This is a free textbook teaching introductory statistics for undergraduates in Psychology. This textbook is part of a larger OER course package for teaching undergraduate statistics in Psychology, including this textbook, a lab manual, and a course website. All of the materials are free and copiable, with source code maintained in Github repositories.
Explores how organizations can use system dynamics to achieve important goals. Student teams work with client managers to tackle the clients' most pressing issues. Students discuss experiences with their clients, and learn modeling and consulting skills they need to be effective. Focus on gaining practical insight from the system dynamics process. Projects are sponsored by diverse organizations from a range of industries and sizes from start-ups to the Fortune 500.
An introduction to bargaining and negotiation in public, business, and legal settings. Combines a "hands-on" skill-building orientation with a look at pertinent social theory. Strategy, communications, ethics, and institutional influences are examined as they influence the ability of actors to analyze problems, negotiate agreements, and resolve disputes in social, organizational, and political circumstances characterized by interdependent interests.
This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
Do you want to think about ways to help solve New OrleansŰŞ problems? Cityscope is a project-based introduction to the contemporary city. "Problem solving in complex (urban) environments" is different than "solving complex problems." As a member of a team, you will learn to assess scenarios for the purpose of formulating social, economic and design strategies to provide humane and sustainable solutions. A visit to New Orleans is planned for spring break 2007.
An introduction to human information processing and learning; topics include the nature of mental representation and processing; the architecture of memory; pattern recognition; attention; imagery and mental codes; concepts and prototypes; reasoning and problem solving.
Why study Quantitative Literacy?
Most students sign up for this course to fulfill a general education mathematics requirement. And this text is certainly aimed at that general audience. But by the time the course is completed, the authors hope that you will have developed some appreciation for the usefulness and elegance of the subject. Without doubt, some level of competency and comfort in working with numerical data is needed to navigate the modern world; and we have tried to cover topics that can be used in day to day life.
In this book, we will focus on problem solving and critical thinking skills. Our goal is not to prepare you just for the next math class, but to equip you with the necessary tools so that you can apply basic mathematical reasoning to a wide variety of commonly encountered problems. Along the way, we will learn basic logic, how to work with percentages and units, the basics of consumer finance, and how to use and interpret basic statistical data.
The goal of this book is to teach you to think like a computer scientist. I like the way computer scientists think because they combine some of the best features of Mathematics, Engineering, and Natural Science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating trade offs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions.The single most important skill for a computer scientist is problem-solving. By that I mean the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. That’s why this chapter is called “The way of the program.”
Introduces students to the theory, tools, and techniques of engineering design and creative problem-solving, as well as design issues and practices in civil engineering. Includes several design cases, with an emphasis on built facilities (e.g., buildings, bridges and roads). Project design explicitly concerns technical approaches as well as consideration of the existing built environment, natural environment, economic and social factors, and expected life span. A large design case is introduced which is used in the subsequent specialty area design subjects (1.031, 1.041, 1.051) and the capstone design subject (1.013).
This course presents the fundamentals of object-oriented software design and development, computational methods and sensing for engineering, and scientific and managerial applications. It cover topics, including design of classes, inheritance, graphical user interfaces, numerical methods, streams, threads, sensors, and data structures. Students use Java programming language to complete weekly software assignments. How is 1.00 different from other intro programming courses offered at MIT? 1.00 is a first course in programming. It assumes no prior experience, and it focuses on the use of computation to solve problems in engineering, science and management. The audience for 1.00 is non-computer science majors. 1.00 does not focus on writing compilers or parsers or computing tools where the computer is the system; it focuses on engineering problems where the computer is part of the system, or is used to model a physical or logical system. 1.00 teaches the Java programming language, and it focuses on the design and development of object-oriented software for technical problems. 1.00 is taught in an active learning style. Lecture segments alternating with laboratory exercises are used in every class to allow students to put concepts into practice immediately; this teaching style generates questions and feedback, and allows the teaching staff and students to interact when concepts are first introduced to ensure that core ideas are understood. Like many MIT classes, 1.00 has weekly assignments, which are programs based on actual engineering, science or management applications. The weekly assignments build on the class material from the previous week, and require students to put the concepts taught in the small in-class labs into a larger program that uses multiple elements of Java together.
