DIGITALIZAÇÃO DO ENSINO SUPERIOR: IMPACTOS NAS PRÁTICAS DE GESTÃO E DESENVOLVIMENTO INSTITUCIONAL. UMA REVISÃO DE LITERATURA

For an extended period, the higher education community has been diligently endeavoring to implement technologically advanced and more efficacious methodologies. The aim is to enhance effectiveness and cultivate a generation of graduates equipped to navigate the evolving labor market dynamics and adapt to the influences of globalization. The outbreak of the Covid-19 pandemic significantly catalyzed the adoption of digital education, often referred to as "E-Learning," as a predominant mode of instruction across a majority of countries. This shift was necessitated by the imperative to adhere to social distancing measures and prevent the potential collapse of the educational infrastructure. In the wake of this transformative paradigm, educational institutions were compelled to engineer inventive management approaches to effectively traverse this altered landscape, marking the dawn of a new era. This era is characterized by a profound dependence on advanced technology and unfettered information accessibility as pivotal factors for sustaining and optimizing performance.This paper aims to explain the basic ideas behind managing higher education while exploring the existing research that supports these ideas. By breaking down the various aspects and tools involved, the goal is to shed light on the complex nature of managing higher education. This exploration eventually leads to an examination of how the digitalization of education impacts different functional areas of education management. Through this in-depth analysis, a clear connection emerges between the need for digitalization and the necessity to update management systems. This connection is crucial for not only achieving but also sustaining effective operation in this new era that combines technology and education.Durante um longo período, a comunidade do ensino superior tem-se esforçado diligentemente por implementar metodologias tecnologicamente avançadas e mais eficazes. O objetivo é aumentar a eficácia e cultivar uma geração de licenciados equipados para navegar na dinâmica do mercado de trabalho em evolução e adaptar-se às influências da globalização. O surto da pandemia de Covid-19 catalisou significativamente a adoção da educação digital, muitas vezes referida como "E-Learning", como um modo predominante de ensino na maioria dos países. Esta mudança foi necessária devido ao imperativo de aderir a medidas de distanciamento social e evitar o potencial colapso da infraestrutura educativa. Na sequência deste paradigma transformador, as instituições de ensino foram obrigadas a conceber abordagens de gestão inventivas para atravessar eficazmente esta paisagem alterada, marcando o início de uma nova era. Esta era caracteriza-se por uma profunda dependência da tecnologia avançada e da acessibilidade ilimitada à informação como factores essenciais para sustentar e otimizar o desempenho. Este documento tem por objetivo explicar as ideias básicas subjacentes à gestão do ensino superior, explorando simultaneamente a investigação existente que apoia estas ideias. Ao decompor os vários aspectos e instrumentos envolvidos, o objetivo é esclarecer a natureza complexa da gestão do ensino superior. Esta exploração acaba por conduzir a um exame da forma como a digitalização do ensino afecta as diferentes áreas funcionais da gestão do ensino. Através desta análise aprofundada, surge uma ligação clara entre a necessidade de digitalização e a necessidade de atualizar os sistemas de gestão. Esta ligação é crucial não só para alcançar, mas também para manter um funcionamento eficaz nesta nova era que combina tecnologia e educação

Visual analytics design for students assessment representation based on supervised learning algorithms

Visual Analytics is very effective in many applications especially in education field and improved the decision making on enhancing the student assessment. Student assessment has become very important and is identified as a systematic process that measures and collects data such as marks and scores in a manner that enables the educator to analyze the achievement of the intended learning outcomes. The objective of this study is to investigate the suitable visual analytics design to represent the student assessment data with the suitable interaction techniques of the visual analytics approach. sheet. There are six types of analytical models, such as the Generalized Linear Model, Deep Learning, Decision Tree Model, Random Forest Model, Gradient Boosted Model, and Support Vector Machine were used to conduct this research. Our experimental results show that the Decision Tree Models were the fastest way to optimize the result. The Gradient Boosted Model was the best performance to optimize the result

