# Backpack Process Model (BPPM): A Process Mining Approach for Curricular Analytics
## 3 Related Work in Process Mining  
Process Mining (PM) is a relatively new research discipline that acts as a bridge between data science and process science [28]. It aims to extract knowledge from the event logs obtained from information systems, in order to discover process models, verify conformance, analyze bottlenecks, compare variants of the same process and suggest improvements [28]. This discipline has been applied in multiple domains, with particular success achieved in fields in which processes are insufficiently structured, such as healthcare [29] and education [16].  
Different techniques have been used to analyze the diverse perspectives of the processes, such as control flow, performance and organizational [30]. For the control flow, which is the focus of this work, petri nets [30; 31], causal nets [31] and process trees [32], among other methods, have been used to represent process models and algorithms have been proposed that can generate them (e.g., alpha for petri nets [28] and Inductive Miner for process trees [28]). Directly Follows Graphs (DFGs) that can be obtained by DFG-based algorithms such as heuristic miner [28] are also widely used because they are one of the easiest notations to interpret by non-expert users of process mining [31]. While DFGs show disadvantages over the other mentioned formalisms because they do not manage concurrency properly (i.e., when several events occur simultaneously), they are especially recommended when concurrency representation is not necessary.  
To guide the application of PM and analyze the results [33], different methodologies have been developed. Well-known generic methodologies are the L* life cycle [28] and PM2 [33]. Both methodologies have a broad scope, covering the entire process management cycle [34]. Different authors have proposed domain-specific methodologies that take into account the particularities of each domain. For education, [PERSON] et al. [35] adapted PM2, narrowing its scope from data extraction to model analysis. [PERSON] et al. [36] extended PM2 to include domain-specific requirements in healthcare, in terms of ethics and participation of domain experts, among other things. [PERSON] et al. [29] established that a process mining methodology in healthcare should highlight usability, in the building of domain-specific event logs and in the management of unstructured data. In manufacturing,Lorenz et al. [37] proposed a methodology with a scope that included the improvement of the processes.  
In recent years, research has been conducted to visualize event logs through domain models or even theoretical frameworks that can be used to represent and analyze the data [38]. The inclusion of shift work operation to model the organizational perspective of processes [30], the construction of a model representing the added value in service processes [39], the modeling of user behavior in MOOCs to identify self-regulated learning strategies [35] and the analysis of dropout behavior through the investment model [40], are examples of that. In this work, we propose to use the backpack metaphor to conceptualize students' curricular trajectories as a novel approach to understand how they manage their failed courses.  
While any general-domain process mining methodology could have been used for this work, the one proposed by [PERSON] [35] was chosen because it is based in PM2, which is widely used, and its scope goes only from data extraction to model analysis. The main contribution of this work is not the sequence of stages in the methodology, but the approach used to understand the curricular trajectories through the backpack metaphor.