Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning

Traditional learning-based approaches to student modeling generalize poorly to underrepresented student groups due to biases in data availability. In this paper, we propose a methodology for predicting student performance from their online learning activities that optimizes inference accuracy over different demographic groups such as race and gender. Building upon recent foundations in federated learning, in our approach, personalized models for individual student subgroups are derived from a global model aggregated across all student models via meta-gradient updates that account for subgroup heterogeneity. To learn better representations of student activity, we augment our approach with a self-supervised behavioral pretraining methodology that leverages multiple modalities of student behavior (e.g., visits to lecture videos and participation on forums), and include a neural network attention mechanism in the model aggregation stage. Through experiments on three real-world datasets from online courses, we demonstrate that our approach obtains substantial improvements over existing student modeling baselines in predicting student learning outcomes for all subgroups. Visual analysis of the resulting student embeddings confirm that our personalization methodology indeed identifies different activity patterns within different subgroups, consistent with its stronger inference ability compared with the baselines.Comment: 10 pages, CIKM 202

Online Learning Communities in the COVID-19 Pandemic: Social Learning Network Analysis of Twitter During the Shutdown

This paper presents a social learning network analysis of Twitter during the 2020 global shutdown due to the COVID-19 pandemic. Research concerning online learning environments is focused on the reproduction of conventional teaching arrangements, whereas social media technologies afford new channels for the dissemination of information and sharing of knowledge and expertise. We examine Twitter feed around the hashtags online learning and online teaching during the global shutdown to examine the spontaneous development of online learning communities. We find relatively small and ephemeral communities on the two topics. Most users make spontaneous contributions to the discussion but do not maintain a presence in the Twitter discourse. Optimizing the social learning network, we find many potential efficiencies to be gained through more proactive efforts to connect knowledge seekers and knowledge disseminators. Considerations and prospects for supporting online informal social learning networks are discussed

Sobre algunas recomendaciones a las estrategias de aprendizaje electrónico en el programa de especialización en Pedagogía para el Desarrollo del Aprendizaje Autónomo (EPDAA) de la Escuela de Ciencias de la Educación de la Universidad Nacional Abierta y a Distancia para el periodo 2018 (II) - 2019 (I)

El presente ejercicio de investigación se dividirá en 3 secciones. En la primera se abordarán los conceptos teóricos y conceptuales que permiten entender la evolución histórica y las ultimas discusiones en torno a la implementación de estrategias didácticas en ambientes virtuales de aprendizaje enfocados a la educación superior. En la segunda sección se busca hacer un algunas recomendaciones a las estrategias de aprendizaje electrónico en el programa de especialización en Pedagogía para el Desarrollo del Aprendizaje Autónomo (EPDAA) de la Escuela de Ciencias de la Educación de la Universidad Nacional Abierta y a Distancia para el periodo 2018 (II) - 2019 (I), con la intención de identificar y describir dichas estrategias, así como analizar la factibilidad de la implementación de nuevas estrategias enfocadas a partir de las ultimas discusiones y experiencias exitosas a nivel mundial. Por último, se consolidaran los diversos hallazgos de la investigación en un apartado de conclusiones.This research exercise will be divided into 3 sections. In the first one, the theoretical and conceptual concepts that allow to understand the historical evolution and the last discussions about the implementation of didactic strategies in virtual learning environments focused on higher education will be addressed. The second section seeks to make some recommendations for e-learning strategies in the specialization program in Pedagogy for the Development of Autonomous Learning (EPDAA) of the School of Education Sciences of the National Open and Distance University for the period 2018 (II) - 2019 (I), with the intention of identifying and describing these strategies, as well as analyzing the feasibility of the implementation of new strategies focused on the latest discussions and successful experiences worldwide. Finally, the various research findings will be consolidated in a conclusions section