Exploring Data Driven Youth Character Education Frameworks: A Systematic Literature Review on Learning Analytics Models and Participatory Design

Character development requires not only high-quality curriculums, but also educators who are able to adapt programs to learners’ needs and context and staff development strategies. Big data and learning analytics strategies may improve youth character development especially in developing countries facilitating educators’ development and practical wisdom, as well as curriculum implementation’s effectiveness in countries with less knowhow in the issue. This study presents a systematic mapping literature review on the models and methods of learning analytics applied in the improvement of youth character education. Based on the literature review results, the research provides insights for future research and implementation of character education programs, and proposes a revised participatory knowledge management data-driven procedure that may facilitate educators to identify and undertake the best character formation actions in specific situations

Do Big Data Analytics Competencies Improve Banks’ Financial Performance? An Investigation using Portuguese data

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementOver the last few years, big data has been a popular technological advancement in games and changes as a virtual 'border' of a wide variety of IT-driven technologies and information-enabling possibilities. Due to technological advances, the internet, handheld machinery, decision support systems, transmission, and computations, the idea of big data was propelled by rapids in data processing and storage. The aim of our study is to determine how Portuguese banks' big data analytics capabilities will help them to improve their financial performance: the efficacy of the audit committee as a mediator and process-oriented dynamic capabilities (PODC) as a moderator. Companies have recently met with unpredictable environmental factors and shifts, and increasingly competitive trends. To obtain a competitive edge, attain superior performance and success and increase long-term survival and longevity, they have to apply, introduce and apply suitable techniques and strategies. Techniques and methods from a variety of fields have been examined rigorously and analytically. In this report, the audit committee is one of the most effective tools and procedures used by businesses, and it is a key driver of successful organizational mechanisms. In comparison, the audit committee's monitoring functions have been considerably greater and more effective. The study adopted a survey research design. Simple random sampling was used to ensure that those employees were found at their workplaces who were used for the study. This design was quantitative to allow for descriptive and inferential analysis. The data has been collected within one month (i.e., February 2021 to May 2021). As it was collected at one time so the design is cross-sectional in nature. Scholars received data from 330 target respondents, including hardcopies and softcopies due to the COVID-19 pandemic in the different public and private banks of Portuguese. The current study makes a substantial addition to big data analytics management and firm performance, and it has a wide range of applications. Because no previous study has directly addressed the moderating behavior of Process-oriented dynamic capability and the mediating role of audit committee effectiveness in this setting, the function played by the current study is quite important. Furthermore, the convergence of organizational resources examined in this study to assess a firm's big data analytics competency has never been attempted before. Using the resource-based Theory (RBT) and dynamic capabilities view (DCV) as theoretical lenses, the study investigates the relationship between BDA capabilities and firm performance. The main justification for adopting these two points of view is that the technological capability of utilizing BDA requires various additional firm-specific resources that can eventually contribute to increased performance

Real-time performance diagnosis and evaluation of big data systems in cloud datacenters

