Personalised feedback in MOOCs is often prevented by the non-scalability of traditional approaches and the lack of pedagogical grounding of the current learning analytics (LA) solutions. One way to tackle such limitations is the adoption of a participatory approach through the active positioning of MOOC instructors in the design and development of LA solutions. Nonetheless, there is a scarcity of empirical proposals supporting these approaches in MOOCs. To that end, the current paper presents e-FeeD4Mi, a web-based tool, incorporating a set of catalogues, recommendations and a process, that aim to support instructors in the design of human-centred LA-informed feedback. We conducted an evaluative study with 6 MOOC instructors who employed the tool into their course designs to assess e-FeeD4Mi usefulness, usability and associated workload. The evidence gathered permitted us to understand how e-FeeD4Mi support the design of human-centred LA-based feedback in MOOCs. Altogether, the results showed a good usability and participants' satisfaction regarding the use of the tool for shaping personalised feedback. At the same time, participants offered ideas for further tool enrichment. This study expands upon the current body of empirical research on the human-centred approaches in the design of LA-driven interventions in MOOCs.

The protection of students’ privacy in learning analytics (LA) applications is critical for cultivating trust and effective implementations of LA in educational environments around the world. However, students’ privacy concerns and how they may vary along demographic dimensions that historically influence these concerns have yet to be studied in higher education. Gender differences, in particular, are known to be associated with people's information privacy concerns, including in educational settings. Building on an empirically validated model and survey instrument for student privacy concerns, their antecedents and their behavioral outcomes, we investigate the presence of gender differences in students’ privacy concerns about LA. We conducted a survey study of students in higher education across five countries (N = 762): Germany, South Korea, Spain, Sweden and the United States. Using multiple regression analysis, across all five countries, we find that female students have stronger trusting beliefs and they are more inclined to engage in self-disclosure behaviors compared to male students. However, at the country level, these gender differences are significant only in the German sample, for Bachelor's degree students, and for students between the ages of 18 and 24. Thus, national context, degree program, and age are important moderating factors for gender differences in student privacy concerns.

This paper presents the H2O Learn (Hybrid and Human-Oriented Learning) project, a coordinated research project funded by the Spanish Research Agency, which just started in 2021 and will last for three years. The main goal of the H2O Learn project is to build Trustworthy and Human-Centered Learning Analytics (TaHCLA) solutions to support human stakeholders when designing, orchestrating and (self-,socially- or co-) regulating learning in Hybrid Learning (HL). The contributions of H2O Learn consider key requirements for trustworthy Artificial Intelligence (AI), as defined by the European Commission: 1) fostering human (i.e., teachers, learners…) agency; 2) enabling transparency of the Learning Analytics (LA) systems; 3) seeking socio-emotional and inclusive wellbeing; and 4) promoting accountability by enabling the assessment of algorithms and design processes.

The provision of feedback in Massive Open Online Courses (MOOCs) is a non-trivial task for instructors. Current Learning Analytics (LA) solutions provide opportunities for scaling up such feedback strategies. Yet, most of these solutions lack course contextualization and guidance for MOOC instructors. This paper presents a conceptual framework and a web-based tool aimed at supporting instructors in the design and enactment of feedback strategies within MOOC learning designs. Both the framework and the tool are based on a structured process and include a set of catalogues facilitating the reflection and (re)design of these strategies. Additionally, this paper reports an evaluation of the framework catalogues and the usefulness of the framework process in an online workshop (N=11). As part of this ongoing work, we also explored the experience of participants with the tool. The results are positive, highlighting the usefulness of the framework catalogues, especially for non-experienced MOOC instructors. We envision that the initial evidence gathered can contribute to research on conceptual and technological tools supporting feedback design, and to promote the synergy among LA and human-centered approaches in MOOC settings.

This paper presents SLEek, an ontology for the context-aware recommendation of learning activities in Smart Learning Environments (SLEs). SLEek creates an actor-artifact network that is especially suitable for the context-aware recommendation of activities across spaces in formal and informal contexts. SLEek implementation reuses vocabularies from the Web of Data and is currently used in a dataset of 17K learning activities.

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.

