Learning analytics to assess students’ behavior with scratch through clickstream

The construction of knowledge through computational practice requires to teachers a substantial amount of time and effort to evaluate programming skills, to understand and to glimpse the evolution of the students and finally to state a quantitative judgment in learning assessment. This suposes a huge problem of time and no adecuate intime feedback to students while practicing programming activities. The field of learning analytics has been a common practice in research since last years due their great possibilities in terms of learning improvement. Such possibilities can be a strong positive contribution in the field of computational practice such as programming. In this work we attempt to use learning analytics to ensure intime and quality feedback through the analysis of students behavior in programming practice. Hence, in order to help teachers in their assessments we propose a solution to categorize and understand students’ behavior in programming activities using business technics such as web clickstream. Clickstream is a technique that consists in the collection and analysis of data generated by users. We applied it in learning programming environments to study students behavior to enhance students learning and programming skills. The results of the work supports this business technique as useful and adequate in programming practice. The main finding showns a first taxonomy of programming behaviors that can easily be used in a classroom. This will help teachers to understand how students behave in their practice and consequently enhance assessment and students’ following-up to avoid examination failures.Peer ReviewedPostprint (published version

Learning Analytics and Recommender Systems toward Remote Experimentation

This paper presents a process based on learning analytics and recom- mender systems to provide suggestions to students about remote laboratories ac- tivities in order to scaffold their performance. For this purpose, the records of remote experiments from the VISIR project were analyzed taking into account one of its installations. Each record is composed of requests containing the as- sembled circuits and the configurations of the measuring equipment, as well as the response provided by the measurement server that evaluates whether a par- ticular request can be performed or not. With the log analysis, it was possible to obtain information in order to determine some initial statistics and provide clues about the student’s behavior during the experiments. Using the concept of rec- ommendation, a service is proposed through request analysis and returns to the students more precise information about possible mistakes in the assembly of circuits or configurations. The process as a whole proves consistent in what re- gards its ability to provide suggestions to the students as they conduct the exper- iments. Furthermore, with the log, relevant information can be offered to teach- ers, thus assisting them in developing strategies to positively impact student’s learning.info:eu-repo/semantics/publishedVersio

Learning Analytics and Recommender Systems toward Remote Experimentation

This paper presents a process based on learning analytics and recom- mender systems to provide suggestions to students about remote laboratories ac- tivities in order to scaffold their performance. For this purpose, the records of remote experiments from the VISIR project were analyzed taking into account one of its installations. Each record is composed of requests containing the as- sembled circuits and the configurations of the measuring equipment, as well as the response provided by the measurement server that evaluates whether a par- ticular request can be performed or not. With the log analysis, it was possible to obtain information in order to determine some initial statistics and provide clues about the student’s behavior during the experiments. Using the concept of rec- ommendation, a service is proposed through request analysis and returns to the students more precise information about possible mistakes in the assembly of circuits or configurations. The process as a whole proves consistent in what re- gards its ability to provide suggestions to the students as they conduct the exper- iments. Furthermore, with the log, relevant information can be offered to teach- ers, thus assisting them in developing strategies to positively impact student’s learning.info:eu-repo/semantics/publishedVersio

Exploring the synergies between gamification and data collection in higher education

In recent years, gamification techniques have been gaining popularity in all kind of educational scenarios, helping students improve their learning process by fostering engagement and attention. Implementing gamification aspects in a course can also provide an opportunity to gather student data that would not have been available otherwise. This paper describes a data gathering process in the context of a university course, as a work-in-progress. Among these data there is information regarding the participation of students in quizzes presented as games in the classroom. These quizzes combined questions covering course con-tents, as well as some regarding self-regulated learning habits. The main advantage observed was a high student participation in the quizzes. As a result, this gamification approach proved to be a more effective way to gather student data compared to other methods applied in previous academic years, which often failed due to many students ignoring optional activities.Xunta de Galicia | Ref. ED431B 2020/3

Hopscotch 2.0: an enhanced version of the Model for the Generation of Research Designs in Social Sciences and Education

