Hands-On Sessions

1. Sequences, Events, and the Notional Machine
Most programming languages are based on the idea that instructions are executed in order (a sequence). App Inventor works this way, and also highlights the use of events. Code sequences are triggered when something happens (e.g., a screen button is clicked, the phone senses a shake, or a text message is received).

Learning objectives: Create programs in App Inventor, using its event-based programming model.

2. Representation and Modeling
One of the central ideas in computer science is that using data to represent and model the physical world and/or a computational process. This is analogous to representation and modeling in mathematics. We'll discuss this conceptually and concretely with programming assignments.

Learning objective: Be able to use variables and lists to represent real-world things (e.g., color; geospatial location).

3. Persistence and Sharing
Once you have data, you want to be able to preserve it—to have it stay around for the next time your program is run. Also, in the internet age, being able to share your app's data with other people is essential. We'll learn how to do these things.

Learning objectives: Be able to use TinyDB to save values from your app and restore them the next time it starts up, and be able to use TinyWebDB to share data across apps.

4. Mobile CT Rubric
We have developed a rubric for assessing the computational thinking embedded in App Inventor projects, including mobile-specific concepts. In this session, you'll be introduced to the rubric and use to code example projects.

Learning objectives: Be able to identify the Mobile CT concepts that are represented in a completed app using the rubric.

5. Curriculum Module Design
Using an infusion model, you'll develop a curriculum module that can be integrated into your mathematics, science, technology, or other subject-area teaching plans. Your module will include App Inventor starter code that will be given to your students, and an activity worksheet for them to complete. Four sample units (two in math and two in science) will be provided.

Learning objectives: Create an original curriculum infusion unit that combines computer science content with another subject.

Talks

Ask The Machine! Automatic Processing of Natural Language Text
Anna Rumshisky (UMass Lowell)
In the contemporary world, unprecedented amounts of electronic text are generated daily. The information contained in these text streams can facilitate many everyday and professional tasks. Anna will describe current challenges and latest advances in natural language processing technology that underlies the automation of many such tasks.

SciFi to HRI: Designing the Robots of Tomorrow
Holly Yanco (UMass Lowell)
For 100 years, imagined robots have appeared in books, plays, and films. How close are we to achieving these dreams? This talk will present the capabilities of conceptualized robots, compare them to today's state of the art, and discuss the future of robot systems through the lens of human-robot interaction (HRI).

Introducing MassCAN: Massachusetts Computing Attainment Network
Jim Stanton (EDC)
MassCAN is a newly-formed partnership with the goal of inspiring and educating Massachusetts K–12 students in the field of computer science. Jim is a founding member of MassCAN, and will give an overview of its mission and core values.

Panel Discussion

Careers in Computing
Four UMass Lowell alumni will discuss their experiences in the field. Joining us are: Jeremy Badessa (BS ’10), Matt Bailey (BS ’07), Adam Dziki (BS ’06, MS ’10), and Veronica Payan (BS ’04).