Formação para o empreendedorismo nas indústrias criativas com base no blended-learning

Atas das II Jornadas Ensino do Empreendedorismo, realizadas em Coimbra, a 6 setembro 2016

O blended-learning aplicado à formação para o empreendedorismo nas indústrias criativas

O Projeto ‘CINet – Redes para o empreendedorismo nas indústrias criativas’ (Programa Leonardo DaVinci) baseia-se na experiência de Lace Market em Nottingham, uma comunidade de empreendedores criativos independentes que tem crescido significativamente devida à forte dinâmica das suas redes de colaboração. Neste sentido, o projeto começou por identificar as vertentes-chave de transferência de inovação com base no ambiente de aprendizagem formal e informal local, com o intuito de desenvolver uma rede de empreendedores criativos, adaptada à realidade encontrada nos países participantes: Portugal, Espanha e Grécia. Com esta base foi possível conceber e implementar o “Programa CINet de aceleração em rede do Empreendedorismo nas Indústrias Criativas” que foi promovido através do lançamento de uma formação desenvolvida na plataforma Moodle da Universidade Aberta, e assente na modalidade de blended-learning (bLearning). Consideramos que a experiência obtida com a conceção e desenvolvimento deste programa apresentou importantes desafios e traduziu-se em significativas vantagens cujo relato e sistematização permitirão enriquecer modelos pedagógicos de aprendizagem centrados nos formandos e nas comunidades colaborativas. A disponibilidade de recursos vídeo e de texto simples de apreender e virados para a prática e a dinamização de fora assíncronos possibilita aos formandos tirar partido da flexibilidade espácio-temporal do eLearning. A possibilidade de partilhar informação e experiências com os colegas, bem como de clarificar dúvidas nos fora permitiu uma experiência da aprendizagem colaborativa em ambiente Moodle. As sessões presenciais neste particular constituíram um complemento útil ao eLearning na medida em que permitiram apresentar o guia de curso, explicar o modelo pedagógico, marcar o ritmo do curso, esclarecer dúvidas relativas os objectivos de cada atividade em cada módulo, contactar de forma síncrona com empreendedores das indústrias criativas de outros países e com empreendedores incubados na DNA Cascais e refletir de forma crítica sobre vertentes-chave específicas do modelo de negócio dos formandos. Neste sentido, elas foram um dos elementos cruciais do desenvolvimento da rede, até porque, a ambientação à plataforma Moodle e a ‘apreensão’ e ‘absorção’ do modelo pedagógico afigura-se como um dos principais desafios, face a formandos que estão habituados a ambientes presenciais de aprendizagem. A ligação deste tipo de cursos a instituições vocacionadas para o apoio a empreendedores revela-se crucial dado que na construção do projeto empresarial deve ser incutida logo de início a predisposição para a ligação ao mercado e às diversas dimensões do ecossistema empreendedor.

Machine Learning methods for hepatocellular malignancies segmentation and MVI prediction

The aim of this thesis project is to automatically localize HCC tumors in the human liver and subsequently predict if the tumor will undergo microvascular infiltration (MVI), the initial stage of metastasis development. The input data for the work have been partially supplied by Sant'Orsola Hospital and partially downloaded from online medical databases. Two Unet models have been implemented for the automatic segmentation of the livers and the HCC malignancies within it. The segmentation models have been evaluated with the Intersection-over-Union and the Dice Coefficient metrics. The outcomes obtained for the liver automatic segmentation are quite good (IOU = 0.82; DC = 0.35); the outcomes obtained for the tumor automatic segmentation (IOU = 0.35; DC = 0.46) are, instead, affected by some limitations: it can be state that the algorithm is almost always able to detect the location of the tumor, but it tends to underestimate its dimensions. The purpose is to achieve the CT images of the HCC tumors, necessary for features extraction. The 14 Haralick features calculated from the 3D-GLCM, the 120 Radiomic features and the patients' clinical information are collected to build a dataset of 153 features. Now, the goal is to build a model able to discriminate, based on the features given, the tumors that will undergo MVI and those that will not. This task can be seen as a classification problem: each tumor needs to be classified either as “MVI positive” or “MVI negative”. Techniques for features selection are implemented to identify the most descriptive features for the problem at hand and then, a set of classification models are trained and compared. Among all, the models with the best performances (around 80-84% ± 8-15%) result to be the XGBoost Classifier, the SDG Classifier and the Logist Regression models (without penalization and with Lasso, Ridge or Elastic Net penalization).

