Quality in MOOCs: Surveying the Terrain

The purpose of this review is to identify quality measures and to highlight some of the tensions surrounding notions of quality, as well as the need for new ways of thinking about and approaching quality in MOOCs. It draws on the literature on both MOOCs and quality in education more generally in order to provide a framework for thinking about quality and the different variables and questions that must be considered when conceptualising quality in MOOCs. The review adopts a relativist approach, positioning quality as a measure for a specific purpose. The review draws upon Biggs’s (1993) 3P model to explore notions and dimensions of quality in relation to MOOCs — presage, process and product variables — which correspond to an input–environment–output model. The review brings together literature examining how quality should be interpreted and assessed in MOOCs at a more general and theoretical level, as well as empirical research studies that explore how these ideas about quality can be operationalised, including the measures and instruments that can be employed. What emerges from the literature are the complexities involved in interpreting and measuring quality in MOOCs and the importance of both context and perspective to discussions of quality

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

Identification of Affective States in MOOCs: A Systematic Literature Review

Massive Open Online Courses (MOOCs) are a type of online coursewere students have little interaction,  no instructor, and in some cases, no deadlines to finisch assignments. For this reason, a better understanding of student affection in MOOCs is importantant could have potential to open new perspectives for this type of course. The recent popularization of tools, code libraries and algorithms for intensive data analysis made possible collect data from text and interaction with the platforms, which can be used to infer correlations between affection and learning. In this context, a bibliographical review was carried out, considering the period between 2012 and 2018, with the goal of identifying which methods are being to identify affective states. Three databases were used: ACM Digital Library, IEEE Xplore and Scopus, and 46 papers were found. The articles revealed that the most common methods are related to data intensive techinques (i.e. machine learning, sentiment analysis and, more broadly, learning analytics). Methods such as physiological signal recognition andself-report were less frequent

Impact of Transparency in the Teamwork Development through Cloud Computing

[EN]Active educational methodologies promote students to take an active role in their own learning, enhance cooperative work, and develop a collective understanding of the subject as a common learning area. Cloud Computing enables the learning space to be supported while also revolutionizing it by allowing it to be used as a link between active methodology and students’ learning activities. A Cloud Computing system is used in conjunction with an active methodology to recognize and manage individual, group, and collective evidence of the students’ work in this research. The key hypothesis shown in this work is that if evidence management is made clear and evidence is consistently and gradually presented to students, their level of involvement will increase, and their learning outcomes will improve. The model was implemented in a university subject of a first academic year using the active Flipped Classroom methodology, and the individual, group and collective evidence is constantly worked with throughout the implementation of a teamwork method

Indicators for enhancing learners’ engagement in massive open online courses: A systematic review

Massive open online courses (MOOCs) have paved a new learning path for the 21st-century world. The potential to reach a massive geographically dispersed audience is one of the major advantages of MOOCs. Moreover, they can be offered on a self-paced and self-regulated basis and have become an integral part of lifelong learning, especially in workplaces. However, one persistent problem is the lack of learners’ engagement. A harmonisation of studies providing a holistic view into aggregating indicators for enhancing learners’ engagement in MOOCs is lacking. The coronavirus pandemic has accelerated MOOC adoption, and learners’ engagement in MOOCs has become even more essential for the success of this educational innovation. We examine the existing literature to derive indicators important for enhancing learners’ engagement in MOOC learning environments. Using a systematic approach, 83 empirical studies were examined, and 10 indicators were identified as important considerations for enhancing learners’ engagement while designing MOOCs—from initiatives for individual learners to platform and instructional design perspectives. We also present a table describing these indicators and offer a structured discussion on each one. We believe the results provide guidelines for MOOC designers and instructors, educational policymakers, higher education institutions, and MOOC engagement researchers.Peer reviewe

Capturing high-level requirements of information dashboards' components through meta-modeling

[EN]Information dashboards are increasing their sophistication to match new necessities and adapt to the high quantities of generated data nowadays.These tools support visual analysis, knowledge generation, and thus, are crucial systems to assist decision-making processes.However, the design and development processes are complex, because several perspectives and components can be involved.Tailoringcapabilities are focused on providing individualized dashboards without affecting the time-to-market through the decrease of the development processes' time. Among the methods used to configure these tools, the software product lines paradigm and model-driven development can be found. These paradigms benefit from the study of the target domain and the abstraction of features, obtaining high-level models that can be instantiated into concrete models. This paper presents a dashboard meta-model that aims to be applicable to any dashboard. Through domain engineering, different features of these tools are identified and arranged into abstract structuresand relationships to gain a better understanding of the domain. The goal of the meta-model is to obtain a framework for instantiating any dashboard to adapt them to different contexts and user profiles.One of the contexts in which dashboards are gaining relevance is Learning Analytics, as learning dashboards are powerful tools for assisting teachers and students in their learning activities.To illustrate the instantiation process of the presented meta-model, a small example within this relevant context (Learning Analytics) is also provided

Collaborative Learning in Virtual Learning Environment using Social Network Analysis

Distance learning is supposed to provide not only independent learning activities but also two-way interaction and collaborative learning based on inquiry model to control students' learning. E-learning is one of the platform to implement two-way interaction and inquiry model. Universitas Terbuka (UT) is the first open distance education university in Indonesia. This paper will study and visualize participation in discussion and interaction on the virtual learning environment (VLE) UT using Social Network Analysis (SNA). This paper also used a questionnaire to detect knowledge sharing behavior (KSB) in the Collaborative Learning Environment (CLE) based on Social Presence, Perceived Online Attachment Motivation, Perceived Online Relationship Commitment, and Altruism indicators. For the perception of students and evaluation about e-learning UT, we use Yilmaz's Transactional Distance. The results of the measurement network in forum discussion can detect that the tutors are most important, and who are mostly reply to other student's posts or which students' post are mostly commented by others. Personal/Informal network shows that students tend to interact only with students on same location registered region office