In Peer Instruction (PI) an instructor displays a challenging multiple-choice question during lecture that students answer individually, discuss verbally with nearby peers, answer individually again, and finally, the instructor leads a discussion of the question. Peer Instruction typically increases student learning and motivation over traditional lecture. We added a text-chat mode to improve PI for remote synchronous learning. This feature assigns students to discussion groups to maximize the number of groups that have members with different answers. The tool was pilot tested in Winter 2022 and revised. In Fall 2022 and Winter 2023, it was tested at one institution. In Fall 2024, it was tested at four institutions. We conducted a log file analysis of student data from 1394 students and analyzed surveys with 848 student responses. We found that questions answered using the text-chat had a significantly higher improvement than those using traditional verbal discussion, although the two modes were tested with different questions. Interestingly, most of the students preferred to discuss the question verbally, although some preferred the text-chat discussion. These results inform efforts to improve the effectiveness of Peer Instruction and increase its adoption.

Despite decades of research on the effectiveness of Peer Instruction (PI), it can be hard to convince computing instructors to try it. In Peer Instruction an instructor displays a hard multiple-choice question during lecture, students answer individually, discuss with peers, and answer independently again. The main reasons computing instructors give for not adopting PI is a lack of awareness, concerns that it would result in less coverage of material in lecture, and the time needed to adopt it. We tried to increase awareness of PI and address instructors' concerns during a three-day summer instructor workshop with 14 instructors in 2024. A pre-survey was administered on the first day of the workshop and a post-survey on the last day. We administered a second post-survey in the fall of 2024. Later semi-structured interviews were conducted with nine instructors. The adoption rate from the 2024 workshop was over 50%, which was much higher than the previous two workshop's adoption rate of 23%. The workshop also increased knowledge of PI and alleviated several concerns. However, instructors wanted more follow-up support, an easier and faster way to create new PI questions, and better integration with their Learning Management System (LMS). Instructors who adopted the free tool, Peer+, reported positive student reactions and better understanding of student misconceptions. All instructors who adopted Peer+ plan to continue to use it.

Even though AI literacy has emerged as a prominent education topic in the wake of generative AI, its definition remains vague. There is little consensus among researchers and practitioners on how to discuss and design AI literacy interventions. The term has been used to describe both learning activities that train undergraduate students to use ChatGPT effectively and having kindergarten children interact with social robots. This paper applies an integrative review method to examine empirical and theoretical AI literacy studies published since 2020. In synthesizing the 124 reviewed studies, three ways to conceptualize literacy-functional, critical, and indirectly beneficial-and three perspectives on AI-technical detail, tool, and sociocultural-were identified, forming a framework that reflects the spectrum of how AI literacy is approached in practice. The framework highlights the need for more specialized terms within AI literacy discourse and indicates research gaps in certain AI literacy objectives.

Despite evidence of its effectiveness, Peer Instruction (PI) has not been widely adopted by undergraduate computing instructors. In PI, an instructor displays a hard multiple-choice question that students answer individually, then discuss their answer with peers, then answer again, and finally an instructor leads a discussion of the question. Even though the benefits of PI are well documented, it can be difficult to convince computing instructors to move away from passive lectures. Major reasons why instructors do not adopt PI include a lack of awareness, lack of time, and concerns over their ability to cover content. We hypothesized that we could encourage the adoption of PI by creating Peer+, a free tool in an ebook platform, a searchable question bank, and running summer instructor workshops. We offered a three-day in-person summer workshop to a total of 37 instructors in 2022 and 2023. Instructors completed a pre-survey, immediate post-survey, and a follow-up post survey after the fall semester. We also conducted semi-structured interviews with 17 instructors. On the immediate post-survey most (33/37, 89%) instructors reported that they were very likely or likely to use the tool in the fall. However, on the follow-up survey, less than a quarter (6/26, 23%) actually did. The number one reason for not using the tool was a lack of time (18/26, 69%). Notably, all of the instructors who used Peer+ planned to use it again. This work informs efforts to increase the adoption of evidence-based pedagogical approaches in computing.

Equity and diversity issues persist in computer science education. Prior research has identified marginalizing factors prevalent in the computing fields that discourage women and people of color, while also highlighting experiences that help them persist. Extending prior studies and applying an intersectional lens, this study aims to center the voice of Black girl high school students and present their experiences taking Advanced Placement (AP) Computer Science (CS) courses. We investigate how and why they persisted by conducting semi-structured interviews with eight Black girl students who have completed at least one AP CS course, and connect their stories to prior work. Applying situated expectancy-value theory (SEVT), our findings reveal examples of marginalizing and empowering social interactions within learning environments, students' awareness and active construction of social support networks, and conscious effort to resist inequity as motivation for studying CS. Our findings build upon prior studies of Black women's experiences in post-secondary and computing careers, and extend this research to secondary education contexts.

The capability of large language models (LLMs) to generate, debug, and explain code has sparked the interest of researchers and educators in undergraduate programming, with many anticipating their transformative potential in programming education. However, decisions about why and how to use LLMs in programming education may involve more than just the assessment of an LLM’s technical capabilities. Using the social shaping of technology theory as a guiding framework, our study explores how students’ social perceptions influence their own LLM usage. We then examine the correlation of self-reported LLM usage with students’ self-efficacy and midterm performances in an undergraduate programming course. Triangulating data from an anonymous end-of-course student survey (n = 158), a mid-course self-efficacy survey (n=158), student interviews (n = 10), self-reported LLM usage on homework, and midterm performances, we discovered that students’ use of LLMs was associated with their expectations for their future careers and their perceptions of peer usage. Additionally, early self-reported LLM usage in our context correlated with lower self-efficacy and lower midterm scores, while students’ perceived over-reliance on LLMs, rather than their usage itself, correlated with decreased self-efficacy later in the course.

Peer Instruction (PI) is a learning activity that lets students solve a difficult multiple-choice question individually, submit their answer, discuss with peers to solve the problem collaboratively, and then submit the answer again. Despite plentiful evidence to support its effectiveness, PI has not been widely adopted by undergraduate computing instructors due to low awareness of PI, the effort needed to create PI questions, the limited instructional time needed for PI activities during lectures, and potential adverse reactions from students.

Decades of research have provided evidence of the effectiveness of Peer Instruction (PI) in many fields, including computing. PI involves an instructor displaying a hard multiple-choice question that students answer individually, then discuss with peers and answer again. The instructor then displays the results from the two votes and leads a discussion.

Data that correspond to real-world scenarios can often be organized in several different ways in a database or program. Appreciating the differences between them and choosing an organization that addresses a system's needs are valuable and necessary computing skills. Unfortunately, little of the computing-education literature seems to deal with this topic.