1. Pattern recognition: As Tim analyzes data from his internet search and reviews the top 10 best skiing locations, he begins to notice patterns. There are several ski resorts in Colorado since it offers a wide range of mountains, but they focus mainly on skiing and don't offer many options for snowboard activities. They also don't really have noteable shopping nearby. He noticed that Park City in Utah, near Salt Lake City, offers many different skiing activities as well as snowboarding activities like the half-pipe since it was home to the 2002 Winter Olympics. He also encountered reviews of recent visitors to Park City and noticed shopping patterns such as clothing stores, local-item specialty stores, and furniture stores in the most frequently visited shops.
1.1. Rationale: Tim researched and recognized trends and patterns for most visited ski resorts that also had shopping nearby. He also identified patterns in type of shopping. Both of these are pattern recognition (Google Computational Thinking for Educators, n.d.).
2. Decomposition: Tim has to break down the problem into manageable parts. Therefore, he needs to find locations that meet both requirements of skiing and snowboarding for the boys and shopping for the girls, so he searches for possible vacations by performing an internet search for the top 10 best skiing locations. He then searches for shopping in the vicinity of those locations.
2.1. Rationale: Tim breaks the dilemma down into parts that he can easily manage, which is decomposition (Google Computational Thinking for Educators, n.d.). The two parts he breaks it down into are online searches for skiing and then shopping.
3. Problem: Two families, the Smiths and the Whites, want to go on vacation, but they have different opinions on location. The boys, Tim and Brad, want to go skiing with snowboarding options, but the girls, Katie and Allison, would rather go shopping. To make the girls happy while still having the opportunity to ski, Tim decides to plan a vacation that will allow everyone to get their way. How does Tim find this location?
4. Algorithm design: As part of the decision-making process, Tim will consider convenience of the shopping area to the ski resort. Based on his internet searches and the reviews he read, Tim decides to create an agenda that details the activities that Park City offers as far as skiing and snowboarding according to the days and times they’re offered. He also maps out a list of popular shops, categorized by type of shopping, distance from the resort, and hours of operation. According to his plan, the boys will spend their first day snowboarding while the girls participate in locally-offered shopping. The second day, the girls will be able to join them for some skiing and shopping at the resort, and the third day the boys would join the girls in traveling a little further from the resort for some outlet and brand-name store shopping.
4.1. Rationale: Tim's day-by-day agenda allows for both families to travel to Salt Lake City for vacation since it meets the requirements for both the boys and the girls and provides time for both skiing and snowboarding along with shopping. This agenda serves as the step-by-step directions for solving the problem which is the definition of algorithm (Yadav, Hong, & Stephenson, 2016).
5. Abstraction: Tim realizes that his solution of researching options and creating an agenda could be applied in many other situations of conflicted vacation or travel plans. For example, a family planning a summer vacation may have different opinions on whether to visit an amusement park or go to a beach. He could help others through this planning process, and he could even become a travel agent!
5.1. Rationale: Tim demonstrated how he could identify the principles of his problem with conflicting travel plans, and he could transfer this solution from his problem. This is an example of abstraction (Yadav, Hong, & Stephenson, 2016).
6. References
6.1. Google Computational Thinking for Educators. (n.d.). What is computational thinking? Retrieved from https://computationalthinkingcourse.withgoogle.com/unit Yadav, A., Hong, H. & Stephenson, C. (2016). Computational thinking for all: Pedagogical approaches to embedding 21st century problem solving in K-12 classrooms. TechTrends, 60(6), 565-568.
