An analysis of students’ behaviour in a Learning Management System through Process Mining

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe exponential growth and transformation of the Internet and information technology in recent years led to the development of several analytical tools. As is the case with process mining, it emerged to fulfill the need to extract and analyze information from event logs by representing it in the form of process models. Process mining is an acclaimed tool and proved crucial in several areas, from healthcare to manufacturing and finance. Nevertheless, and despite the crucial role of digital systems in supporting learning activities and generating large amounts of data about learning processes, limited research focused on process mining applied to the educational context. Therefore, the aim of this dissertation is to apply a process-oriented approach and demonstrate the applicability of process mining techniques to explore and analyze students’ behavior and interaction patterns, based on data collected from Moodle, the widely used Learning Management System. We cover definitions of process mining, education, and a detailed search of the existing literature on educational process mining during this work. Furthermore, the paper analyzes and discusses the findings of the study that combines process mining techniques, specifically process discovery implanted in the Disco tool, with cluster analysis. Through the application of these two techniques, it was possible to recognize the relationship between the students’ behavior registered in the process models and the success of the students in the course, along with the general and specific information about the students’ learning paths. Besides, we obtained findings that allow us to predict the group of students at risk of failing. Finally, with the analysis of these results, we were able to provide improvement proposals and recommendations to enhance the learning experience

Educational Process Mining based on Moodle courses: a review of literature

With the prevalence of E-Learning, it is important to analyze how students progress in this environment. These systems collect data about the students’ learning path, and Process Mining (PM) can provide a detailed model of this path. Based on the analysis of ten Educational Process Mining (EPM) case studies involving Moodle event logs, this article aims to contribute a literature review on EPM’s research. Beyond a theoretical introduction to PM and its implications for educational data, the review concludes on what PM tools and techniques are used, as well as the challenges faced in practice. The technical options include software, process discovery algorithms and representation models. These results aim to create a list of available options for future EPM endeavors, in addition to a list of issues to consider in future research involving Moodle

Designing Open Educational Resources through Knowledge Maps to enhance Meaningful learning

This paper demonstrates some pedagogical strategies for developing Open Educational Resources (OERs) using the knowledge mapping tool Compendium. It also describes applications of Knowledge Maps to facilitate meaningful learning by focusing on specific OER examples. The study centres on the OpenLearn project, a large scale online environment that makes a selection of higher education learning resources freely available via the internet. OpenLearn, which is supportedby William and Flora Hewlett Foundation, was launched in October 2006 and in the two year period of its existence hasreleased over 8,100 learning hours of the OU's distance learning resources for free access and modification by learnersand educators under the Creative Commons license. OpenLearn also offers three knowledge media tools: Compendium(knowledge mapping software), MSG (instant messaging application with geolocation maps) and FM (web-based videoconferencing application). Compendium is a software tool for visual thinking, used to connect ideas, concepts, arguments, websites and documents. There are numerous examples of OERs that have been developed and delivered by institutions across the world, for example, MIT, Rice, Utah State, Core, Paris Tech, JOCW. They present a wide variety of learning materials in terms of styles as well as differing subject content. Many such offerings are based upon original lecture notes, hand-outs and other related papers used in face-to-face teaching. Openlearn OERs, however, are reconstructed from original self study distance learning materials developed at the Open University and from a vast academic catalogue of materials. Samples of these “units” comprise a variety of formats: text, images, audio and video. In this study, our findings illustratethe benefits of sharing some OER content through knowledge maps, the possibility of condensing high volumes of information,accessing resources in a more attractive way, visualising connections between diverse learning materials, connecting new ideas to familiar references, organising thinking and gaining new insights into subject specific content

