Toward Using Virtual Identities in Computer Science Learning for Broadening Participation

• In Event: 47.027 - CS for All: An Intersectional Approach to Unpacking Equity in Computer Science Education

Abstract

This paper presents an overview of key aspects of our computer science education project. It elaborates in-workshop breakout groups not yet reported elsewhere and summarizes key outcomes. Our research seeks to discover best practices for using avatars to enhance performance, engagement, and STEM identity development for diverse public middle and high school computer science students. At sites of our research we run workshops in which students:

•   learn computer science in fun, relevant ways, and
•   develop self-images as computer scientists.

Our workshops involve eliciting student-generated themes, questions, challenges, and goals. This process includes taking an anti-deficit ideological stance on students and their achievement. We have also developed our own custom platform called MazeStar, used in the workshops, that allows students to explore their own ideas by creating customized games while learning about human-computer interaction, web design, privacy, coding, debugging, and more (we utilize aspects of the nationally recognized Exploring Computer Science (ECS) curriculum). Students ideas were guided within the context of the following set of topics:

a. how companies make money off your data (Mo’ Money, Mo’ Problems)
b.  how people are represented in computer science (Stereotypes)
c.  how people and companies manage privacies and related issues (Surveillance)
d.  how we socialize with others online (Social Connections)

Student discussion of these topics was facilitated with students ultimately choosing which topic to focus their own game on. They then were led through a paper prototyping process in which they brainstormed themes, consolidated ideas, conceptualized their own game, then coded it. Subsequently, they went through a round of quality assurance by exchanging their games with other student participants who gave feedback. All students then refined their game based on the feedback and presented their game to the group.

A core component of MazeStar is our game for learning programming called Mazzy in which play requires learning building blocks of coding. Ultimately, we approach STEM education and access to high quality, relevant learning opportunities as a social justice issue of our time.

Using qualitative, quantitative, and AI/machine learning analysis techniques, we have already formulated a few best practices and guidelines when it comes to avatar use in education. We have also systematically explored the impacts of different avatar types on users, beginning with distinctions between anthropomorphic vs. non-anthropomorphic avatars, user likeness vs. non-likeness avatars, and other conditions informed by insights from the learning sciences and sociology in crowd-sourced studies (with over 10,000 participants).

Taken together, our studies have revealed that avatars can support, or harm, student performance and engagement. A few notable trends are:

1) ‘role model’ avatars (in particular scientist avatars) are positively effective,
2) ‘likeness’ avatars (avatars in a user’s likeness) are not always positively effective,
3) simple ‘abstract’ avatars (such as geometric shapes) are especially positively effective when the player is undergoing failure, e.g., ‘debugging,’ and
4) ‘successful likeness’ avatars that look like the user when doing well and appear ‘abstract’ otherwise are very positively effective.

In summary, our poster elaborates key design features and a summary of project results.

Authors

• Sneha Veeragoudar, Massachusetts Institute of Technology

• Maya Wagoner

• Dominic Kao, Purdue University

• Danielle Olson

• Aziria Rodriguez

• D. Fox Harrell, Massachusetts Institute of Technology