and colleagues (2020) present an approach that uses a process mining lens to examine learning process differences of students and to identify and recommend subsets of ...doi:10.18608/jla.2021.7279 fatcat:2jzbjhvza5ce5isdy2k5wjismq
Proceedings of the Ninth ACM Conference on Learning @ Scale
Increased enrolments in higher education, and the shift to online learning that has been escalated by the recent COVID pandemic, have made it challenging for instructors to assist their students with their learning needs. Contributing to the growing literature on instructor-facing systems, this paper reports on the development of a learning analytics (LA) technique called Student Inspection Facilitator (SIF) that provides an explainable interpretation of students learning behaviour to supportdoi:10.1145/3491140.3528292 fatcat:r43qarz4i5dsribiry2djhmspu