Consumers have invited AI into their lives with voice-activated personal assistants like Siri and Alexa, but how do they feel about computer vision technologies that can provide visual coaching and feedback in their homes? Sanjay Nichani, vice president of artificial intelligence and computer vision at Peloton Interactive, describes one compelling use case in the at-home fitness space. Sanjay Nichani is vice president of artificial intelligence and computer vision at Peloton Interactive. In that role, he leads an AI/computer vision team focused on human pose estimation, activity recognition, and movement-tracking technologies for the fitness domain. He also leads the ongoing development of Peloton Guide, a new camera-based interactive strength-training product. Previously, Nichani was vice president of the computer vision and machine learning team at Acuant, working on document forensics technologies for detecting fraud. Before that, he was vice president of the Mitek Labs R&D group, where he led the development of a deep learning-based image-processing pipeline for identity verification. He also founded 3D sensor technology company Merakona and cofounded Pelfunc, developer of a photo-sharing app/service. He has advanced degrees in business from Babson College and computer science from the University of South Florida.

Explainability of AI systems is critical for users to take informed actions and hold systems accountable. While "opening the opaque box" is important, understanding who opens the box can govern if the Human-AI interaction is effective. In this paper, we conduct a mixed-methods study of how two different groups of whos--people with and without a background in AI--perceive different types of AI explanations. These groups were chosen to look at how disparities in AI backgrounds can exacerbate the creator-consumer gap. We quantitatively share what the perceptions are along five dimensions: confidence, intelligence, understandability, second chance, and friendliness. Qualitatively, we highlight how the AI background influences each group's interpretations and elucidate why the differences might exist through the lenses of appropriation and cognitive heuristics. We find that (1) both groups had unwarranted faith in numbers, to different extents and for different reasons, (2) each group found explanatory values in different explanations that went beyond the usage we designed them for, and (3) each group had different requirements of what counts as humanlike explanations. Using our findings, we discuss potential negative consequences such as harmful manipulation of user trust and propose design interventions to mitigate them. By bringing conscious awareness to how and why AI backgrounds shape perceptions of potential creators and consumers in XAI, our work takes a formative step in advancing a pluralistic Human-centered Explainable AI discourse.

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.

Thus, one of the most fundamental tasks in statistics is to provide bounds on the sample size which is sufficient to soundly represent the population, and probabilistic tools are used to derive such guarantees, under a variety of assumptions. Virtually all of these guarantees are based on classical probabilistic models which assume that the target population is fixed in advance and does not depend on the sample collected throughout the process . Such an assumption, that the setting is offline (or oblivious or static), is however not always realistic. In this work we explore an abstract framework which removes this assumption, and prove that natural and efficient sampling processes produce samples which soundly represent the target population. Situations where the sampling process explicitly or implicitly affects the target population are abundant in modern data analysis. Consider, for instance, navigation apps that optimize traffic by routing drivers to less congested routes: such apps collect statistics from drivers to estimate the traffic-load on the routes, and use these estimates to guide their users through faster routes.

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.

This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences. Learn how to perform supervised and reinforcement learning, with images and temporal sequences. This course includes lectures, lecture notes, exercises, labs, and homework problems.

We propose VarFA, a variational inference factor analysis framework that extends existing factor analysis models for educational data mining to efficiently output uncertainty estimation in the model's estimated factors. Such uncertainty information is useful, for example, for an adaptive testing scenario, where additional tests can be administered if the model is not quite certain about a students' skill level estimation. Traditional Bayesian inference methods that produce such uncertainty information are computationally expensive and do not scale to large data sets. VarFA utilizes variational inference which makes it possible to efficiently perform Bayesian inference even on very large data sets. We use the sparse factor analysis model as a case study and demonstrate the efficacy of VarFA on both synthetic and real data sets. VarFA is also very general and can be applied to a wide array of factor analysis models.

