Presented by:

Irene Ortega

from UC Berkeley

Irene is a fourth year Cognitive Science major with a focus on Computer Science. She is the Snap! team lead for Computer-Based Testing at UC Berkeley.

Gurkaran Singh Goindi

from UC Berkeley

Hi! I'm a rising Junior at UC Berkeley studying CS and Economics and have been working with the CBT Team for about a year now. I'm really excited to share what we've been working on, so do spend some time listening to what we have to share. Hopefully you'll find it interesting!

Summer 2020 Instructor for CS10.

Shannon Hearn

from UC Berkeley

Shannon is a fourth year Electrical Engineering and Computer Science major.


from University of California, Berkeley

Bojin Yao

from UC Berkeley

Master in C.S.

Eduardo Huerta

from UC Berkeley

I am a Senior majoring in EECS. I was born and raised in Lima, Peru.

Dan Garcia

from UC Berkeley

Dan Garcia is a Teaching Professor in the EECS department at UC Berkeley. He was selected as an ACM Distinguished Educator in 2012 and ACM Distinguished Speaker in 2019, and is a national leader in the "CSforALL" movement, bringing engaging computer science to students normally underrepresented in the field.

Thanks to four National Science Foundation grants, the "Beauty and Joy of Computing (BJC)" non-majors course he co-developed has been shared with over 800 high school teachers. He is delighted to regularly have more than 50% female enrollment in BJC, with a high mark of 65% in the Spring of 2018, shattering the campus record for an intro computing course, and is among the highest in the nation! He is humbled by the international exposure he and the course have received in the New York Times, PBS, NPR, and others media outlets. He is working on the BJC Middle School curriculum.

Volunteer Hosts
Thanks for helping with Snap!Con 2020!

Bryant Bettencourt

from UC Berkeley

In STEM higher education, courses conduct both formative and summative assessments in a manner that thwarts mastery learning and magnifies equity gaps in student preparation. In short, this is “constant time, variable learning”—course pacing is the same for all students regardless of learning speed, all students receive a small number of “one-shot” summative assessments at the same time, and not all will master the material (or even pass). In contrast, mastery learning is “constant learning over variable time”—some students may take longer than others to reach the same level of mastery, but they can eventually do so with increased practice and instructor support. The challenge with implementing mastery learning is that increased practice in STEM courses means solving more practice problems, but developing good practice problems requires instructor effort, to say nothing of giving the students feedback on their performance on those problems.

To address these challenges, Dan Garcia’s lab at UC Berkeley is developing paradigm-based question generators (PQGs) to enable both formative-assessment mastery learning and summative-assessment mastery learning for “The Beauty and Joy of Computing.” Students will have as much practice and time with Snap! concepts as necessary to achieve mastery rather than a rigid schedule that may result in variable mastery. Our hypothesis for this project is that PQGs will result in higher retention, stronger learning outcomes, higher participation in computing for underrepresented and minority students, and more effective use of instructor time to identify and assist struggling students.

Discuss on the Snap! Forum

1 h
Zoom 1
Snap!Con 2020