Personalizing the Learning Process With Wihi

Societies are changing rapidly because of automation and digitalization, but local and global business environments are also becoming more volatile. Changing societies also place requirements on education: the number of atypical learners is growing all the time, and lifelong careers have been changed to lifelong learning. Traditional education approaches do not support part-time learners or lifelong learning; personalizing the learning process for every student separately is too laborious. In this paper, we study a flexible, personalized learning approach and an information system (Wihi) to support it. Wihi is a thesis management tool for students to plan and schedule their theses and for the thesis supervisor to centrally monitor the progress of different theses. In addition, it allows curriculum management to follow the whole thesis situation. Although Wihi was developed for a specific need, the personalized learning assumptions behind it are also applicable in other education cases

Automatic management tool for attribution and monitorization of projects/internships

No último ano académico, os estudantes do ISEP necessitam de realizar um projeto final para obtenção do grau académico que pretendem alcançar. O ISEP fornece uma plataforma digital onde é possível visualizar todos os projetos que os alunos se podem candidatar. Apesar das vantagens que a plataforma digital traz, esta também possui alguns problemas, nomeadamente a difícil escolha de projetos adequados ao estudante devido à excessiva oferta e falta de mecanismos de filtragem. Para além disso, existe também uma indecisão acrescida para selecionar um supervisor que seja compatível para o projeto selecionado. Tendo o aluno escolhido o projeto e o supervisor, dá-se início à fase de monitorização do mesmo, que possui também os seus problemas, como o uso de diversas ferramentas que posteriormente levam a possíveis problemas de comunicação e dificuldade em manter um histórico de versões do trabalho desenvolvido. De forma a responder aos problemas mencionados, realizou-se um estudo aprofundado dos tópicos de sistemas de recomendação aplicados a Machine Learning e Learning Management Systems. Para cada um desses grandes temas, foram analisados sistemas semelhantes capazes de solucionar o problema proposto, tais como sistemas de recomendação desenvolvidos em artigos científicos, aplicações comerciais e ferramentas como o ChatGPT. Através da análise do estado da arte, concluiu-se que a solução para os problemas propostos seria a criação de uma aplicação Web para alunos e supervisores, que juntasse as duas temáticas analisadas. O sistema de recomendação desenvolvido possui filtragem colaborativa com factorização de matrizes, e filtragem por conteúdo com semelhança de cossenos. As tecnologias utilizadas no sistema centram-se em Python no back-end (com o uso de TensorFlow e NumPy para funcionalidades de Machine Learning) e Svelte no front-end. O sistema foi inspirado numa arquitetura em microsserviços em que cada serviço é representado pelo seu próprio contentor de Docker, e disponibilizado ao público através de um domínio público. O sistema foi avaliado através de três métricas: performance, confiabilidade e usabilidade. Foi utilizada a ferramenta Quantitative Evaluation Framework para definir dimensões, fatores e requisitos(e respetivas pontuações). Os estudantes que testaram a solução avaliaram o sistema de recomendação com um valor de aproximadamente 7 numa escala de 1 a 10, e os valores de precision, recall, false positive rate e F-Measure foram avaliados em 0.51, 0.71, 0.23 e 0.59 respetivamente. Adicionalmente, ambos os grupos classificaram a aplicação como intuitiva e de fácil utilização, com resultados a rondar o 8 numa escala de 1 em 10.In the last academic year, students at ISEP need to complete a final project to obtain the academic degree they aim to achieve. ISEP provides a digital platform where all the projects that students can apply for can be viewed. Besides the advantages this platform has, it also brings some problems, such as the difficult selection of projects suited for the student due to the excessive offering and lack of filtering mechanisms. Additionally, there is also increased difficulty in selecting a supervisor compatible with their project. Once the student has chosen the project and the supervisor, the monitoring phase begins, which also has its issues, such as using various tools that may lead to potential communication problems and difficulty in maintaining a version history of the work done. To address the mentioned problems, an in-depth study of recommendation systems applied to Machine Learning and Learning Management Systems was conducted. For each of these themes, similar systems that could solve the proposed problem were analysed, such as recommendation systems developed in scientific papers, commercial applications, and tools like ChatGPT. Through the analysis of the state of the art, it was concluded that the solution to the proposed problems would be the creation of a web application for students and supervisors that combines the two analysed themes. The developed recommendation system uses collaborative filtering with matrix factorization and content-based filtering with cosine similarity. The technologies used in the system are centred around Python on the backend (with the use of TensorFlow and NumPy for Machine Learning functionalities) and Svelte on the frontend. The system was inspired by a microservices architecture, where each service is represented by its own Docker container, and it was made available online through a public domain. The system was evaluated through performance, reliability, and usability. The Quantitative Evaluation Framework tool was used to define dimensions, factors, and requirements (and their respective scores). The students who tested the solution rated the recommendation system with a value of approximately 7 on a scale of 1 to 10, and the precision, recall, false positive rate, and F-Measure values were evaluated at 0.51, 0.71, 0.23, and 0.59, respectively. Additionally, both groups rated the application as intuitive and easy to use, with ratings around 8 on a scale of 1 to 10

An outcome of expert-oriented digitalization of university processes

Digitalization challenges the way in which business processes are seen. The potential for enhancement is even recognized in business areas that traditionally have little to do with IT. Even though universities have long-standing traditions of how work is organized, they have not been eager to adopt digitalized processes. Because core university processes rely on highly skilled experts, digitalizing processes are not as straightforward as they would be in more mechanical work. We developed an expert-oriented digitalization model (EXOD) for the digitalization of university processes and tested it using a case study. After digitalizing a core process, we interviewed the experts involved. The results show the usefulness and adaptability of the model. Based on the results, we recommend further studies to refine and test the model more comprehensively. In addition, based on the adaptability of the model, we recommend it as a baseline for university process digitalization projects in general

Motivational Factors of At-Risk Students in Blended High School Credit Recovery

Although the number of high school students taking online courses for an initial course or credit recovery (CR) is growing, it is not clear why at-risk students are not successful in blended CR courses. The purpose of this qualitative multiple case study was to explore teachersâ perceptions and studentsâ experiences related to at-risk studentsâ motivation in blended CR courses. Kellerâs ARCS model of motivation for instructional design provided the framework for the study. Data were collected from interviews with 2 teachers, 5 students and from 2 school sites, face-to-face classroom observations, and online CR curricula. Data were analyzed through a priori coding and cross-case analysis aligned to the conceptual framework. Findings showed at-risk high school studentsâ experiences related to motivation in blended CR courses were influenced by their attention being captured, finding relevance in the course, experiencing confidence while completing tasks, and finding satisfaction (internally and externally) throughout the course. Findings may provide school districts with information related to motivational strategies in CR courses. Findings may also provide an increased understanding of what motivates high school students in these courses and how teachers can better support at-risk students