Development of an institutional framework to guide transitions into enhanced blended learning in higher education

The rapidly changing digital landscape is having a significant influence on learning and teaching. Our study assesses the response of one higher education institution (HEI) to the changing digital landscape and its transition into enhanced blended learning, which seeks to go beyond the early implementation stage to make the most effective use of online learning technologies to enhance the student experience and student learning outcomes. Evidence from a qualitative study comprising 20 semi-structured interviews, informed by a literature review, has resulted in the development of a holistic framework to guide HEIs transitioning into enhanced blended learning. The proposed framework addresses questions relating to the why (change agents), what (institutional considerations), how (organisational preparedness) and who (stakeholders) of transitions into enhanced blended learning. The involvement of all stakeholder groups is essential to a successful institutional transition into enhanced blended learning.

Blended Learning Essentials Project reports

Blended Learning Essentials is a free suite of online courses for the Vocational Education and Training sector to promote effective practice and pedagogy in blended learning. The courses were run and supported from 2016 onwards by a consortium of partners funded by Ufi Charitable Trust. The lead partners were the University of Leeds, the UCL Institute of Education, the Association for Learning Technology (ALT), and FutureLearn. The Blended Learning Essentials (BLE) courses are for anyone working in further education, skills training, vocational education, workplace learning, lifelong learning or adult education, who wants to learn about and implement blended learning. The project reports cover engagement and marketing work undertaken during this project phase to reach the courses’ key audiences and work undertaken during this project phase to develop and promote the pathways to accreditation available to course participants. These reports are shared by ALT as a project partner on behalf of the BLE Project under a CC-BY-NC-ND licence. �

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.

A B-learning strategy for Therapeutics at the Bachelor Level

Background Information:The incorporation of distance learning activities by institutions of higher education is considered an important contribution to create new opportunities for teaching at both, initial and continuing training. In Medicine and Nursing, several papers illustrate the adaptation of technological components and teaching methods are prolific, however, when we look at the Pharmaceutical Education area, the examples are scarce. In that sense this project demonstrates the implementation and assessment of a B-Learning Strategy for Therapeutics using a “case based learning” approach. Setting: Academic Pharmacy Methods:This is an exploratory study involving 2nd year students of the Pharmacy Degree at the School of Allied Health Sciences of Oporto. The study population consists of 61 students, divided in groups of 3-4 elements. The b-learning model was implemented during a time period of 8 weeks. Results:A B-learning environment and digital learning objects were successfully created and implemented. Collaboration and assessment techniques were carefully developed to ensure the active participation and fair assessment of all students. Moodle records show a consistent activity of students during the assignments. E-portfolios were also developed using Wikispaces, which promoted reflective writing and clinical reasoning. Conclusions:Our exploratory study suggests that the “case based learning” method can be successfully combined with the technological components to create and maintain a feasible online learning environment for the teaching of therapeutics.

B-learning quality: dimensions, criteria and pedagogical approach

Measuring the quality of a b-learning environment is critical to determine the success of a b-learning course. Several initiatives have been recently conducted on benchmarking and quality in e-learning. Despite these efforts in defining and examining quality issues concerning online courses, a defining instrument to evaluate quality is one of the key challenges for blended learning, since it incorporates both traditional and online instruction methods. For this paper, six frameworks for quality assessment of technological enhanced learning were examined and compared regarding similarities and differences. These frameworks aim at the same global objective: the quality of e-learning environment/products. They present different perspectives but also many common issues. Some of them are more specific and related to the course and other are more global and related to institutional aspects. In this work we collected and arrange all the quality criteria identified in order to get a more complete framework and determine if it fits our b-learning environment. We also included elements related to our own b-learning research and experience, acquired during more than 10 years of experience. As a result we have create a new quality reference with a set of dimensions and criteria that should be taken into account when you are analyzing, designing, developing, implementing and evaluating a b-learning environment. Besides these perspectives on what to do when you are developing a b-learning environment we have also included pedagogical issues in order to give directions on how to do it to reach the success of the learning. The information, concepts and procedures here presented give support to teachers and instructors, which intend to validate the quality of their blended learning courses.