The general minimum prerequisite for understanding this book is the intellectual maturity of a junior-level (third-year) college student in an accredited four-year engineering curriculum. A mathematical second-order system is represented in this book primarily by a single second-order ODE, not in the state-space form by a pair of coupled first-order ODEs. Similarly, a two-degrees-of-freedom (fourth-order) system is represented by two coupled second-order ODEs, not in the state-space form by four coupled first-order ODEs. The book does not use bond graph modeling, the general and powerful, but complicated, modern tool for analysis of complex, multidisciplinary dynamic systems. The homework problems at the ends of chapters are very important to the learning objectives, so the author attempted to compose problems of practical interest and to make the problem statements as clear, correct, and unambiguous as possible. A major focus of the book is computer calculation of system characteristics and responses and graphical display of results, with use of basic (not advanced) MATLAB commands and programs. The book includes many examples and homework problems relevant to aerospace engineering, among which are rolling dynamics of flight vehicles, spacecraft actuators, aerospace motion sensors, and aeroelasticity. There are also several examples and homework problems illustrating and validating theory by using measured data to identify first- and second-order system dynamic characteristics based on mathematical models (e.g., time constants and natural frequencies), and system basic properties (e.g., mass, stiffness, and damping). Applications of real and simulated experimental data appear in many homework problems. The book contains somewhat more material than can be covered during a single standard college semester, so an instructor who wishes to use this as a one-semester course textbook should not attempt to cover the entire book, but instead should cover only those parts that are most relevant to the course objectives.
This seminar explores experiments in education and discusses how education and learning might be done, through reading and discussion. This seminar is not a survey of experiments in education, but rather, its goal is to determine how learning should happen and what kinds of contexts allow it to happen.
This course was originally developed for the Open Course Library project. The text used is Math in Society, edited by David Lippman, Pierce College Ft Steilacoom. Development of this book was supported, in part, by the Transition Math Project and the Open Course Library Project. Topics covered in the course include problem solving, voting theory, graph theory, growth models, finance, data collection and description, and probability.
Matrix Algebra with Computational Applications is a collection of Open Educational Resource (OER) materials designed to introduce students to the use of Linear Algebra to solve real-world problems. These materials were developed specifically for students and instructors working in a "flipped classroom" model that emphasizes hands-on problem-solving activities during class meetings, with students watching lectures and completing readings and assignments outside of the classroom. To access the Matrix Algebra with Computational Applications website, please go to http://colbrydi.github.io/MatrixAlgebra
Introduces the fundamentals of machine tool and computer tool use. Students work with a variety of machine tools including the bandsaw, milling machine, and lathe. Instruction given on the use of the Athena network and Athena-based software packages including MATLABĺ¨, MAPLEĺ¨, XESSĺ¨, and CAD. Emphasis on problem solving, not programming or algorithmic development. Assignments are project-oriented relating to mechanical engineering topics. It is recommended that students take this subject in the first IAP after declaring the major in Mechanical Engineering. From the course home page: This course was co-created by Prof. Douglas Hart and Dr. Kevin Otto.
My Math GPS: Elementary Algebra Guided Problem Solving is a textbook that aligns to the CUNY Elementary Algebra Learning Objectives that are tested on the CUNY Elementary Algebra Final Exam (CEAFE). This book contextualizes arithmetic skills into Elementary Algebra content using a problem-solving pedagogy. Classroom assessments and online homework are available from the authors.
Precalculus is adaptable and designed to fit the needs of a variety of precalculus courses. It is a comprehensive text that covers more ground than a typical one- or two-semester college-level precalculus course. The content is organized by clearly-defined learning objectives, and includes worked examples that demonstrate problem-solving approaches in an accessible way.
This class builds a bridge between the recreational world of algorithmic puzzles (puzzles that can be solved by algorithms) and the pragmatic world of computer programming, teaching students to program while solving puzzles. Python syntax and semantics required to understand the code are explained as needed for each puzzle.
Introduction to the theoretical and practical sides of public policy controversies and their resolution. Offers a multidisciplinary perspective on a wide range of difficult public policy disputes including racial and ethnic conflict, resource management disputes, and science-intensive policy disagreements such as those surrounding the disposal of nuclear waste, the nature of the risks associated with resource recovery plants, and the cultural impacts of hydroelectric development. Simulations, case studies, and role plays provide numerous opportunities for students to develop their own dispute handling capabilities.