Person:
Maldonado Mahauad, Jorge Javier

Birth Date

1980-05-13

ORCID

0000-0003-1953-390X

Scopus Author ID

57190294959

Afiliación

Universidad de Cuenca, Cuenca, Ecuador
Universidad de Cuenca, Departamento de Ciencias de la Computación, Cuenca, Ecuador
Universidad Católica de Chile, Departamento de Informática, Pontificia Santiago, Chile

País

Ecuador

Organizational Units

Organizational Unit

Facultad de Ingeniería

La Facultad de Ingeniería, a inicios de los años 60, mediante resolución del Honorable Consejo Universitario, se formalizó la Facultad de Ingeniería de la Universidad de Cuenca, conformada por las escuelas de Ingeniería Civil y Topografía. Esta nueva estructura permitió una mayor especialización y fortalecimiento en áreas clave para el desarrollo regional. Cuenta con programas académicos reconocidos internacionalmente, que promueven y lideran actividades de investigación. Aplica un modelo educativo centrado en el estudiante y con procesos de mejora continua. Establece como prioridad una educación integra, la formación humanística es parte del programa de estudios que complementa a la sólida preparación científico-técnica. Las actividades culturales pertenecen a un programa permanente y activo al interior de nuestras dependencias, a la par de proyectos que desde el alumnado y bajo la supervisión de docentes cumplen con servicios de apoyo a nivel local y regional; promoviendo así una vinculación estrecha con la comunidad.

Job Title

Profesor (T)