Discovering the campus together: a mobile and computer-based learning experience

One of the most relevant difficulties faced by first-year undergraduate students is to settle into the educational environment of universities. This paper presents a case study that proposes a computer-assisted collaborative experience designed to help students in their transition from high school to university. This is done by facilitating their first contact with the campus and its services, the university community, methodologies and activities. The experience combines individual and collaborative activities, conducted in and out of the classroom, structured following the Jigsaw Collaborative Learning Flow Pattern. A specific environment including portable technologies with network and computer applications has been developed to support and facilitate the orchestration of a flow of learning activities into a single integrated learning setting. The result is a Computer-Supported Collaborative Blended Learning scenario, which has been evaluated with first-year university students of the degrees of Software and Audiovisual Engineering within the subject Introduction to Information and Communications Technologies. The findings reveal that the scenario improves significantly students’ interest in their studies and their understanding about the campus and services provided. The environment is also an innovative approach to successfully support the heterogeneous activities conducted by both teachers and students during the scenario. This paper introduces the goals and context of the case study, describes how the technology was employed to conduct the learning scenario, the evaluation methods and the main results of the experience.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Learning and Experiential Outcomes of Face-to-Face Versus Online Communications Courses

Higher education is rapidly trending toward the implementation of online (OL) courses and a blended facilitation style that incorporates both OL and face-to-face (FTF) classes. Though previous studies have explored the benefits and pitfalls of OL and blended learning formats from institutional, teacher, and student perspectives, scant research has examined learning outcomes for OL and FTF courses sharing identical content. This study used an explanatory mixed methods design—including pre- and post-test assessments, a questionnaire, and interviews—to explore similarities and differences in participant and teacher perceptions and outcomes (gain scores and final grades) of OL versus traditional FTF Communications courses, and to examine effects of students’ age and gender on learning preference and performance. Data collection occurred over a 4-month period and involved 183 student and 2 professor participants. The study used an SPSS program for data analysis and created a Microsoft Excel document to record themes derived from the questionnaire and interviews. Quantitative findings suggest there are no significant differences in gain scores, final grades, or other learning outcomes when comparing OL and FTF versions of identical Communications courses; however, qualitative findings indicate differences between facilitation styles based on student and professor perception. The study sheds light on student and faculty perceptions of facilitation styles and suggests areas for potential improvements in FTF- and OL-facilitated courses. The study ultimately recommends that students and faculty should have options when it comes to preferred delivery of course material.

Plataforma para o suporte de blended peer assisted learning

Este projecto foi realizado na Universidade da Madeira, no âmbito do Mestrado em Engenharia Informática e tem como título “Plataforma para o suporte de Blended Peer Assisted Learning”. Ao longo da nossa pesquisa sobre os vários métodos de aprendizagem online, deparamo-nos com uma grande lacuna que abrange a maior parte dos sistema de ensino assistidos por computador, ou seja, todas elas preocupam-se com a passagem de conhecimentos, mas raras são aquelas que têm em atenção o tipo de utilizador, qual a seu percurso académico e profissional, qual a metodologia que fará com que o mesmo capte melhor os conteúdos, etc. Com este objectivo em mente, e tendo em atenção as diversas plataformas e metodologias de ensino existentes, optou-se por elaborar uma arquitectura de uma plataforma capaz de centralizar na mesma, um conjunto de funcionalidades e metodologias que possibilitassem um acompanhamento mais específico do utilizador, proporcionando um maior conhecimento, através do qual poderia encaminhar o utilizador para a estratégia de aprendizagem que mais se adequasse a um utilizador com as suas características. Como se poderá constatar no decorrer desta dissertação, a plataforma desenhada e o módulo desenvolvido têm como base teórica o Peer Assisted Learning (PAL) e as suas estratégias de aprendizagem. O PAL é um conceito relativamente novo, que se encontra em plena ascensão, sendo cada vez maior o número de instituições/organizações que adoptam o PAL como metodologia de ensino para a formação dos seus membros. Este crescimento deve-se em grande parte às várias estratégias PAL que visam uma maior adequação ao tipo de utilizador, contribuindo assim para uma aprendizagem mais rápida e eficaz. Uma vez que a implementação da plataforma na sua totalidade seria de todo impossível, optouse por desenvolver apenas um dos módulos referente à estratégia de PAL – Peer Tutoring (PT). No final, o objectivo principal, passa não só por fornecer as bases necessárias ao desenvolvimento da referida plataforma, mas também pela disponibilização do módulo de PT que servirá de referência para o desenvolvimento das restantes estratégias. Tendo em atenção toda a investigação efectuada, facilmente se depreende as inúmeras vantagens que podem advir da utilização do PAL, das quais se salienta, a adequação da estratégia PAL mais indicada para cada tipo de utilizador.

