Online judges in programming courses allow students to improve their coding abilities and instructors to analyze student work and detect challenging topics. Although several online judge platforms are available, most are limited in that they cannot support a large number of students simultaneously working on an assignment during a fixed time period, or can only do so at a significant cost, making the use of such systems in developing countries non-viable. This paper presents Clearn, a new platform that is (1) cost-conscious, as we have focused on lowering costs, (2) student-led, as we have empowered students and teaching assistants to lead its development and maintenance, and (3) highly simultaneous, as it allows over 1,000 students to work simultaneously on a timed assignment. This paper presents the platform, as well as the lessons learned during its development and deployment, and its reception by the students.

Over the years, patient satisfaction has become a key factor when evaluating the quality of healthcare. There is a constant desire to further analyze patients' needs and expectations, with the aim of improving their healthcare experience. With tools such as customer journey maps (CJM), two objectives are addressed: a) identify how patients interact through the phases of the care cycle and b) execute multiple analyses to deliver a wide variety of outcomes focused on improving patient experience, through the combination of these tools with emerging disciplines such as process mining. We propose a new method based on a predefined framework to optimize the creation of healthcare indicators through process mining. Our method focuses on identifying touchpoints, defining, calculating, validating, and visualizing Key Performance Indicators (KPIs) in a clinical process. The proposed method was applied to analyze an emergency room process as a case study. Results demonstrate the usefulness of the method to discover the main process interaction points, to create key indicators, and generate dashboards that support the goal of understanding and optimizing patient care.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

A common belief among students is that computing is a boring subject that lacks a connection to the real world. The first class (one 80-minute session) in an introductory computer science course may be an appropriate instance to combat such a belief. Previous studies have used coursewide interventions, e.g., games and physical/tangible devices to improve students’ motivation. However, although other approaches help motivate students, they may lack real-world context or have a high cost of deployment. This article proposes a novel real-world based approach to introduce programming concepts in the first class of the introductory computer science course. This approach, called Protobject based, is applicable to courses with over 100 students, has a low deployment entry barrier, requires low investment, and may be used creatively to implement different experiences. Furthermore, the Protobject-based approach has an equivalent motivational effect—at least in the short-term—to a Game-based approach even if it is entirely focused on the real world. The low requirements of the approach make it especially suitable for an 80-minute first class in an introductory computer science course. The Protobject-based approach has been preliminarily validated and compared to a pure game-based approach with a study with 376 participants, and we present the analysis of motivation questionnaires, a pre-test and post-test, and a homework assignment given to the students. We posit that more research into initiatives such as this one—that can show students how computer science can impact the real world around them—is warranted.

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.

Project-based courses can provide valuable learning experiences for computing majors as well as for faculty and community partners. However, proper coordination between students, stakeholders and the academic team is very difficult to achieve. We present an integral study consisting of a twofold approach. First, we propose a proven capstone course framework implementation in conjunction with an educational software tool to support and ensure proper fulfillment of most academic and engineering needs. Second, we propose an approach for mining process data from the information generated by this tool as a way of understanding these courses and improving software engineering education. Moreover, we propose visualizations, metrics and algorithms using Process Mining to provide an insight into practices and procedures followed during various phases of a software development life cycle. We mine the event logs produced by the educational software tool and derive aspects such as cooperative behaviors in a team, component and student entropy, process compliance and verification. The proposed visualizations and metrics (learning analytics) provide a multi-faceted view to the academic team serving as a tool for feedback on development process and quality by students

Process mining has been around for more than a decade now, and, in that period, several discovery algorithms have been introduced that work fairly well on average-sized event logs, that is, event logs that contain ∼50 different activities. Nevertheless, these algorithms have problems dealing with big event logs, that is, event logs that contain 200 or more different activities. For this reason, a generic approach has been developed which allows such big problems to be decomposed into a series of smaller (say, average-sized or even smaller) problems. This approach offers formal guarantees for the results obtained by it and makes existing algorithms also tractable for larger logs. As a result, discovery problems may become feasible, or may become easier to handle. This paper introduces a tool framework, called Divide And Conquer that fully supports this generic approach and that has been implemented in ProM 6. Using this novel framework, this paper demonstrates that significant speed-ups can be achieved for discovery. This paper also discusses the fact that decomposition may lead to different results, but that this may even turn out to have a positive effect.

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.

Process Conformance is becoming a crucial area due to the changing nature of processes within an Information System. By confronting specifications against system executions (the main problem tackled in process conformance), both system bugs and obsolete/incorrect specifications can be revealed. This paper presents novel techniques to enrich the process conformance analysis for the precision dimension. The new features of the metric proposed in this paper provides a complete view of the precision between a log and a model. The techniques have been implemented as a plug-in in an open-source Process Mining platform and experimental results witnessing both the theory and the goals of this work are presented.