PhD ThesisModern big data processing systems are becoming very complex in terms of largescale, high-concurrency and multiple talents. Thus, many failures and performance reductions only happen at run-time and are very difficult to capture. Moreover, some issues may only be triggered when some components are executed. To analyze the root cause of these types of issues, we have to capture the dependencies of each component in real-time. Big data processing systems, such as Hadoop and Spark, usually work in large-scale, highly-concurrent, and multi-tenant environments that can easily cause hardware and software malfunctions or failures, thereby leading to performance degradation. Several systems and methods exist to detect big data processing systems’ performance degradation, perform root-cause analysis, and even overcome the issues causing such degradation. However, these solutions focus on specific problems such as stragglers and inefficient resource utilization. There is a lack of a generic and extensible framework to support the real-time diagnosis of big data systems. Performance diagnosis and prediction of big data systems are highly complex as these frameworks are typically deployed in cloud data centers that are large-scale, highly concurrent, and follows a multi-tenant model. Several factors, including hardware heterogeneity, stochastic networks and application workloads may impact the performance of big data systems. The current state-of-the-art does not sufficiently address the challenge of determining complex, usually stochastic and hidden relationships between these factors. To handle performance diagnosis and evaluation of big data systems in cloud environments, this thesis proposes multilateral research towards monitoring and performance diagnosis and prediction in cloud-based large-scale distributed systems by involving a novel combination of an effective and efficient deployment pipeline.The key contributions of this dissertation are listed below: - i - • Designing a real-time big data monitoring system called SmartMonit that efficiently collects the runtime system information including computing resource utilization and job execution information and then interacts the collected information with the Execution Graph modeled as directed acyclic graphs (DAGs). • Developing AutoDiagn, an automated real-time diagnosis framework for big data systems, that automatically detects performance degradation and inefficient resource utilization problems, while providing an online detection and semi-online root-cause analysis for a big data system. • Designing a novel root-cause analysis technique/system called BigPerf for big data systems that analyzes and characterizes the performance of big data applications by incorporating Bayesian networks to determine uncertain and complex relationships between performance related factors. The key contributions of this dissertation are listed below: - i - • Designing a real-time big data monitoring system called SmartMonit that efficiently collects the runtime system information including computing resource utilization and job execution information and then interacts the collected information with the Execution Graph modeled as directed acyclic graphs (DAGs). • Developing AutoDiagn, an automated real-time diagnosis framework for big data systems, that automatically detects performance degradation and inefficient resource utilization problems, while providing an online detection and semi-online root-cause analysis for a big data system. • Designing a novel root-cause analysis technique/system called BigPerf for big data systems that analyzes and characterizes the performance of big data applications by incorporating Bayesian networks to determine uncertain and complex relationships between performance related factors. The key contributions of this dissertation are listed below: - i - • Designing a real-time big data monitoring system called SmartMonit that efficiently collects the runtime system information including computing resource utilization and job execution information and then interacts the collected information with the Execution Graph modeled as directed acyclic graphs (DAGs). • Developing AutoDiagn, an automated real-time diagnosis framework for big data systems, that automatically detects performance degradation and inefficient resource utilization problems, while providing an online detection and semi-online root-cause analysis for a big data system. • Designing a novel root-cause analysis technique/system called BigPerf for big data systems that analyzes and characterizes the performance of big data applications by incorporating Bayesian networks to determine uncertain and complex relationships between performance related factors.State of the Republic of Turkey and the Turkish Ministry of National Educatio

Um modelo de perfil de aluno voltado a aplicações de técnicas de learning analytics

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Florianópolis, 2019.A análise das interações dos alunos com os ambientes virtuais de aprendizagem assumiu um papel relevante para decisões educacionais. A grande disponibilidade de cursos a distância permite o uso da tecnologia a fim de explorar os dados produzidos a partir dessas alterações. Pode assim, maximizar o aprendizado dos alunos, sugerindo atividades de acordo com o perfil de cada um. Entretanto, a utilização do perfil do aluno para análises mais abrangentes ainda é insipiente. Neste sentido, o presente trabalho propõe um modelo de dados de perfil de aluno voltado a aplicação de técnicas de Learning Analytics em Sistemas de Aprendizagem Online. O modelo, elaborado por meio do desenvolvimento de artefatos, teve como suporte a metodologia Design Science Research. Para a sua avaliação, utilizou-se uma base de dados de uma instituição de ensino que possui atividades ativas em um ambiente virtual de aprendizagem. A partir desses dados, foi possível a aplicação das técnicas escolhidas, obtendo-se informações relevantes para subsidiar os gestores no âmbito educacional. Análises estatísticas, análise de agrupamentos e sistemas de recomendação foram as técnicas aplicadas. De maneira geral, os resultados produzidos estão centrados na identificação e geração de grupos de perfis similares, considerando o estilo de aprendizagem e o tipo de personalidade dos alunos. Esta estratégia permitiu a obtenção de resultados promissores para a tomada de decisão no contexto educacional e com potencial para gerar uma contribuição efetiva para a área de Learning Analytics.Abstract: The analysis of students' interactions with virtual learning environments has assumed a relevant role for educational decisions. The wide availability of distance learning courses allows the use of technology to exploit the data produced from these interactions. It can thus maximize students' learning by suggesting activities according to their profile. However, using the student profile for broader analysis is still incipient. In this sense, the present work proposes a student profile data model, focused on the application of Learning Analytics techniques in Online Learning Systems. The model, created through the development of artifacts, was supported by the Design Science Research methodology. For its evaluation, it was used a database from an educational institution that has active activities in a virtual learning environment. From these data, it was possible to apply the chosen techniques, obtaining relevant information to support managers in the educational field. Statistical analyzes, cluster analysis and recommendation systems were the applied techniques. In general, the results produced focus on the identification and generation of similar profile groups, considering the students' learning style and personality type. This strategy allowed promising results for decision making in the educational context and with the potential to generate an effective contribution to the area of Learning Analytics

