An xAPI application profile to monitor self-regulated learning strategies

Self-regulated learning (SRL) is being promoted and adopted increasingly due to the needs of current education, student centered and focused on competence development. One of the main components of SRL is learners' self-monitoring, which eventually contributes to a better performance. Monitoring is also important for teachers, as it enables them to know to what extent their learners are doing well and progressing properly. At the same time, the use of technology for learning is now common and facilitates monitoring. Nevertheless, the available software still offers poor support from the SRL point of view, especially, for SRL monitoring. This clashes with the growth of learning analytics and educational data mining. The main issue is the wide variety of SRL actions that need to be captured, commonly performed in different tools, and the need to integrate them to support the development of analytics and data mining developments, making imperative the search of interoperable solutions. This paper focuses on the standardization of SRL traces to enable data collection from multiple sources and data analysis with the goal of easing the monitoring process for teachers and learners. First, the paper analyzes current monitoring software and its limitations for SRL. Then, after a brief analysis of available standards on this area, an application profile for the eXperience API specification is proposed to enable the interoperable recording of the SRL traces. The paper describes the process followed to create the profile, from the analysis to the final implementation, including the selection of the interactions that represent relevant SRL actions, the selection of vocabularies to record them and a case study.Xunta de Galicia | Ref. ED431B 2017/67Xunta de Galicia | Ref. ED431D 2017/1

Affordances and limitations of learning analytics for computer-assisted language learning: a case study of the VITAL project

Learning analytics (LA) has emerged as a field that offers promising new ways to support failing or weaker students, prevent drop-out and aid retention. However, other research suggests that large datasets of learner activity can be used to understand online learning behaviour and improve pedagogy. While the use of LA in language learning has received little attention to date, available research suggests that understanding language learner behaviour could provide valuable insights into task design for instructors and materials designers, as well as help students with effective learning strategies and personalised learning pathways. This paper first discusses previous research in the field of language learning and teaching based on learner tracking and the specific affordances of LA for CALL, as well as its inherent limitations and challenges. The second part of the paper analyses data arising from the European Commission (EC) funded VITAL project that adopted a bottom-up pedagogical approach to LA and implemented learner activity tracking in different blended or distance learning settings. Referring to data arising from 285 undergraduate students on a Business French course at Hasselt University which used a flipped classroom design, statistical and process-mining techniques were applied to map and visualise actual uses of online learning resources over the course of one semester. Results suggested that most students planned their self-study sessions in accordance with the flipped classroom design, both in terms of their timing of online activity and selection of contents. Other metrics measuring active online engagement – a crucial component of successful flipped learning - indicated significant differences between successful and non-successful students. Meaningful learner patterns were revealed in the data, visualising students’ paths through the online learning environment and uses of the different activity types. The research implied that valuable insights for instructors, course designers and students can be acquired based on the tracking and analysis of language learner data and the use of visualisation and process-mining tools

Proceedings of the 4th Workshop on Awareness and Reflection in Technology Enhanced Learning. In conjunction with the 9th European Conference on Technology Enhanced Learning: Open Learning and Teaching in Educational Communities

Awareness and reflection can be viewed from the differing perspectives of the disciplines informing Technology-Enhanced Learning, such as Psychology, Educational and Learning Sciences, or Computer Science. A common denominator can be identified, though, and enhancing ’awareness’ of learners and other participants involved in learning processes by technology means augmenting formal or informal learning experiences, typically in real-time, with information on progress, presence, outcomes, workspace, and the like. Supporting ’reflection’ then means enabling learners to capture, adapt, re-evaluate, and share experience in anticipation of future situations it will prove relevant to. Reflection supported digitally is a creative act, adding sense and meaning to experiences made. Combining support for 'awareness' and 'reflection' bears huge potential for improving the learning and training with respect to utility, self-regulation, usability, and user experience. The ARTEL workshop series brings - for the 4th time in 2014 - together researchers and professionals from different backgrounds to provide a forum for discussing the multi-faceted area of awareness and reflection. For this year 2014, the workshop organizes discussion and meta-reflection amongst researchers around the application of awareness and reflection in practice, its impact on learners and questions of feasibility, and sustainability for awareness and reflection in education and work. This year's workshop theme is: How does computer-support for awareness and reflection need to be embedded into practical (working or learning) contexts in order for learners to benefit from such computer support

Adaptive Learning Supported by Learning Analytics for Student Teachers’ Personalized Training during in-School Practices

This paper presents the results of the second phase of the international project “Improving Educational Innovation, Competitiveness, and Quality of Higher Education through Collaboration between University and Companies (EKT)”. The use of adaptive learning supported by learning analytics is proposed as a pedagogical strategy to work on the collaborative and personalized learning process that takes place during the school placement period of initial teacher education. Learning analytics is expected to facilitate the analysis of the different sources of information and data generated in the learning process. The collected data will be centralized in a learning record store (LRS), which will serve as a repository for xAPI compatible traces from the tools that make up EKT intelligent system. The system is expected to provide a strong support to decision-making so that participant agents can collaborate, advise, and contribute to the future teacher’s personalized training according to his or her progress and the context in which the practice takes place. The need analysis of tutors in the five pilot countries is presented, which has made it possible to define the process variables that make up the learning analysis architecture of the EKT systemS

