Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

Introduction to Data Analytics and Emerging Real-World Use Cases

Data analytics is a rapidly emerging interdisciplinary research area that involves advances in engineering, computer science, statistics and operations research. This webinar is focused on introducing the foundation of data analytics and emerging real-world use cases of data analytics. This presentation will begin with a discussion of the mathematical and statistical modeling aspects of various levels of data analytics (i.e., descriptive, predictive and prescriptive). In this webinar, you will hear an overview of data analytics in real world problems ranging from healthcare analytics, retail analytics and financial analytics

Enhancing student learning and engagement of scientific concepts through case studies in integrated science

Context-based learning activities, such as case studies, that bring to light the relevance of science increase student engagement, improve student performance, and attract students to study science at the university level. Meanwhile, integrated science education is based on an approach that emphasizes the interconnectedness of scientific fields, such as astronomy, chemistry, physics, biology, Earth sciences, and computer science. By incorporating case studies in integrated science courses, students are provided with real-world scenarios that enable them to explore the interdisciplinary nature of science while acquiring a deeper understanding of foundational scientific concepts and their application to real-world situations. These case studies foster active and collaborative learning, helping students develop their problem-solving and critical thinking skills by analyzing and interpreting data from multiple scientific perspectives. Furthermore, this approach can stimulate students to formulate innovative solutions to problems, enhancing their creativity and scientific curiosity. Overall, an integrated science approach that centers on case studies creates a more engaging and effective learning environment that can lead to improved outcomes in science education. This presentation discusses the implementation of this approach at the university level and provides practical ideas on its implementation

Use of collaborative computer simulation activities by high school science students learning relative motion.

Galileo\u27s contemporaries as well as today\u27s students have difficulty understanding relative motion. It is hypothesized that construction of visual models, resolution of these visual models with numeric models, and, in many cases, rejection of epistemological commitments such as the belief in one true velocity, are necessary for students to form integrated mental models of relative motion events. To investigate students\u27 relative motion problem solving, high school science students were videotaped in classroom and laboratory settings as they performed collaborative predict-observe-explain activities with relative motion computer simulations. The activities were designed to facilitate conceptual change by challenging common alternative conceptions. Half of the students interacted with simulations that provided animated feedback; the other half received numeric feedback. Learning, as measured by a diagnostic test, occurred following both conditions. There was no statistically significant difference between groups on the measure. It is hypothesized that students did not show statistically significant performance differences on the relative motion test because (a) many students were able to solve numeric problems through algorithm use; (b) many numeric condition students were aided in their ability to visualize problems by interaction with the treatment; and (c) the animation condition fostered little learning because the activities were too easy for students to perform. Students\u27 problem solving was examined through analyses of protocols and through statistical analyses of written responses. Evidence supported the following findings: (1) Numeric condition students had more difficulty with the computer activities than animation condition students. (2) Many students in both groups were able to construct accurate mental models of relative motion events. (3) A number of numeric condition students used faulty mechanical algorithms to solve problems. (4) A number of animation condition students used visualization to solve problems, mapping dynamic visual features of the animations onto posttest problems. Thus, there is evidence that presentation of numeric data can foster students\u27 use of mechanical algorithms. Presentation of animations can foster visualization of target problems solved off-line. These results suggest that, in addition to the structure of the simulations, how computer simulations are used may have a great impact on students\u27 cognition

Rhetorical structures in academic research writing by non-native writers

Writers of research articles are expected to present research information in a structured manner by following a certain rhetorical patterns determined by the discourse community.Failures to keep to the writing standard and rhetorical pattern are likely to lower the acceptance rate. While producing a research article is understandably a complex task, it is even more difficult if one is writing in his or her second or third language. Even if grammatical mistakes can be ironed out by a language editor, researchers are on their own when it comes to rhetorical presentation of their research ideas. The available research writing guidelines constructed in the native speaker context often fall short in addressing rhetorical aspects related to cultural issues that have been known to influence most non native English (NNE) writings. Motivated by the complexity of rhetorical presentation in research articles and the problems on writing research articles by NNE writers, this paper is aimed to explore the rhetorical moves used by the Malaysian writers in the introduction section of Computer Science research articles for journal publication.CARS model (Swales, 2004) is used to analyze the rhetorical moves in the introduction section of Computer Science research articles by the Malaysian writers.The study begins with a corpus compilation of five research articles (RA) by the writers followed by move analysis use in the CARS model (Swales, 2004) was conducted to analyze the articles.The analysis revealed that majority of the writers adopted most of the rhetorical strategies in Swales model (2004).The paper concludes that CARS model is suitable in identifying the rhetorical moves in the RA by Malaysian writers