Big data in education and organizational change: Evidence from private K12 schools in China

China is a time-honored civilization with a long history of private education. In China, private education has played an important role in preserving Chinese civilization. At the end of the 20th century, private education in China began to develop thanks to government support. As such, remarkable progress was made during the past decade. Due to specific conditions within the education industry, however, the administration of private edu-cation - and basic education, in particular - has remained rudimentary compared with other more mature service industries. To address the many problems in basic education, such as rig-id teaching methods, heavy teacher workloads and long, repetitive working hours, it is imper-ative in this information era to conduct innovative explorations with the help of the “internet of things” (IoT), big data and other scientific and technological means to carry out organiza-tional reform in schools and to establish contemporary organizational structures and manage-ment modes. Doing so will comprehensively improve the administration of basic education, which will in turn promote the quality of education and teaching. This thesis examines Tianli Education Group, a typical example of private, basic educa-tion in China. By adopting experimental research methods, the behavior of students and teachers in Tianli’s schools were experimentally analyzed. IoT technology was employed to collect data about student behavior at school. Likewise, after collecting and analyzing big data on the behavior of teachers at school, the content and processes of their work were analyzed. Based on these experiments, this thesis explores a new 5G era-appropriate mode of stu-dent selection and training that makes use of big data technology. It outlines the standard work scenario for teachers and improves both their work efficiency and salaries by “trimming staff and streamlining administration,” thus rekindling enthusiasm among teachers for their work. Finally, as a part of this thesis, a series of organizational changes were implemented at Tianli Education Group and its schools to boost organizational vitality, improve overall levels of education, teaching and operational efficiency, raise teachers’ salaries and enhance student happiness.A China é uma civilização muito antiga, com uma longa história de educação privada. A educação privada desempenhou um papel importante na preservação da civilização chinesa. No final do século 20, a educação privada na China começou a desenvolver-se com o apoio do governo. Nos últimos dez anos, devido ao apoio concedido temos assistido a um grande progresso. Contudo e em virtude das condições específicas da indústria da educação, a administração da educação privada – a educação básica em particular – permaneceu rudimentar quando comparada com outras indústrias de serviços. Para resolver os muitos problemas da educação básica, tais como os métodos rígidos de ensino, as cargas de trabalho pesadas e horas de trabalho repetitivas, torna-se imperativo nesta era da informação realizar pesquisas inovadoras com a ajuda da “Internet das Coisas”, do “Big Data” e meios científicos e tecnológicos que nos permitam realizar a reforma nas escolas e estabelecer estruturas organizacionais e métodos de gestão adaptados aos tempos em que vivemos. Os resultados destas pesquisas irão contribuir para melhorar de uma forma abrangente a administração da educação básica, o que por sua vez promoverá a qualidade da educação e do ensino. Esta tese estuda o Tianli Education Group, que consideramos um bom exemplo do ensino privado na educação básica na China. Adoptando métodos experimentais de pesquisa, o comportamento dos estudantes e professores das escolas Tianli foram analisados. Aplicamos a tecnologia da “Internet das Coisas” para recolher informações sobre comportamento dos alunos na escola. Da mesma forma, após a recolha e análise dos dados sobre o comportamento dos professores na escola, efetuamos a análise do conteúdo e dos processos do seu trabalho. Tendo por base estas experiências, esta tese explora na nova era 5G, um modo apropriado para seleção e formação dos alunos. Esta tese descreve o cenário padrão de trabalho para professores e melhora não somente a eficiência do trabalho como também os seus salários ao “reduzir o pessoal e simplificar a administração”, reacendendo assim o entusiasmo dos professores pelo seu trabalho. Finalmente, como parte desta tese, uma série de mudanças organizacionais foram implementadas nas escolas do grupo Tianli Education Group com a finalidade de impulsionar a vitalidade organizacional, melhorar todos os níveis gerais de educação, aumentar a eficiência operacional e de ensino, aumentar os salários dos professores e aumentar a felicidade dos alunos

Artificial Intelligence And Big Data Technologies To Close The Achievement Gap.

We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts

ADO-Tutor: Intelligent Tutoring System for leaning ADO.NET

This paper describes an Intelligent Tutoring System for helping users with ADO.NET called ADO-Tutor. The Intelligent Tutoring System was designed and developed using (ITSB) authoring tool for building intelligent educational systems. The user learns through the intelligent tutoring system ADO.NET, the technology used by Microsoft.NET to connect to databases. The material includes lessons, examples, and questions. Through the feedback provided by the intelligent tutoring system, the user's understanding of the material is assessed, and accordingly can be guided to different difficulty level of exercises and/or the lessons. The Intelligent Tutoring System was evaluated by a group of users and the results were more than satisfactory in terms of the quality of the material and the design of the system

Multiple Users’ Experiences of an AI-Aided Educational Platform for Teaching and Learning

