Browsing by Author "Munoz Gama, Jorge"

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    Clinical Guidelines: A Crossroad of Many Research Areas. Challenges and Opportunities in Process Mining for Healthcare
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2019) Gatta, Roberto; Vallati, Mauro; Fernandez Llatas, Carlos; Martinez Millana, Antonio; Orini, Stefania; Sacchi, Lucia; Lenkowicz, Jacopo; Marcos, Mar; Munoz Gama, Jorge; Cuendet, Michel; de Bari, Berardino; Marco Ruiz, Luis; Stefanini, Alessandro; Castellano, Maurizio; DiFrancescomarino, C; Dijkman, R; Zdun, U
    Clinical Guidelines, medical protocols, and other healthcare indications, cover a significant slice of physicians daily routine, as they are used to support clinical choices also with relevant legal implications. On the one hand, informatics have proved to be a valuable mean for providing formalisms, methods, and approaches to extend clinical guidelines for better supporting the work performed in the healthcare domain. On the other hand, due to the different perspectives that can be considered for addressing similar problems, it lead to an undeniable fragmentation of the field. It may be argued that such fragmentation did not help to propose a practical, accepted, and extensively adopted solutions to assist physicians. As in Process Mining as a general field, Process Mining for Healthcare inherits the requirement of Conformance Checking. Conformance Checking aims to measure the adherence of a particular (discovered or known) process with a given set of data, or vice-versa. Due to the intuitive similarities in terms of challenges and problems to be faced between conformance checking and clinical guidelines, one may be tempted to expect that the fragmentation issue will naturally arise also in the conformance checking field. This position paper is a first step on the direction to embrace experience, lessons learnt, paradigms, and formalisms globally derived from the clinical guidelines challenge. We argue that such new focus, joint with the even growing notoriety and interest in PM4HC, might allow more physicians to make the big jump from user to protagonist becoming more motivated and proactive in building a strong multidisciplinary community.
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    Exploring differences in how learners navigate in MOOCs based on self-regulated learning and learning styles: A process mining approach
    (IEEE, 2016) Maldonado Mahauad, Jorge Javier; Palta, René; Vázquez, Jorge; Bermeo, Jorge L.; Pérez Sanagustín, Mar; Munoz Gama, Jorge
    Study in a Massive Open and Online Courses (MOOCs) is challenging, since participants take the course without the support of a teacher. Taking a MOOC require the students to have the ability to self-regulate their learning. However, every person has its own learning style and the way each one interacts and self-regulate in a MOOC varies. In this work we present an exploratory study from a process-oriented perspective to study whether students with different learning styles and SRL profiles show differences in navigating through a MOOC. Specifically, we investigate using Process Mining Techniques to analyze log files recording the course behavior of 99 learners across an Open edX MOOC combined with data from self-reported surveys. Our findings show that learners with different SRL profiles follow similar navigation paths, but there are differences when differentiating students by their learning styles.
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    Influence of Student Diversity on Educational Trajectories in Engineering High-Failure Rate Courses that Lead to Late Dropout
    (IEEE, 2019) Salazar Fernandez, Juan Pablo; Sepúlveda Cárdenas, Marcos Daniel; Munoz Gama, Jorge
    Global growth in participation in higher education has helped to increase diversity of students, and traditionally underrepresented minorities on gender, income and math skills have expanded their presence in engineering education. Nevertheless, late dropout has increased and the number of engineering graduates remains low in western world. The analysis of educational trajectories using process mining techniques can help to explain the relationship between a sequence of academic results and late dropout. This case study seeks to answer how gender, income and entry math skills may explain differences on educational trajectories of engineering students in high-failure rate courses that lead to late dropout. Academic records for 794 engineering students at Universidad Austral de Chile that belongs to cohorts 2007 to 2009, were extracted and analyzed using process mining discovery techniques. Models of educational trajectories on high-failure rate courses were created and then analyzed using the Investment Model as a reference framework. Findings reveal the following: late dropout is related to the number of consecutive semesters that a student maintain pending failed courses; low-income students and those with low entry math skills tend to be more persistent, even if they have unsatisfactory trajectories; female students tend to be more risk-averse when they have unsatisfactory results. Understanding the educational trajectories of students who end in late dropout can help managers and policy makers to improve the curriculum, entry conditions and programs that support disadvantaged students.
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    OSTIA: A Low Cost Alternative for Short Summative Assessments in Massive Programming Courses
    (IEEE, 2020) Salas, Juan Carlos; Munoz Gama, Jorge
    The following topics are dealt with: computer aided instruction; educational courses; educational institutions; teaching; computer science education; Internet; further education; learning (artificial intelligence); engineering education; pattern classification.
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    Understanding Undesired Procedural Behavior in Surgical Training: The Instructor Perspective
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2019) Galvez, Victor; Meneses, Cesar; Fagalde, Gonzalo; Munoz Gama, Jorge; Sepulveda, Marcos; Fuentes, Ricardo; de la Fuente, Rene; DiFrancescomarino, C; Dijkman, R; Zdun, U
    In recent years, a new approach to incorporate the process perspective in the surgical procedural training through Process Mining has been proposed. In this approach, training executions are recorded, to later generate end-to-end process models for the students, describing their execution. Although those end-to-end models are useful for the students, they do not fully capture the needs of the instructors of the training programs. This article proposes a taxonomy of activities for surgical process models, analyzes the specific questions instructors have about the student execution and their undesired procedural behavior, and proposes the Procedural Behavior Instrument, an instrument to answer them in an easy-to-interpret way. A real case was used to test the approach, and a preliminary validity was developed by a medical expert.