Teses não defendidas no IFAL

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Navegando Teses não defendidas no IFAL por Departamento "Campus Maceió"

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    Estratégias de aprendizagem no ensino remoto emergencial do Instituto Federal de Alagoas
    (CENTRO UNIVERSITÁRIO DE MACEIÓ – UNIMA | Afya, 2025-09-23) Melo, Helisabety Barros Mendes de; Mendonça, Adriana de Lima; http://lattes.cnpq.br/0381713043828464; Mendes Junior, Walcler de Lima; http://lattes.cnpq.br/6938727316741941; Pereira, Jesana Batista; http://lattes.cnpq.br/2329848572181989; Junkes, Janaína Accordi; http://lattes.cnpq.br/7777461824456062; Santos, Pedro Henrique Simonard; http://lattes.cnpq.br/0615985683640773
    Emergency Remote Teaching (ERT) during the COVID-19 pandemic posed significant challenges for education. Students, for instance, had to adapt to the sudden transition to an online environment, and many struggled to maintain focus and manage their studies independently, making it necessary to adopt Learning Strategies. In this context, this research investigates the extent to which students enrolled in technical programs at the Federal Institute of Alagoas (IFAL) - Maceió Campus - made use of any type of Learning Strategy during ERT, aiming to understand how they coped with the demands of this new teaching format. With a qualitative-quantitative approach, a case study methodology was adopted, articulating two main procedures: Content Analysis of the students’ reports submitted as part of formative assessments during ERT, based on Bardin (2016), and the application of the Learning Strategies Assessment Scale for Vocational Education Students (EAVAP-EP) developed by Pereira; Santos; Ferraz (2020). The qualitative analysis of the students' narratives highlighted issues related to infrastructure and technology, teaching methodology, learning difficulties, and mental health—factors that compromised the appropriate use of these strategies. Students’ perceptions also revealed weaknesses in the institutional planning of ERT and gaps in the provision of technological, pedagogical, and psychological support. The EAVAP-EP, a validated instrument composed of 33 items distributed across three dimensions (Cognitive Strategies, Metacognitive Strategies, and Absence of Dysfunctional Metacognitive Strategies), was completed by 189 students. Statistical analysis was performed using R software (version 4.3.3), employing descriptive techniques. Quantitative data revealed satisfactory use of Metacognitive Strategies and, to a lesser extent, Cognitive Strategies. However, some students exhibited behaviors detrimental to learning (Absence of Dysfunctional Metacognitive Strategies). The results reinforce the urgent need, in light of the challenges faced, to implement pedagogical policies that systematically develop Learning Strategies, promote student autonomy, and foster better conditions for coping with adverse educational contexts.
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    Estudantes do Ensino Superior: variáveis pessoais e contextuais relacionadas ao insucesso acadêmico e evasão escolar
    (2022) Faria, Ana Amália Gomes de Barros Torres; Almeida, Leandro da Silva; Lima, Licínio Carlos Viana da Silva; Almeida, Leandro da Silva; Silva, José Maria de Castro; Monteiro, Sílvia Correia; Couñago, Maria Adelina Guisande
    Over the past few decades, research around adaptation and academic success in higher education has increased. A greater number and diversity of students enter this level of education, justifying greater concerns about their academic performance and consequent completion of training. Data collection took place longitudinally at the Instituto Federal de Alagoas-Ifal (Campus Maceió) and included three studies with different samples, between 317 and 420 students from different courses, distributed between degrees or bachelors. At first, even before the pandemic, we applied the questionnaires in person. Subsequently, with the Covid-19 pandemic and the transition of activities to Emergency Remote Teaching (ERE), all the research was done from contacts and the application of online questionnaires. The study considered the following steps: the first took place at the time of enrollment, between the 1st and 3rd week after admission, where students were invited to participate in the study and provide information regarding sociodemographic characteristics; your previous academic career; as well as expectations and anticipated difficulties related to the chosen course. The second stage took place between the 6th and 8th week of classes since the beginning of the course. In this phase, information related to the academic experiences of these students was collected; and inserted questions about the socio-emotional impact of the Covid-19 pandemic. In the third stage, at the end of the school year, the process of adapting students to their courses and their intention to drop out of the course was evaluated. The thesis compiles four articles, including an introduction, a theoretical reference chapter and methodological course and a conclusion. The articles that made up the thesis were: 1- “Academic Adaptation of 1st Year Students: Promoting Success and Permanence at the University”; 2- “Academic Expectations and Difficulties in Higher Education Entrants: Analysis in Function of Gender and Quota System”; 3- “Effects of Remote Learning on Higher Education students”; 4-"Difficulties in the academic adaptation of higher education students in times of a pandemic". The results point to the complexity of the convergence of personal and contextual variables in the process of academic success and school dropout. In this process, the need for public policies to gradually decrease the retention and dropout rates stands out, as well as the design of institutional strategies to support the career development of these students, mainly through integration activities and support services. specialized.
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    Sistema de computação em borda para controle preditivo de veículos autoguiados em redes sem fio sujeitas à degradação
    (2023) Omena, Rômulo Afonso Luna Vianna de; Santos, Danilo Freire de Souza; Perkusich, Angelo; Lima, Antonio Marcus Nogueira; Silva, Jaidilson Jó da; Brito, Alisson Vasconcelos de; Pereira, Carlos Eduardo; Valadares, Dalton Cézane Gomes
    Automated Guided Vehicles (AGVs) are essential for industry material transportation. In the Industry 4.0 and Industrial Internet of Things scenario, the AGV fleet is expected to be connected and integrated into the factory management system, being flexible and adapting to new demands. AGV control systems with fixed path navigation may not meet these requirements. Edge computing brings cloud resources to the network’s edge, making them closer to users. These resources can be accessed through a wireless network and applied to industrial demands. The AGVs can benefit from this when offloading tasks that require more computing resources to the edge server. However, the wireless network in the industrial environment is subject to degradation due to interference, signal reflections, shadowing effects, and electromagnetic wave absorption, among other challenges. The AGV, as a mobile robot, may traverse areas where the signal is degraded, increasing risks of collisions and accidents. Results of experiments suggest that Model Predictive Control (MPC) executed at the edge server, combined with a delay and packet loss compensation strategy implemented in the robot, can be used to mitigate these network degradations. In sequence, a two-tier architecture with MPCs is proposed to control multiple AGVs. The first tier, executed on the edge server, plans the trajectory of the AGVs globally, preventing collisions of the AGVs with fixed obstacles and each other. In the computer embedded in the AGV, the compensator used in the previous experiments gives place to a trajectory-tracking MPC, which must receive the trajectory of the respective AGV from the edge server and track it. Results of experiments carried out in four validation scenarios indicate that from the proposed architecture, it is possible to drive the AGVs without collisions, even in the communication network’s occurrence of delays and packet losses. In addition, tasks that demand more computational resources are offloaded to the edge server so that the computer embedded in the AGV can have more restricted resources, reducing costs and battery consumption.