Representation of virtual choreographies in learning dashboards of interoperable LMS analytics

Learning management systems (LMS) collect a large amount of data from user interaction, and it isn't easy to analyze this data in a reliable and context-independent manner. This research seeks to comprehend how virtual choreographies can be represented in interoperable LMS analytics dashboards. In order to gain a better understanding of the problem, this objective has been divided into three sub-goals: determining which interactions can be gathered from LMS contexts, identifying virtual choreographies from LMS logs, and representing virtual choreographies in learning dashboards. To achieve these objectives, we first conducted a Systematic Literature Review to comprehend the behaviors and interactions other authors have investigated in LMS contexts. Then, by applying these findings to this dissertation's case study, a methodical procedure for extracting valuable choreographies from the logs was outlined. The Design Science Research methodology was then applied to transforming logs into virtual choreographies and their representation in learning dashboards. It was implemented two services: one responsible for identifying virtual choreographies from data logs and transforming the logs into statements, recipes, and choreographies, following xAPI specification elements; and the other translates the information from the backend service into dashboard visualizations, allowing the user to view representations for statements, recipes, choreographies, and various visualizations. These artifacts provide a new flexible and cost-efficient solution for the identification of virtual choreographies, thereby facilitating the widespread adoption of their use

Next Generation Learning Platform - Reference Architecture Based on Open Standard

This paper was created as part of the Online Master of Science - Computer Science (OMS CS) CS6460 Educational Technology at Georgia Tech.There are hundreds of companies developing learning tools and capabilities; however, there are not many papers published on how these technologies are interconnected to provide a complete learning architecture. Because of the lack of comprehensive open learning architecture, education companies are forced to piece together many technologies and hardwire them through a non-standard integration. In recognizing the lack of progress on learning management tools, Educause proposed a conceptual framework called the next-generation digital learning environment (NGDLE). This paper explores NDGLE and suggests a reference architecture based on open standards

Logros y retos en analítica del aprendizaje en España: La perspectiva de SNOLA

As in other research fields, the development of learning analytics is influenced by the networks of researchers that contribute to it. This paper describes one of such networks: the Spanish Network of Learning Analytics (SNOLA). The paper presents the research lines of the members of SNOLA, as well as the main challenges that learning analytics has to address in the next few years as perceived by these researchers. This analysis is based on SNOLA’s archival data and on a survey carried out to the current members of the network. Although this approach does not cover all the activity related to learning analytics in Spain, the results provide a representative overview of the current state of research related to learning analytics in this context. The paper describes these trends and the main challenges, among which we can point out the need to adopt an ethical commitment with data, to develop systems that respond to the requirements of the end users, and to reach a wider institutional impact.Tal y como ocurre en otros campos de investigación, el desarrollo de la analítica del aprendizaje está influido por las redes de investigadores que contribuyen al mismo. Este artículo describe una de estas redes: la Red Española de Analítica de Aprendizaje (SNOLA). El artículo presenta las líneas de investigación de los miembros de SNOLA, así como los principales retos que la analítica del aprendizaje tiene que afrontar en los próximos años desde la visión de estos investigadores. Este análisis está basado en datos de archivo de SNOLA y en una encuesta realizada a los actuales miembros de la red. Aunque esta aproximación no cubre toda la actividad relacionada con analítica del aprendizaje en España, los resultados proporcionan una visión general representativa del estado de la investigación relacionada con analítica del aprendizaje en dicho contexto. El artículo muestra cuáles son estas tendencias y los principales retos, entre los que se encuentran la necesidad de adoptar un compromiso ético con los datos, desarrollar sistemas que respondan a las necesidades de los usuarios y alcanzar mayor impacto institucional