Peer reviews offer many learning benefits. Understanding students’ engagement in them can help design effective practices. Although learning analytics can be effective in generating such insights, its application in peer reviews is scarce. Theory can provide the necessary foundations to inform the design of learning analytics research and the interpretation of its results. In this paper, we followed a theory-based learning analytics approach to identifying students’ engagement patterns in a peer review activity facilitated via a web-based tool called Synergy. Process mining was applied on temporal learning data, traced by Synergy. The theory about peer review helped determine relevant data points and guided the top-down approach employed for their analysis: moving from the global phases to regulation of learning, and then to micro-level actions. The results suggest that theory and learning analytics should mutually relate with each other. Mainly, theory played a critical role in identifying a priori engagement patterns, which provided an informed perspective when interpreting the results. In return, the results of the learning analytics offered critical insights about student behavior that was not expected by the theory (i.e., low levels of co-regulation). The findings provided important implications for refining the grounding theory and its operationalization in Synergy.

Collaborative learning can improve the pedagogical effectiveness of MOOCs. Group formation, an essential step in the design of collaborative learning activities, can be challenging in MOOCs given the scale and the wide variety in such contexts. We discuss the need for considering the behaviours of the students in the course to form groups in MOOC contexts, and propose a grouping approach that employs homogeneity in terms of students’ engagement in the course. Two grouping strategies with different degrees of homogeneity are derived from this approach, and their impact to form successful groups is examined in a real MOOC context. The grouping criteria were established using student activity logs (e.g. page-views). The role of the timing of grouping was also examined by carrying out the intervention once in the first and once in the second half of the course. The results indicate that in both interventions, the groups formed with a greater degree of homogeneity had higher rates of task-completion and peer interactions, Additionally, students from these groups reported higher levels of satisfaction with their group experiences. On the other hand, a consistent improvement of all indicators was observed in the second intervention, since student engagement becomes more stable later in the course.

Gamification strategies have been proposed to mitigate student disengagement and dropouts in massive online environments, due to the positive results shown by these strategies at lower scales. Among various gamification strategies, redeemable rewards have been identified as an effective element to intrinsically motivate students and increase their engagement in educational settings, including MOOCs. Yet, effective design, implementation and enactment of this gamification strategy in MOOC contexts might face new challenges, given the unique characteristics of these learning settings such as massiveness. As an attempt to help teachers use redeemable rewards in MOOCs, this paper analyzes the characteristics of MOOCs that influence its integration and presents a proposal of a system supporting the design, implementation and enactment of such rewards. The envisioned system is illustrated by a scenario that describes the main features of this system for teachers and students.

SNOLA is a Thematic Network of Excellence recognized by the Spanish Ministry of Economy and Competitiveness composed of the main Spanish researchers in the field of learning analytics. This network emerged in 2013 focusing mainly on the technology underlying learning analytics development, but also interested on the integration of other views and disciplines that give the network a wider scope. During these years, SNOLA has carry out numerous actions with the goal of promoting the collaboration on this field, the diffusion of knowledge and initiatives, the collection of resources and the provision of broad support. Remarkable examples of these efforts have been LASI Spain events, workshops at TEEM conferences or the numerous webinars. In this case, the EDUCON 2018 has given us the opportunity to organize this special session where 5 papers have been selected for presentation.

Collaborative learning flow patterns (CLFPs) encode solutions to recurrent pedagogical problems, which have been successfully applied to the design of learning experiences. However, the pedagogical knowledge encoded in these patterns has seldom been exploited in learning analytics (LA). This paper analyzes four of the most common CLFPs to extract the intrinsic constraints that lead to a successful collaborative learning activity, and use them to enhance existing LA solutions. To understand the added value of applying such codified knowledge in LA, we present evidence from five authentic case studies in which such constraints aided university teachers in monitoring complex collaborative scripts. The results not only illustrate quantitatively such added value but also unearth qualitative benefits, such as raising practitioners' awareness about how the current state of activities may affect future phases of the script.

In blended learning scenarios, evidence needs to be gathered from digital and physical spaces to obtain a more complete view of the teaching and learning processes. However, these scenarios are highly heterogeneous, and the varying data sources available in each particular context can condition the accuracy, relevance, interpretability and actionability of the Learning Analytics (LA) solutions, affecting also the user's sense of agency and trust in such solutions. To aid stakeholders in making use of learning analytics, we propose a process to involve teachers in customizing multimodal LA (MMLA) solutions, adapting them to their particular blended learning situation (e.g., identifying relevant data sources and metrics). Since measuring the added value of adopting an LA solution is not straightforward, we also propose a concrete method for doing so. The results obtained from two case studies in authentic, blended computer-supported collaborative learning settings show an improvement in the sensitivity and F1 scores of the customized MMLA solution. Aside from these quantitative improvements, participant teachers reported both an increment in the effort involved, but also increased relevance, understanding and actionability of the results.

