Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are important words in a given lesson material. To validate that the system is not perverse, five lesson materials were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the teacher were compared to the auto-generated keywords and the result shows that the system was capable of extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a user-friendly interface for easy accessibility.

Science and technology curricula in UK schools are'not fit for purpose' and need to be updated to help pupils'change the world for the better', teachers have warned. Polls taken amid COVID-19 on behalf of the Amazon Longitude Explorer Prize found 47 per cent of teachers think the technology curriculum, specifically, is out of date. More than half of teachers said they had not the support to ensure lessons kept pace with advances and 59 per cent said resource shortages were limiting lesson plans. And 59 per cent said the STEM (Science, Technology, Engineering and Mathematics) curricula constrain their ability to help students reach their potential. The findings also flagged issues that have arisen specifically as a consequence of the COVID-19 pandemic -- including the impact such has had on practical teaching.

Description Would you like to be able to develop and prepare the data you need to pose, explore, and answer the most pressing and complex questions in your field of research? This course concerns itself with one of the most demanding and least covered parts of developing a predictive model for precision agriculture, or just about anything: sampling. When studying machine learning through video tutorials you normally access somebody's dataset and learn how to apply algorithms. But how were those neat datasets created? This course details how to use and adapt to your unique needs some tools I developed to sample just about any spatially explicit variable through the Google Earth Engine Platform.

The original version of this article appeared in Toward Data Science. When I started teaching data science and artificial intelligence in Duke University's Pratt School of Engineering, I was frustrated by how little insight I actually felt I had into how effective my teaching was, until the end-of-semester final exam grades and student assessments came in. Being new to teaching, I spent time reading up on pedagogical best practices and how methods like mastery learning and one-on-one personalized guidance could drastically improve student outcomes. Yet even with my relatively small class sizes I did not feel I had enough insight into each individual student's learning to provide useful personalized guidance to them. In the middle of the semester, if you had asked me to tell you exactly what a specific student had mastered from the class to date and where he or she was struggling, I would not have been able to give you a very good answer.

This seminar will be run via zoom, when you register on Eventbrite you will receive a zoom registration email alongside your standard Eventbrite registration email. Where speakers have given permission to be recorded, their talks will be made available on our AI3SD YouTube Channel. Abstract: Prediction of protein functional properties from sequence is a central challenge that would allow us to discover new proteins with specific functionality. Experimental breakthroughs allow data on the relationship between sequence and function to be rapidly acquired that can be used to train and validate machine learning models that predict protein function directly from sequence. However, the cost and latency of wet-lab experiments require methods that find good sequences in few experimental rounds, where each round contains large batches of sequence designs.

Machine Learning is one of the hottest technology field in the world right now! This field is exploding with opportunities and career prospects. Machine Learning techniques are widely used in several sectors now a days such as banking, healthcare, finance, education transportation and technology. This course covers several technique in a practical manner, the projects include coding sessions as well as Algorithm Intuition: So, if you've ever wanted to play a role in the future of technology development, then here's your chance to get started with Machine Learning. Because in a practical life, machine learning seems to be complex and tough,thats why we've designed a course to help break it down into real world use-cases that are easier to understand.

Potts is a doctoral candidate in the Department of Mathematical Sciences who studies the development of machine learning and data science …

If you have access to Oracle Database, then you probably already have access to Oracle Machine Learning (OML). In this hands-on workshop based on Autonomous Database, you will learn how to use OML to build a machine learning model and integrate it into an APEX application. The workshop is suitable for Oracle data professionals looking to build their first machine learning model, and data scientists who want the simplest way to apply machine learning to enterprise data.

Artificial Intelligence for Simple Games - Learn how to use powerful Deep Reinforcement Learning and Artificial Intelligence tools on examples of AI simple games! Created by Jan Warchocki, Ligency TeamPreview this Course - GET COUPON CODE Ever wish you could harness the power of Deep Learning and Machine Learning to craft intelligent bots built for gaming? If you're looking for a creative way to dive into Artificial Intelligence, then'Artificial Intelligence for Simple Games' is your key to building lasting knowledge. Learn and test your AI knowledge of fundamental DL and ML algorithms using the fun and flexible environment of simple games such as Snake, the Travelling Salesman problem, mazes and more. Whether you're an absolute beginner or seasoned Machine Learning expert, this course provides a solid foundation of the basic and advanced concepts you need to build AI within a gaming environment and beyond.

Prediction tasks about students have practical significance for both student and college. Making multiple predictions about students is an important part of a smart campus. For instance, predicting whether a student will fail to graduate can alert the student affairs office to take predictive measures to help the student improve his/her academic performance. With the development of information technology in colleges, we can collect digital footprints which encode heterogeneous behaviors continuously. In this paper, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem. To this end, we propose a variant of LSTM and a soft-attention mechanism. The proposed LSTM is able to learn the student profile-aware representation from heterogeneous behavior sequences. The proposed soft-attention mechanism can dynamically learn different importance degrees of different days for every student. In this way, heterogeneous behaviors can be well modeled. In order to model interactions among multiple prediction tasks, we propose a co-attention mechanism based unit. With the help of the stacked units, we can explicitly control the knowledge transfer among multiple tasks. We design three motivating behavior prediction tasks based on a real-world dataset collected from a college. Qualitative and quantitative experiments on the three prediction tasks have demonstrated the effectiveness of our model.