Peer Assessment is a task of analysis and commenting on student's writing by peers, is core of all educational components both in campus and in MOOC's. However, with the sheer scale of MOOC's & its inherent personalised open ended learning, automatic grading and tools assisting grading at scale is highly important. Previously we presented survey on tasks of post classification, knowledge tracing and ended with brief review on Peer Assessment (PA), with some initial problems. In this review we shall continue review on PA from perspective of improving the review process itself. As such rest of this review focus on three facets of PA namely Auto grading and Peer Assessment Tools (we shall look only on how peer reviews/auto-grading is carried), strategies to handle Rogue Reviews, Peer Review Improvement using Natural Language Processing. The consolidated set of papers and resources so used are released in https://github.com/manikandan-ravikiran/cs6460-Survey-2.

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.

The New York Institute of Finance (NYIF) and Google Cloud announced a new Machine Learning for Trading Specialization available exclusively on the Coursera platform. The Specialization helps learners leverage the latest AI and machine learning techniques for financial trading. Amid the Fourth Industrial Revolution, nearly 80 percent of financial institutions cite machine learning as a core component of business strategy and 75 percent of financial services firms report investing significantly in machine learning. The Machine Learning for Trading Specialization equips professionals with key technical skills increasingly needed in the financial industry today. Composed of three courses in financial trading, machine learning, and artificial intelligence, the Specialization features a blend of theoretical and applied learning.

Artificial intelligence offers a way to solve that problem.

January 25, 2020 - The AI revolution is upon us. How much is Croatia lagging behind, and are we going to do something about it? But even if we start those processes, where would we be in comparison to the rest of the world? What are other countries already doing and what should we be aware of? Fortunately, a fear of missing out is spreading around the globe or at least among some countries.

Conversations around artificial intelligence's potential in higher education are growing, and a report outlines some of the ways in which AI could revolutionize higher education. Artificial Intelligence in Higher Education: Current Uses and Future Applications, from The Learning House, casts a critical eye on the immediate and future applications of AI in higher ed, and it also examines implementation challenges. The report also highlights important policy guidance and recommendations that are likely to accelerate AI innovation or, if unrealized, stifle its growth and adoption. Related content: Is your campus ready for AI and other tech trends? For example, the Family Educational Rights and Privacy Act (FERPA) last updated in 2001, predates many common education technologies including smartphones, tablets, wireless data, MOOCs, and even online education programs in general.

This article will discuss about basic programming languages that you need for doing data science. For essential math skills needed, please see the following: Essential Math Skills for Machine Learning.

This paper presents several strategies that can improve neu - ral network-based predictive methods for MOOC student course trajectory modeling, applying multiple ideas previ - ously applied to tackle NLP (Natural Language Processing) tasks. In particular, this paper investigates LSTM network s enhanced with two forms of regularization, along with the more recently introduced Transformer architecture.

Two years ago, I graduated college where I studied Economics and Finance. I was all set for a career in finance. Investment Banking and Global Markets -- those were the dream jobs. Months into the job, I picked up some Excel VBA and learnt how to use Tableau, Power BI and UiPath (a Robotics Process Automation software). I realized I was more interested in picking up these tools and learning to code rather than learning about banking products.

Machine Learning is a fast growing, rapidly advancing field that touches nearly everyone's lives. There has recently been an explosion of successful machine learning applications - in everything from voice recognition to text analysis to deeper insights for researchers. While common and frequently talked about, most people have only a vague concept of how machine learning actually works. In this tutorial, Dr. Artemy Kolchinsky and Dr. Brendan Tracey outline exactly what it is that makes machine learning so special in an accessible way. The principles of training and generalization in machine learning are explained with ample metaphors and visual intuitions, an extended analysis of machine learning in games provides a thorough example, and a closer look at the deep neural nets that are the core of successful machine learning.