Search

Producción Científica, Background:Providing feedback in massive open online courses (MOOCs) is chal-lenging due to the massiveness and heterogeneity of learners' population. Learninganalytics (LA) solutions aim at scaling up feedback interventions and supportinginstructors in this endeavour.Paper Objectives:This paper focuses on instructor-led feedback mediated by LAtools in MOOCs. Our goal is to answer how, to what extent data-driven feedback isprovided to learners, and what its impact is.Methods:We conducted a systematic literature review on the state-of-the-art LA-informed instructor-led feedback in MOOCs. From a pool of 227 publications, weselected 38 articles that address the topic of LA-informed feedback in MOOCs medi-ated by instructors. We applied etic content analysis to the collected data.Results and Conclusions:The results revealed a lack of empirical studies exploring LA todeliver feedback, and limited attention on pedagogy to inform feedback practices. Our find-ings suggest the need for systematization and evaluation of feedback. Additionally, there isa need for conceptual tools to guide instructors' in the design of LA-based feedback.Takeaways:We point out the need for systematization and evaluation of feedback. Weenvision that this research can support the design of LA-based feedback, thus contribut-ing to bridge the gap between pedagogy and data-driven practice in MOOCs., Consejo de Investigación de Estonia (PSG286), Ministerio de Ciencia e Innovación - Fondo Europeo de Desarrollo Regional y la Agencia Nacional de Investigación (grant PID2020-112584RB-C32) and (grant TIN2017-85179-C3-2-R), Junta de Castilla y León - Fondo Social Europeo y el Consejo Regional de Educación (grant E-47-2018-0108488)

From June 22 to June 24, 2021, Hasso Plattner Institute, Potsdam, hosted the seventh European MOOC Stakeholder Summit (EMOOCs 2021) together with the eighth ACM Learning@Scale Conference. Due to the COVID-19 situation, the conference was held fully online. The boost in digital education worldwide as a result of the pandemic was also one of the main topics of this year’s EMOOCs. All institutions of learning have been forced to transform and redesign their educational methods, moving from traditional models to hybrid or completely online models at scale. The learnings, derived from practical experience and research, have been explored in EMOOCs 2021 in six tracks and additional workshops, covering various aspects of this field. In this publication, we present papers from the conference’s Experience Track, the Policy Track, the Business Track, the International Track, and the Workshops.

Providing adequate support to MOOC participants is often a challenging task due to massiveness of the learners’ population and the asynchronous communication among peers and MOOC practitioners. This workshop aims at discussing common learners’ problems reported in the literature and reflect on designing adequate feedback interventions with the use of learning data. Our aim is three-fold: a) to pinpoint MOOC aspects that impact the planning of feedback, b) to explore the use of learning data in designing feedback strategies, and c) to propose design guidelines for developing and delivering scaffolding interventions for personalized feedback in MOOCs. To do so, we will carry out hands-on activities that aim to involve participants in interpreting learning data and using them to design adaptive feedback. This workshop appeals to researchers, practitioners and MOOC stakeholders who aim to providing contextualized scaffolding. We envision that this workshop will provide insights for bridging the gap between pedagogical theory and practice when it comes to feedback interventions in MOOCs.

Producción Científica, Detecting learners who face problems in MOOCs usually poses difficulties due to the high instructor-learners ratio, the diversity of the population, and the asynchronous participation mode. Existing solutions mainly draw on self-reported problems in discussion forums and on dashboards displaying learners’ activity traces. However, these approaches cannot scale up easily or do not consider the course learning design. This paper presents a conceptual framework aimed at guiding MOOC instructors in the identification of potential learners' problems and indicators of such problems, considering the learning design of the course (e.g., types of activities, difficulty, etc.). An instrumental qualitative case study served for the evaluation and refinement of the framework. The results showed that the framework positively helped instructors to reflect on potential learners’ problems they had not considered beforehand, and to associate such problems with a set of indicators related to their learning designs., Junta de Castilla y León (grant VA257P18), Fondo Europeo de Desarrollo Regional - Agencia Estatal de Investigación (TIN2017-85179-C3-2-R, PID2020-112584RB-C32)

Producción Científica Detecting learners who face problems in MOOCs usually poses difficulties due to the high instructor-learners ratio, the diversity of the population, and the asynchronous participation mode. Existing solutions mainly draw on self-reported problems in discussion forums and on dashboards displaying learners’ activity traces. However, these approaches cannot scale up easily or do not consider the course learning design. This paper presents a conceptual framework aimed at guiding MOOC instructors in the identification of potential learners' problems and indicators of such problems, considering the learning design of the course (e.g., types of activities, difficulty, etc.). An instrumental qualitative case study served for the evaluation and refinement of the framework. The results showed that the framework positively helped instructors to reflect on potential learners’ problems they had not considered beforehand, and to associate such problems with a set of indicators related to their learning designs. Junta de Castilla y León (grant VA257P18) Fondo Europeo de Desarrollo Regional - Agencia Estatal de Investigación (TIN2017-85179-C3-2-R, PID2020-112584RB-C32)

