Browsing by Author "Hidalgo Sepúlveda, Luciano"

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    A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction
    (MDPI, 2023) Hidalgo Sepúlveda, Luciano; Muñoz Gama, Jorge
    Interest in studying Massive Online Open Courses (MOOC) learners' sessions has grown as a result of the retention and completion issues that these courses present. Applying process mining to study this phenomenon is difficult due to the freedom of navigation that these courses give their students. The goal of this research is to provide a domain-driven top-down method that enables educators who are unfamiliar with data and process analytics to search for a set of preset high-level concepts in their own MOOC data, hence simplifying the use of typical process mining techniques. This is accomplished by defining a three-stage process that generates a low-level event log from a minimum data model and then abstracts it to a high-level event log with seven possible learning dynamics that a student may perform in a session. By examining the actions of students who successfully completed a Coursera introductory programming course, the framework was tested. As a consequence, patterns in the repetition of content and assessments were described; it was discovered that students' willingness to evaluate themselves increases as they advance through the course; and four distinct session types were characterized via clustering. This study shows the potential of employing event abstraction strategies to gain relevant insights from educational data.
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    Predicting Students' Outcome in an Introductory Programming Course: Leveraging the Student Background
    (MDPI, 2023) Kohler, Jacqueline; Hidalgo Sepúlveda, Luciano; Jara, Jose Luis
    For a lot of beginners, learning to program is challenging; similarly, for teachers, it is difficult to draw on students' prior knowledge to help the process because it is not quite obvious which abilities are significant for developing programming skills. This paper seeks to shed some light on the subject by identifying which previously recorded variables have the strongest correlation with passing an introductory programming course. To do this, a data set was collected including data from four cohorts of students who attended an introductory programming course, common to all Engineering programmes at a Chilean university. With this data set, several classifiers were built, using different Machine Learning methods, to determine whether students pass or fail the course. In addition, models were trained on subsets of students by programme duration and engineering specialisation. An accuracy of 68% was achieved, but the analysis by specialisation shows that both accuracy and the significant variables vary depending on the programme. The fact that classification methods select different predictors depending on the specialisation suggests that there is a variety of factors that affect a student's ability to succeed in a programming course, such as overall academic performance, language proficiency, and mathematical and scientific skills.