This paper presents the basis of the GENIE Learn project, a coordinated three-year research project funded by the Spanish Research Agency. The main goal of GENIE Learn is to improve Smart Learning Environments (SLEs) for Hybrid Learning (HL) support by integrating Generative Artificial Intelligence (GenAI) tools in a way that is aligned with the preferences and values of human stakeholders. This article focuses on analyzing the problems of this research context, as well as the affordances that GenAI can bring to solve these problems, but considering also the risks and challenges associated with the use of GenAI in education. The paper also details the objectives, methodology, and work plan, and expected contributions of the project in this context.

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.

Cultural Heritage learners can highly benefit from ubiquitous learning approaches that connect in-classroom activities with active on-site learning opportunities. However, teachers face the problem that the current landscape of technological support for learning Cultural Heritage mostly consists of mobile applications for informal contexts, completely decoupled from curricular activities. This paper presents the results of an exploratory-sequential mixed-methods study that focuses on the feasibility, for non-technical expert teachers, of repurposing an existing mobile application in formal Cultural Heritage ubiquitous learning situations. Such feasibility is explored from the perspective of the orchestration metaphor. More specifically, we used the “5+3 aspects orchestration framework” to understand the orchestration challenges of four ubiquitous learning situations about Cultural Heritage carried out in two secondary schools. These situations involved five teachers and 139 students who used a mobile application called Casual Learn for several weeks. The results of the study suggest that, after a brief training, teachers can design, enact, and orchestrate ubiquitous learning situations that involve both in-classroom and out-classroom activities. The teachers were able to transfer to the students most of the orchestration load of out-classroom activities. The results also showed that the flow of learning artifacts and information between in- and out-classroom activities is a critical issue that needs to be addressed by developers of ubiquitous learning technological solutions.

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.

New generations should learn about the Cultural Heritage (CH) that surrounds them to value and preserve it. Common strategies for learning CH include lectures, textbooks, and also cultural visits to CH sites. Cultural visits allow very rich learning experiences but are quite challenging to prepare and carry out. This paper presents Cultural Heritage Educational Semantic Tool (CHEST), a web application that enables teachers to identify monuments of interest and annotate them with learning tasks. The created learning tasks will be carried out by students using an existing application, named Casual Learn, that makes use of a dataset of learning tasks created from information available as Linked Open Data. CHEST overcomes an identified limitation of Casual Learn by giving the teachers the possibility of enriching the existing semantic dataset of monuments and associated learning tasks with customized content while hiding the technical complexity of Semantic Web technologies. Additionally, CHEST promotes the reusability and adaptation of learning tasks by different teachers. An early version of CHEST was used by two secondary school teachers in real-life situations to design customized tasks for their students. These tasks were then carried out by the students via Casual Learn. These teachers gave a high usability score to CHEST and were interviewed to obtain further feedback. They were keen to publish new tasks, share them with other teachers, and reuse tasks from other teachers.

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.

Learners have ubiquitous informal learning opportunities, but it is difficult to take advantage from them and relate them to their formal education. The connection of formal and informal learning is one of the aims of SLEs, but how to do it is still a question. This paper explores such connection by integrating the mobile application Casual Learn to the SLE SCARLETT and it discusses the challenges faced in such integration.

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.

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.

With the aim of supporting instructional designers in setting up collaborative learning activities in MOOCs, this paper derives prediction models for student participation in group discussions. The salient feature of these models is that they are built using only data prior to the learning activity, and can thus provide actionable predictions, as opposed to post-hoc approaches common in the MOOC literature. Some learning design scenarios that make use of this actionable information are illustrated.

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.

Linked Data is a methodology to publish data on the Web that facilitates the federation of data sources. Following this methodology, a huge quantity of data is being published on the Web of Data, but very little is currently consumed by educational applications. This paper proposes to overcome this situation by relating educational vocabularies to the ones already used on the Web of Data. This way, educational applications can take advantage of the data published on the Web even if such data was not published for educational purposes. As a case study, the vocabulary used by an educational tool search system, called Ontoolsearch, is related to ICT tool descriptions published by DBpedia and Factforge. Evaluation results show that 2905 tool descriptions can be retrieved from the Web of Data to be consumed by Ontoolsearch. In addition, most of these descriptions have a similar quality to the ones manually published by an educational tool expert.

This paper proposes a novel method to detect anomalies in network traffic, based on a nonrestricted α-stable first-order model and statistical hypothesis testing. To this end, we give statistical evidence that the marginal distribution of real traffic is adequately modeled with α-stable functions and classify traffic patterns by means of a Generalized Likelihood Ratio Test (GLRT). The method automatically chooses traffic windows used as a reference, which the traffic window under test is compared with, with no expert intervention needed to that end. We focus on detecting two anomaly types, namely floods and flash-crowds, which have been frequently studied in the literature. Performance of our detection method has been measured through Receiver Operating Characteristic (ROC) curves and results indicate that our method outperforms the closely-related state-of-the-art contribution described in. All experiments use traffic data collected from two routers at our university-a 25,000 students institution-which provide two different levels of traffic aggregation for our tests (traffic at a particular school and the whole university). In addition, the traffic model is tested with publicly available traffic traces. Due to the complexity of α-stable distributions, care has been taken in designing appropriate numerical algorithms to deal with the model.