Producción Científica The use of Conversational Agents (CAs) in computer-supported collaborative learning (CSCL) has shown promising results regarding students' productive dialogue and learning. Yet, limited work has explored the connection between the configuration of the CA behavior, the nature of the learning task, and the student behavior in authentic educational settings. In this work, we describe a pedagogical design space of CAs for collaborative learning composed of three dimensions: task design, domain model, and agent intervention strategies. We conduct an initial field study in a university classroom comparing two types of agent intervention strategies based on student participation, dialogue, and satisfaction. 54 university students worked in pairs in the same collaborative brainstorming task with a CA tool and were randomly assigned in two CA conditions with a) knowledge-based prompts to connect two domain concepts, b) social prompts to link their partners' contributions. The results show that students who received knowledge-based prompts significantly exchanged more messages with evidence of explicit reasoning and were more satisfied with the agent and their discussion during the task. Students from both conditions reported problems like the lack of context-awareness and timely interventions by the agent. We discuss the relation between the agent intervention strategies and the task design towards seeking design recommendations for CAs in CSCL. European Commission under project grant 588438-EPP-1-2017-1-EL-EPPKA2-KA Ministerio de Ciencia, Innovación y Univerisades (project grant TIN2017-85179-C3-2-R and TIN2014-53199-C3-2-R) Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. Project VA257P18)

Producción Científica Multimodal Learning Analytics (MMLA) uncovers the possibility to get a more holistic picture of a learning situation than traditional Learning Analytics, by triangulating learning evidence collected from multiple modalities. However, current MMLA solutions are complex and typically tailored to specific learning situations. In order to overcome this problem we are working towards an infrastructure that supports MMLA and can be adapted to different learning situations. As a first step in this direction, this paper analyzes four MMLA scenarios, abstracts their data processing activities and extracts a Data Value Chain to model the processing of multimodal evidence of learning. This helps us to reflect on the requirements needed for an infrastructure to support MMLA. European Union’s Horizon 2020 research and innovation programme (grant 669074) Ministerio de Ciencia, Innovación y Universidades - Fondo Europeo de Desarrollo Regional (projects TIN2017-85179-C3-2-R / TIN2014-53199- C3-2-R) Junta de Castilla y León - Fondo Europeo de Desarrollo Regional (project VA257P18) Comisión Europea (project 588438-EPP-1-2017-1-EL-EPPKA2- KA)

Producción Científica Massive Open Online Courses (MOOCs) learners often face problems that can result to dropout when proper and timely support is not received. In traditional classrooms, teachers can monitor learners’ progress and assist them timely. Yet in MOOCs, the massiveness and diversity of the learning population and the instructors’ high workload make impossible the assistance of each learner separately. The early identification of learners’ facing difficulties could facilitate instructors manage their limited time by assisting them and preventing a possible dropout. So far, techniques based on automatic predictive models are commonly applied to identify critical cohort of learners in these contexts. However, the existing research has several limitations that hinder the identification and support of those learners really involved in the course. The present doctoral thesis aims to address the issue of how to help MOOC instructors to identify and support learners facing difficulties during the course runtime. This paper presents a brief motivation, the general thesis’ schema including the objectives and envisioned contributions, the methodology followed and the current state of the thesis. Ministerio de Ciencia, Innovación y Univerisades (project grant TIN2017-85179-C3-2-R and TIN2014-53199-C3-2-R) Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. Project VA257P18) European Commission under project grant 588438-EPP-1-2017-1-EL-EPPKA2-KA

Producción Científica, Multimodal Learning Analytics (MMLA) uncovers the possibility to get a more holistic picture of a learning situation than traditional Learning Analytics, by triangulating learning evidence collected from multiple modalities. However, current MMLA solutions are complex and typically tailored to specific learning situations. In order to overcome this problem we are working towards an infrastructure that supports MMLA and can be adapted to different learning situations. As a first step in this direction, this paper analyzes four MMLA scenarios, abstracts their data processing activities and extracts a Data Value Chain to model the processing of multimodal evidence of learning. This helps us to reflect on the requirements needed for an infrastructure to support MMLA., European Union’s Horizon 2020 research and innovation programme (grant 669074), Ministerio de Ciencia, Innovación y Universidades - Fondo Europeo de Desarrollo Regional (projects TIN2017-85179-C3-2-R / TIN2014-53199- C3-2-R), Junta de Castilla y León - Fondo Europeo de Desarrollo Regional (project VA257P18), Comisión Europea (project 588438-EPP-1-2017-1-EL-EPPKA2- KA)