Quranic education and technology : reinforcement learning system for non-native Arabic children

We built a simulator based on reinforcement learning to improve teaching experience in Quranic and Islamic education for non-native Arabic speakers to evaluate their strength and weaknesses and allow the system to help improving the child in one hand, and provide an accurate actual report for each child on the other hand

Student Behavior Simulation in English Online Education Based on Reinforcement Learning

In class, every student's action is not the same. In this era, most courses are taken online; tracking and identifying students’ behavior is a significant challenge, especially in language classes (English). In this study, Student Behaviors’ Simulation-Based on Reinforcement Learning Framework (SBS–BRLF) has been proposed to track and identify students’ online class behavior. The simulation model is generated with various trained sets of behavior that are categorized as positive and negative with Reinforcement Learning (RL). Reinforcement learning (RL) is a field of machine learning dealing with how intelligent agents act in an environment for cumulative rewards. With a web camera and microphone, the students are tracked in the simulation model, and collected data is executed with RL’s aid. If the action is assessed as good, the pupil is praised, or given a warning three times, and then, if repeated, suspended for a day. Hence, the pupil is monitored easily without complications. The research and comparative analysis of the proposed and the current framework have proved that SBSBRLF works efficiently and accurately with the behavioral rate of 93.2%, the performance rate of 96%, supervision rate of 92%, reliability rate of 89.7 % for students, and a higher action and reward acceptance rate of 89.9 %

A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining

Educational Data Mining (EDM) has emerged as a vital field of research, which harnesses the power of computational techniques to analyze educational data. With the increasing complexity and diversity of educational data, Deep Learning techniques have shown significant advantages in addressing the challenges associated with analyzing and modeling this data. This survey aims to systematically review the state-of-the-art in EDM with Deep Learning. We begin by providing a brief introduction to EDM and Deep Learning, highlighting their relevance in the context of modern education. Next, we present a detailed review of Deep Learning techniques applied in four typical educational scenarios, including knowledge tracing, undesirable student detecting, performance prediction, and personalized recommendation. Furthermore, a comprehensive overview of public datasets and processing tools for EDM is provided. Finally, we point out emerging trends and future directions in this research area.Comment: 21 pages, 5 figure

Aplicação de algoritmo de machine learning na identificação de alunos em risco de evasão

Este trabalho é um estudo da aplicação de ferramentas de machine learning em um modelo de predição de evasão de alunos no contexto de um curso pré-vestibular online. O objetivo é a identificação das principais variáveis indicativas de uma possível evasão, assim como prever quais alunos estão em risco de evadir, tendo como base os dados gerados pelo aluno após quatro semanas de utilização da plataforma de ensino. Pautado na metodologia KDD e se valendo do método random forest, trabalhou-se os dados de cadastro e comportamentais dos alunos que utilizaram a plataforma como principal ferramenta de estudo por, pelo menos, cinco meses. Obteve-se como resultado uma lista das variáveis mais influentes na previsão da evasão, assim como um modelo com acurácia de 79% de previsão, identificando 66% dos alunos que vieram a evadir. Conclui-se que o estudo gerou uma ferramenta com capacidade preditiva significativa para o curso pré vestibular, assim como informações úteis e novas em relação às variáveis relevantes na predição da evasão

A Transparency Index Framework for Machine Learning powered AI in Education

The increase in the use of AI systems in our daily lives, brings calls for more ethical AI development from different sectors including, finance, the judiciary and to an increasing extent education. A number of AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI, such as fairness, explainability and safety. However, the abstract nature of these existing ethical AI guidelines often makes them difficult to operationalise in real-world contexts. The inadequacy of the existing situation with respect to ethical guidance is further complicated by the paucity of work to develop transparent machine learning powered AI systems for real-world. This is particularly true for AI applied in education and training. In this thesis, a Transparency Index Framework is presented as a tool to forefront the importance of transparency and aid the contextualisation of ethical guidance for the education and training sector. The transparency index framework presented here has been developed in three iterative phases. In phase one, an extensive literature review of the real-world AI development pipelines was conducted. In phase two, an AI-powered tool for use in an educational and training setting was developed. The initial version of the Transparency Index Framework was prepared after phase two. And in phase three, a revised version of the Transparency Index Framework was co- designed that integrates learning from phases one and two. The co-design process engaged a range of different AI in education stakeholders, including educators, ed-tech experts and AI practitioners. The Transparency Index Framework presented in this thesis maps the requirements of transparency for different categories of AI in education stakeholders, and shows how transparency considerations can be ingrained throughout the AI development process, from initial data collection to deployment in the world, including continuing iterative improvements. Transparency is shown to enable the implementation of other ethical AI dimensions, such as interpretability, accountability and safety. The 3 optimisation of transparency from the perspective of end-users and ed-tech companies who are developing AI systems is discussed and the importance of conceptualising transparency in developing AI powered ed-tech products is highlighted. In particular, the potential for transparency to bridge the gap between the machine learning and learning science communities is noted. For example, through the use of datasheets, model cards and factsheets adapted and contextualised for education through a range of stakeholder perspectives, including educators, ed-tech experts and AI practitioners

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