E-Learning praktisch erlebbar machen

Dieser Projektbericht beschreibt eine Lehrveranstaltung, die den Studierenden in der Theorie genau das vermittelte, was sie direkt in der Praxis erfahren konnten: E-Learning. Die enge Koppelung von Wissensvermittlung und praktischer Umsetzung setzte auf ein ungewöhnliches Modell der Lernzeitorganisation. Von den Lernenden wie vom Lehrenden verlangte das Blended-Learning-Seminar die Bereitschaft, das übliche Selbstverständnis in Lehr-Lern-Kontexten an Hochschulen zu überdenken. Dieser Bericht stellt die inhaltliche und strukturelle Ausgangssituation dar, beschreibt die Organisation der Veranstaltung sowie die eingesetzten Methoden und Mittel und reflektiert die Lernerfolge.

Rahmenwerk für zielgruppenorientiertes Blended E-Learning im MINT-Bereich im Kontext des Lebenslangen Lernens

Das heutige Leben der Menschen ist vom Internet durchdrungen, kaum etwas ist nicht „vernetzt“ oder „elektronisch verfügbar“. Die Welt befindet sich im Wandel, die „Informationsgesellschaft“ konsumiert in Echtzeit Informationen auf mobilen Endgeräten, unabhängig von Zeit und Ort. Dies gilt teilweise auch für den Aus- und Weiterbildungssektor: Unter „E-Learning“ versteht man die elektronische Unterstützung des Lernens. Gelernt wird „online“; Inhalte sind digital verfügbar. Zudem hat sich die Lebenssituation der sogenannten „Digital Natives“, der jungen Individuen in der Informationsgesellschaft, verändert. Sie fordern zeitlich und räumlich flexible Ausbildungssysteme, erwarten von Bildungsinstitutionen umfassende digitale Verfügbarkeit von Informationen und möchten ihr Leben nicht mehr Lehr- und Zeitplänen unterordnen – das Lernen soll zum eigenen Leben passen, lebensbegleitend stattfinden. Neue „Lernszenarien“, z.B. für alleinerziehende Teilzeitstudierende oder Berufstätige, sollen problemlos möglich werden. Dies soll ein von der europäischen Union erarbeitetes Paradigma leisten, das unter dem Terminus „Lebenslanges Lernen“ zusammengefasst ist. Sowohl E-Learning, als auch Lebenslanges Lernen gewinnen an Bedeutung, denn die (deutsche) Wirtschaft thematisiert den „Fachkräftemangel“. Die Nachfrage nach speziell ausgebildeten Ingenieuren im MINT-Bereich soll schnellstmöglich befriedigt, die „Mitarbeiterlücke“ geschlossen werden, um so weiterhin das Wachstum und den Wohlstand zu sichern. Spezielle E-Learning-Lösungen für den MINT-Bereich haben das Potential, eine schnelle sowie flexible Aus- und Weiterbildung für Ingenieure zu bieten, in der Fachwissen bezogen auf konkrete Anforderungen der Industrie vermittelt wird. Momentan gibt es solche Systeme allerdings noch nicht. Wie sehen die Anforderungen im MINT-Bereich an eine solche E-Learning-Anwendung aus? Sie muss neben neuen Technologien vor allem den funktionalen Anforderungen des MINTBereichs, den verschiedenen Zielgruppen (wie z.B. Bildungsinstitutionen, Lerner oder „Digital Natives“, Industrie) und dem Paradigma des Lebenslangen Lernens gerecht werden, d.h. technische und konzeptuelle Anforderungen zusammenführen. Vor diesem Hintergrund legt die vorliegende Arbeit ein Rahmenwerk für die Erstellung einer solchen Lösung vor. Die praktischen Ergebnisse beruhen auf dem Blended E-Learning-System des Projekts „Technische Informatik Online“ (VHN-TIO).