Knowledge tracing (KT) refers to the problem of predicting future learner performance given their past performance in educational applications. Recent developments in KT using flexible deep neural network-based models excel at this task. However, these models often offer limited interpretability, thus making them insufficient for personalized learning, which requires using interpretable feedback and actionable recommendations to help learners achieve better learning outcomes. In this paper, we propose attentive knowledge tracing (AKT), which couples flexible attention-based neural network models with a series of novel, interpretable model components inspired by cognitive and psychometric models. AKT uses a novel monotonic attention mechanism that relates a learner's future responses to assessment questions to their past responses; attention weights are computed using exponential decay and a context-aware relative distance measure, in addition to the similarity between questions. Moreover, we use the Rasch model to regularize the concept and question embeddings; these embeddings are able to capture individual differences among questions on the same concept without using an excessive number of parameters. We conduct experiments on several real-world benchmark datasets and show that AKT outperforms existing KT methods (by up to $6\%$ in AUC in some cases) on predicting future learner responses. We also conduct several case studies and show that AKT exhibits excellent interpretability and thus has potential for automated feedback and personalization in real-world educational settings.

> Science's COVID-19 coverage is supported by the Pulitzer Center. In a normal summer, Appledore Island, a 39-hectare outcrop 12 kilometers off the coast of Maine and New Hampshire, becomes a classroom. Students from high school to graduate level live in close quarters, eat in a communal dining hall, and work shoulder to shoulder to explore the biology of the shore and waters in 18 courses organized by the Shoals Marine Laboratory. But this summer, with the pandemic surging, students have stayed home. Instead, a skeleton staff on Appledore is streaming field trips and dissections of fish and invertebrates and setting up cameras to gather data for students. Rather than leading students around the island, coastal restoration ecologist Gregg Moore from the University of New Hampshire (UNH), Durham, hauls a backpack full of equipment: “a dual modem with two different cellular carriers, a signal-boosting directional antenna, and a large DC power source,” he says. The equipment allows him to teach 12 remote students—twice the course's usual enrollment—basic techniques of coastal ecology. Moore's is just one of hundreds of lab and field courses forced online by COVID-19—“a seismic shift for those who were not already involved in distance or online education,” says Martin Storksdieck, a science education researcher at Oregon State University, Corvallis. Some researchers worry students will miss out on certain practical and problem-solving skills and won't be able to judge whether the hands-on work of a scientist is a good fit for them. But instructors are developing high-tech ways to simulate the field and lab experiences. “I would say [these courses] are not virtual,” says Jennifer Seavey, director of the Shoals lab. “They are real.” And some advantages are emerging. By lowering geographical and financial barriers, Seavey says, “Virtual field courses are democratizing fieldwork.” The shift has taken ingenuity. “Professors must get creative and use a combination of what is available,” including online videos and free or commercially available online labs, says Mildred Pointer, a physiologist at Howard University who is working on a fall course in general biology. No single tool meets all their needs, Pointer says. As the pandemic gained momentum, emails flew among the leaders of the National Association of Geoscience Teachers. Many U.S. geology majors must take a “capstone” field course to graduate. The cancellation of more than three-quarters of these courses jeopardized graduation for many majors. So the association invited instructors to develop learning objectives that did not depend on students doing fieldwork. It also compiled online exercises to help the 29 field courses that have moved online this summer. Lessons range from “Orienteering in Minecraft” to “Geology of Yosemite Valley,” which includes a 43-stop Google Earth tour with photos and embedded text. Like Moore, geoscientist Jim Handschy wanted to give remote students “as close to the real experience as possible.” He runs Indiana University's Judson Mead Geologic Field Station in Montana, which had enrolled 60 students before classes were canceled in March. He and a few instructors visited each outcrop in their course plan, filmed the rocks and landscape, and captured magnified views of samples. Each week, the class delves deeper into the rock layers and their history. For their final project, students digitally map a 3100-hectare landscape. Shannon Dulin, a geologist at the University of Oklahoma, Norman, who just finished teaching a field course, sees the value of learning how to survey a landscape without setting foot on it. On their class evaluations, her students said they gained unexpected skills. “And these are skills they are going to need on the job,” she adds, as geologists are