The integration of existing educational computer-based tools is a current research trend aimed at increasing the range of learning situations that can be supported by most widely adopted Virtual Learning Environments (VLEs). Nevertheless, due to the technological and functional heterogeneity of both tools and VLEs, there is an integration cost that must be assumed and that varies depending on several design issues. This paper identifies the issues to be tackled when designing a VLE or a tool and the effects that the different alternatives may have in the integration cost. Besides, the paper discusses the advantages and drawbacks of previous research proposals when dealing with such issues. The final goal is to understand the impact of design decisions in the chances for acceptance of the adopted integration approaches.

Many studies show the advantages of using a learning design approach to help teachers and instructional designers to create technology enhanced collaborative learning activities. However, some of these studies highlight lacks in such approach. This paper addresses one of them: the representation of the flow of data in the learning activity models. A generic specification is proposed that aims at pointing out the conceptual needs that should be taken into account in the languages to improve the approaches.

In line with the suggestions deriving from the new problem-solving approaches to ID, this paper points out the need of integrating formalized descriptions of good practices, such as Collaborative Learning Flow Patterns (CLFP) with other representations able to support unskilled designers in selecting and instantiating scripts, setting up effective collaborative groups and their “social structures”. Based on these premises, the paper analyzes a real course designed with the backing of a CLFP-based authoring tool for the creation of CSCL scripts and describes the support offered by the Social Structures Representations (SSRs) in selecting and reusing scripts.

Advances in educational technology have enabled new forms of organizing and managing collaborative learning environments. One of the currents trends in research on Technology Enhanced Learning is the application of computer-interpretable scenarios, such as those formalized with IMS Learning Design (IMSLD), for the description and deployment of Computer Supported Collaborative Learning (CSCL) scripts. The life-cycle of IMS-LD scripts involves several phases, from the conception to their enactment in actual learning situations. In the context of collaborative learning, group/roles management and composition have special relevance, and are in fact some of the tasks that the teacher must accomplish before students can follow the scripts. This paper presents a software tool (InstanceCollage) that implements a pattern-based approach, which has been previously validated for the task of script design. In this way, it aims at facilitating the population of roles and groups generally described in IMS-LD scripts.

With the aim of facilitating teachers the design of units of learning and assessment, this paper proposes the use of different educational pattern types. Depending on the type of patterns, the resulting didactic materials are specified using IMS Learning Design or IMS Question and Test Interoperability extended with integrated Web2.0-like services.

Collaborative learning models are widely used in educational institutions. These models require a high interaction level among students and are mainly oriented towards in-class scenarios. But when collaborative models are deployed in a distant scenario, user expressiveness is significantly reduced thus creating a gap that hinders the effectiveness of this collaboration. A computer-supported model provides a set of tools to compensate for the distant scenario and reduce this gap. This paper presents the issues and solutions derived from the design and deployment of a complex collaborative model in a distant scenario. The course structure was captured using the Learning Design specification, and an architecture based on virtual network computing was used to provide the required collaborative tools. The course was included as part of a regular undergraduate program in three higher educational institutions.

This paper presents the design of a new simulation environment based on the well-known ns-2 network simulator with two significant characteristics. First, it enables the possibility of running a large number of simulations even if the computational and storage resources locally available are scarce. Second, it provides a number of features in order to facilitate collaboration among students. In this way, the proposed simulation environment enables the realization of many collaborative learning scenarios with pedagogical interest for computer networks education.

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.

The success of groupware software largely depends on its capability for being reused in different collaborative scenarios without requiring significant software development efforts or user technical involvement. During the last decade several toolkits, frameworks, and specialized middleware software layers have been proposed as potential solutions to the groupware reusability problem. Those solutions cover common groupware functionality such as group formation and management, group awareness, shared workspace management, etc. This paper is focused on the reusable support to one of these common functionalities: the so-called object-level coordination that deals with multiple participants' sequential or simultaneous access to the same set of shared objects in a collaborative setting. The paper compares existing object-level coordination proposals and analyzes their capabilities and level of reuse. Starting from that analysis, and using a bottom-up approach, the paper also proposes a high-level reference model that identifies the desirable set of required functional elements, as well as their relationships, that reusable object-level coordination support should contain. The purpose of this reference model is twofold: to be used as a comparison framework for analyzing existing or yet-to-come object-level coordination building blocks; and, to be used as a starting point for developing new object-level coordination solutions (or redesigning existing ones) satisfying the whole set of requirements implicitly collected in the model. In addition to detailing the proposed reference model, the paper also illustrates and discusses both potential ways of using it

