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Identifying Cohorts that Differ in their Behaviour: Tool Support

Sander J. J. Leemans, Shiva Shabaninejad, Kanika Goel, Hassan Khosravi, Shazia W. Sadiq, Moe Thandar Wynn
2020 International Conference on Conceptual Modeling  
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

Recommending Insightful Drill-Downs Based on Learning Processes for Learning Analytics Dashboards [chapter]

Shiva Shabaninejad, Hassan Khosravi, Sander J. J. Leemans, Shazia Sadiq, Marta Indulska
2020 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 users
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.
doi:10.1007/978-3-030-52237-7_39 fatcat:wnyb4mrkvvdzta7oityc4vwria

Intelligent Learning Analytics Dashboards: Automated Drill-Down Recommendations to Support Teacher Data Exploration

Hassan Khosravi, Shiva Shabaninejad, Aneesha Bakharia, Shazia Sadiq, Marta Indulska, Dragan Gašević
2021 Journal of Learning Analytics  
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

Incorporating Explainable Learning Analytics to Assist Educators with Identifying Students in Need of Attention

Shiva Shabaninejad, Hassan Khosravi, Solmaz Abdi, Marta Indulska, Shazia Sadiq
2022 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 support
more » ... structors with the identification of students in need of attention. Unlike many previous predictive systems that automatically label students, our approach provides explainable recommendations to guide data exploration while still reserving judgement about interpreting student learning to instructors. The insights derived from applying SIF in an introductory Information Systems course with 407 enrolled students suggest that SIF can be utilised independent of the context and can provide a meaningful interpretation of students' learning behaviour towards facilitating proactive support of students.
doi:10.1145/3491140.3528292 fatcat:r43qarz4i5dsribiry2djhmspu