A soft computing decision support framework for e-learning

Tesi per compendi de publicacions.Supported by technological development and its impact on everyday activities, e-Learning and b-Learning (Blended Learning) have experienced rapid growth mainly in higher education and training. Its inherent ability to break both physical and cultural distances, to disseminate knowledge and decrease the costs of the teaching-learning process allows it to reach anywhere and anyone. The educational community is divided as to its role in the future. It is believed that by 2019 half of the world's higher education courses will be delivered through e-Learning. While supporters say that this will be the educational mode of the future, its detractors point out that it is a fashion, that there are huge rates of abandonment and that their massification and potential low quality, will cause its fall, assigning it a major role of accompanying traditional education. There are, however, two interrelated features where there seems to be consensus. On the one hand, the enormous amount of information and evidence that Learning Management Systems (LMS) generate during the e-Learning process and which is the basis of the part of the process that can be automated. In contrast, there is the fundamental role of e-tutors and etrainers who are guarantors of educational quality. These are continually overwhelmed by the need to provide timely and effective feedback to students, manage endless particular situations and casuistics that require decision making and process stored information. In this sense, the tools that e-Learning platforms currently provide to obtain reports and a certain level of follow-up are not sufficient or too adequate. It is in this point of convergence Information-Trainer, where the current developments of the LMS are centered and it is here where the proposed thesis tries to innovate. This research proposes and develops a platform focused on decision support in e-Learning environments. Using soft computing and data mining techniques, it extracts knowledge from the data produced and stored by e-Learning systems, allowing the classification, analysis and generalization of the extracted knowledge. It includes tools to identify models of students' learning behavior and, from them, predict their future performance and enable trainers to provide adequate feedback. Likewise, students can self-assess, avoid those ineffective behavior patterns, and obtain real clues about how to improve their performance in the course, through appropriate routes and strategies based on the behavioral model of successful students. The methodological basis of the mentioned functionalities is the Fuzzy Inductive Reasoning (FIR), which is particularly useful in the modeling of dynamic systems. During the development of the research, the FIR methodology has been improved and empowered by the inclusion of several algorithms. First, an algorithm called CR-FIR, which allows determining the Causal Relevance that have the variables involved in the modeling of learning and assessment of students. In the present thesis, CR-FIR has been tested on a comprehensive set of classical test data, as well as real data sets, belonging to different areas of knowledge. Secondly, the detection of atypical behaviors in virtual campuses was approached using the Generative Topographic Mapping (GTM) methodology, which is a probabilistic alternative to the well-known Self-Organizing Maps. GTM was used simultaneously for clustering, visualization and detection of atypical data. The core of the platform has been the development of an algorithm for extracting linguistic rules in a language understandable to educational experts, which helps them to obtain patterns of student learning behavior. In order to achieve this functionality, the LR-FIR algorithm (Extraction of Linguistic Rules in FIR) was designed and developed as an extension of FIR that allows both to characterize general behavior and to identify interesting patterns. In the case of the application of the platform to several real e-Learning courses, the results obtained demonstrate its feasibility and originality. The teachers' perception about the usability of the tool is very good, and they consider that it could be a valuable resource to mitigate the time requirements of the trainer that the e-Learning courses demand. The identification of student behavior models and prediction processes have been validated as to their usefulness by expert trainers. LR-FIR has been applied and evaluated in a wide set of real problems, not all of them in the educational field, obtaining good results. The structure of the platform makes it possible to assume that its use is potentially valuable in those domains where knowledge management plays a preponderant role, or where decision-making processes are a key element, e.g. ebusiness, e-marketing, customer management, to mention just a few. The Soft Computing tools used and developed in this research: FIR, CR-FIR, LR-FIR and GTM, have been applied successfully in other real domains, such as music, medicine, weather behaviors, etc.Soportado por el desarrollo tecnológico y su impacto en las diferentes actividades cotidianas, el e-Learning (o aprendizaje electrónico) y el b-Learning (Blended Learning o aprendizaje mixto), han experimentado un crecimiento vertiginoso principalmente en la educación superior y la capacitación. Su habilidad inherente para