# Backpack Process Model (BPPM): A Process Mining Approach for Curricular Analytics
## 1 Introduction  
In the last decade, different techniques have progressively emerged for the analysis of data recorded by information systems, with the purpose of supporting informed decision-making in Higher Education Institutions (HEIs) [1]. In this context, Learning Analytics (LA) is the \"measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs\" [1]. Many HEIs have high hopes that LA can play an important role in supporting institutional processes, where data analysis can improve teaching [2], curricula [3] and learning outcomes [2] and reduce dropouts [2]. Curricular Analytics (CA) emerged as an area of LA that focuses on analyzing curricula and improving them through continuous improvement processes [4]. For this purpose, analytical tools are used to collect different types of educational data, e.g., data on curricular structure and/or data on course grading [5]. This allows the HEIs to analyze the strengthsand weaknesses of their curricula and to justify the curricular decisions and changes made when designing learning pathways for students [2]. The main objective of CA is to improve the curricular design to provide teaching and learning trajectories according to the needs of the students. For this purpose, it seeks to assess the consistency between course-level outcomes and program-level outcomes, determining whether learning outcomes have been achieved and eventually detecting gaps with respect to the assessments [5]. In addition, it is possible to identify blind spots and bottlenecks of students' trajectories, thus demonstrating the value of the curriculum for different stakeholders [6]. Traditionally, curricular analysis has been a painstaking, laborious, time-consuming, manual process. However, with the emergence of new analytical techniques that take advantage of different data sources (both structured and unstructured), new possibilities have emerged for analyzing the curriculum and students' curricular trajectories [7]. There are often significant differences between the curricular trajectories proposed (ideal trajectories) by academic institutions and the curricular trajectories carried out (actual trajectories) by students [8; 9]. Not everyone is able to follow these curricular trajectories in the same way, so it is important to detect when these deviations happen and to understand why they happen. In this way, it is possible to support better decision-making at the curricular level (e.g., what courses students are expected to take in the same semester, prerequisite courses and number of available vacancies to be offered in a course, among other things). So far, some tools have been developed that integrate different techniques for the analysis of curricular trajectories (e.g., data mining techniques such as clustering and classification) [10; 11], with the purpose of obtaining a \"snapshot of the student\" at a specific moment in time with respect to their progress. Some of these tools are, e.g., academic advising systems, which allow to see the progress status of a student according to their curriculum [4; 7; 12] or early dropout prediction systems in MOOC courses [13]. However, while these tools are promising, the models implemented to define curricular trajectories as processes (i.e., an ordered sequence of events that describe a student's curricular history) and not just as a snapshot of curricular progress, are scarce. Moreover, several authors consider that the development of LA requires that the selection of data and analysis models be more grounded in learning sciences [1], in order for tool design decisions to be based on them [14]. Therefore, it is believed necessary to develop analysis models that see curricular trajectories as processes and are based on concepts that come from learning sciences.