Kouluverkoista oppimisverkkoihin : koulutuksen asiantuntijoiden käsityksiä Lapin perusopetuksen tulevaisuudesta

Perusopetuksen tulevaisuus on ollut vahvasti esillä sekä julkisessa keskustelussa että tieteellisessä tutkimuksessa. Asiaa on tarkasteltu väestörakennemuutosten, digitaalisuuden, pedagogisen kehityksen ja yhteiskunnallisten megatrendien kautta. Vuonna 2023 keskusteluun ovat nousseet erityisesti suomalaisen peruskoulutuksen oppimistuloksien taso ja digitaalisuuden vaikutukset. Tämän tutkimuksen keskiössä on perusopetuksen tulevaisuus Lapissa. Teoreettisena taustana tarkastellaan koulutuksen tulevaisuuden tutkimusta, ennakointityötä, opetusteknologian ja digitaalisuuden merkitystä sekä perusopetuksen kehitystä Suomessa ja Lapin maakunnan perusopetuksen järjestämistä ohjaavia erityispiirteitä. Tutkimuksen tarkoituksena on kuvata perusopetuksen tulevaisuuskuvaa Lapissa. Tulevaisuuskuvan hahmottamiseksi tutkimuksessa haetaan Lapissa vaikuttavien koulutuksen asiantuntijoiden käsityksiä siitä, millaisista tekijöistä peruskoulun tulevaisuus koostuu sekä miten peruskoulutus toteutetaan käytännössä. Näiden tutkimuskysymysten avulla tavoitteena on luoda Lapin perusopetuksen tulevaisuuskuvan aihio ja vastata pääkysymykseen: ”Millainen on perusopetuksen tulevaisuus Lapin maakunnassa koulutuksen asiantuntijoiden käsitysten mukaan?” Tutkimus edustaa design-tutkimusta, jossa aineisto on kerätty kahdessa syklissä. Ensimmäinen syklin aineisto hankittiin toukokuussa 2022 pidetyssä digitaalisessa työpajassa, johon osallistui Lapin kuntien sivistysjohtajia tai vastaavassa asemassa olevia henkilöitä sekä Lapin aluehallintoviraston opetustoimen tarkastajia tai johtajia (N=14). Heille annettiin virikemateriaalia, ja sen perusteella tutkimushenkilöt muodostivat ryhmätyöskentelynä dialogisen vuoropuhelun avulla käsityksiä perusopetuksen tulevaisuudesta. Ensimmäinen sykli sisälsi neljä vaihetta. Ensimmäisen syklin aineisto analysoitiin fenomenografisella tutkimusotteella. Alkuperäisistä ilmauksista muodostettiin merkitysyksiköt, niistä alakategoriat sekä näistä kolme tuloskategoriaa, jotka nimettiin seuraavasti: opetuksen ja oppimisen uudistamien, koulutuksen saavutettavuuden parantaminen sekä digitalisaation luomat mahdollisuudet. Näistä luotiin fenomenografisen tulosavaruus yhdistämällä tuloskategoriat pääkategoriaksi. Näin syntyi perusopetuksen tulevaisuuskuvan aihio. Toisen syklin aineisto kerättiin kyselyllä, jossa tutkimushenkilöitä pyydettiin arvioimaan syntyneen perusopetuksen tulevaisuuskuvan aihion toteutumisen todennäköisyyttä. Pääkategoria vahvistui tietyin täydennysehdotuksin. Tutkimustulosten mukaan perusopetuksen tulevaisuuskuva korostaa 1) opetussuunnitelman, opetuksen ja oppimisen uudistamista edellyttäen muiden muassa opettajan roolin ja opettajankoulutuksen muuttumista, oppimisen yksilöllisyyden vahvistamista sekä oppimis- ja opiskelutaitojen kehittämistä. Perusopetuksen tulevaisuuskuvaan kuuluu myös 2) koulutuksen saavutettavuuden takaaminen ja järjestäminen uudella tapaa, mikä tarkoittaa sivistysalueiden muodostamista, kuntien yhteistyötä koulutuksen järjestämisessä ja toteutuksessa. Niin ikään keskeinen tulevaisuuskuvan tekijä on 3) digitaalisuus, joka nähdään kuntien yhteistyön, koulujen ja kuntien yhteisten opetusryhmien ja laadukkaan koulutustoiminnan edellytyksenä ja mahdollistajana. Tulevaisuuden peruskoulussa huolehditaan myös digitalisaation inhimillisyydestä ja toimivasta oppilashuollosta. Johtopäätöksinä todetaan, että perusopetuksen tulevaisuus edellyttää mittavia muutoksia niin koulutuksen järjestämiseen, sen perusteisiin kuin myös sen pedagogiikkaan. Digitaalinen kehitys lienee muutoksen kärjessä

