Kanika Sood

Socially Responsible Computing (SRC) education entails the infusion of Computer Science (CS) education with interwoven attention to ethical, social, and political issues to position students to reflect and take action individually and collaboratively to create a more just world. Our approach to SRC supports students to explore computing design/development in early CS courses with a communal goal orientation (in contrast to agentic/individualized), shown to improve achievement and retention for students with identities that are minoritized in CS. Grounded in our own experiences as co-developers and implementers of this pedagogical transformation and as co-facilitators of a Faculty Learning Community (FLC) across six minority-serving institutions in California, we share how we use an iterative design and implementation process modeled from social design experimentation as research and development method. Initial results are presented as a set of promising practices for incorporating SRC into introductory CS courses: 1) choose the domain mindfully; 2) design for synergy with technical material; 3) scaffold for inclusivity; 4) structure with a framework; 5) avoid othering SRC elements; and 6) reuse and build on existing resources. We share how these promising practices guide our efforts; how they can address challenges and concerns for new and continuing SRC implementers; and the ways in which we have and will continue to test and co-design this approach.

Ambient sensors positioned throughout a residential residence can assist in detecting human activity without the need for wearables or direct communication with the people being watched. Elderly or disabled folks who have trouble utilizing conventional monitoring equipment will mostly benefit from this. The sensors are simple to deploy in various interior settings, making it possible to monitor huge populations or many sites simultaneously. This methodology applies to smart homes, and we suggest gathering ambient data from participants' houses while they go about their regular lives. Based on the activity in the residents' smart homes, the sensors can identify their activities. To group the movements detected by the sensors, we will utilize a variety of classification algorithms based on the UCI dataset.