As has happened with almost all areas of our life, technology came to change forever, also education. Being one of the most rigid industries in society, it is not entirely easy for the changes that are taking place to take shape in the short or medium term. However, the technological revolution of recent decades, and especially the advances of recent years, provide a good number of tools that, well used, can be very useful for educational purposes. Video games, applications and platforms to solve tasks or communication with parents, flexible spaces that adapt to the needs of increasingly collaborative work and even robots that correct tests and send feedback almost in real time are some of the many changes that are being implemented and that are coming, here and in the world. This is the great premise from which almost all technological changes in education emerge. The model of the boy sitting on a bench with a teacher who explains how things are out of date.

Before the global pandemic started, I did not think anything could break my heart as thoroughly as watching my daughter struggle with remote kindergarten. In the grand scheme of things, not being able to operate Google Meet is a privileged problem to have. After all, my daughter has a quiet room, reliable internet, active adult support, and her own electronic device. One of us has to babysit her on the computer, as she can't manage Google Meet, writing on her whiteboard, and studying the class material by herself. There's always one poorly lit, hyperactive student who never mutes.

On this course, you will learn how AI technology and AI processes can help businesses with both human and automated business planning and decision-making. As you learn the concepts of data sources, knowledge acquisition and types of machine learning algorithms, you will develop an understanding of the process of moving from data to knowledge. You will then explore how this process can be used to inform your professional decision-making and business planning.

The editors at Solutions Review have compiled this list of the best machine learning courses on Coursera to consider if you're looking to grow your skills. Machine learning involves studying computer algorithms that improve automatically through experience. It is a sub-field of artificial intelligence where machine learning algorithms build models based on sample (or training) data. Once a predictive model is constructed it can be used to make predictions or decisions without being specifically commanded to do so. Machine learning is now a mainstream technology with a wide variety of uses and applications.

So you want to learn Data Science with R? Good Decision! Because R programming has various statistical and graphical capabilities. R has a huge variety of libraries to perform statistical analysis. Some most powerful visualization packages in R are ggplot2, ggvis, googleVis, and rCharts. So if you are looking for the Best Online Courses for Data Science with R, then this article will help you.

INTRODUCTION With increasing awareness of the societal risks of algorithmic bias and encroaching automation, issues of fairness, accountability, and transparency in data-driven AI systems have received growing academic attention in multiple high-stakes contexts, including healthcare, loan-granting, and hiring (e.g., Barocas & Selbst, 2016; Holstein, Wortman Vaughan, Daumé III, Dudik, & Wallach, 2019; Veale, Van Kleek, & Binns, 2018). Given these noble intentions, why might AIEd systems have inequitable impacts? In this chapter, we ask whether AIEd systems will ultimately serve to A mplify I nequities in Ed ucation, or alternatively, whether they will help to A lleviate existing inequities. We discuss four lenses that can be used to examine how and why AIEd systems risk amplifying existing inequities: (1) factors inherent to the overall socio-technical system design; (2) the use of datasets that reflect historical inequities; (3) factors inherent to the underlying algorithms used to drive machine learning and automated decision-making, and (4) factors that emerge through a complex interplay between automated and human decision-making. Building from these lenses, we then outline possible paths towards more equitable futures for AIEd, while highlighting debates surrounding each proposal. In doing so, we hope to provoke new conversations around the design of equitable AIEd, and to push ongoing conversations in the field forward. PATHWAYS TOWARD INEQUITY IN AIED We begin by presenting four lenses to understand how AIEd systems might amplify existing inequities or even create new ones (cf. While each lens provides a different way of examining pathways towards inequity in AIEd, all are pointed at the same underlying socio-technical system. Figure 1 provides a coarse-grained overview of the broader social-technical systems in which AIEd systems are embedded, and some of the components we will refer to in the four lenses. The accumulated, collective decisions of designers, researchers, policy-makers, and other stakeholders shape these systems' designs. In addition to using or being affected by AIEd systems, on-the-ground stakeholders such as students, teachers, or school administrators may also play a role in shaping their designs; whether directly, through participatory design processes, or indirectly through the passive generation of training data while interacting with an AIEd interface. In turn, decisions regarding what data is used to shape an AIEd system's design (e.g., when used as training data for use with machine learning methods) can shape an AIEd system's algorithmic behavior (e.g., instructional policies learned from data).

