2020 Seventh International Conference on Social Network Analysis, Management and Security (SNAMS)

International audienc

Learning analytics on coursera event data: a process mining approach

Massive Open Online Courses (MOOCs) provide means to offer learning material in a highly scalable and flexible manner. Learning Analytics (LA) tools can be used to understand a MOOC's effectiveness and suggest appropriate intervention measures. A key dimension of such analysis is through profiling and understanding students' learning behavior. In this paper, we make use of process mining techniques in order to trace and analyze students' learning habits based on MOOC data. The objective of this endeavor is to provide insights regarding students and their learning behavior as it relates to their performance. Our analysis shows that successful students always watch videos in the recommended sequence and mostly watch in batch. The opposite is true for unsuccessful students. Moreover, we identified a positive correlation between viewing behavior and final grades supported by Pearson's, Kendall's and Spearman's correlation coefficients

Enhancing process mining results using domain knowledge

Process discovery algorithms typically aim at discovering process models from event logs. Most discovery algorithms discover the model based on an event log, without allowing the domain expert to influence the discovery approach in any way. However, the user may have certain domain expertise which should be exploited to create a better process model. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. We firstly present a modification algorithm to modify a discovered process model. Furthermore, we present a verification algorithm to verify the presence of user specified constraints in the model. The outcome of our approach is a Pareto front of process models based on the constraints specified by the domain expert and common quality dimensions of process mining

Detecting change in processes using comparative trace clustering

Real-life business processes are complex and show a high degree of variability. Additionally, due to changing conditions and circumstances, these processes continuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Besides changes over time, case data (e.g. treating physician, patient age) also influence how processes are executed. Existing process mining techniques assume processes to be static and therefore are less suited for the analysis of contemporary flexible business processes. This paper presents a novel comparative trace clustering approach that is able to expose changes in behavior. Valuable insights can be gained and process improvements can be made by finding those points in time where behavior changed and the reasons why. Evaluation on real-life event data shows our technique can provide these insights