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What Can Innovation in Engineering Education Do for You as a Student and What can You Do as a Student for Innovation in Engineering Education?
Carlos Alario-Hoyos
[email protected] / Universidad Carlos III de Madrid. España
Recepción: 17-6-2019 / Aceptación: 3-7-2019
ABSTRACT. Innovation in education in general and innovation in engineering education in particular must be supported by properly collected and analyzed data to guide decision-making processes. Today it is possible to collect data from many more stakeholders (not just students), and also to collect much more data from each stakeholder. Nevertheless, low-level data collected by monitoring the interactions of the multiple stakeholders with learning platforms and other computing systems must be transformed into meaningful high-level indicators and visualizations that guide decision-making processes. The aim of this paper is to discuss some notable trends in data-driven innovation in engineering education, including 1) improvement of educational content; 2) improvement of learners’ social interactions; 3) improvement of learners’ self-regulated learning skills; and 4) prediction of learners’ behavior. However, there are also significant risks associated with data collection and processing, such as privacy, transparency, biases, misinterpretations, etc., which must also be taken into account, and require creating specialized units and training the personnel in data management.
KEYWORDS: Data-driven innovation, learning analytics, digital education
¿Qué puede hacer la innovación de la educación de la ingeniería para el estudiante y qué puede hacer el estudiante para la misma?
RESUMEN. La innovación en la educación, en general, y la innovación en la educación de ingeniería, en particular, deben estar respaldadas por datos, debidamente recopilados y analizados para guiar los procesos de toma de decisiones. Hoy es posible recopilar datos de muchos grupos de interés (no solo estudiantes), y también recopilar muchos más datos de cada interesado. Sin embargo, los datos de bajo nivel recopilados al monitorear las interacciones de los múltiples interesados con las plataformas de aprendizaje y otros sistemas informáticos deben transformarse en indicadores y visualizaciones de alto nivel que guíen los procesos de toma de decisiones. El objetivo de este documento es discutir algunas tendencias notables en la innovación basada en datos en la educación de ingeniería, que incluyen: 1) mejora del contenido educativo; 2) mejora de las interacciones sociales de los alumnos; 3) mejora de las habilidades de aprendizaje autorreguladas de los alumnos; y 4) predicción del comportamiento de los alumnos. Sin embargo, también existen riesgos significativos asociados con la recopilación y el procesamiento de datos, que incluyen privacidad, transparencia, sesgos, malas interpretaciones, etc., que también deben tenerse en cuenta y que requieren la creación de unidades especializadas y la capacitación del personal en la gestión de datos.
PALABRAS CLAVE: innovación basada en datos, análisis de aprendizaje, educación digital
1. INTRODUCTION
The rapid changes taking place in today’s world have important consequences for education in general, and for engineering education in particular. Traditional “chalk and talk” teaching methodologies are not effective in engineering education, where it is necessary to combine the explanation of complex concepts with the practice of these concepts in realistic scenarios. In order to do this, it is necessary to apply learner-centered methodologies that promote active learning (Alario-Hoyos, Estévez-Ayres, Delgado Kloos, Villena-Román, Muñoz-Merino, et al., 2019), and that can be tailored to different education contexts, including face-to-face education in the classroom, online education, and blended (or hybrid) education (Pérez-Sanagustín, Hilliger, Alario-Hoyos, Delgado Kloos, & Rayyan, 2017).
Innovation in engineering education cannot take place without the support of a multi-disciplinary team of specialized professionals. Instructional designers must advise teachers on how to redesign their traditional course to turn it into a blended learning experience, making the most of the available resources with the aim to promote active learning (Baepler, Walker, & Driessen, 2014). Pedagogues and psychologists must contribute with the understanding and development of self-regulated learning (SRL) skills to ensure student success, especially in engineering education where further development of SRL skills is required to succeed (Zimmerman, 2013). Developers must create applications and simulators to put into practice and evaluate the key concepts of each course. Researchers and data scientists must collect and analyze data, offering visualizations so that the main stakeholders of the educational process (students, teachers, managers, etc.) can make informed decisions. All in all, innovation in engineering education is a multidisciplinary effort in which all supporting professionals and actions must be aligned with the ultimate goal to benefit students.
Among the abovementioned professionals, the role of researchers and data scientists is becoming more and more important in the last few years. Over the course of time, many efforts to innovate in engineering education have been undertaken without considering the research advances in the field of technology-enhanced learning. Today, with the amount of data that can be collected for the particular context of each educational institution, there is no longer an excuse for not implementing data-driven decision-making processes at the different levels: 1) teachers must rely on data to improve their content and the methodologies used in their classes; 2) students must rely on data to detect their knowledge gaps and improve their SRL skills; and 3) managers must rely on data to detect unbalanced programs or unsatisfactory courses, and to offer personalized pathways for students.