Last Name

Maldonado Mahauad

First Name

Jorge Javier

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Search Results

Now showing 1 - 10 of 43

Proposal of an assistant for the automation of the design and creation process of learning objects
(Institute of Electrical and Electronics Engineers Inc., 2018) Bermeo Conto, Jorge; Maldonado Mahauad, Jorge Javier; Cabrera, Boris; Zúñiga Prieto, Miguel Ángel
Learning Objects (LOs) are digital educational materials that enable the teaching and learning process to be mediated. Howev-er, their design and creation often exceeds the possibilities that a teacher has for their production. Teachers are experienced users in the educational field and have little or no experience in pro-gramming tools, so it is expected that the production of an OA can be scalable. The aim of this paper is to present an intermedi-Ate software solution that supports the systematic application and documentation of the phases proposed in the DICREVOA meth-odology for the design, analysis and implementation of OA. For this a case study with the proposed graphic editors is presented. As a result, this wizard for the automation of the design and creation process of OA will allow teachers to exploit and improve the productivity, reliability, maintainability and portability of this type of educational resource from their experience in the domain of the educational context. © 2018 IEEE.
Generación de un modelo de calidad para la evaluación de objetos de aprendizaje utilizando notación i* e ISO/IEC 25010
(2018) Solis Cabrera, Vanessa Alexandra; Carvallo, Juan Pablo; Maldonado Mahauad, Jorge Javier
This study describes the process for the creation of a quality model that evaluates Learning Objects in terms of technology and didactics by using i* notation. In order to achieve the objective, quality models from both aspects were reviewed along with LO concepts and quality characteristics that a LO must accomplish were determined in order to consider the evaluation characteristics. The IQMC method was used to construct the diagram for strategic (SD) i* sections for the final quality model. With the SD model created, it was established the traceability between the generated model criteria and the ISO/IEC 25010 matrix to keep a correlation between the i* notation characteristics and the standard ISO matrix mentioned before. The matrix obtained was enriched with conceptual characteristics and with the attributes that a LO should fulfill. For each quality property included in final model was asigned appropiate metrics for your evaluation. As a result, a model through LO evaluation with each one of the selected metrics was generated.
Designing a moodle plugin for promoting learners’ self-regulated learning in blended learning
(Springer Science and Business Media Deutschland GmbH, 2022) Pérez Álvarez, Ronald Antonio; Sanza, Cédric; Villalobos, Esteban; Maldonado Mahauad, Jorge Javier; Pérez Sanagustín, Mar
After the COVID-19 pandemic, universities moved towards online and Blended Learning (BL) modes to offer greater curricular flexibility. Yet, recent research shows that students have difficulties regulating their learning strategies to adapt to the different learning modes that BL entails, which mixes face-to-face with online activities taking place in different learning contexts and environments. Prior work on Self-Regulated Learning (SRL) has explored the use of dashboard-based scaffolds for supporting students’ learning strategies. However, most existing solutions are designed for supporting students in online settings (i.e., MOOCs), disregarding the teachers’ role in BL settings and the support they need to monitor and promote students’ SRL. This paper presents the design process followed for transforming a tool designed for supporting students’ SRL in MOOCs into a Moodle plugin for BL. Following a design-based research methodological approach, we describe all the phases conducted for identifying the most appropriate indicators and visualizations for supporting SRL in BL practices, implementing and evaluating a first prototype. Results of a local evaluation with 114 teachers and a broad evaluation with 311 students shed some light on the type of indicators, dashboards and functionalities that should be considered when designing solutions for supporting SRL in BL settings
Lessons Learned from the Educational Experience during COVID-19 from the Perspective of Latin American University Students
(2023) Maldonado Mahauad, Jorge Javier
The COVID-19 pandemic impacted the educational context. University students were exposed to an educational transition from a face-to-face context to emergency remote teaching (ERT). This change affected the educational experience of students and teachers in general, and impacted their educational performance, as well as their emotional and mental health, among other aspects. However, learning from the successes during the ERT and reflecting on good and bad practices will allow us to configure effective learning scenarios that respond to the new normal. The objective of this paper is to describe and present the lessons learned during ERT from the experience of university students in Latin America who have already returned to face-to-face instruction. The study used a qualitative inductive approach and a phenomenographic design. The sample consisted of 640 undergraduate students (63% women) of higher education who experienced online education during the year 2021 and a face-to-face modality during the first semester of 2022, belonging to universities in Chile, Venezuela, and Ecuador. The results suggest that new learning scenarios should consider specific pedagogical practices, including active, collaborative, meaningful, and problem-based strategies, together with a diversity of feedback practices. It is concluded that the ERT brought good practices that should guide university educational policies
Backpack process model (Bppm). A process mining approach for curricular analytics
(2021) Sepúlveda, Marcos; Maldonado Mahauad, Jorge Javier; Salazar Fernandez, Juan Pablo; Munoz Gama, Jorge; Bustamante, Diego
