ACM Transactions on Computing Education最新文献

The benefits of service learning in computer and information science (CIS) are believed to be significant, ranging from providing students with real-world experiences, to retaining students, to positively impacting community partners. While there are many benefits of service learning, the CIS domain does impose unique costs for integrating service learning into the curriculum. Yet there is little systematic research to help the CIS community understand best practices for maximizing benefits while minimizing costs. Experience reports about service learning courses in CIS have appeared in the literature annually since 2000 and so we address this gap in knowledge by conducting a systematic review and content analysis of 84 experience reports from the The ACM Guide to Computing Literature. We synthesize the current state of service learning in CIS as well as derive recommendations for best practices and future research directions.

Assessing team software development projects is notoriously difficult and typically based on subjective metrics. To help make assessments more rigorous, we conducted an empirical study to explore relationships between subjective metrics based on peer and instructor assessments, and objective metrics based on GitHub and chat data. We studied 23 undergraduate software teams (n = 117 students) from two undergraduate computing courses at two North American research universities. We collected data on teams’ (a) commits and issues from their GitHub code repositories, (b) chat messages from their Slack and Microsoft Teams channels, (c) peer evaluation ratings from the CATME peer evaluation system, and (d) individual assignment grades from the courses. We derived metrics from (a) and (b) to measure both individual team members’ contributions to the team, and the equality of team members’ contributions. We then performed Pearson analyses to identify correlations among the metrics, peer evaluation ratings, and individual grades. We found significant positive correlations between team members’ GitHub contributions, chat contributions, and peer evaluation ratings. In addition, the equality of teams’ GitHub contributions was positively correlated with teams’ average peer evaluation ratings and negatively correlated with the variance in those ratings. However, no such positive correlations were detected between the equality of teams’ chat contributions and their peer evaluation ratings. Our study extends previous research results by providing evidence that (a) team members’ chat contributions, like their GitHub contributions, are positively correlated with their peer evaluation ratings; (b) team members’ chat contributions are positively correlated with their GitHub contributions; and (c) the equality of team’ GitHub contributions is positively correlated with their peer evaluation ratings. These results lend further support to the idea that combining objective and subjective metrics can make the assessment of team software projects more comprehensive and rigorous.

What if “regular” Computer Science (CS) faculty each taught elements of inclusive design in “regular” CS courses across an undergraduate curriculum? Would it affect the CS program's climate and inclusiveness to diverse students? Would it improve retention? Would students learn less CS? Would they actually learn any inclusive design? To answer these questions, we conducted a year-long Action Research investigation, in which 13 CS faculty integrated elements of inclusive design into 44 CS/IT offerings across a 4-year curriculum. The 613 affected students’ educational work products, grades, and/or climate questionnaire responses revealed significant improvements in students’ course outcomes (higher course grades and fewer course fails/incompletes/withdrawals), especially for marginalized groups; revealed that most students did learn and apply inclusive design concepts to their CS activities; and revealed that inclusion and teamwork in the courses significantly improved. These results suggest a new pathway for significantly improving students’ retention, their knowledge and usage of inclusive design, and their experiences across CS education—for marginalized groups and for all students.

Motivation: Enrollments in Brazilian computing degrees are at an all-time high, but graduation numbers have not increased at the same rate. Moreover, enrollment growth has mainly attracted male students, steadily expanding the gender gap in Brazilian computing programs. Such high attrition rates have a great economic impact and may disproportionately affect women and students of color. Previous works investigated reasons for student drop-out and retention in specific courses or barriers to entrance in computing programs in narrower contexts or in a single institution. Objectives: We investigate potential actionable factors for intent to drop out in computing programs and what factors might lead students to remain in a computing program in several Brazilian institutions. We investigated how such factors may be affected by students’ race/ethnicity, gender, and socioeconomic status. Method: We analyzed Likert-style answers from an online survey with 3193 students currently enrolled in Brazilian computing programs. Results: The results show that students value salary/job-related factors as the most important factors to potentially remain in a computing program. The excess of theoretical courses and the difficulty of programming and mathematics courses are the top-ranked factors by students to potentially abandon a computing degree. However, while there is little effect of gender, race/ethnicity, or socioeconomic status in retention factors, potential drop-out factors such as a male-dominated field, harassment, and the difficulty of courses were rated as more important by women. Also, costs and the difficulty of courses are relevant factors for the intent to drop-out when analyzing students’ race/ethnicity and socioeconomic status. Discussion: We explore the implications of our findings for Computing programs, particularly (but not restricted to) the Brazilian context. We conjecture reasons for such students’ perspectives regarding intent to drop-out and retention factors and provide recommendations of actions for instructional designers, curriculum developers, and other key stakeholders to address issues related to gender, students’ wellness, perceived authenticity of courses, and other relevant factors. Since we only observed small interactions between race/ethnicity and retention and intent to drop-out factors, which may indicate a lack of sensitivity from the instrument, we lay suggestions to address such limitations in future work.

The disciplinary identity as a computer science student has recently received increasing attention as a well-developed subject identity can help with increasing retention, interest and motivation. Besides, identity theory can serve as an analytical lens for issues around diversity. However, identity is also often perceived as a vague, overused concept with a variety of theories to build upon. In addition, connections to other topics, such as computer science conceptions, remain unclear and there seems to be little intra-disciplinary exchange about the concept. This article therefore attempts to provide a starting point by presenting a so far missing systematic literature review of identity in Computing Education Research (CER). We analyzed a corpus of 41 papers published since 2005 with a focus on the variety of identity theories that are used, the reasons for using them and the overall theoretical framing of the concept in the CER literature up to this point. We use content analysis with both inductive and deductive coding to derive categories from the corpus to answer our research questions. The results show that there is less variety in the theories than originally expected, most publications refer to the theory of “Communities of Practice”. The reasons for employing identity theory are also rather canonical, in particular, there is only little theoretical development of the theories within CER and also only little empirical work. Finally, we also present an extended version of a computing identity that can be theoretically derived from the work in our corpus.