Massive Open Online Courses (MOOCs) have become a popular choice for e-learning thanks to their great flexibility. However, due to large numbers of learners and their diverse backgrounds, it is taxing to offer real-time support. Learners may post their feelings of confusion and struggle in the respective MOOC forums, but with the large volume of posts and high workloads for MOOC instructors, it is unlikely that the instructors can identify all learners requiring intervention. This problem has been studied as a Natural Language Processing (NLP) problem recently, and is known to be challenging, due to the imbalance of the data and the complex nature of the task. In this paper, we explore for the first time Bayesian deep learning on learner-based text posts with two methods: Monte Carlo Dropout and Variational Inference, as a new solution to assessing the need of instructor interventions for a learner's post. We compare models based on our proposed methods with probabilistic modelling to its baseline non-Bayesian models under similar circumstances, for different cases of applying prediction. The results suggest that Bayesian deep learning offers a critical uncertainty measure that is not supplied by traditional neural networks. This adds more explainability, trust and robustness to AI, which is crucial in education-based applications. Additionally, it can achieve similar or better performance compared to non-probabilistic neural networks, as well as grant lower variance.

Data poisoning is a type of adversarial attack on training data where an attacker manipulates a fraction of data to degrade the performance of machine learning model. Therefore, applications that rely on external data-sources for training data are at a significantly higher risk. There are several known defensive mechanisms that can help in mitigating the threat from such attacks. For example, data sanitization is a popular defensive mechanism wherein the learner rejects those data points that are sufficiently far from the set of training instances. Prior work on data poisoning defense primarily focused on offline setting, wherein all the data is assumed to be available for analysis. Defensive measures for online learning, where data points arrive sequentially, have not garnered similar interest. In this work, we propose a defense mechanism to minimize the degradation caused by the poisoned training data on a learner's model in an online setup. Our proposed method utilizes an influence function which is a classic technique in robust statistics. Further, we supplement it with the existing data sanitization methods for filtering out some of the poisoned data points. We study the effectiveness of our defense mechanism on multiple datasets and across multiple attack strategies against an online learner.

In educational applications, Knowledge Tracing (KT), the problem of accurately predicting students' responses to future questions by summarizing their knowledge states, has been widely studied for decades as it is considered a fundamental task towards adaptive online learning. Among all the proposed KT methods, Deep Knowledge Tracing (DKT) and its variants are by far the most effective ones due to the high flexibility of the neural network. However, DKT often ignores the inherent differences between students (e.g. memory skills, reasoning skills, ...), averaging the performances of all students, leading to the lack of personalization, and therefore was considered insufficient for adaptive learning. To alleviate this problem, in this paper, we proposed Leveled Attentive KNowledge TrAcing (LANA), which firstly uses a novel student-related features extractor (SRFE) to distill students' unique inherent properties from their respective interactive sequences. Secondly, the pivot module was utilized to dynamically reconstruct the decoder of the neural network on attention of the extracted features, successfully distinguishing the performance between students over time. Moreover, inspired by Item Response Theory (IRT), the interpretable Rasch model was used to cluster students by their ability levels, and thereby utilizing leveled learning to assign different encoders to different groups of students. With pivot module reconstructed the decoder for individual students and leveled learning specialized encoders for groups, personalized DKT was achieved. Extensive experiments conducted on two real-world large-scale datasets demonstrated that our proposed LANA improves the AUC score by at least 1.00% (i.e. EdNet 1.46% and RAIEd2020 1.00%), substantially surpassing the other State-Of-The-Art KT methods.

Roorkee: Indian Institute of Technology (IIT) Roorkee has launched two online certificate programs in high-demand topics -- Data Science and Machine Learning and Advanced Machine Learning and AI -- on Coursera, one of the world's leading online learning platform. "We are happy to announce two certificate courses in data science, machine learning, and AI in partnership with Coursera. This will enable a large number of aspirants to acquire these relevant areas for their professional growth," said Prof. Ajit K. Chaturvedi, Director, IIT Roorkee IIT Roorkee is among 150 top universities, including Yale, University of Michigan, University of Pennsylvania, and Imperial College of London -- that offer programs on Coursera. "For over 170 years, IIT Roorkee has been a leading Indian institution, known for its rigorous technical programs," said Betty Vandenbosch, Chief Content Officer at Coursera. "Through our partnership, we are expanding access and allowing more students to learn from IIT Roorkee's renowned faculty. Learners will gain the cutting-edge skills they need to advance their careers while creating powerful networks with their peers."