increasingly being asked to evaluate sites they don't visit. In other fields, hands-on learning takes place in labs. Typically, students work in pairs and share equipment, “so there are a lot of issues about virus transmission,” says Heather Lewandowski, a physicist at the University of Colorado (CU), Boulder. At her university this fall, lab exercises as diverse as building an electrical circuit or analyzing solar flare data will most likely be completely remote. Luckily, physics already had a foot in the virtual lab world—especially at CU. There, back in 2002, Nobel laureate Carl Wieman developed the Physics Education Technology (PhET) Interactive Simulations project to provide “games” that teach students basic physics concepts. The PhET web portal now has 106 physics-based simulations and another 50 or so for other disciplines. It became a go-to place this spring for faculty shifting to online teaching; traffic increased fivefold, says Director Katherine Perkins. In addition, several universities have adopted a handheld device called the iOLab that rents for $50 a semester. With it, students can measure magnetism, light intensity, acceleration, temperature, gravity, and atmospheric pressure, and do basic physics experiments at home. “They like that we trust them and are not just giving them instructions,” says iOLab inventor and physicist Mats Selen at the University of Illinois, Urbana-Champaign. Lewandowski and her colleagues surveyed physics instructors and students about their experiences and posted their findings on arXiv, the physics preprint server, on 2 July. Respondents said online labs work best when projects are open-ended, and online class meetings are kept small. They complained about technical difficulties, students having unequal access to the internet and materials, and longer prep times for both students and instructors. But they reported they could meet most key learning objectives, Lewandowski says, even though “there are lots of things we can't replicate in remote experiments,” such as such as building vacuum chambers or troubleshooting equipment. Some institutions decided this spring that virtual just wouldn't do. The Marine Biological Laboratory (MBL) in Woods Hole, Massachusetts, simply canceled its summer courses. “MBL courses are world-renowned for the intensity of the hands-on nature of the lab work,” says Director Nipam Patel. Students spend long hours with famous faculty and do their own projects using organisms collected locally. “We felt that it would be exceedingly difficult to replicate these experiences as a virtual lab course.” Other institutions will try for a mix of in-person and virtual labs. Suely Black, chemistry chair at Norfolk State University, expects only half of his students will be in lab each week this fall, while the other half will be in online classes analyzing data and writing reports. “The crisis has caused us to more critically evaluate what activities students must experience in the lab setting,” he says. Similarly, this fall, organic chemistry students at the University of Michigan (UM), Ann Arbor, will rotate into the lab in small groups, giving each a taste of the hands-on experience. Personal protection equipment is standard for this course and all the work is done in hoods with excellent air exchange, so “they are really fully protected,” says UM biochemist Kathleen Nolta. Storksdieck, an advocate of online learning, questions the value of smelling fumes or using a pipette. “We have to ask whether all the hands-on taught so far was all that great,” he says. Dominique Durand, a biomedical engineer at Case Western Reserve University, says after he put a master's program in biomedical engineering completely online 5 years ago, he concluded that solving problems was more important than hands-on experience. And University of California, Santa Cruz, ecologist Erika Zavaleta thinks virtual courses will open fieldwork to far more students. “There are things you can do online that you can't do in person,” she adds, such as visiting more places than possible by driving. Even so, Handschy laments that his geology students will not have the 12-hour-a-day immersive interactions with each other and faculty that past classes have had. Natalie White, a rising junior at UNH who took Moore's course on Appledore last year, agrees: “You don't have all the time in between when you walk around the island and can ask impromptu questions.” Appledore Island is the source of some her fondest memories. “I think they are missing out on the community.”

Learning Management Systems (LMS) and Educational Data Mining (EDM) are two important parts of online educational environment with the former being a centralised web-based information systems where the learning content is managed and learning activities are organised (Stone and Zheng,2014) and latter focusing on using data mining techniques for the analysis of data so generated. As part of this work, we present a literature review of three major tasks of EDM (See section 2), by identifying shortcomings and existing open problems, and a Blumenfield chart (See section 3). The consolidated set of papers and resources so used are released in https://github.com/manikandan-ravikiran/cs6460-Survey. The coverage statistics and review matrix of the survey are as shown in Figure 1 & Table 1 respectively. Acronym expansions are added in the Appendix Section 4.1.