This submission introduces CS+Social Good, a student organization at Stanford University, which works at the intersection of tech and social impact. In this paper, we introduce one of our educational initiatives that might be of interest to the SIGCSE community, focusing specifically on our Studio program, which is offered as CS51 and CS52 from the Stanford University Computer Science Department. For this student-taught class, student teams partner with nonprofits and social ventures to build impactful technical projects over the course of (...) two quarters. (shrink)

Which pedagogical techniques better engage computer science students in computing for social good? We examine this question with students enrolled in classes using the Collaborating Across Boundaries to Engage Undergraduates in Computational Thinking pedagogical model, that pairs CS and non-CS courses with a community partner to propose solutions to a local problem. Pre- and post-tests of self-assessed concerns about civic responsibility, global responsibility, and local civic efficacy were administered to the students in a three-year long pedagogical experiment, which paired five (...) CS courses with five journalism courses. While CS students were not statistically different from their journalism peers in pre-test measures of social and global responsibility, they lagged behind in local efficacy. In the posttest, CS students had significantly increased their sense of local efficacy to the extent that they were statistically indistinguishable from journalism students. Community-engaged learning projects, such as the one in the CABECT model, show great potential for attracting students to computing for social good. (shrink)

ABSTRACTResearch that dissociates different types of processes within a given task using a processing tree approach suggests that attitudes may be acquired through evaluative conditioning in the absence of explicit encoding of CS-US pairings in memory. This research distinguishes explicit memory for the CS-US pairings from CS-liking acquired without encoding of CS-US pairs in explicit memory. It has been suggested that the latter effect may be due to an implicit misattribution process that is assumed to operate when US evocativeness is (...) low. In the present research, the latter assumption was supported neither by two high-powered experiments nor by complementary meta-analytic evidence, whereas evocativeness exerted an influence on explicit memory. This pattern of findings is inconsistent with the view that CS-liking acquired without encoding of CS-US pairs in explicit memory reflects an implicit misattribution process at learning. Hence, the underlying learning process is awaiting further empirical scrutiny. (shrink)