In the setting of online learning, Implicit algorithms turn out to be highly successful from a practical standpoint. However, the tightest regret analyses only show marginal improvements over Online Mirror Descent. In this work, we shed light on this behavior carrying out a careful regret analysis. We prove a novel static regret bound that depends on the temporal variability of the sequence of loss functions, a quantity which is often encountered when considering dynamic competitors. We show, for example, that the regret can be constant if the temporal variability is constant and the learning rate is tuned appropriately, without the need of smooth losses. Moreover, we present an adaptive algorithm that achieves this regret bound without prior knowledge of the temporal variability and prove a matching lower bound.

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Educational technology leadership is by no means uniform across institutions. The work is variously distributed among CIOs, CTOs, teaching and learning centers, academic administration, online learning outfits and sometimes even smaller-scale labs, institutes or departments. On most campuses there is not yet an ed tech center of gravity around which the others orbit. Institutions must empower chief educational technology leaders as true partners in developing the core university strategy for the next era of learning. The modern era of ed tech parallels the development of information technology in general.

In the coming years, there won't be a single industry in the world untouched by Machine Learning. A transformative force, you can either choose to understand it now, or lose out on a wave of incredible change. You probably already use apps many times each day that rely upon Machine Learning techniques. So why stay in the dark any longer? There are many courses on Machine Learning already available.

The time has come for Artificial Intelligence (AI)-driven teaching assistants to help ease a human teacher's workload in the age of online learning, however, such virtual machines have to be effective and communicate well to be accepted by the society in a broad way, argue researchers. The increase in online education has allowed a new type of teacher to emerge -- an artificial one. But just how accepting students are of an artificial instructor remains to be seen, said researchers at the University of Central Florida's Nicholson School of Communication and Media who are working to examine student perceptions of AI-based teachers. Some of their findings, published in the'International Journal of Human-Computer Interaction', indicated that for students to accept an AI teaching assistant, it needs to be effective and easy to talk to. "The hope is that by understanding how students relate to AI-teachers, engineers and computer scientists can design them to easily integrate into the education experience," said Jihyun Kim, an associate professor in the school and lead author of the study.

With the emergence of e-learning and personalised education, the production and distribution of digital educational resources have boomed. Video lectures have now become one of the primary modalities to impart knowledge to masses in the current digital age. The rapid creation of video lecture content challenges the currently established human-centred moderation and quality assurance pipeline, demanding for more efficient, scalable and automatic solutions for managing learning resources. Although a few datasets related to engagement with educational videos exist, there is still an important need for data and research aimed at understanding learner engagement with scientific video lectures. This paper introduces VLEngagement, a novel dataset that consists of content-based and video-specific features extracted from publicly available scientific video lectures and several metrics related to user engagement. We introduce several novel tasks related to predicting and understanding context-agnostic engagement in video lectures, providing preliminary baselines. This is the largest and most diverse publicly available dataset to our knowledge that deals with such tasks. The extraction of Wikipedia topic-based features also allows associating more sophisticated Wikipedia based features to the dataset to improve the performance in these tasks. The dataset, helper tools and example code snippets are available publicly at https://github.com/sahanbull/context-agnostic-engagement

Advances in AI, making use of machine learning (ML) and natural language processing (NLP) have proven adept for voice recognition and identifying …

BagyaTech is not just another online training institute that offers courses on Testing Tools and other software technologies. We are transforming and redefining the online education. We are making learning a fun and interactive activity through which learners gain maximum and succeed in their careers.

Distance learning and remote teaching have increased reliance on tech making it a reality, and able to traverse borders with less regard for physical geo-locations. There are numerous restrictions that prevent online learning from being ubiquitous such as internet accessibility, access to learning platforms, adequate attention for learners individually, and language barriers. Video-based learning could be enough for urban pupils, but for rural areas, connectivity becomes low, less reliable, and interrupted lessons. For international students, pursuing higher education or probably taking vocational courses, a lack in fluency in English or any other intermediary languages can play a significant role in limiting proper online learning. Learning a new language is the objective for work or to further studies, but the bigger question is how technology can bridge the language learning divide.

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