Curricular analytics is the area of learning analytics that looks for insights and evidence on the relationship between curricular elements and the degree of achievement of curricular outcomes. For higher education institutions, curricular analytics can be useful for identifying the strengths and weaknesses of the curricula and for justifying changes in learning pathways for students. This work presents the study of curricular trajectories as processes (i.e., sequence of events) using process mining techniques. Specifically, the Backpack Process Model (BPPM) is defined as a novel model to unveil student trajectories, not by the courses that they take, but according to the courses that they have failed and have yet to pass. The usefulness of the proposed model is validated through the analysis of the curricular trajectories of N = 4466 engineering students considering the first courses in their program. We found differences between backpack trajectories that resulted in retention or in dropout; specific courses in the backpack and a larger initial backpack sizes were associated with a higher proportion of dropout. BPPM can contribute to understanding how students handle failed courses they must retake, providing information that could contribute to designing and implementing timely interventions in higher education institutions.
Characterizing learners' engagement in MOOCs: an observational case study using the notemyprogress tool for supporting self-regulation
(2020) Pérez Álvarez, Ronald Antonio; Maldonado Mahauad, Jorge Javier; Sharma, Kshitij; Sapunar Opazo, Diego; Pérez Sanagustín, María del Mar
IEEE Recent research shows that learners who are able to self-regulate their learning show greater levels of engagement with Massive Open Online Course (MOOC) content. To improve support for learners in their self-regulatory processes, researchers have proposed technological solutions to transform recorded MOOC data into actionable knowledge. However, studies providing empirical evidence on how these solutions impact learners' engagement with the course and their self-regulatory behavior remain scarce. In this paper, we present the results of an observational case study in which NoteMyProgress (NMP), a web-based tool designed to support learners' self-regulation in MOOCs, is applied as an intervention in two MOOCs. The main aim of this study is to provide insights into how the support of learners' Self-regulated Learning (SRL) strategies correlates with course engagement. We performed the evaluation using a sample of 263 learners and utilized distinct data sources in order to propose indicators for learners' engagement with the course and NMP. Results show a positive correlation between learners' final grades with NMP functionalities that support goal setting, organization (note-taking), and self-reflection (social comparison) SRL strategies. Furthermore, we found no significant behavioral differences in how learners with low SRL and high SRL profiles engage with the course or NMP. Finally, we discuss how these results relate to prior work and the implications for future technological solutions that seek to promote engagement in MOOCs.
Estudio exploratorio sobre el involucramiento de los estudiantes en el aula de clase, desde la mirada de la observación áulica
(Egregius, 2024) Arteaga Auquilla, María Teresa; Maldonado Mahauad, Jorge Javier; Suquilanda Villa, Doris
Can feedback based on predictive data improve learners' passing rates in MOOCs? a preliminary analysis
(Association for Computing Machinery, 2021) Pérez Sanagustín, María del Mar; Pérez Álvarez, Ronald Antonio; Maldonado Mahauad, Jorge Javier; Villalobos, Esteban; Hilleger, Isabel; Hernández Correa, Josefina; Sapunar, Diego; Moreno Marcos, Pedro Manuel; Muñoz Merino, Pedro; Delgado Kloos, Carlos; Imaz, Jon
This work in progress paper investigates if timely feedback increases learners’ passing rate in a MOOC. An experiment conducted with 2,421 learners in the Coursera platform tests if weekly messages sent to groups of learners with the same probability of dropping out the course can improve retention. These messages can contain information about: (1) the average time spent in the course, or (2) the average time per learning session, or (3) the exercises performed, or (4) the video-lectures completed. Preliminary results show that the completion rate increased 12% with the intervention compared with data from 1,445 learners that participated in the same course in a previous session without the intervention. We discuss the limitations of these preliminary results and the future research derived from them.
Building Institutional Capacity for Learning Analytics: Top-Down & Bottom-Up Initiatives
(2022) Perez Alvarez, Ronald; Pérez Sanagustín, Mar; Hilliger, Isabel; Maldonado Mahauad, Jorge Javier
Capacity building for Learning Analytics (LA) in Higher Education Institutions requires the coordination of organizational aspects and infrastructure development. This also depends on the organizational maturity of the institution and its leadership regarding LA adoption. LA capacity building can follow two approaches: (1) top-down, led by institutional managers; and (2) bottom-up, led by ground-level staff. This article studies two LA initiatives of each type conducted in the same institution to compare the deployment of organizational processes and infrastructure. The lessons learned that were captured from each approach are shared to inform other universities in Latin America on developing LA capabilities.
Detection of learning strategies: a comparison of process, sequence and network analytic approaches
(Springer Link, 2019) Pérez Sanagustín, Mar; Matcha, Wannisa; Gasevic, Dragan; Ahmad Uzir, Noraayu; Jovanovic, Jelena; Pardo, Abelardo; Maldonado Mahauad, Jorge Javier
© 2019, Springer Nature Switzerland AG. Research in learning analytics proposed different computational techniques to detect learning tactics and strategies adopted by learners in digital environments through the analysis of students’ trace data. While many promising insights have been produced, there has been much less understanding about how and to what extent different data analytic approaches influence results. This paper presents a comparison of three analytic approaches including process, sequence, and network approaches for detection of learning tactics and strategies. The analysis was performed on a dataset collected in a massive open online course on software programming. All three approaches produced four tactics and three strategy groups. The tactics detected by using the sequence analysis approach differed from those identified by the other two methods. The process and network analytic approaches had more than 66% of similarity in the detected tactics. Learning strategies detected by the three approaches proved to be highly similar.