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks

Achievements and challenges in learning analytics in Spain: The view of SNOLA

[EN] As in other research fields, the development of learning analytics is influenced by the networks of researchers that contribute to it. This paper describes one of such networks: the Spanish Network of Learning Analytics (SNOLA). The paper presents the research lines of the members of SNOLA, as well as the main challenges that learning analytics has to address in the next few years as perceived by these researchers. This analysis is based on SNOLA’s archival data and on a survey carried out to the current members of the network. Although this approach does not cover all the activity related to learning analytics in Spain, the results provide a representative overview of the current state of research related to learning analytics in this context. The paper describes these trends and the main challenges, among which we can point out the need to adopt an ethical commitment with data, to develop systems that respond to the requirements of the end users, and to reach a wider institutional impact.[ES] Tal y como ocurre en otros campos de investigación, el desarrollo de la analítica del aprendizaje está influido por las redes de investigadores que contribuyen al mismo. Este artículo describe una de estas redes: la Red Española de Analítica de Aprendizaje (SNOLA). El artículo presenta las líneas de investigación de los miembros de SNOLA, así como los principales retos que la analítica del aprendizaje tiene que afrontar en los próximos años desde la visión de estos investigadores. Este análisis está basado en datos de archivo de SNOLA y en una encuesta realizada a los actuales miembros de la red. Aunque esta aproximación no cubre toda la actividad relacionada con analítica del aprendizaje en España, los resultados proporcionan una visión general representativa del estado de la investigación relacionada con analítica del aprendizaje en dicho contexto. El artículo muestra cuáles son estas tendencias y los principales retos, entre los que se encuentran la necesidad de adoptar un compromiso ético con los datos, desarrollar sistemas que respondan a las necesidades de los usuarios y alcanzar mayor impacto institucional.SIMinisterio de Ciencia, Innovación y Universidades ( RED2018-102725-T)Ministerio de Ciencia, Innovación y Universidades (TIN2016-80172-R)Junta de Castilla y León (VA257P18)Gobierno Vasco (IT980-16)Generalitat de Catalunya (2017SGR1619)Gobierno de la Comunidad de Madrid (S2018/TCS-4307

Oppimisanalytiikan käynnistäminen, Tapaus: Aalto Online Learning

The digital transformation of learning brings forth data having unprecedented granularity and coverage of learning activity. The research area of Learning Analytics (LA) uses this data to understand and improve learning. The practice of LA is a cyclic process where learning data is collected from different sources and analytics is developed according to stakeholder objectives. Finally, current results are delivered that lead into action which improves learning and produces new data. The goal of this thesis is to bootstrap LA in multiple courses that implement different weekly online learning activities. The term bootstrap underlines the aim to support continuity, further development, and expansion of LA. The research questions were: what learning data the courses currently instrument, and what LA objectives the course staff find most important. This thesis conducts software engineering to construct an LA solution for the research case. Requirements are defined via examination of the case and interviews of the course staff. The developed solution enables real time access to learning data and possibility to integrate data from both Moodle and A-plus learning environments for joined analysis. Novel interactive visualizations are developed according to the user requirements. The work in bootstrapping LA at course level lead to two general findings. First, the integration of learning data from multitude of sources is a common challenge that requires design. Second, teachers' initial LA objectives include aims to monitor expected progress, improve allocation of learning material, identify problematic areas in learning material, and improve interaction with learners.Opetuksen digitaalinen murros synnyttaää ennennäkemättömän tarkkaa ja kattavaa tietoa oppimisaktiviteeteista. Oppimisanalytiikan (OA) tutkimusalue käyttää tätä aineistoa oppimisen ymmärtämiseen ja parantamiseen. OA:n soveltaminen käytäntöön on toistuva prosessi, jossa oppimisaineistoa kerätään erilaisista lähteistä ja analytiikkaa kehitetään omistajiensa tavoitteiden mukaisesti. Lopuksi tuotetaan ajantasaisia tuloksia, jotka johtavat toimintaan, joka parantaa oppimista ja tuottaa uutta aineistoa. Tämän diplomityön tavoitteena on käynnistää OA usealla kurssilla, jotka toteuttavat erilaisia viikoittaisia verkko-oppimisen ratkaisuja. Käynnistäminen pyrkii elinvoimaiseen, kehittyvään ja laajenevaan analytiikkaan. Tutkimuskysymykset olivat, mitä dataa kurssit tällä hetkellä keräävät ja mitkä OA–tavoitteet ovat kurssihenkilökunnalle tärkeimpiä. Työssä rakennetaan ohjelmistotuotannon keinoin OA–ratkaisu tutkittavalle tapaukselle. Ratkaisun vaatimukset määritellään tarkastelemalla tapausta ja haastattelemalla kurssien henkilökuntaa. Kehitetyn ratkaisun avulla aineisto on saatavilla reaaliaikaisesti. Lisäksi ratkaisu mahdollistaa aineiston yhdistämisen Moodle ja A-plus oppimisympäristöistä yhteistä analyysiä varten. Työssä suunnitellaan uusia interaktiivisia tiedon visualisointeja käyttäjävaatimusten mukaisesti. Tutkimus OA:n käynnistämiseksi kurssitasolla tuotti kaksi yleistä tulosta. Ensiksi aineiston yhdistäminen eri lähteistä on tyypillinen haaste, joka vaatii suunnittelua. Toiseksi opettajien tavoitteita OA:ta aloittaessa ovat valvoa odotettua edistymistä, parantaa oppimateriaalin mitoitusta, tunnistaa ongelmakohtia oppimateriaalissa ja parantaa vuorovaikutusta opiskelijoiden kanssa