The Virtual Tutor: Combining Conversational Agents with Learning Analytics to support Formative Assessment in Online Collaborative Learning

The objective of this design science research project is to combine Learning Analytics data with a conversational agent communication interface, the Virtual Tutor, which is able to support formative assessment for educators and learners in online collaborative learning (OCL) environments. The main benefit for educators is providing user-adaptable Learning Analytics data requests to fit the information needs for formative assessment. Learners receive semi-automated feedback on their platform activity in form of reports, which shall trigger self-reflection processes. By extracting requirements from the potential users and deriving design principles, a conversational agent is implemented and evaluated in an online collaborative learning course. The results indicate that the Virtual Tutor reduces the task load of educators, supports formative assessment and gives scaffolded guidance to the learners by reflecting their performance, thus triggering self-reflection processes. This research provides a first step towards data supported (semi-)automated feedback systems for formative assessment in OCL courses

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?

Innovation in education in general and innovation in engineering education in particular must be supported by properly collected and analyzed data to guide decisionmaking 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.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

Using Alexa for flashcard-based learning

Despite increasing awareness of Alexa’s potential as an educational tool, there remains a limited scope for Alexa skills to accommodate the features required for effective language learning. This paper describes an investigation into implementing ‘spaced-repetition’, a non-trivial feature of flashcard-based learning, through the development of an Alexa skill called ‘Japanese Flashcards’. Here we show that existing Alexa development features such as skill persistence allow for the effective implementation of spaced-repetition and suggest a heuristic adaptation of the spaced-repetition model that is appropriate for use with voice assistants (VAs). We also highlight areas of the Alexa development process that limit the facilitation of language learning, namely the lack of multilingual speech recognition, and offer solutions to these current limitations. Overall, the investigation shows that Alexa can successfully facilitate simple L2-L1 flashcard-based language learning and highlights the potential for Alexa to be used as a sophisticated and effective language learning tool

What Can You Do with Educational Technology that is Getting More Human?

Proceeding of: Tenth IEEE Global Engineering Education Conference (EDUCON 2019), 9-11 April, 2019, Dubai, UAE.Technology is advancing at an ever-increasing speed. The backend capabilities and the frontend means of interaction are revolutionizing all kinds of applications. In this paper, we analyze how the technological breakthroughs seem to make educational interactions look smarter and more human. After defining Education 4.0 following the Industry 4.0 idea, we identify the key breakthroughs of the last decade in educational technology, basically revolving around the concept cloud computing, and imagine a new wave of educational technologies supported by machine learning that allows defining educational scenarios where computers interact and react more and more like humans.The authors would like to primarily acknowledge the support of the eMadrid Network, which is funded by the Madrid Regional Government (Comunidad de Madrid) with grant No. S2018/TCS-4307. This work has also received partial support from FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación through Project RESET (TIN2014-53199-C3-1-R) and Project Smartlet (TIN2017-85179-C3-1-R). Partial support has also been received from the European Commission through Erasmus+ projects, in particular, projects COMPASS (Composing Lifelong Learning Oppor-tunity Pathways through Standards-based Services, 2015-1-EL01-KA203-014033), COMPETEN-SEA (Capacity to Organize Massive Public Educational Opportunities in Universities in Southeast Asia, 574212-EPP-1-2016-1-NL-EPPKA2-CBHE-JP), LALA (Building Capacity to use Learning Analytics to Improve Higher Education in Latin America, 586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), and InnovaT (Innovative Teaching across Continents: Universities from Europe, Chile, and Peru on an Expedition, 598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP). UNESCO Chair "Scalable Digital Education for All" at Universidad Carlos III de Madrid is also gratefully acknowledged.Publicad

The Importance of Educational Data Mining and Learning Analytics for Improving Teaching and Learning: An Issue Brief

“The words educational data mining and learning analytics are frequently used interchangeably, despite their being an increase in their investigation and implementation. This may be as a result of the fact that both areas have similar conceptual components. One way to ensure precision, homogeneity, and consistency It aims to pinpoint themes that are similar to and different from one other in the two domains as they develop. This a topic modelling study of papers on educational data mining and learning analytics was carried out in the elucidate the two areas' respective themes. In particular, we used structural topic modelling to find the two domains' subjects from the abstracts. For instructional purposes, we use structural topic modelling on N 1 4192 articles. For both educational data and survey data, we infer five-topic models analytics for mining and learning. While there may be disciplinary variations in research, our findings show that beyond their various lineages, there is no evidence to indicate a clear separation between the two disciplines. the area of educational research on the uses of advanced statistical methods is trending toward convergence for improving teaching and learning, discover how to mine massive data streams for insights that may be put to use. Over the past five years, both areas have converged on a growing emphasis on student behaviour. This study topic has advanced greatly, and a variety of related words, including Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science, are now used in the paper. The main publications, significant turning points, cycle of knowledge discovery, primary educational settings, specialised tools, freely accessible datasets, widely used methodologies, primary goals, and anticipated trends in this field of study are reviewed to provide the state of the art at this time

La tecnología educativa en la era de las interfaces naturales y el aprendizaje profundo

