Browsing by Author "Sepúlveda Cárdenas, Marcos Daniel"

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    A Mobile Cloud Shared Workspace to Support Lessons-Learned in Construction Companies
    (IEEE, 2014) Nuñez, D.; Neyem, A.; Sepúlveda Cárdenas, Marcos Daniel; Serpell, A.; Ferrada, X.
    Construction is a project-based industry, and for each project, companies have to cope with specific challenges. Nevertheless, most of these challenges are similar to each other and share many common elements. Thus, projects are an important source of expert know-how and organizational knowledge, but lessons learned in each one are not systematically incorporated into the design and construction phases of subsequent projects. Typically, construction companies do not have effective tools to make an appropriate transfer of this valuable knowledge for future projects. Therefore, shared workspaces could play an important role to do this by increasing communication among workers, organizing work more efficiently, reducing the coordination cost, and keeping an updated overview of the project. This paper presents a mobile cloud shared workspace to support knowledge management along each project. The platform is the result of the research and development work conducted by the authors, supported by a construction company, during the last two years.
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    Constraint Bag Process Model: An Interdisciplinary Process Mining Approach to Lean Construction
    (IEEE, 2018) Pérez Diarrart, Daniel José; Ruiz-Tagle Molina, Camilo José; Munoz-Gama, J.; Arias, M.; Alarcon, L. F.; Sepúlveda Cárdenas, Marcos Daniel
    Computer science tecniques, methodologies, and approaches, are directly applied to improve other enginnering disciplines. Construction is no exception, where software and data analysis are used to improve the processes of control and monitoring of construction projects. Most of the analysis are based on a key-value perspective analysis of the data. However, an emerging Process Mining discipline has proven to be able to capture a different process perspective. This interdisciplinary work is a step on this direction, proposing the Constraint Bag Process Model (CBPM), a novel use of Process Mining for answering process-oriented questions on construction projects.
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    Development of a comprehensive Percutaneous Dilatational Tracheostomy process model for procedural training: A Delphi-based experts consensus
    (2021) Fuente Sanhueza, René Francisco de la; Kattan Tala, Eduardo José; Muñoz Gama, Jorge; Puente, Ignacio; Navarrete, Matías; Kychenthal, Catalina; Fuentes, Ricardo; Bravo Morales, Sebastián; Gálvez Yanjari, Víctor Andrés; Sepúlveda Cárdenas, Marcos Daniel
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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.