This chapter aims to provide a better understanding of how AI technology can be used to assist in teaching and learning at schools. The Smart Learning Partner (SLP) educational platform is based on AI technology to provide new possibilities for individualized learning and more educational resources. We used a case study approach to investigate how this AI-aided SLP platform helped to assist in teaching and learning from the multiple users’ perspectives of students, teachers, and the principal at a Chinese school. This chapter starts with a description of AI used in education and learning. The AI-aided SLP educational platform is then presented to illustrate in what ways it works to assist in teaching and learning. Based on the users’ self-reported experience, this platform can support students’ learning by providing students with individualized diagnostic feedback and assessments as well as information about the progress of their learning. In addition, students receive recommendations of micro lectures from the platform to aid improvement based on the students’ assessment results. Additionally, students can also access various micro lectures according to their interests. This platform provides teachers with reports of real-time learning situations and progress at the individual or class level. Accordingly, teachers can better adjust their pedagogical decision and teaching according to the students’ needs. The principal used the information to allocate resources and assist in curriculum planning at school. In conclusion, all users positively stated that this AI-aided SLP platform assisted in teaching and learning at school even though there were still certain challenges. At the end of the chapter, recommendations for the future platform design are offeredPeer reviewe

Design and Development of an Intelligent Tutoring System for C# Language

Learning programming is thought to be troublesome. One doable reason why students don’t do well in programming is expounded to the very fact that traditional way of learning within the lecture hall adds more stress on students in understanding the Material rather than applying the Material to a true application. For a few students, this teaching model might not catch their interest. As a result, they'll not offer their best effort to grasp the Material given. Seeing however the information is applied to real issues will increase student interest in learning. As a consequence, this may increase their effort to be taught. In the current paper, we try to help students learn C# programming language using Intelligent Tutoring System. This ITS was developed using ITSB authoring tool to be able to help the student learn programming efficiently and make the learning procedure very pleasing. A knowledge base using ITSB authoring tool style was used to represent the student's work and to give customized feedback and support to students

Research on Personalized Learning Resource Recommendation Based on Knowledge Graph Technology

In the face of the dilemma of learners\u27 learning loss and information overload in information resources, a personalized learning resource recommendation algorithm is proposed by conducting in-depth and extensive research on the knowledge graph. This algorithm relies on the similarity or correlation between learners\u27 characteristics and course knowledge (learning resources) for recommendation. It analyzes learners\u27 characteristics in depth from four aspects: data collection and processing, model construction, resource and path recommendation, and model application, and establishes a multi layered dynamic feature model for learners; Analyze the core elements of the curriculum knowledge graph, decompose the curriculum knowledge into nanoscale knowledge granularity, and construct a curriculum knowledge graph model. The experimental results indicate that this algorithm improves learners\u27 learning efficiency and promotes their personalized development

Research on the New Eco-construction of College English Teaching in the Data Age

Along with the in-depth application of computer and network technology in the field of education, the concept and strategy of College English teaching is quietly undergoing a major change. The era of big data has brought a new perspective and direction to college English teaching reform. Under the development trend of continuous integration of education and digital technology, College English teachers need to build a new ecology of College English based on the era of data, explore a hybrid teaching model by breaking the time and space constraints, reconstruct the evaluation mode of education quality by applying data mining and develop teaching team building by changing self-role. In this way, a systematic, open, dynamic and three-dimensional College English curriculum system can be established to better meet the needs of college students getting high-quality and diversified college English teaching, and to meet the requirements of national economic and social development for talent training

Artificial intelligent based teaching and learning approaches: A comprehensive review

The goal of this study is to investigate the potential effects that Artificial intelligence (AI) could have on education. The narrative and framework for investigating AI that emerged from the preliminary research served as the basis for the study’s emphasis, which was narrowed down to the use of AI and its effects on administration, instruction, and student learning. According to the findings, artificial intelligence has seen widespread adoption and use in education, particularly by educational institutions and in various contexts and applications. The development of AI began with computers and technologies related to computers; it then progressed to web-based and online intelligent education systems; and finally, it applied embedded computer systems in conjunction with other technologies, humanoid robots, and web-based chatbots to execute instructor tasks and functions either independently or in partnership with instructors. By utilizing these platforms, educators have been able to accomplish a variety of administrative tasks. In addition, because the systems rely on machine learning and flexibility, the curriculum and content have been modified to match the needs of students. This has led to improved learning outcomes in the form of higher uptake and retention rates

Multi-Armed Bandits for Intelligent Tutoring Systems

We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two algorithms that rely on the empirical estimation of the learning progress, RiARiT that uses information about the difficulty of each exercise and ZPDES that uses much less knowledge about the problem. The system is based on the combination of three approaches. First, it leverages recent models of intrinsically motivated learning by transposing them to active teaching, relying on empirical estimation of learning progress provided by specific activities to particular students. Second, it uses state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the exploration/exploitation challenge of this optimization process. Third, it leverages expert knowledge to constrain and bootstrap initial exploration of the MAB, while requiring only coarse guidance information of the expert and allowing the system to deal with didactic gaps in its knowledge. The system is evaluated in a scenario where 7-8 year old schoolchildren learn how to decompose numbers while manipulating money. Systematic experiments are presented with simulated students, followed by results of a user study across a population of 400 school children