This paper describes an approach for modeling and implementing a collaborative learning situation, which is part of a real lifelong learning scenario in astronomy. We adopt and slightly adjust IMS Learning Design for computationally representing the script so that collaborative tools can be satisfactorily employed. We use Collage editor, which enables the re-use of general structures proposed by Collaborative Learning Flow Patterns, for authoring the script and Gridcole system for executing it. The paper also shows the log-files produced by the employed tools. The teacher can use these log-files to monitor the evolution of the process for evaluation and regulation purposes.

This paper proposes collaborative learning flow patterns (CLFPs), which represent best practices in collaborative learning structuring, as a central element of a kind of bi-directional linkage that facilitates that teachers can play the role of designers influencing in the behavior of CSCL (computer-supported collaborative learning) technological solutions. Additionally, this paper describes a technological approach for achieving such a scenario. That approach is based on the Collage authoring tool that provides CLFPs as IMS LD templates and the Gridcole system, capable of interpreting the resulting CLFP-based LDs and integrating the service-oriented tools needed to support the (collaborative) learning activities as prescribed in those LDs

The use of patterns in e-learning is being recently proposed with different purposes and scopes. This paper provides a unifying view of several representative proposals in order to situate and introduce the types of patterns that can be used for generating collaboration scripts that are suitable of being computationally represented and interpreted by learning management systems (LMSs). The paper also presents a hierarchical structure that relates these patterns according to their focus and granularity. The structure shows how the different types of patterns are complementary and should be considered in the process of creating a script. In addition to authoring tools and LMSs, there are other types of tools in which the application of patterns may be useful. This paper points out these tools locating them along the "life cycle" of the scripts

CSCL systems can benefit from using a grid, since it offers a common infrastructure allowing an extended pool of resources that can provide supercomputing capabilities as well as specific hardware resources. Adopting a service oriented architecture such as OGSA can further benefit CSCL systems, enabling increased flexibility to adapt and reuse learning software. However, proliferation of services enormously challenges the search of appropriate ones. Common service discovery mechanisms, such as the Index Service or UDDI, perform poor searches since they rely on keyword matching and do not properly describe service functionality. To address these difficulties, ontologies can be used to formally describe services allowing for semantic searches. This paper proposes an ontology that describes learning services using educators abstractions in order to support the search. This way, service providers can describe their services in terms of the ontology, while educators can search for them using domain concepts. The integration of this enhanced service search mechanism in Gridcole, a real grid-based CSCL system, is described to illustrate its performance.

Research in the computer supported collaborative learning (CSCL) field is currently tackling three important problems which are closely related: difficult integration of CSCL tools, scarce software reuse and technification. This work presents a Grid Collaborative Learning Environment (GRIDCOLE) that combines IMS learning design (IMS-LD) and Open Grid Services Architecture (OGSA) technologies in order to address these issues. More specifically, GRIDCOLE allows users with no technical skills easy integration and use of applications that effectively support collaborative learning processes. IMS-LD is employed to formally describe the design of these collaborative learning processes while applications are built using tools provided by third-party suppliers as OGSA-compliant Grid services. With this aim, the GRIDCOLE architecture and its main functionalities are introduced. A simple prototype is also presented in order to show the feasibility of the most important concepts introduced in this paper. Furthermore, a real collaborative learning scenario in which we plan to validate GRIDCOLE is introduced.

Collaborative learning patterns (CLPs) are detailed descriptions of best practices in collaborative learning. These patterns provide a way for a representation of key aspects of CSCL (computer-supported collaborative learning) that is easy to understand by software developers. To formalize these CLPs we have focused our attention on IMS learning design (IMS-LD). IMS-LD provides a means of expressing many different pedagogical approaches (including collaborative learning), however we have found some limitations in reflecting learning experiences that are group-based. Although this specification supports multiple roles in a learning activity, it is not possible to specify how they are going to interact. This paper points out this deficiency and proposes an extension of IMS-LD. The process that can be followed in order to obtain a unit of learning based on a CLP is illustrated with an example.

This paper describes the design and implementation of an SNMP-based management system for the CORBA-based electronic brokerage application that has been implemented within the scope of the ABS (Architecture for Information Brokerage Service) ACTS Project. The management system is based on the use of an SNMP-CORBA gateway that uses the JIDM (joint inter-domain management) specification translation algorithms and incorporates ideas from the services and facilities contained in the SNMP part of the JIDM interaction translation documents. The paper also focuses on the way the management information has been instrumented within the CORBA application trying to make it transparent with respect to the developers of the functional aspects of the managed application.