# A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction
## 6 Conclusions  
This study proposes a domain-driven event abstraction framework for creating a high-level event log to analyze learning dynamics in MOOC sessions. The framework, in specific, is divided into three stages: (1) the minimal data model required; (2) the creation of a low-level event log; and (3) the classification into seven high-level activities that can be utilized to construct an abstracted high-level event log. A case study that examined the learning dynamics of the sessions of students who completed the course successfully validated the application of the framework in a real-world setting. The examination of the dynamics of the resources that are reviewed again lets us identify problems in the course and reveals that most students start a study session with a certain goal in mind (advance in the course, complete assessments, clear up questions, etc.). Similar distinctions were made between student behaviors at the start of the course and those at the end, since early on, students prioritized content consumption over working on assessments. However, when involvement to the course increases, students begin to actively participate in evaluation activities as their interest in the subject grows. Finally, the sessions could be divided into 4 different clusters, highlighting that while the development of relatively short sessions with a specific goal is the most common behavior of students in MOOCs, there are also sessions of extended duration in which students invest more time in the course by either progressing through it, attempting to improve their assessment results, or navigating through the course and revisiting content and assessments. Among the limitations of this study is the use of sessions as the unit of analysis in our proposed event abstraction; thus, reviewing lower-level behavior patterns, such as behavior within an assessment, or higher-level ones, such as behavior during a course week or an overview of the entire course, requires their own domain-driven abstractions, while the framework was intended to be expanded and modified, as previously said, it begins with a separation between two basic categories of resources: content and assessment. This means that applying similar learning dynamics to courses that do not follow this order, such as project-based courses, may not make sense. However, the proposed abstraction approach would still be a valuable alternative for carrying out process analysis without having to deal with the complexities of data generated by students with total freedom of navigation through MOOC resources. Similarly, while the concepts explored in this research might be useful in synchronous and hybrid courses, such as b-learning courses hosted in LMS systems like MOODLE, our approach does not take into account the possibility of activities that might exist outside the system and that are not recorded in the database. This restriction must be taken into consideration by any application in a setting where there is interaction with parties outside the system, such as in a classroom. This research demonstrates that utilizing event abstraction techniques on educational data enables the discovery of outcomes that would not be obvious when using data mining or process discovery techniques directly. One potential route for expanding on this research would be to identify behaviors that necessitate student interactions, such as forums, group projects, or peer assessments, as their own learning dynamics. Another alternative is to compare how the top-down learning dynamics described are correlated with a bottom-up abstraction output. This might allow for the detection of previously unknown learning dynamics or indicate the necessity for intermediate abstraction layers between the low-level of resources viewed and the activities conducted by a student during a MOOC session.