Comprendiendo el potencial y los desafíos del Big Data en las escuelas y la educación

In recent years, the world has experienced a huge revolution centered around the gathering and application of big data in various fields. This has affected many aspects of our daily life, including government, manufacturing, commerce, health, communication, entertainment, and many more. So far, education has benefited only a little from the big data revolution. In this article, we review the potential of big data in the context of education systems. Such data may include log files drawn from online learning environments, messages on online discussion forums, answers to open-ended questions, grades on various tasks, demographic and administrative information, speech, handwritten notes, illustrations, gestures and movements, neurophysiologic signals, eye movements, and many more. Analyzing this data, it is possible to calculate a wide range of measurements of the learning process and to support various educational stakeholders with informed decision-making. We offer a framework for better understanding of how big data can be used in education. The framework comprises several elements that need to be addressed in this context: defining the data; formulating data-collecting and storage apparatuses; data analysis and the application of analysis products. We further review some key opportunities and some important challenges of using big data in educationEn los últimos años, el mundo ha experimentado una gran revolución centrada en la recopilación y aplicación de big data en varios campos. Esto ha afectado muchos aspectos de nuestra vida diaria, incluidos el gobierno, la manufactura, el comercio, la salud, la comunicación, el entretenimiento y muchos más. Hasta ahora, la educación se ha beneficiado muy poco de la revolución del big data. En este artículo revisamos el potencial de los macrodatos en el contexto de los sistemas educativos. Dichos datos pueden incluir archivos de registro extraídos de entornos de aprendizaje en línea, mensajes en foros de discusión en línea, respuestas a preguntas abiertas, calificaciones en diversas tareas, información demográfica y administrativa, discurso, notas escritas a mano, ilustraciones, gestos y movimientos, señales neurofisiológicas, movimientos oculares y muchos más. Analizando estos datos es posible calcular una amplia gama de mediciones del proceso de aprendizaje y apoyar a diversos interesados educativos con una toma de decisiones informada. Ofrecemos un marco para una mejor comprensión de cómo se puede utilizar el big data en la educación. El marco comprende varios elementos que deben abordarse en este contexto: definición de los datos; formulación de aparatos de recolección y almacenamiento de datos; análisis de datos y aplicación de productos de análisis. Además, revisamos algunas oportunidades clave y algunos desafíos importantes del uso de big data en la educació

iFocus: A Framework for Non-intrusive Assessment of Student Attention Level in Classrooms

The process of learning is not merely determined by what the instructor teaches, but also by how the student receives that information. An attentive student will naturally be more open to obtaining knowledge than a bored or frustrated student. In recent years, tools such as skin temperature measurements and body posture calculations have been developed for the purpose of determining a student\u27s affect, or emotional state of mind. However, measuring eye-gaze data is particularly noteworthy in that it can collect measurements non-intrusively, while also being relatively simple to set up and use. This paper details how data obtained from such an eye-tracker can be used to predict a student\u27s attention as a measure of affect over the course of a class. From this research, an accuracy of 77% was achieved using the Extreme Gradient Boosting technique of machine learning. The outcome indicates that eye-gaze can be indeed used as a basis for constructing a predictive model

o caso de uma Escola Secundária do Distrito de Leiria

Este estudo tem como objetivo analisar o potencial dos dados digitais de uma escola na aplicação de técnicas de análise provenientes da ciência de dados para identificar precocemente, possíveis casos de insucesso escolar. Ao explorar esta abordagem, espera-se entender se os dados registados no software de gestão de uma escola têm qualidade para poder produzir padrões e correlações significativas que possam ser utilizados para intervenções precoces e personalizadas, com vista a melhorar os resultados académicos e promover o sucesso dos alunos. Os objetivos serão avaliar a qualidade da informação dos dados registado no software de gestão escolar de uma escola e analisar a possibilidade de correlação entre diversas variáveis contidas nesses dados com o sucesso escolar. Para a realização deste estudo, foram recolhidos dados do software de gestão escolar, os quais foram posteriormente sujeitos a um processo de tratamento para identificar o seu potencial na identificação de eventuais padrões. Adicionalmente, foi conduzida uma entrevista com a responsável pela inserção dos dados relativos aos alunos no software de gestão escolar, com o propósito de analisar potenciais falhas nesse procedimento. Os resultados e conclusões deste estudo identificam que o uso dos dados digitais em algoritmos de inteligência artificial depende não apenas da qualidade e da uniformização da introdução da informação nos registos digitais, mas também de um grande volume de dados. No futuro, as técnicas de análise existentes na ciência de dados podem contribuir significativamente para auxiliar na tomada de decisões relacionadas ao combate do insucesso escolar

Supporting learning in intelligent tutoring systems with motivational strategies.

Motivation and affect detection are prominent yet challenging areas of research in the field of Intelligent Tutoring Systems (ITSs). Devising strategies to engage learners and motivate them to practice regularly are of great interest to researchers. In the learning and education domain, where students use ITSs regularly, motivating them to engage with the system effectively may lead to higher learning outcomes. Therefore, developing an ITS which provides a complete learning experience to students by catering to their cognitive, affective, metacognitive, and motivational needs is an ambitious yet promising area of research. This dissertation is the first step towards this goal in the context of SQL-Tutor, a mature ITS for tutoring SQL. In this research project, I have conducted a series of studies to detect and evaluate learners' affective states and employed various strategies for increasing motivation and engagement to improve learning from SQL-Tutor. Firstly, I established the reliability of iMotions to correctly identify learners' emotions and found that worked examples alleviated learners' frustration while solving problems with SQL-Tutor. Gamification is introduced as a motivational strategy to persuade learners to practice with the system. Gamification has emerged as a strong engagement and motivation strategy in learning environments for young learners. I evaluated the effects of gamified SQL-Tutor on undergraduate students and found that gamification indirectly improved learning by influencing learners’ time on task. It helped students by increasing their motivation which produce similar effects as intrinsically motivated students. Additionally, prior knowledge, gamification experience, and interest in the topic moderated the effects of gamification. Lastly, self-regulated learning support is presented as another strategy to affect learners’ internal motivation and skills. The support provided in the form of interventions improved students’ learning outcomes. Additionally, the learners' challenge-accepting behaviour, problem selection, goal setting, and self-reflection have improved with support without experiencing any negative emotions. This research project contributes to the latest trends of motivation and learning research in ITS

The Impacts of Advancements in Digital Technologies on Students’ Self-Regulated Learning

This study examined student digital technology usage and student self-regulated learning in 2012 and 2020. Digital technologies have become a staple in both our learning environment, as well as our personal environment. In order to promote and enhance student self-regulated learning, two domains were examined in this study: the student’s personal environment and the educational provider’s learning environment. This holistic examination led to the development a Dual Model of Self-Regulated Learning for supporting student learning