romper distancias tanto físicas como culturales, para diseminar conocimiento y disminuir los costes del proceso enseñanza aprendizaje le permite llegar a cualquier sitio y a cualquier persona. La comunidad educativa se encuentra dividida en cuanto a su papel en el futuro. Se cree que para el año 2019 la mitad de los cursos de educación superior del mundo se impartirá a través del e-Learning. Mientras que los partidarios aseguran que ésta será la modalidad educativa del futuro, sus detractores señalan que es una moda, que hay enormes índices de abandono y que su masificación y potencial baja calidad, provocará su caída, reservándole un importante papel de acompañamiento a la educación tradicional. Hay, sin embargo, dos características interrelacionadas donde parece haber consenso. Por un lado, la enorme generación de información y evidencias que los sistemas de gestión del aprendizaje o LMS (Learning Management System) generan durante el proceso educativo electrónico y que son la base de la parte del proceso que se puede automatizar. En contraste, está el papel fundamental de los e-tutores y e-formadores que son los garantes de la calidad educativa. Éstos se ven continuamente desbordados por la necesidad de proporcionar retroalimentación oportuna y eficaz a los alumnos, gestionar un sin fin de situaciones particulares y casuísticas que requieren toma de decisiones y procesar la información almacenada. En este sentido, las herramientas que las plataformas de e-Learning proporcionan actualmente para obtener reportes y cierto nivel de seguimiento no son suficientes ni demasiado adecuadas. Es en este punto de convergencia Información-Formador, donde están centrados los actuales desarrollos de los LMS y es aquí donde la tesis que se propone pretende innovar. La presente investigación propone y desarrolla una plataforma enfocada al apoyo en la toma de decisiones en ambientes e-Learning. Utilizando técnicas de Soft Computing y de minería de datos, extrae conocimiento de los datos producidos y almacenados por los sistemas e-Learning permitiendo clasificar, analizar y generalizar el conocimiento extraído. Incluye herramientas para identificar modelos del comportamiento de aprendizaje de los estudiantes y, a partir de ellos, predecir su desempeño futuro y permitir a los formadores proporcionar una retroalimentación adecuada. Así mismo, los estudiantes pueden autoevaluarse, evitar aquellos patrones de comportamiento poco efectivos y obtener pistas reales acerca de cómo mejorar su desempeño en el curso, mediante rutas y estrategias adecuadas a partir del modelo de comportamiento de los estudiantes exitosos. La base metodológica de las funcionalidades mencionadas es el Razonamiento Inductivo Difuso (FIR, por sus siglas en inglés), que es particularmente útil en el modelado de sistemas dinámicos. Durante el desarrollo de la investigación, la metodología FIR ha sido mejorada y potenciada mediante la inclusión de varios algoritmos. En primer lugar un algoritmo denominado CR-FIR, que permite determinar la Relevancia Causal que tienen las variables involucradas en el modelado del aprendizaje y la evaluación de los estudiantes. En la presente tesis, CR-FIR se ha probado en un conjunto amplio de datos de prueba clásicos, así como conjuntos de datos reales, pertenecientes a diferentes áreas de conocimiento. En segundo lugar, la detección de comportamientos atípicos en campus virtuales se abordó mediante el enfoque de Mapeo Topográfico Generativo (GTM), que es una alternativa probabilística a los bien conocidos Mapas Auto-organizativos. GTM se utilizó simultáneamente para agrupamiento, visualización y detección de datos atípicos. La parte medular de la plataforma ha sido el desarrollo de un algoritmo de extracción de reglas lingüísticas en un lenguaje entendible para los expertos educativos, que les ayude a obtener los patrones del comportamiento de aprendizaje de los estudiantes. Para lograr dicha funcionalidad, se diseñó y desarrolló el algoritmo LR-FIR, (extracción de Reglas Lingüísticas en FIR, por sus siglas en inglés) como una extensión de FIR que permite tanto caracterizar el comportamiento general, como identificar patrones interesantes. En el caso de la aplicación de la plataforma a varios cursos e-Learning reales, los resultados obtenidos demuestran su factibilidad y originalidad. La percepción de los profesores acerca de la usabilidad de la herramienta es muy buena, y consideran que podría ser un valioso recurso para mitigar los requerimientos de tiempo del formador que los cursos e-Learning exigen. La identificación de los modelos de comportamiento de los estudiantes y los procesos de predicción han sido validados en cuanto a su utilidad por los formadores expertos. LR-FIR se ha aplicado y evaluado en un amplio conjunto de problemas reales, no todos ellos del ámbito educativo, obteniendo buenos resultados. La estructura de la plataforma permite suponer que su utilización es potencialmente valiosa en aquellos dominios donde la administración del conocimiento juegue un papel preponderante, o donde los procesos de toma de decisiones sean una pieza clave, por ejemplo, e-business, e-marketing, administración de clientes, por mencionar sólo algunos. Las herramientas de Soft Computing utilizadas y desarrolladas en esta investigación: FIR, CR-FIR, LR-FIR y GTM, ha sido aplicadas con éxito en otros dominios reales, como música, medicina, comportamientos climáticos, etc.Postprint (published version

