There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.
Artificial intelligence has entered every industry, and the educational sector is no exception. The administrative staff, management, teachers, and students are all using AI in different ways to achieve similar goals. During the last few years, AI has spread its roots much wider and deeper in this sector. Markets and Markets has predicted that the global market share of AI in education is estimated to reach $3.68 billion by 2023 at a CAGR (Compound Annual Growth Rate) of 47%. Another platform, Market Search Engine, has predicted that the share will reach $5.80 billion by 2025.
Are you looking for Best Free Coursera Courses 2021? You can earn a Coursera Certificate with Coursera free courses by applying for Coursera scholarship and by doing Coursera paid courses. You are going to get a 7-day free trial on Coursera when you join and start your very first subscription to do a Coursera Specializations for free. If you do not cancel your free trial you will be automatically transferred to paid subscription on the 8th Day. You can continue your Coursera Classes either by using Coursera App on mobile or on any other devices. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Learn and launch your career in Data Science with these best Coursera courses. A nine-course introduction to data science developed and taught by leading instructors. Develop programs to gather, clean, analyze, and visualize data. You will get new insights into your data. Learn to apply data science methods and techniques, and acquire analytical skills. This program is designed to take beginner learners to job readiness in about eight months. Design, develop and manage cloud solutions to drive business objectives. Learn to solve real business problems. Master Excel to add a highly valuable asset to your employability portfolio.
Datasets play a critical role in shaping the perception of performance and progress in machine learning (ML)--the way we collect, process, and analyze data affects the way we benchmark success and form new research agendas (Paullada et al., 2020; Dotan & Milli, 2020). A growing appreciation of this determinative role of datasets has sparked a concomitant concern that standard datasets used for training and evaluating ML models lack diversity along significant dimensions, for example, geography, gender, and skin type (Shankar et al., 2017; Buolamwini & Gebru, 2018). Lack of diversity in evaluation data can obfuscate disparate performance when evaluating based on aggregate accuracy (Buolamwini & Gebru, 2018). Lack of diversity in training data can limit the extent to which learned models can adequately apply to all portions of a population, a concern highlighted in recent work in the medical domain (Habib et al., 2019; Hofmanninger et al., 2020). Our work aims to develop a general unifying perspective on the way that dataset composition affects outcomes of machine learning systems.
On the arts side of things, you can get a free four-week look (or listen!) to German and/or Italian opera. There's also the ability to study the classic literature of the 19th century. The Georgia Institute of Technology, or Georgia Tech, is one of the United States' leading research universities, offering a technologically based education to over 25,000 students. You can join those thousands of scholars online through edX's GTx portal. GTx offers a large selection of free technology-based classes and courses that range from five-week courses you can study part-time at home, as well as paid-for options, on topics such as human-computer interaction and an introduction to Python programming, that can lead to a professional qualification, or even a master's degree.
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to ultimately meet her desired goal. This concept emerged several years ago and is being adopted by a rapidly-growing number of educational institutions around the globe. In recent years, the boost of artificial intelligence (AI) and machine learning (ML), together with the advances in big data analysis, has unfolded novel perspectives to enhance personalized education in numerous dimensions. By taking advantage of AI/ML methods, the educational platform precisely acquires the student's characteristics. This is done, in part, by observing the past experiences as well as analyzing the available big data through exploring the learners' features and similarities. It can, for example, recommend the most appropriate content among numerous accessible ones, advise a well-designed long-term curriculum, connect appropriate learners by suggestion, accurate performance evaluation, and the like. Still, several aspects of AI-based personalized education remain unexplored. These include, among others, compensating for the adverse effects of the absence of peers, creating and maintaining motivations for learning, increasing diversity, removing the biases induced by the data and algorithms, and the like. In this paper, while providing a brief review of state-of-the-art research, we investigate the challenges of AI/ML-based personalized education and discuss potential solutions.
Since its introduction in 2011, there have been over 4000 MOOCs on various subjects on the Web, serving over 35 million learners. MOOCs have shown the ability to democratize knowledge dissemination and bring the best education in the world to every learner. However, the disparate distances between participants, the size of the learner population, and the heterogeneity of the learners' backgrounds make it extremely difficult for instructors to interact with the learners in a timely manner, which adversely affects learning experience. To address the challenges, in this thesis, we propose a framework: educational content linking. By linking and organizing pieces of learning content scattered in various course materials into an easily accessible structure, we hypothesize that this framework can provide learners guidance and improve content navigation. Since most instruction and knowledge acquisition in MOOCs takes place when learners are surveying course materials, better content navigation may help learners find supporting information to resolve their confusion and thus improve learning outcome and experience. To support our conjecture, we present end-to-end studies to investigate our framework around two research questions: 1) can manually generated linking improve learning? 2) can learning content be generated with machine learning methods? For studying the first question, we built an interface that present learning materials and visualize the linking among them simultaneously. We found the interface enables users to search for desired course materials more efficiently, and retain more concepts more readily. For the second question, we propose an automatic content linking algorithm based on conditional random fields. We demonstrate that automatically generated linking can still lead to better learning, although the magnitude of the improvement over the unlinked interface is smaller.
The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.
MOOCs have been around since 2008, when 25 students attended a course on connectivism at the University of Manitoba - with 2,300 joining online worldwide. They really hit the public consciousness around 2012, when Coursera was created. It partnered with universities to offer courses online, typically with a mix of active participation and self-paced study using filmed lectures and reading lists. Its closest competitor, edX, is a joint venture of Harvard and MIT. As the name suggests, MOOCs are designed for unlimited participation and are free, subsidized, or much cheaper than traditional higher education courses. Although they have been criticized for their low completion rate - an MIT study found an average quit rate of 96% over five years - that has done nothing to stop the demand. In 2019, MOOCs had attracted 110 million students and more than 900 universities around the world had submitted 13,500 courses.
From chatbots to discussion platforms, artificial intelligence (AI) is popping up at campuses all over the globe. In fact, the recent AI in Education Market Research Report from Research and Markets predicts that the global AI in education market will reach $25.7 billion in 2030, up from just $1.1 billion in 2019. The report shows that the largest demand for AI has been for learning platforms, mainly because of the increasing preference for remote and online education courses--even before the pandemic. It predicts that the next AI area to explode will be intelligent tutoring systems applications. A chatbot is a computer program that imitates human conversation and continually learns from every conversation it has, improving the efficiency of its responses.