Las herramientas son un soporte esencial en cualquier actividad humana. A medida que la tecnología avanza, podemos diseñar herramientas más avanzadas que nos ayuden a realizar las actividades de manera más eficiente. Recientemente, hemos visto avances en los dos componentes principales de las herramientas, la interfaz y el motor computacional que hay detrás. Las interfaces naturales nos permiten comunicarnos con las herramientas de una forma más adaptada a los humanos. En relación con el motor, estamos pasando del paradigma de la computación a otro basado en la inteligencia artificial, que aprende a medida que se utiliza. En este documento, examinamos cómo estos avances tecnológicos tienen un impacto en la educación, lo que conduce a entornos de aprendizaje inteligentes (smart learning environments).Los autores agradecen el apoyo de FEDER/Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación a través del Proyecto Smartlet (TIN2017-85179-C3-1-R). Este artículo también ha recibido apoyo parcial de la Red eMadrid (e-Madrid-CM), financiada por la Comunidad de Madrid mediante el proyecto S2018/TCS-4307. Este último proyecto también está cofinanciado por los Fondos Estructurales (FSE y FEDER). También se ha recibido apoyo parcial de la Comisión Europea a través de proyectos Erasmus+"Capacity Building in the Field of Higher Education", más específicamente a través de los proyectos COMPETEN-SEA, LALA e InnovaT (574212-EPP-1-2016-1-NL-EPPKA2-CBHE-JP) (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP) (598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP

Application of Voice Personal Assistants in the Context of Smart University

Los asistentes personales de voz basados en técnicas avanzadas de comprensión del lenguaje natural se muestran como un recurso prometedor frente al reto del diseño de plataformas virtuales de aprendizaje. Específicamente, estos recursos pueden servir de apoyo para la mejora del proceso de enseñanza-aprendizaje. El objetivo principal de este trabajo ha sido el de estudiar los desafíos actuales para la utilización de este tipo de asistentes en el ámbito de las universidades inteligentes. Asimismo, se ha analizado cómo esta nueva tecnología puede ayudar a los estudiantes en su proceso de aprendizaje y grado de satisfacción. Los resultados de este trabajo se presentan en tres artículos de investigación publicados en revistas científicas indexadas en Web of Science. También se aporta un Registro de la Propiedad Intelectual registrado en el Ministerio de Cultura de España, en la categoría de programa de ordenador, cuyos derechos fueron cedidos a la Universidad de Burgos.Personal voice assistants based on advanced natural language comprehension techniques are shown as a promising resource with regard to the challenge of designing virtual learning platforms. In particular, these resources can support the improvement of the teaching-learning process. The main objective of this work has been to study the current challenges for the use of this type of assistant in the field of smart universities. Likewise, it has been analyzed how this innovative technology can help students in their learning process and their degree of satisfaction. The results of this work are presented in three research articles published in scientific journals indexed on the Web of Science. Also, an Intellectual Property Registry registered with the Ministry of Culture of Spain in the category of computer programs is provided, whose rights were transferred to the University of Burgos

O impacto da inteligência artificial no negócio eletrónico

Pela importância que a Inteligência Artificial exibe na atualidade, revela-se de grande interesse verificar até que ponto ela está a transformar o Negócio Eletrónico. Para esse efeito, delineou-se uma revisão sistemática com o objetivo de avaliar os impactos da proliferação destes instrumentos. A investigação empreendida pretendeu identificar artigos científicos que, através de pesquisas realizadas a Fontes de Dados Eletrónicas, pudessem responder às questões de investigação implementadas: a) que tipo de soluções, baseadas na Inteligência Artificial (IA), têm sido usadas para melhorar o Negócio Eletrónico (NE); b) em que domínios do NE a IA foi aplicada; c) qual a taxa de sucesso ou fracasso do projeto. Simultaneamente, tiveram de respeitar critérios de seleção, nomeadamente, estar escritos em inglês, encontrarem-se no intervalo temporal 2015/2021 e tratar-se de estudos empíricos, suportados em dados reais. Após uma avaliação de qualidade final, procedeu-se à extração dos dados pertinentes para a investigação, para formulários criados em MS Excel. Estes dados estiveram na base da análise quantitativa e qualitativa que evidenciaram as descobertas feitas e sobre os quais se procedeu, posteriormente, à sua discussão. A dissertação termina com as conclusão e discussão de trabalhos futuros.Due to the importance that Artificial Intelligence exhibits today, it is of great interest to see to what extent it is transforming the Electronic Business. To this end, a systematic review was designed to evaluate the impacts of the proliferation of these instruments. The research aimed to identify scientific articles that, through research carried out on Electronic Data Sources, could answer the research questions implemented: a) what kind of solutions, based on Artificial Intelligence, have been used to improve the Electronic Business; b) in which areas of the Electronic Business Artificial Intelligence has been applied; c) what the success rate or failure of the project is. At the same time, they must comply with selection criteria, to be written in English, to be found in the 2015/2021-time interval and to be empirical studies supported by actual data. After a final quality evaluation, the relevant data for the investigation were extracted for forms created in MS Excel. These data were the basis of the quantitative and qualitative analysis that evidenced the findings found and on which they were subsequently discussed. The dissertation ends with the conclusion and discussion of future works