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

Predicting Learners' Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learning

Proceeding of: 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK, September 3-5, 2018.In the past years, predictive models in Massive Open Online Courses (MOOCs) have focused on forecasting learners' success through their grades. The prediction of these grades is useful to identify problems that might lead to dropouts. However, most models in prior work predict categorical and continuous variables using low-level data. This paper contributes to extend current predictive models in the literature by considering coarse-grained variables related to Self-Regulated Learning (SRL). That is, using learners' self-reported SRL strategies and MOOC activity sequence patterns as predictors. Lineal and logistic regression modelling were used as a first approach of prediction with data collected from N = 2,035 learners who took a self-paced MOOC in Coursera. We identified two groups of learners: (1) Comprehensive, who follow the course path designed by the teacher; and (2) Targeting, who seek for the information required to pass assessments. For both type of learners, we found a group of variables as the most predictive: (1) the self-reported SRL strategies 'goal setting', 'strategic planning', 'elaboration' and 'help seeking'; (2) the activity sequences patterns 'only assessment', 'complete a video-lecture and try an assessment', 'explore the content' and 'try an assessment followed by a video-lecture'; and (3) learners' prior experience, together with the self-reported interest in course assessments, and the number of active days and time spent in the platform. These results show how to predict with more accuracy when students reach a certain status taking in to consideration not only low-level data, but complex data such as their SRL strategies.This work was supported by FONDECYT (Chile) under project initiation grant No.11150231, the MOOC-Maker Project (561533-EPP-1-2015-1-ES-EPPKA2-CBHE-JP), the LALA Project (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), and CONICYT/DOCTORADO NACIONAL 2016/21160081, the Spanish Ministry of Education, Culture and Sport, under an FPU fellowship (FPU016/00526) and the Spanish Ministry of Economy and Competiveness (Smartlet project, grant number TIN2017-85179-C3-1-R) funded by the Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER).Publicad

Exploring key parameters influencing student performance in a blended learning environment using learning analytics

Understanding the factors that influence students' results in hybrid learning environments is becoming increasingly important in today's educational environment.  The goal of this research is to examine factors that influence students' academic performance as well as their level of participation in blended learning environments.  A comprehensive study was conducted with 330 interested participants from the prestigious government polytechnics of the state of Karnataka in order to achieve this goal. Our data acquisition approach relied on the administration of a meticulously crafted survey questionnaire. The conceptual framework underpinning this study seamlessly integrates Transactional Distance Theory (TDT) principles with valuable insights derived from prior research. The Welch test and one-way ANOVA (Analysis of Variance) are two statistical approaches that we used selectively to reinforce our research which produced surprising results.  These findings underscore the pivotal role played by certain specific factors. The geographical location of learners and the medium through which they pursue their studies have emerged as critical determinants significantly influencing academic performance. Aspects like the frequency of login activities and active engagement in forum discussions have been found to exert a positive influence on learners' academic performance. In contrast, the duration of sleep did not show a significant impact on performance. These insights bear tangible implications for teachers and policymakers who are dedicated to the enhancement of the quality of BL programs with the ultimate goal of enriching the overall educational experience

Analysing the predictive power for anticipating assignment grades in a massive open online course

The learning process in a MOOC (Massive Open Online Course) can be improved from knowing in advance learners' grades on different assignments. This would be very useful to detect problems with enough time to take corrective measures. In this work, the aim is to analyse how different course scores can be predicted, what elements or variables affect the predictions and how much and in which way it is possible to anticipate scores. To do that, data from a MOOC about Java programming have been used. Results show the importance of indicators over the algorithms and that forum-related variables do not add power to predict grades, unlike previous scores. Furthermore, the type of task can vary the results. Regarding the anticipation, it was possible to use data from previous topics but with worse performance, although values were better than those obtained in the first seven days of the current topic.EACEA through the Erasmus+ Programme of the European Union, projects MOOC-Maker (561533-EPP-1-2015-1-ES-EPPKA2-CBHE-JP), SHEILA (562080-EPP-1-2015-BE-EPPKA3-PI-FORWARD) and LALA (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), by the Consejeria de Educacion, Juventud y Deporte, Comunidad de Madrid (Madrid Regional Government), through the eMadrid Excellence Network (S2013/ICE-2715), and by the Ministry of Economy and Competitiveness in Spain, projects RESET (TIN2014-53199-C3-1-R), SNOLA (TIN2015-71669-REDT) and Smartlet (TIN2017-85179-C3-1-R). The latter is financed by the Agencia Estatal de Investigacion (State Research Agency) in Spain, and the European Regional Development Fund (FEDER). It has also been supported by the Ministry of Education, Culture and Sport in Spain, under an FPU fellowship (FPU016/00526)