Análisis de datos educativos aplicado en el estudio de la incidencia de factores socioeconómicos en el rendimiento escolar

La investigación que corresponde con esta tesis se desarrolló en el campo de la Minería de Datos Educativos, en un sentido más amplio, en la Ciencia Informática aplicada en la Educación. El documento articula el análisis de datos con el problema multifactorial del rendimiento académico en las escuelas. Así, el objetivo general es el análisis de la incidencia de los factores socioeconómicos en el aprovechamiento académico a nivel escolar, con la finalidad de contribuir a su entendimiento y mejora, mediante la aplicación de modelos de análisis de datos predictivos o supervisados y descriptivos o no supervisados. También se ha incluido un análisis confirmatorio que tiene relaciones entre sus elementos, a priori sustentados en las exploraciones estadísticas de los datos que anteceden al desarrollo de los modelos supervisados y no supervisados y también a dichos modelos. Los datos objeto de estudio corresponden a dos escuelas de Ecuador, dado que la cantidad de datos entre una y otra difería considerablemente no se presenta un análisis comparativo, sino uno con base en la información consolidada que totaliza 6808 instancias o registros de calificaciones y 88 columnas que lo describen. El análisis gira en torno a cada registro de calificaciones y no de cada alumno, porque en el sistema escolar ecuatoriano las bajas calificaciones en una materia, simplificadas como rendimiento académico, pueden llegar a determinar la reprobación del año básico cursado por el alumno. El proceso de análisis ejecutado es iterativo, permite ir hacia adelante y hacia atrás entre las fases que lo componen, siempre que resulte necesario tener mejores resultados. Se basa en el ciclo de vida conocido como CRISP-DM, siglas del Proceso Estándar Intersectorial para Minería de Datos. Además, se adicionó algunas prácticas sugeridas en el Proceso Estándar Intersectorial para el Desarrollo de Aplicaciones de Aprendizaje Automático con Metodología de Garantía de Calidad o CRISP-ML (Q), cómo, por ejemplo, cumplir con requisitos que promuevan la calidad de datos, robustez del modelo y evaluación de riesgos, para así aminorar problemas de sesgo, sobreajuste y falta de reproducibilidad de los modelos hacia nuevas escuelas y regiones. Se utilizó el modelado predictivo para ayudar a las instituciones educativas con la identificación temprana de los estudiantes con dificultades para sostener su rendimiento académico escolar. Se desarrolló modelos predictivos que utilizan datos de calificaciones, factores socioeconómicos y de comportamiento de los estudiantes, mismos que se han recopilado de sistemas provistos por el Estado y del departamento de orientación estudiantil de las escuelas ecuatorianas. Con ello se buscó clasificar con precisión si un estudiante está en riesgo de reprobar un curso o experimentar problemas en cierta materia del curso. La identificación de patrones de estudiantes en riesgo es de ayuda a los docentes y más actores educativos en la toma de medidas proactivas que favorezcan la participación efectiva en las aulas de clases y en que se aminore las eventuales brechas educativas relacionadas con el rendimiento académico. Se recurrió a 13 modelos supervisados, 5 no supervisados y un análisis confirmatorio. La relación entre los resultados obtenidos a partir de