# Backpack Process Model (BPPM): A Process Mining Approach for Curricular Analytics
## 7 Conclusions  
From the PM perspective, the contribution of BPPM to CA is twofold: Firstly, it systematizes the analysis of curricular trajectories based on the backpack metaphor, characterizing students with similar behaviors in similar contexts. BPPM can improve the understanding of how students handle failed courses they must retake in each study program and in which sequences students stay or drop out most often. Furthermore, the BPPM shows how it is possible to integrate methodologies for sequence analysis and give them a specific meaning in study contexts such as higher education. It is a way of complementing other study metrics to seek an understanding of the educational trajectories that lead to dropout [52]. Secondly, BPPM can help managers and policymakers because the analysis of educational data can help to design and implement timely interventions. Backpack monitoring could be implemented in HEIs, to support counseling. While academic performance is a strong predictor of retention [53], student's beliefs about the usefulness of effort have a significant influence on academic performance [19]. Good counseling services use technology to identify students at risk [6; 27] and BPPM could be used as a complement to identify these students. Additionally, understanding how students handle their backpacks could be used to redesign the curriculum, to reduce the risk of students getting a very large backpack size and to improve student satisfaction with the curriculum. These kinds of decisions could help to reduce early dropout. We believe that the BPPM and BPPM-S models could be used to analyze longer curriculum trajectories that include the entire study plan. This analysis could help to understand the impact of failed courses that students must retake on late dropout and stop out decisions. The application of trace clustering techniques, in combination with DFGs, could be useful to reduce the complexity of longer BPPM educational trajectories, decomposing traces into smaller and more understandable backpack trajectories. In this context, hierarchical clustering [54] looks promising for future works. Furthermore, qualitative analysis could expand the understanding of students' beliefs about effort and the nature of intelligence [19] on decisions about course taking, stop out and dropout. The contributions to this paper are as follows: Conceptualization, J.P.S.-F., J.M.-G. and J.M.-M.; Data curation, J.P.S.-F., J.M.-G. and D.B. ; Formal analysis, J.P.S.-F., J.M.-G. and J.M.-M.; Funding acquisition, J.P.S.-F., J.M.-G. and M.S. ; Investigation, J.P.S.-F. and J.M.-G.; Methodology, J.P.S.-F. and J.M.-G.; Project administration, J.M.-G. and M.S. ; Resources, J.P.S.-F., J.M.-G. and M.S. ; Software, J.P.S.-F., J.M.-G. and D.B. ; Supervision, J.M.-G. and M.S. ; Validation, [PERSON]-F., [PERSON] and [PERSON]-[PERSON]; Visualization, [PERSON]-G.; Writing--original draft, [PERSON]-F., [PERSON]-G. and [PERSON]-M.; Writing--review & editing. [PERSON] and M.S. All authors have read and agreed to the published version of the manuscript. This study was funded by National Agency for Research and Development (ANID)-Scholarship Program/Doctorado Nacional 2015--21150985 and supported by National Agency for Research and Development (ANID)/FONDECYT--Chile Regular Project--1200206, Escuela de Ingenieria UC/Investigacion en Pregrado (IPRE) 2019-1546 and Direccion de Investigacion de la Universidad de Cuenca (DIUC) under the project \"Analitica de aprendizaje para el estudio de estrategias de aprendizaje autoregulado en un contexto de aprendizaje hibrio\" (DIUC_XVIII_2019_54). Not applicable. [PERSON]; [PERSON] Student Perceptions Matter: Early Signs of Undergraduate Student Retention/Attrition. _J. Coll. Stud. Retention: Res. Theory Pr._**2013**, _14_, 467-493. [CrossRef]
* _[PERSON] et al. (2018)_ [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression. _Res. High.