O quanto eu quero este certificado? caçadores de certificados no Lúmina

Com o grande aumento do número de alunos inscritos em Massive Open Online Courses (MOOCs), e o crescimento na quantidade de plataformas de distribuição, a oferta deste tipo de curso está cada vez maior, pois representam uma possibilidade de disseminação de conhecimento especializado, de forma flexível e aberta. Todavia, com o aumento do uso de plataformas on-line de aprendizagem o estudo da desonestidade acadêmica se torna relevante, neste contexto, pois estes cursos podem ser mais facilmente burlados do que cursos presenciais. Por isso, o objetivo geral desta Tese é identificar parâmetros de configuração de MOOCs que desestimulem estudantes que têm comportamentos de “caçadores de certificados” a obter certificações, ao mesmo tempo em que não desestimulem estudantes engajados, na Plataforma de MOOCs da Universidade Federal do Rio Grande do Sul (UFRGS), o Lúmina. Para tanto, é preciso identificar e caracterizar o perfil dos “caçadores de certificados”, estudantes que buscam explorar características da plataforma e dos cursos, para obter um certificado sem se dedicar a sua aprendizagem. Nesta tese, levantou-se a hipótese de haver um “comportamento de caçador” (independente do aluno) e um perfil que pode ser chamado de “estudante-caçador” (um indivíduo que sempre exibe este comportamento). Para caracterizar o comportamento de caçador e dos estudantes-caçadores foi desenvolvido um processo metodológico iterativo com as seguintes etapas: Seleção e Processamento dos dados; Aplicação de Técnicas de Mineração de Dados Educacionais; e Considerações sobre o Processo. Como técnica de Mineração de Dados Educacionais para identificar este comportamento e estes estudantes, foram utilizados algoritmos de Aprendizagem de Máquina não supervisionados, mais especificamente algoritmos de agrupamento hierárquico. Em relação à identificação do "comportamento de caçador", o algoritmo de agrupamento não foi capaz de identificar características que permitam identificar os usuários, pois os grupos formados apresentam níveis parecidos na maioria das variáveis utilizadas, à exceção das variáveis "curso tem mais de 10 questões", que é um indicador de dificuldade do curso. Em relação à identificação de estudantes caçadores, entende-se que a obtenção de pelo menos 3 certificados em menos de 35 dias é um bom indicador para classificar um estudante como caçador de certificados. Em relação ao modelo que ajusta a presença de caçadores às configurações dos cursos, conclui-se que não há indícios suficientes para indicar que as restrições nas configurações sejam eficazes para inibir caçadores de certificados.With the large increase in the number of students enrolled in Massive Open Online Courses (MOOCs), and the growth in the number of distribution platforms, the offer of this type of course is increasing, as they represent a possibility of disseminating specialized knowledge, of flexible and open way. However, with the increased use of online learning platforms, the study of academic dishonesty becomes relevant in this context, as these courses can be more easily circumvented than face-to-face courses. Therefore, the general objective of this Thesis is to identify MOOCs configuration parameters that discourage students who have “certificate hunter” behaviors to obtain certifications, while not discouraging engaged students, in the MOOCs Platform of the Federal University of Rio de Janeiro. Grande do Sul (UFRGS), the Lúmina. To do so, it is necessary to identify and characterize the profile of “certificate hunters”, students who seek to explore features of the platform and courses, in order to obtain a certificate without dedicating themselves to learning. In this thesis, it was hypothesized that there is a “hunter behavior” (independent of the student) and a profile that can be called “student-hunter” (an individual who always exhibits this behavior). To characterize the behavior of hunters and student-hunters, an iterative methodological process was developed with the following steps: Selection and Processing of data; Application of Educational Data Mining Techniques; and Process Considerations. As an Educational Data Mining technique to identify this behavior and these students, unsupervised Machine Learning algorithms were used, more specifically hierarchical clustering algorithms. Regarding the identification of "hunter behavior", the grouping algorithm was not able to identify characteristics that allow identifying users, since the groups formed have similar levels in most of the variables used, with the exception of the variables "course has more than 10 questions", which is an indicator of course difficulty. Regarding the identification of student hunters, it is understood that obtaining at least 3 certificates in less than 35 days is a good indicator to classify a student as a certificate hunter. Regarding the model that adjusts the presence of hunters to the course settings, it is concluded that there is not enough evidence to indicate that the restrictions in the settings are effective in inhibiting certificate hunters