Abstract--In Intelligent Tutoring System (ITS), tracing the student's knowledge state during learning has been studied for several decades in order to provide more supportive learning instructions. In this paper, we propose a novel model for knowledge tracing that i) captures students' learning ability and dynamically assigns students into distinct groups with similar ability at regular time intervals, and ii) combines this information with a Recurrent Neural Network architecture known as Deep Knowledge Tracing. Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art techniques for student modelling. ITS is an active field of research that aims to provide personalized instructions to students. A wide array of Artificial Intelligence and Knowledge Representation techniques have been explored, of which we can mention rule-based and Bayesian representation of student knowledge and misconceptions, skills modeling with logistic regression in Item Response Theory, case-based reasoning, and, more recently reinforcement learning and deep learning [1], [2]. One can even argue that most of the main techniques found in Artificial Intelligence and Data Mining have found their way into the field of ITS, and in particular for the problem of knowledge tracing, which aims to model the student's state of mastery of conceptual or procedural knowledge from observed performance on tasks [3].

This paper presents a pure neural solver for arithmetic expression calculation (AEC) problem. Previous work utilizes the powerful capabilities of deep neural networks and attempts to build an end-to-end model to solve this problem. However, most of these methods can only deal with the additive operations. It is still a challenging problem to solve the complex expression calculation problem, which includes the adding, subtracting, multiplying, dividing and bracketing operations. In this work, we regard the arithmetic expression calculation as a hierarchical reinforcement learning problem. An arithmetic operation is decomposed into a series of sub-tasks, and each sub-task is dealt with by a skill module. The skill module could be a basic module performing elementary operations, or interactive module performing complex operations by invoking other skill models. With curriculum learning, our model can deal with a complex arithmetic expression calculation with the deep hierarchical structure of skill models. Experiments show that our model significantly outperforms the previous models for arithmetic expression calculation.

Virtual Reality (VR) is increasingly being recognized for its educational potential and as an effective way to convey new knowledge to people, it supports interactive and collaborative activities. Affordable VR powered by mobile technologies is opening a new world of opportunities that can transform the ways in which we learn and engage with others. This paper reports our study regarding the application of VR in stimulating interdisciplinary communication. It investigates the promises of VR in interdisciplinary education and research. The main contributions of this study are (i) literature review of theories of learning underlying the justification of the use of VR systems in education, (ii) taxonomy of the various types and implementations of VR systems and their application in supporting education and research (iii) evaluation of educational applications of VR from a broad range of disciplines, (iv) investigation of how the learning process and learning outcomes are affected by VR systems, and (v) comparative analysis of VR and traditional methods of teaching in terms of quality of learning. This study seeks to inspire and inform interdisciplinary researchers and learners about the ways in which VR might support them and also VR software developers to push the limits of their craft.

Tens of thousands of college students nationwide will cheer for their football teams this weekend. Some of those who show up for the game after tailgate drinking may not remember the highlight touchdowns that they cheered so loudly for. Others may have trouble remembering even a rousing celebration of victory. Binge drinking, the leading type of alcohol misuse for college students, is the culprit. Drinking too much too fast can cause memory loss, sometimes called a blackout, erasing any recollection of an enjoyable life event.

For those outside the fold, the Rubik's cube is cognitive kryptonite. Until this week, I'd certainly never solved one. Even now, saying that I solved a Rubik's cube feels like a grievous overstatement of my accomplishments. The truth is that we--a patient pre-teen "cuber" whose solve time is 47 seconds, her slightly-less-patient middle school teacher (whose solve time, she's embarrassed to admit, is closer to a minute and a half), and me--completed a cube together. The site of my public humiliation could not have been more incongruous with the task at hand.

Join me as I teach this free 10-week reinforcement learning course I've called Move 37. I'll take you on a journey through the basics up to modern day techniques. Every week, we'll build apps together that will cover both toy and industry problems. You'll be able to measure your progress along the way by chatting with your peers both online and offline at the School of AI chapters globally, taking quizzes, coding challenges, and 2 graded projects. I'll have weekly coding live streams to help answer any questions, and my assistant instructors will be available to help in our community slack channel. Students who successfully complete the course will receive their own certificate signed by Siraj Raval.

Artificial intelligence promises an even bigger revolution than the internet yet could be stifled in the UK by a fear-driven public backlash, according to a leading scientist and broadcaster. Prof Jim Al-Khalili, a physicist and the incoming president of the British Science Association, warns that without greater transparency and public engagement the full potential of AI may not be realised. In the absence of concerted action by academics, the government and industry, the rapidly advancing technology could end up "uncontrolled and unregulated" in the hands of a few supremely powerful companies, he says. Previewing his presidential address at this year's British Science festival in Hull, which begins next week, Khalili spoke of the dream and dangers of AI. He said the UK was at the forefront of the technology, which is predicted to contribute up to $15tn (£11.7tn) to the global economy by 2030.

Using AI to create better, faster, more accurate content is already a reality. The Associated Press reports that it has gone from publishing 300 earnings reports a quarter to 3,000 thanks to the help of AI systems. Meanwhile, in 2016, The Washington Post published high school sports stories, Olympics, and election coverage that once again machines had written. Clearly, the future is already here. If you have any kind of data-driven content, you need to get on board right now.

The University of New England is launching an undergraduate degree program in data science on its Biddeford campus to open up more job opportunities for graduates. Michael Arciero, associate professor and chair of UNE's mathematical sciences department, said that project-based experiential learning through partnerships with employers will be an important part of the program. He noted that partnerships with companies such as UNUM Group, a Fortune 500 insurer, and healthcare IT provider Cotiviti Holdings Inc., have already begun to create pathways to permanent employees for recent UNE math sciences graduates. Data science involves the use of mathematical modeling and computing to analyze data and to design systems for making predictions, extracting information and decision-making. Beginning with foundational skills in math and computing, students will develop techniques in data mining, statistical and machine learning, predictive modeling and data visualization.

Teaching anyone about "fairness" is a laudable goal. As humans, we may not necessarily agree on what's fair. It sometimes depends on the context. Teaching kids to be fair -- both at home and in school -- is fundamental, but it's easier said than done. With this in mind, how can we, as a society, communicate the nuances of "being fair" to artificial intelligence (AI) systems?