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.

This preliminary study investigates the impact of a collaborative guided inquiry learning (CGIL) approach on the retention and performance of underrepresented racial minority (URM) students in an Object-Oriented programming course at a Hispanic-serving institution. The study showed that this teaching method significantly improved academic performance for both URM and non-URM students, with no notable differences in retention rates. Student surveys highlight the method's effectiveness in promoting communication and collaboration skills, which are the foundations of inclusion and diversity in the workplace. The results also show that it was fairly easy to replicate the positive learning experiences across multiple sections of a course. This approach can potentially increase retention, improve performance, and promote diversity within the Computer Science discipline, contributing to a more inclusive and skilled workforce in the technology industry.

Programming is a complex skill requiring computational thinking, knowledge of the programming language, and the ability to distinguish, combine, and manipulate appropriate components to solve a problem. Practice is an effective strategy for teaching programming because it fosters skill mastery especially when students get sufficient experience and feedback. However, properly implementing programming practice is difficult because instructors need to assign appropriate problems for each student, evaluate solutions, and provide feedback. We conducted a pilot study on an introductory C++ programming class that applied mastery learning to optimize practice, used a repository of programming problems to reduce instructor effort, and leveraged unit tests to provide feedback and facilitate grading. The class had a lecture component to introduce programming concepts and a lab component for practice. We used mastery learning to control the number of practice-problems students solved on a topic before moving to a more advanced one. We assumed mastery when students answered three programming problems correctly in a row without help. If they sought help, they had to successfully solve a new set of three problems in a row. We also assumed mastery if they solved ten problems because it should have given them sufficient practice. We observed that experienced students asked fewer questions and quickly progressed across topics, while other students got much-needed help from instructors. The repository saved time for preparing problems and the unit tests helped answer students' low-level questions. Unit tests already checked students' solutions against expected results, so it significantly reduced checking time.

Self-reflection and self-evaluation are effective processes for identifying good learning behavior. These are essential in self-directed learning scenarios because students have to be responsible for their own learning. Although students benefit from doing fine-grained analysis of their own behavior, which we observed in our previous work, asking them to perform tasks such as analysis and making annotations are tedious and take significant amount of time and effort. In this paper, we present our work on the development of incremental affect models that can be used to minimize effort in analyzing and annotating behavior. Incremental models have an added benefit of adaptability to new information, which can be used by future systems to provide up-to-date affect-related feedback in real time.

Learning is commonly associated with knowledge transfer involving guidance from a teacher. However, as people grow older they are expected to know how to learn by themselves. In this research, we analyzed student learning in an unsupervised learning environment, i.e., performing academic research, wherein students have complete control over their learning thus requiring them to manage it. Transition likelihood metrics were used to analyze the interplay between emotion, learning and non-learning related activities while students did research. Several observations were seen from students learning in this environment such as students experiencing cognitive disequilibrium but experiencing disengagement faster. Non-learning related activities were also shown to have the potential of motivating students to resume learning. Lastly, user-specific traits and context seem to affect the interplay between learning and non-learning activities in an unsupervised learning environment. This highlights the need to not only create general models to predict student behavior but also user-specific models to allow future systems to provide appropriate feedback in this environment.

Commonly attributed to digital natives is the ability to quickly, yet effectively, shift from one task to another. However, several works have debunked this assumption by showing that multitasking even among digital natives led to poor learning performance and productivity. Our aim is to provide a tool to help digital natives be self-aware of desirable, while curbing undesirable, learning behaviors. Our tool is infused with self-annotation and feedback mechanisms that allow students to reflect upon their entire learning history. Our results indicate that the annotation process with the tool helped students understand their learning behaviors better and identify ways in which their behaviors can be improved.

Students who performed well in their college mathematics subjects, referred to here as academic achievers, were divided into two groups according to the self-reported level of difficulty faced by them while performing several programming tasks in LOGO - a programming language using turtle-graphics. It is shown that, to some extent, the level of difficulty of tasks faced by academic achievers can be predicted, based on their measured affective levels of excitement, frustration and engagement. These affective states are measured using brainwaves sensors that are attached to the head of the student. Those who assessed the learning experience as easy tend to have higher levels of excitement than those who reported to have experienced difficulty in learning the language. On the other hand, the level of frustration among those having difficulty with the tasks registered slightly higher frustration levels. Three machine learning algorithms were used to predict whether or not a learner finds the tasks to be easy. The average predictive accuracy is 70%.

Students have different ways of learning and have varied reactions to feedback. Thus, allowing a system to predict how students would appraise certain feedback gives it the capability to adapt to what would help a student learn better. This research focuses on the prediction of a student’s appraisal of feedback provided in an intelligent tutoring system (ITS). A regression model for frustration and excitement is created to perform prediction. The frustration model was able to achieve a 0.724 correlation with a 0.164 RMSE and the excitement model was able to achieve 0.6 a correlation with a 0.189 RMSE. These results indicate the potential of using these models for allowing systems to adjust feedback automatically based on student’s reactions while using an ITS.