Process mining analysis of conceptual modeling behavior of novices – empirical study using JMermaid modeling and experimental logging environment

Abstract

a b s t r a c tPrevious studies on learning challenges in the field of modeling focus on cognitive perspectives, such as model understanding, modeling language knowledge and perceptual properties of graphical notation by novice business analysts as major sources affecting model quality. In the educational context outcome feedback is usually applied to improve learning achievements. However, not many research publications have been written observing the characteristics of a modeling process itself that can be associated with better/worse learning outcomes, nor have any empirically validated results been reported on the observations of modeling activities in the educational context. This paper attempts to cover this gap for conceptual modeling. We analyze modeling behavior (conceptual modeling event data of 20 cases, 10.000 events in total) using experimental logging functionality of the JMermaid modeling tool and process mining techniques. The outcomes of the work include modeling patterns that are indicative for worse/better learning performance. The results contribute to (1) improving teaching guidance for conceptual modeling targeted at process-oriented feedback, (2) providing recommendations on the type of data that can be useful in observing a modeling behavior from the perspective of learning outcomes. In addition, the study provides first insights for learning analytics research in the domain of conceptual modeling.

Supporting the Collaborative Construction of a Shared Understanding About Work with a Guided Conceptual Modeling Technique

“…The process of process modeling has also been examined from a cognitive perspective, focusing on the development of understanding on the subject of modeling for the individual modeler (Soffer et al 2012), where the authors discuss the cognitive fit of available modeling constructs as a factor influencing the process of modeling. Other approaches focus on the process of model creation and collect their data solely from observing the manipulation of the model Sedrakyan et al 2014;). They do not consider any aspects that do not have immediate impact on the model.…”

Educational Theories and Learning Analytics: From Data to Knowledge

“…Feedback is also viewed as an important component of self-regulated learning (Butler & Winne, 1995). Sedrakyan et al (2014) examined whether quality of work can be predicted by differences in students' learning patterns. Based on a three-dimensional analysis (i.e.…”

Towards a Structured Process Modeling Method: Building the Prescriptive Modeling Theory

“…Pinggera, et al coded modeling observations in sequences of adding, deleting and laying out model elements and used clustering techniques to define three modeling styles: slower modeling/more reconciliation, slower modeling/less reconciliation, and faster modeling/less reconciliation [5]. Sedrakyan, et al performed a process mining analysis on observational data and concluded that process modeling seems to require more effort than data modeling, and that in general it seems to be a successful tactic to start with the essence and iteratively add details to your model, which results in a quick growth of the model at the start and a slower pace of adding elements later on when thinking about the more challenging details [21]. To conclude, Claes, et al described various patterns of process modeling, ranging from timing and spread of creation, movement and deletion of model elements to more general modeling patterns such as the process modeling styles discussed in Section 3.1 [9,11].…”