The development of educational research designs might be daunting for novice researchers that have to make choices among the plethora of philosophical frameworks, research traditions, and different methods existing in the field. In this article we describe the process followed to formally evaluate Hopscotch, a model and a web-tool created by the author in 2015 to help novice researchers in the generation of solid and well-informed qualitative research designs. To do so, a responsive evaluation based on case study methods was conducted. The obtained results led us build a new 2.0 version of the Hopscotch\u27s web-tool overcoming the limitations identified by the users of the initial version launched in 2015. Among others, the web-tool now offers the possibility of creating not only qualitative research designs, but also quantitative and mixed-methods designs. The system also allows for the collaboration and sharing of research designs among its users. It also provides the option of generating visual representations of the key components of six different types of qualitative research designs, four types of quantitative research designs, and four types of mixed-methods research designs. This newly developed tool based on the principles of Open Science, aims at helping novice researchers to deeply reflect on the research designs for their dissertations, research studies and even capstone projects

Addressing Fine-Grained Variability in User-Centered Software Product Lines: A Case Study on Dashboards

Software product lines provide a theoretical framework to generate and customize products by studying the target domain and by capturing the commonalities among the potential products of the family. This domain knowledge is subsequently used to implement a series of configurable core assets that will be systematically reused to obtain products with different features to match particular user requirements. Some kind of interactive systems, like dashboards, require special attention as their features are very fine-grained. Having the capacity of configuring a dashboard product to match particular user requirements can improve the utility of these products by providing the support to users to reach useful insights, in addition to a decrease in the development time and an increase in maintainability. Several techniques for implementing features and variability points in the context of SPLs are available, and it is important to choose the right one to exploit the SPL paradigm benefits to the maximum. This work addresses the materialization of fine-grained variability in SPL through code templates and macros, framed in the particular domain of dashboards

Exploring the Problems Experienced by Learners in a MOOC Implementing Active Learning Pedagogies

Although Massive Open Online Courses (MOOCs) have been reported as an effective educational tool offering numerous opportunities in online learning, the high dropout rates and the lack of learners' motivation are factors concerning researchers and instructors. The one-size-fits-all instructional approach that most courses follow, failing to address the individual needs of learners, has been seen as their weakest point. Recent efforts focus on the inclusion of active learning pedagogies in MOOCs to stimulate the interaction among the participants and to keep them engaged. However, taking into account that in these massive contexts the learners face several issues while trying to keep up with the course, the incorporation of active learning strategies may introduce additional problems to the learning process. This study explores the problems that learners experienced in a MOOC implementing collaboration and gamification strategies. As the results reveal, the introduction of collaborative learning activities can generate additional problems to learners and for that reason, a careful design and a proper scaffolding is needed in an early stage to overcome the problems that will occur. No significant problems were reported regarding the implementation of gamification elements

Sistema informática de apoyo a las analíticas para el aprendizaje (learning analytics) para entornos educativos on-line

Los cursos MOOC (Massive Open Online Courses) son una herramienta de aprendizaje en constante crecimiento en oferta (número de cursos ofertados), demanda (número de estudiantes matriculados) y relevancia en la disciplina del aprendizaje en línea. En este contexto, se han detectado potenciales factores relacionados con la insuficiente interacción estudiante-profesor y el aislamiento que sienten los estudiantes que pueden afectarles negativamente y que precisan de un amplio estudio para ser analizados, comprendidos y, si se requiere, mitigados o solucionados. Si este objetivo se cumple, se habrá conseguido dar un gran paso para hacer del aprendizaje con este tipo de recursos un proceso eficiente y útil para cualquier estudiante que desee utilizarlos. Además, los MOOC recogen gran cantidad de información en relación con la interactividad del estudiante con sus recursos, con lo que son una gran fuente de datos en este campo. La disciplina que facilita poder afrontar el problema planteado es la Analítica del Aprendizaje o Learning Analytics. El presente Trabajo de Fin de Grado busca avanzar dentro de la comprensión de este tipo de sensaciones. Para ello, se propone el desarrollo, implementación y explotación de una plataforma escalable (edX-LIMS: Learning Intervention Monitoring Service for edX MOOCs) que permita realizar un proceso de acompañamiento de los estudiantes de un MOOC. Dicha plataforma proporciona periódicamente a los estudiantes de un MOOC información visual en un Dashboard o Panel de aprendizaje en la Web, mostrándoles su progreso y participación en el MOOC. Esta información proporcionada es parte de una estrategia de intervención sobre el aprendizaje de estos estudiantes. El sistema ofrece también a los instructores de MOOC acceso a un Dashboard o Panel de Instructores en la Web que muestra el interés en este servicio por parte de los estudiantes y, por lo tanto, facilita la evaluación del éxito o el fracaso de la estrategia de intervención

Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning

[EN] Data analysis is a key process to foster knowledge generation in particular domains or fields of study. With a strong informative foundation derived from the analysis of collected data, decision-makers can make strategic choices with the aim of obtaining valuable benefits in their specific areas of action. However, given the steady growth of data volumes, data analysis needs to rely on powerful tools to enable knowledge extraction. Information dashboards offer a software solution to analyze large volumes of data visually to identify patterns and relations and make decisions according to the presented information. But decision-makers may have different goals and, consequently, different necessities regarding their dashboards. Moreover, the variety of data sources, structures, and domains can hamper the design and implementation of these tools. This Ph.D. Thesis tackles the challenge of improving the development process of information dashboards and data visualizations while enhancing their quality and features in terms of personalization, usability, and flexibility, among others. Several research activities have been carried out to support this thesis. First, a systematic literature mapping and review was performed to analyze different methodologies and solutions related to the automatic generation of tailored information dashboards. The outcomes of the review led to the selection of a modeldriven approach in combination with the software product line paradigm to deal with the automatic generation of information dashboards. In this context, a meta-model was developed following a domain engineering approach. This meta-model represents the skeleton of information dashboards and data visualizations through the abstraction of their components and features and has been the backbone of the subsequent generative pipeline of these tools. The meta-model and generative pipeline have been tested through their integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully integrated with other meta-model to support knowledge generation in learning ecosystems, and as a framework to conceptualize and instantiate information dashboards in different domains. In terms of the practical applications, the focus has been put on how to transform the meta-model into an instance adapted to a specific context, and how to finally transform this later model into code, i.e., the final, functional product. These practical scenarios involved the automatic generation of dashboards in the context of a Ph.D. Programme, the application of Artificial Intelligence algorithms in the process, and the development of a graphical instantiation platform that combines the meta-model and the generative pipeline into a visual generation system. Finally, different case studies have been conducted in the employment and employability, health, and education domains. The number of applications of the meta-model in theoretical and practical dimensions and domains is also a result itself. Every outcome associated to this thesis is driven by the dashboard meta-model, which also proves its versatility and flexibility when it comes to conceptualize, generate, and capture knowledge related to dashboards and data visualizations

Towards a Technological Ecosystem to Provide Information Dashboards as a Service: A Dynamic Proposal for Supplying Dashboards Adapted to Specific Scenarios

[EN]Data are crucial to improve decision-making and obtain greater benefits in any type of activity. However, the large amount of information generated by new technologies has made data analysis and knowledge generation a complex task. Numerous tools have emerged to facilitate this generation of knowledge, such as dashboards. Although dashboards are useful tools, their effectiveness can be affected by poor design or by not taking into account the context in which they are placed. Therefore, it is necessary to design and create custom dashboards according to the audience and data domain. This paper presents an application of the software product line paradigm and the integration of this approach into a web service to allow users to request source code for customized information dashboards. The main goal is to introduce the idea of creating a holistic ecosystem of different services to craft and integrate information visualizations in a variety of contexts. One of the contexts that can be especially favored by this approach is the educational context, where learning analytics, data analysis of student performance, and didactic tools are becoming very relevant. Three different use cases of this approach are presented to illustrate the benefits of the developed generative service