Digital Youth in Digital Schools: Literacy, Learning, and all That Noise

Educational researchers, practitioners, and policymakers face increasing pressure to determine the role of new media in America\u27s schools. Despite widespread agreement that digital media are transforming how young people learn and communicate, little evidence exists that digital media have markedly changed how we do school. In the last decade, extensive research focused on increasing access to and integrating technology in schools, suggesting that digital media support new contexts for knowledge development. Yet little empirical research examined how adolescents actually engage digital media in their everyday lives in schools. In a two-year study in a Philadelphia public high school, I researched what it means for literacy learning when youth attend a digitally comprehensive school, and what happens when we shift our focus away from new media as discrete tools, and instead consider them as part of the social and cultural fabric of doing school. I followed and learned from tenth-graders in English and History classes taught by the same teacher. Through the theoretical frames of socio-cultural constructions of literacy, youth culture, and media ecologies, I examine three interrelated dimensions significant to adolescents\u27 experiences as students in what I call a new culture of literacy learning: (1) Noise, (2) Navigation, and (3) Negotiation. Noise refers to the intense, multilayered, and highly saturated nature of this context. Navigation represents the range of moves, tools, and roles that adolescents engaged to accomplish their intellectual work in these classrooms. Negotiation illustrates how adolescents leveraged digital media to participate with others. The findings can support the work of teachers to redesign classrooms that harness digital media to cultivate adolescents\u27 literacies and foster meaningful participation. This study raises questions for educators, researchers, and policymakers about how to assess literacies that are multimodal, fluid, and collaborative. My results also can contribute to conversations about designing new ways to study adolescents\u27 literacies within and across the dynamic contexts associated with digital media. Finally, this study suggests that we will need new theoretical frameworks to understand adolescents\u27 literacy work in schools

HYPE-On-Campus: A Pilot Online Learning Program Designed for Helping Youth on the Path to Employment (HYPE)

Helping Youth on the Path to Employment (HYPE) is a research-based program housed at the University of Massachusetts Medical School. The program aims to provide support and services to young adults with mental health conditions as they identify meaningful career paths and achieve goals for sustained employment. HYPE has developed a multi-stage trial program called Hype-On-Campus (HOC) that will expand the HYPE model to colleges and universities across the United States beginning with the fall semester of 2020. Graduate-level Student Practitioners, selected to participate in the initial HOC feasibility trial, will be enrolled in an HOC online distance training program to assimilate the HYPE model and provide support and services to these students. With the learning content of the new HOC online training program being duplicated from a previously designed in-person HYPE training series, the design and development of this project required specific focus on the instructional needs of the HYPE training staff. The primary need identified was to establish a productive means for scheduled synchronous training sessions with the related secondary need of populating and organizing all learning content within the Learning Management System (LMS). A successful online training program ran from mid-October to early December 2020 with two participating universities and an anticipated continuation/expansion will begin in early January 2021

From a Pedagogical Experience of a Photography Course on Architectural and Public Space into a Research Project focused on Communication of Public Space's State and Evolution, Architecture and Urban Cultures

This paper will explain how, from a blended teaching experience1 in CFM (Communication,Photography and Multimedia) Course at FAUP (Faculty of Architecture, University of Porto) that lead to several didactic experiences and research projects on architecture and public space (seminars,workshops, publications and editorial projects), CCRE (Centre for Spatial Communication and Representation) Research Group has been able to interrelate a set of significant experiences and results coming from the former work andactivities with the on-going R&D project DARC, more specifically with its City Spaces|Culture module, directed towards the communication of content related to cultural events and spaces as architectural heritage, identity and historical places. All these projects, together with CCRE's communication philosophy, its online platforms and physical outputs - such as publications and exhibitions - constitute a blend between direct and "indirect" pedagogy strategies on architecture, public space and the way they are experienced by the general public. As we will explain in this paper, all these research projects were fundamental to assess the possibilities and limits of our pedagogic/didactic philosophy, as well as to trigger new and more effective ways of communicating and sharing substantial information between students and the general public. This paper will therefore explore the blended approach that we've taken in CFM classes, how it translated into the various platforms that we've used for it and for other parallel activities, and how this knowledge is being applied to the on-going DARC research project and to its set of communication and presentation operators. The goal will be to verify how the combined operators that we've used in our platform(ranging from geo-referenced maps, images and videos - focused on visual literacy - and social networking - focused on communication) allowed for a deeper involvement and participation of both academic community and general public