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 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.

This PhD research explores how Smart Learning Environments can support the connection between formal and informal learning. Thanks to the information offered by learning systems and tools such as Virtual Learning Environments, mobile and Internet of Things devices, SLEs can characterize the individual learning needs and context of students to provide them with personalized support across the boundaries of the classroom. In a similar fashion to approaches related with mobile learning, the connection offered by SLEs can help students to reflect on learning concepts in real scenarios, but also adapting the offered resources to their progression and performance throughout the learning situation. However, existing attempts in SLEs face difficulties regarding the preparation of possible interventions by teachers or the understanding of the formal learning situation. This work attempts to overcome this limitations with the usage of the learning design.

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.

Predicting the decrease of students' engagement in typical MOOC tasks such as watching lecture videos or submitting assignments is key to trigger timely interventions in order to try to avoid the disengagement before it takes place. This paper proposes an approach to build the necessary predictive models using students' data that becomes available during a course. The approach was employed in an experimental study to predict the decrease of three different engagement indicators in a MOOC. The results suggest its feasibility with values of area under the curve for different predictors ranging from 0.718 to 0.914.

Virtual Learning Environments (VLEs) such as Moodle or Sakai are now commonplace in many educational settings, but the limited set of built-in tools (e.g. chats or forums) is often mentioned as a drawback. While successful solutions to integrate Software as a Service (SaaS) tools are reported in the literature (e.g. IMS Basic LTI and GLUE!), there are other non-SaaS tools with potential for education that cannot be integrated in VLEs. These include single-user standalone tools that are run on local computers without a web-based interface (e.g. Matlab), but also virtual machines and laboratories that are common in Computer Science-related disciplines. This paper addresses the integration of these non-SaaS tools in VLEs, proposing an architecture to help teachers to select and configure non-SaaS tools using existing integration approaches from VLEs, but that also relies on cloud infrastructures for provisioning and deploying these tools transparently to the end users.

Flood events are registered worldwide, bringing great human and material losses. Warning systems play a key role in minimizing these impacts. The CEOPS/FURB is responsible for warning system of Itajaí's Valley. This system demand services in real time of hydrologic forecasting and simulation researches. The goal of this work is to propose a model based on Cloud Computing to meet the requirements of CEOPS.

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.

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.

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.

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

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.

In this paper we present the design of fuzzy adaptive system ellipsoid ARTMAP (FASEAM), a novel neural architecture based on ellipsoid ARTMAP (EAM) that is equipped with concepts utilized in the fuzzy adaptive system ART (FASART) architecture. More specifically, we derive a new category choice function appropriate for EAM categories that is non-constant in a category's representation region. Additionally, we augment the EAM category description with a centroid vector, whose learning rate is inversely proportional to the number of training patterns accessing the category. Finally, we demonstrate the merits of our design choices by comparing FASART, EAM and FASEAM in terms of generalization performance and final structural complexity on a set of classification problems.

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.

This paper describes the integration of the Grid topic within a computer architecture engineering course. Students are engaged in a project of design and evaluation of computing systems for a specific customer/market sector. The contents of the course and the case studies have been adapted in order to motivate the study of the Grid. In particular two case studies have been analyzed: one concerns an Internet-based music distribution enterprise, while the other is related to the assembly of genome sequences. A preliminary analysis of this experience shows that most of the students chose to evaluate the suitability of the Grid for their case study, and were able to argue on the benefits and drawbacks of this and other alternatives. In the future, we plan to set up a Grid-based learning environment so the Grid is not only the object of study, but also a tool for learning.

A new architecture called muARTMAP is proposed to impact a category proliferation problem present in Fuzzy ARTMAP. Under a probabilistic setting, it seeks a partition of the input space that optimizes the mutual information with the output space, but allowing some training error, thus avoiding overfitting. It implements an inter-ART reset mechanism that permits handling exceptions correctly, thus using few categories, especially in high dimensionality problems. It compares favorably to Fuzzy ARTMAP and Boosted ARTMAP in several synthetic benchmarks, being more robust to noise than Fuzzy ARTMAP and degrading less as dimensionality increases. Evaluated on a real-world task, the recognition of handwritten characters, it performs comparably to Fuzzy ARTMAP, while generating a much more compact rule set.

A new architecture, called MicroARTMAP, is proposed to impact the category proliferation problem present in Fuzzy ARTMAP. It handles probabilistic information through the optimization of the mutual information between the input and output spaces, but allowing a small training error, thus avoiding overfitting. While reducing the number of categories used by Fuzzy ARTMAP, it holds several desirable properties, such as a correct treatment of exceptions and a fast algorithm, as opposed to other approaches like BARTMAP. In addition, it is shown that MicroARTMAP is less sensitive than Fuzzy ARTMAP with respect to the the pattern presentation order, and that it degrades less if the training set is noisy.