Despite the educational affordances that ubiquitous learning has shown, it is still hampered by several orchestration difficulties. One of these difficulties is that teachers lose awareness of what the students perform across the multiple technologies and spaces involved. Monitoring can help in such awareness, and it has been highly explored in face-to-face and blended learning. Nevertheless, in ubiquitous learning environments, monitoring has been usually limited to activities taking place in a specific type of space (e.g., outdoors). In this article, we propose a monitoring system for ubiquitous learning, which was evaluated in three authentic studies, supporting the participants in the affordable monitoring of learning situations involving web, augmented-physical, and 3D virtual world spaces. The work carried out also helped identify a set of guidelines, which are expected to be useful for researchers and technology developers aiming to provide participants’ support in ubiquitous learning environments.

Recent technological advances in mobile devices enable the connection of classrooms with other virtual and physical spaces. Some approaches aim at helping teachers carry out learning situations across such spaces. However, these proposals tend to be isolated from other activities in teachers’ current common practices, and do not allow teacher-controlled flexibility of what students do during the enactment. Aiming to overcome such limitations, the Bucket-Server is a system that enables teachers to include learning buckets in their learning situations: containers of learning artifacts generated and/or consumed across-spaces by students during the enactment. Teachers create learning buckets at design time, configuring them with constraints to regulate the degree of freedom offered to the students. These learning buckets can be integrated into multiple existing technologies used in different educational spaces (e.g., web, physical and 3D virtual world spaces), thus helping embed buckets in the teachers’ current common practices.

Since the emergence of learning analytics in North America, researchers and practitioners have worked to develop an international community. The organization of events such as SoLAR Flares and LASI Locals, as well as the move of LAK in 2013 from North America to Europe, has supported this aim. There are now thriving learning analytics groups in North American, Europe and Australia, with smaller pockets of activity emerging on other continents. Nevertheless, much of the work carried out outside these forums, or published in languages other than English, is still inaccessible to most people in the community. This panel, organized by Europe's Learning Analytics Community Exchange (LACE) project, brings together researchers from five European countries to examine the field from European perspectives. In doing so, it will identify the benefits and challenges associated with sharing and developing practice across national boundaries.

Communities of Practice (CoP) are nowadays widely used for implementing collaborative learning approaches, under the scope of contemporary learning theories. Several communication and/or collaboration web modules are used in order to support the organizational structure and facilitate the operational purpose of such communities. Following the same approach, Scientific Networks (SN) are widely used as a common method of expanding research and sharing academic resources. The need for appropriate tools in order to support the members of a CoP or a SN and further facilitate their collaborative tasks, or even resulted learning is often highlighted by the literature. In this paper we propose the use of Interaction Analysis indicators for that matter. Our experience from the implementation of such indicators, supporting the Kaleidoscope Network of Excellence is presented.

Interaction Analysis (IA) is an essential element for formative evaluation, self-regulation and other important functions, especially in Computer Supported Collaborative Learning (CSCL) scenarios. However, several problems have impeded its regular use in real environments, in spite of the significant advancement of the field. This paper analyzes the main problems and alternatives encountered in the process of effectively supporting IA in CSCL, based on lessons learned from the authors’ active involvement in this field during the last decade. Thus, it proposes a decoupled architecture together with the use of a standard common data format for interaction as main components of a new framework for a global design process that will support practitioners, researchers and technology providers in a sustainable and transparent way.

Data flow between tools cannot be specified using the current IMS Learning Design specification (LD). Nevertheless by specifying this data flow between tools, several degrees of activity automation may augment the system intervention opportunities for data flow management. Service automation, data flow automation and data flow validation may enhance the continuity of the learning design realization, reduce the student's cognitive load and obtain system-support for error prone situations. In this paper a novel approach based on the composition of LD and a standard workflow technology is proposed. Unlike other current approaches, our approach maintains interoperability with both LD and workflow standards. Then an architectural solution based on the composition approach is presented.

Interaction analysis has become a basic function in the field of collaborative learning, as a means for supporting both regulation and evaluation of collaborative learning processes. In spite of the fact that these processes rely on the same basic functionalities, there is a lack of proposals or systems that integrate them. The most outstanding difference between the tools that support either of the approaches is that they are oriented to different types of users. Then, an interaction analysis tool able to adapt to different roles would be able to perform both functionalities. This paper presents an experience performed in an authentic learning scenario using interaction analysis for supporting regulation for students and evaluation for teachers adapting its functionality depending on analysis needs of teacher and learners that participate in the activity. A first evaluation carried out shows that the participants assess positively this experience and consider the information received easy to understand, reliable, useful and capable to modify their way of collaboration.