# A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction
###### Abstract  
Interest in studying Massive Online Open Courses (MOOC) learners' sessions has grown as a result of the retention and completion issues that these courses present. Applying process mining to study this phenomenon is difficult due to the freedom of navigation that these courses give their students. The goal of this research is to provide a domain-driven top-down method that enables educators who are unfamiliar with data and process analytics to search for a set of preset high-level concepts in their own MOOC data, hence simplifying the use of typical process mining techniques. This is accomplished by defining a three-stage process that generates a low-level event log from a minimum data model and then abstracts it to a high-level event log with seven possible learning dynamics that a student may perform in a session. By examining the actions of students who successfully completed a Coursera introductory programming course, the framework was tested. As a consequence, patterns in the repetition of content and assessments were described; it was discovered that students' willingness to evaluate themselves increases as they advance through the course; and four distinct session types were characterized via clustering. This study shows the potential of employing event abstraction strategies to gain relevant insights from educational data. event abstraction; MOOC; learning dynamics; process mining; in-session behavior +
Footnote †: journal: Medical  
process discovery tools, applying filters to routes and activities, or altering activity and case ID until they obtain a readable, albeit incomplete, model, as depicted in Figure 2. This narrows the scope of questions that can be answered using these techniques. This raw, unfiltered data does not fit more complex questions, so we need techniques that allow us to group low-level events into high-level activities, i.e., event abstractions. There are numerous event abstraction methods described in process mining literature (for a review, see [13]). Most event abstraction approaches are data-driven (bottom-up), that is, they use domain-agnostic or unsupervised methods to detect frequent patterns in data. However, in some circumstances, high-level activities have already been specified in a domain-driven (top-down) manner, and the objective is to find those activities in the data. Such approaches are not appropriate in these cases. For example, the same behavior of accessing a MOOC resource may indicate if a learner is studying from it or just scanning it to understand what will come next in a lesson. Finally, constructing an event abstraction might be a difficult task for educational decision-makers who are not specialists in process mining. This is due to the fact that it requires a complete understanding of concepts such as case ID, activity ID, and event ID. As a result, easy-to-follow recipes must be defined in order to apply process mining in interdisciplinary contexts such as education. We propose a domain-driven event abstraction framework to analyze learning dynamics in MOOC sessions, involving three steps: (1) a minimal data model that can be  
Figure 1: Conventional unfiltered spaghetti model obtained from raw data. The model is generated in Disco using data from an introductory Python programming course, with the resource name field serving as the activity and the student ID serving as the case ID. The source data is the same as that used in the case study section. Figure 2: Oversimplification of the previous model, using the same data but this time with the resource type field as activity. adapted to most MOOC systems; (2) the definition of a low-level event log, including the definition of case ID and user sessions; and (3) the definition of seven high-level activities, our learning dynamics, and its corresponding high-level event log. The framework maps all low-level events to the seven learning dynamics discussed here, making it straightforward enough for educational managers to replicate.