Generating actionable predictions regarding MOOC learners' engagement in peer reviews

Peer review is one approach to facilitate formative feedback exchange in MOOCs; however, it is often undermined by low participation. To support effective implementation of peer reviews in MOOCs, this research work proposes several predictive models to accurately classify learners according to their expected engagement levels in an upcoming peer-review activity, which offers various pedagogical utilities (e.g. improving peer reviews and collaborative learning activities). Two approaches were used for training the models: in situ learning (in which an engagement indicator available at the time of the predictions is used as a proxy label to train a model within the same course) and transfer across courses (in which a model is trained using labels obtained from past course data). These techniques allowed producing predictions that are actionable by the instructor while the course still continues, which is not possible with post-hoc approaches requiring the use of true labels. According to the results, both transfer across courses and in situ learning approaches have produced predictions that were actionable yet as accurate as those obtained with cross validation, suggesting that they deserve further attention to create impact in MOOCs with real-world interventions. Potential pedagogical uses of the predictions were illustrated with several examples

Generating actionable predictions regarding MOOC learners’ engagement in peer reviews

Producción CientíficaPeer review is one approach to facilitate formative feedback exchange in MOOCs; however, it is often undermined by low participation. To support effective implementation of peer reviews in MOOCs, this research work proposes several predictive models to accurately classify learners according to their expected engagement levels in an upcoming peer-review activity, which offers various pedagogical utilities (e.g. improving peer reviews and collaborative learning activities). Two approaches were used for training the models: in situ learning (in which an engagement indicator available at the time of the predictions is used as a proxy label to train a model within the same course) and transfer across courses (in which a model is trained using labels obtained from past course data). These techniques allowed producing predictions that are actionable by the instructor while the course still continues, which is not possible with post-hoc approaches requiring the use of true labels. According to the results, both transfer across courses and in situ learning approaches have produced predictions that were actionable yet as accurate as those obtained with cross validation, suggesting that they deserve further attention to create impact in MOOCs with real-world interventions. Potential pedagogical uses of the predictions were illustrated with several examples.European Union’s Horizon 2020 research and innovation programme (Marie Sklodowska-Curie grant 793317)Ministerio de Ciencia, Innovación y Universidades (projects TIN2017-85179-C3-2-R / TIN2014-53199-C3-2-R)Junta de Castilla y León (grant VA257P18)Comisión Europea (grant 588438-EPP-1-2017-1-EL-EPPKA2-KA

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

Multimodal data as a means to understand the learning experience

publishedVersionPaid Open AccessUNIT-agreemen

Assessment of cognitive, behavioral, and affective learning outcomes in massive open online courses: a systematic literature review

This systematic review on massive open online courses (MOOCs) in higher education examinedthe research on the assessment of learning outcomes based on 65 peer-reviewed articles publishedbetween 2017 and 2019. This study aims to investigate the learning outcomes, related instruments,and assessment characteristics of these instruments in MOOCs. Learning outcomes thatwere examined in the studies that were reviewed concerned cognitive, behavioral, and affectivelearning outcomes. Twenty-five types of assessment approaches were employed to examine theseoutcomes and to identify the assessment characteristics. The results indicate that a considerationof the assessment of learning outcomes at the beginning of course design could support theformulation of explicit assessment goals and, in this way, instruct learners to work towardlearning outcomes. A combination of knowledge tests and skill tasks can be used to examinecognitive outcomes in a particular MOOC. Outcome-oriented feedback rubrics are beneficial tosupport learner essay performance and interpretations of the utilization of rubrics could betterguide providers to give peer feedback. A variety of behavioral and affective outcomes reflectmultiple aspects of participant learning in MOOCs, which might contribute to better understandingby teachers and the provision of learning support. Furthermore, assessment tasksthroughout the course may differ in difficulty and complexity, which could align with differentlevels of learner motivation. The findings provide a holistic picture of learning outcomes andrelated assessment instruments in current MOOCs. Curriculum designers and teachers couldbenefit from this study to consider appropriate learning outcome variables and instruments toapply in their MOOC practices. Future research might investigate the motivation of learners toparticipate in a MOOC and how this changes during a MOOC. This could help MOOC designersand teachers to align how learners are motivated, what they want to learn, and what they actuallydo learn.Teaching and Teacher Learning (ICLON