ellos guarda consistencia. Los datos fueron estudiados desde cinco ejes (1) Modelos no supervisados, (2) Modelos de clasificación considerando notas intermedias, (3) Modelos de clasificación sin considerar notas intermedias, (4) Modelos de regresión sin considerar notas intermedias y (5) Modelos de clasificación con datos reducidos en su dimensionalidad, balanceados y sin considerar notas intermedias. Cuando no se incluyó a las notas intermedias fue porque era de esperar que el promedio final se vea muy influenciado por las calificaciones progresivas de los alumnos, por tanto, la no inclusión de dichas calificaciones ilustra de mejor manera la incidencia de los factores socioeconómicos sobre el rendimiento académico. Existen calificaciones que en el sistema escolar ecuatoriano se registran, pero no condicionan la aprobación del año básico por parte del alumno, estas son el comportamiento de cada alumno y la calificación de su participación en los denominados proyectos escolares, que tienen como finalidad evaluar a las habilidades sociales de los alumnos. Con la reducción de la dimensionalidad se favoreció los tiempos de entrenamiento de los modelos supervisados a la par de prevenir la indisponibilidad de ciertos datos para los análisis posteriores. La información resultante de los modelos se combinó con el aporte de la revisión sistemática de la literatura. De modo general, los métodos de ensamblado reportaron los mejores valores en las diversas métricas, entonces, los resultados de las clasificaciones y regresiones logradas son confiables y no casuales, reflejan los patrones en los datos, porque en tales métodos de ensamblado se empleó 50 estimadores basados en árboles de decisión. Como referencia a una métrica, la Exactitud de la clasificación siempre superó el 90% y las regresiones tuvieron una efectividad de hasta el 85% porque las predicciones de promedios en los mejores casos pueden efectuase con un error de hasta 1.5 puntos sobre 10 posibles. En esta investigación doctoral, se ha combinado la objetividad de las métricas en las tareas de clasificación y regresión, con la subjetiva pero importante interpretabilidad de los resultados, apoyados en estudios referidos a técnicas de puntuación de características y su respectiva ilustración visual, con ello se ha pretendido que los modelos resulten interpretables por los usuarios posibles al tiempo de fortalecer su confianza en las decisiones de los modelos de las instituciones escolares. Parte de los resultados obtenidos muestran que los alumnos que no alcanzan los aprendizajes requeridos, es decir, que obtienen las calificaciones más bajas posibles, tienen como tendencia a un padre en estado civil de unión libre, un bajo número de hermanos, suelen presentar alguna discapacidad, su comportamiento en principio es A o el más alto, pero tiende a bajar conforme avanza el periodo lectivo, en sus proyectos escolares tienen una muy buena calificación B pero que no es la mejor A, su padre suele tener una ocupación laboral informal (por ejemplo, guardia de seguridad), el ingreso familiar suele ser bajo y también suelen vivir en familias reconstruidas. A futuro, estudios como el presente pueden ser fortalecidos con la incorporación de más escuelas de distintas regiones para obtener un abordaje más significativo por disponer de más datos y así producir resultados más fiables y extrapolables.Facultad de Informátic