Computational Thinking for High School Educators: Creating Future Problem Solvers – MA DLCS Series in partnership with Teachers21
This course is designed for educators interested in teaching computer science as well as those that are looking for ways to incorporate the computational thinking standards in their own disciplines. Throughout the course, participants will learn about the five broad topics associated with the Computational Thinking strand of the Digital Literacy & Computer Science (DLCS) standards: Abstraction, Algorithms, Data, Programming & Development, and Modeling & Simulation. Participants will learn about these strands via a combination of weekly readings and/or hands-on learning activities similar to those experienced by students in a high school computer science course. Participants will engage in weekly reflections or assignments designed to best illustrate the computational thinking standards. Discussion boards will be used so that participants can share their thoughts about the standards and how they might look in their own classrooms. As a result of taking this course, participants will be better equipped to integrate these standards within their own teaching.
REGISTRATION DEADLINE: July 15, 2019
|Instructors||James Looney and Steve Ouellette|
|Dates||July 22 – August 22, 2019|
|Earn||15 hours of certificate of participation|
|MassCUE Member Cost||$150|
|Graduate Credit (Pending)||At the first class, participants may choose to register for 1 graduate credit through Worcester State University for an additional fee of $125. All participants, including those who do not opt for graduate credit, will receive a certificate documenting the number of course hours attended.|
Jim Looney has been teaching Intro to Computer Science and AP Computer Science A for five years at Westwood High School in Westwood, MA. He has an undergraduate degree in Earth & Planetary Science from Harvard University and is currently pursuing a master’s in computer science from Georgia Tech. He is a member of the Computer Science Teachers’ Association of Greater Boston and is licensed to teach Digital Literacy & Computer Science by the MA Department of Education.
Steve Ouellette has been the Director of Technology, Learning, and Innovation for Westwood Public Schools since 2010. Before that, he held the position of Math Department Chair in the Walpole and Westwood school districts. Prior to entering the education field, Steve worked as an electrical engineer for Otis Elevator Corp. In his current role, Steve oversees a staff of 13 people with responsibilities that range from maintaining the district’s technology infrastructure to working with staff to leverage technology in meaningful ways to empower students to be the best learners possible. Accomplishments include the implementation of a coding special in the elementary grades, the development of Westwood’s Guiding Principles for Instructional Technology, the implementation of a grade 3 – 12 one-to-one Chromebook program, and a focus on comprehensive responsible use instruction for all students. Steve holds a BS in Electrical Engineering from Worcester Polytechnic Institute and a Master’s of Arts in Teaching, Mathematics from Boston University. This year Steve also earned his Certified Educational Technology Leader (CETL) certification from the Consortium of School Networking
In this four week online course, participants will take a deep dive into the Computational Thinking strand for the Massachusetts DLCS frameworks for grades 9 – 12 (see above). Using a combination of readings, structured activities, and discussion responses to a prompt(s), participants will learn about the standards according to the following weekly schedule:
Week 1: Abstraction and Modeling & Simulation
Week 2: Algorithms
Week 3: Data
Week 4: Programming & Development
Project Description: Those educators wishing to receive one credit from WSU will be creating a comprehensive portfolio of lesson plans in the area of computational thinking.
9-12.CT.a.1 Discuss and give an example of the value of generalizing and decomposing aspects of a problem in order to solve it more effectively.
9-12.CT.b.1 Recognize that the design of an algorithm is distinct from its expression in a programming language.
9-12.CT.b.2 Represent algorithms using structured language, such as pseudocode.
9-12.CT.b.3 Explain how a recursive solution to a problem repeatedly applies the same solution to smaller instances of the problem.
9-12.CT.b.4 Describe that there are ways to characterize how well algorithms perform and that two algorithms can perform differently for the same task.
9-12.CT.b.5 Explain that there are some problems, which cannot be computationally solved.
9-12.CT.c.1 Describe how data types, structures, and compression in programs affect data storage and quality (e.g., digital image file sizes are affected by resolution and color depth).
9-12.CT.c.2 Create an appropriate multidimensional data structure that can be filtered, sorted, and searched (e.g., array, list, record).
9-12.CT.c.3 Create, evaluate, and revise data visualization for communication and knowledge.
9-12.CT.c.4 Analyze a complex data set to answer a question or test a hypothesis (e.g., analyze a large set of weather or financial data to predict future patterns).
9-12.CT.c.5 Identify different problems (e.g., large or multipart problems, problems that need specific expertise, problems that affect many constituents) that can benefit from collaboration when processing and analyzing data to develop new insights and knowledge. 9-12.CT.d Programming and Development
9-12.CT.d.1 Use a development process in creating a computational artifact that leads to a minimum viable product and includes reflection, analysis, and iteration (e.g., a data-set analysis program for a science and engineering fair, capstone project that includes a program, term research project based on program data).
9-12.CT.d.2 Decompose a problem by defining functions, which accept parameters and produce return values.
9-12.CT.d.3 Select the appropriate data structure to represent information for a given problem (e.g., records, arrays, lists).
9-12.CT.d.4 Analyze trade-offs among multiple approaches to solve a given problem (e.g., space/time performance, maintainability, correctness, elegance).
9-12.CT.d.5 Use appropriate looping structures in programs (e.g., FOR, WHILE, RECURSION).
9-12.CT.d.6 Use appropriate conditional structures in programs (e.g., IF-THEN, IF-THENELSE, SWITCH).
9-12.CT.d.7 Use a programming language or tool feature correctly to enforce operator precedence.
9-12.CT.d.8 Use global and local scope appropriately in program design (e.g., for variables).
9-12.CT.d.9 Select and employ an appropriate component or library to facilitate programming solutions (e.g., turtle, Global Positioning System [GPS], statistics library).
9-12.CT.d.10 Use an iterative design process, including learning from making mistakes, to gain a better understanding of the problem domain.
9-12.CT.d.11 Engage in systematic testing and debugging methods to ensure program correctness.
9-12.CT.d.12 Demonstrate how to document a program so that others can understand its design and implementation.
9-12.CT.e Modeling and Simulation
9-12.CT.e.1 Create models and simulations to help formulate, test, and refine hypotheses.
9-12.CT.e.2 Form a model from a hypothesis generated from research and run a simulation to collect and analyze data to test that hypothesis.
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Computational Thinking for High School Educators: Creating Future Problem Solvers – MA DLCS Series in partnership with Teachers21 July 22, 2019 – August 22, 2019Register Now