AI Thinking for Cloud Education Platform with Personalized Learning

Artificial Intelligence (AI) thinking is a framework beyond procedural thinking and based on cognitive and adaptation to automatically learn deep and wide rules and semantics from experiments. This paper presents Cloud-eLab, an open and interactive cloud-based learning platform for AI Thinking, aiming to inspire i) Deep and Wide learning, ii) Cognitive and Adaptation learning concepts for education. It has been successfully used in various machine learning courses in practice, and has the expandability to support more AI modules. In this paper, we describe the block diagram of the proposed AI Thinking education platform, and provide two education application scenarios for unfolding Deep and Wide learning as well as Cognitive and Adaptation learning concepts. Cloud-eLab education platform will deliver personalized content for each student with flexibility to repeat the experiments at their own pace which allow the learner to be in control of the whole learning process

Discovery Learning Experiments in a New Machine Design Laboratory

A new Machine Design Laboratory at Marquette University has been created to foster student exploration with hardware and real-world systems. The Laboratory incorporates areas for teaching and training, and has been designed to promote “hands-on” and “minds-on” learning. It reflects the spirit of transformational learning that is a theme in the College of Engineering. The goal was to create discovery learning oriented experiments for a required junior-level “Design of Machine Elements” course in mechanical engineering that would give students practical experiences and expose them to physical hardware, actual tools, and real-world design challenges. In the experiments students face a range of real-world tasks: identify and select components, measure parameters (dimensions, speed, force), distinguish between normal and used (worn) components and between proper and abnormal behavior, reverse engineer systems, and justify design choices. The experiments serve to motivate the theory and spark interest in the subject of machine design. This paper presents details of the experiments and summarizes student reactions and our experiences in the Machine Design Laboratory. In addition, the paper provides some insights for others who may wish to develop similar types of experiments

Using Remote Access for Sharing Experiences in a Machine Design Laboratory

A new Machine Design Laboratory at Marquette University has been created to foster student exploration and promote “hands-on” and “minds-on” learning. Laboratory experiments have been developed to give students practical experiences and expose them to physical hardware, actual tools, and design challenges. Students face a range of real-world tasks: identify and select components, measure parameters (dimensions, speed, force), distinguish between normal and used (worn) components and between proper and abnormal behavior, reverse engineer systems, and justify design choices. The experiments serve to motivate the theory, spark interest, and promote discovery learning in the subject of machine design. This paper presents details of the experiments in the Machine Design Laboratory and then explores the feasibility of sharing some of the experiences with students at other institutions through remote access technologies. The paper proposes steps towards achieving this goal and raises issues to be addressed for a pilot-study offering machine design experiences to students globally who have access to the internet

Feature Selection with Evolving, Fast and Slow Using Two Parallel Genetic Algorithms

Feature selection is one of the most challenging issues in machine learning, especially while working with high dimensional data. In this paper, we address the problem of feature selection and propose a new approach called Evolving Fast and Slow. This new approach is based on using two parallel genetic algorithms having high and low mutation rates, respectively. Evolving Fast and Slow requires a new parallel architecture combining an automatic system that evolves fast and an effortful system that evolves slow. With this architecture, exploration and exploitation can be done simultaneously and in unison. Evolving fast, with high mutation rate, can be useful to explore new unknown places in the search space with long jumps; and Evolving Slow, with low mutation rate, can be useful to exploit previously known places in the search space with short movements. Our experiments show that Evolving Fast and Slow achieves very good results in terms of both accuracy and feature elimination