Assessing and responding to the growth of computer science undergraduate enrollments
Abstract
The field of computer science (CS) is currently experiencing a surge in undergraduate degree production and course enrollments, which is straining program resources at many institutions and causing concern among faculty and administrators about how best to respond to the rapidly growing demand. There is also significant interest about what this growth will mean for the future of CS programs, the role of computer science in academic institutions, the field as a whole, and U.S. society more broadly. Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments seeks to provide a better understanding of the current trends in computing enrollments in the context of past trends. It examines drivers of the current enrollment surge, relationships between the surge and current and potential gains in diversity in the field, and the potential impacts of responses to the increased demand for computing in higher education, and it considers the likely effects of those responses on students, faculty, and institutions. This report provides recommendations for what institutions of higher education, government agencies, and the private sector can do to respond to the surge and plan for a strong and sustainable future for the field of CS in general, the health of the institutions of higher education, and the prosperity of the nation. © 2018 by the National Academy of Sciences. All rights reserved.
... The prompts are discussed with Partner B (B), who is viewing and optionally editing her code. This intervention is compared against autograders, a common strategy for addressing the rising enrollment of CS majors [36]. The study consisted of: a conceptual assessment pre-test; a short programming assignment; an intervention in which peers work in pairs using prompts; and a conceptual assessment post-test. ...
... Some of the strategies adopted to manage demand may not adequately consider equity or learning outcomes. The National Academies of Science, Engineering, Medicine, and others [36] found a doubling of CS enrollment from the 2012-13 academic year to the 2014-15 academic year. ...
... When considering how to scale CS courses, the National Academies Press (NAP) [36] calls faculty and professional staff the "most significant resource constraint in CS departments." Automated grading, an approach to managing rising enrollments, has been shown to provide benefits [19]. ...
... The lack of diversity in gender and race/ethnicity in Computer Science majors is a widely acknowledged challenge [1]. The first programming course, Computer Science 1 (CS1), has been a central topic in computing education research [2]. ...
... This paper is organized as follows: Section II describes the methodology; Section III present our findings answering the 1 research question; Section IV describes some important observations based on the results; Section V presents the limitations of our paper; and Section VI reports our conclusions. ...
... Over the past decade the popularity of CS majors has increased and enrollments have skyrocketed [2]. This has cre-ated challenges for instructors with increasing demands for individual support, collaborative learning, and automated guidance [12,2,16,15]. As the size of courses and cohorts have increased, the demand for office hours has begun to exceed the time that instructors and staff have available [12,13]. ...
As Computer Science has increased in popularity so too have class sizes and demands on faculty to provide support. It is therefore more important than ever for us to identify new ways to triage student questions, identify common problems, target students who need the most help, and better manage instructors' time. By analyzing interaction data from office hours we can identify common patterns, and help to guide future help-seeking. My Digital Hand (MDH) is an online ticketing system that allows students to post help requests, and for instructors to prioritize support and track common issues. In this research, we have collected and analyzed a corpus of student questions from across six semesters of a CS2 with a focus on object-oriented programming course [17]. As part of this work, we grouped the interactions into five categories, analyzed the distribution of help requests, balanced the categories by Synthetic Minority Oversampling Technique (SMOTE) , and trained an automatic classifier based upon LightGBM to automatically classify student requests. We found that over 69% of the questions were unclear or barely specified. We proved the stability of the model across semesters through leave one out cross-validation and the target model achieves an accuracy of 91.8%. Finally, we find that online office hours can provide more help for more students.
... There is a growing interest in developing students' interest in computer science, programming, and computational thinking as computing has become ubiquitous in various fields. 1 Within engineering fields, there is a growing recognition of the need to provide an undergraduate education that supports the development of computational and mathematical modeling skills. 2 Figure 1 shows examples of STEM+C programs available across the lifespan. Such interest has led to several online educational platforms offering computer science content for learning. ...
... Despite an ever-increasing demand for STEM and computer science workers, a concerning number of students in these disciplines leave the field before finishing their degree, sometimes citing discrimination and unwelcome environments [1]. Only around half of students enrolled in these programs actually graduate with the degree [2]. Underrepresented minorities (URM) are disproportionately represented in this group. ...
This article delves into the issues of equity, diversity, and inclusiveness (EDI) in the engineering disciplines in Canada and Spain and presents the challenges faced by underrepresented individuals and ways to promote an inclusive and diverse environment. Two strategic lines are identified: (a) facilitating university education access to underrepresented and minority groups and (b) guiding such students during university training to set them up for successful future careers. Accordingly, this article shows how the strategies mentioned above are implemented in some selected Canadian and Spanish universities, clearly distinguishing the approach taken in the two countries. In Canada, there is a more decentralized approach to addressing EDI issues, wherein the universities devise their agendas independently. In Spain, on the other hand, there is a stronger and more direct involvement of the government to ensure a comprehensive, system-wide approach to tackling EDI issues in academia. This article helps education policymakers to devise and implement pragmatic strategies for achieving EDI and the relevant UN-defined sustainable development goals.
... These started to fill the need for graduates with specific skills in those areas. Following this, with so much innovation and change taking place in the 2010's, part of what some call the 'fourth industrial revolution' [58] computing has since taken a new, more empirically driven path with the maturing of machine learning, the emergence of data science, and "the big data" revolution [47]. As a result, has come the need for increased security of data and computer systems. ...
Higher education institutions (HEIs) internationally are increasingly
recognising the importance of understanding student study
choices. This is especially true in the field of computing in which
skills shortages in the labour market are high but so too are student dropout rates. Loss of interest in the computing field has been reported among the reasons for students dropping out. The aim of this study is to offer a fresh perspective of the factors influencing undergraduate student’s interest and choice of specialisation in computing. Previous studies have mainly focused on five ACM identified computing disciplines: Computer Science, Information Systems, Information Technology, Computer Engineering and Software Engineering. With the ever-growing nature of computing, two more disciplines recently emerged: Cybersecurity and Data Science.
HEIs continuously endeavour to expand computing programmes
into specialist areas within these disciplines such as machine learning, artificial intelligence, gaming, robotics and creative computing. For prospective students, this maze of options can make for a difficult decision. 137 first-year computing students were invited to participate in a mixed-methods survey to explore their choices around cybersecurity and other newer specialisations. The results of the survey were matched with findings from recent literature, and show that personal interest, family, media, career prospects and prior experiences still influence student choices, with media appearing to have a greater impact compared to earlier studies. HEIs can use this when developing effective recruitment strategies in computing.
... Soaring Enrolments The relatively young field of computer science has become one of the largest study programmes around the globe. The increase of student enrolments is dramatic [27,25] while employment of new teaching staff often lags behind. At TUM, the number of new enrolments in computer science more than doubled between 2013 and 2021 from 1110 to 2644 (an increase of 138%) while academic staff only increased from 439 to 573 (31%) [1]. ...
Worldwide, computer science departments have experienced a dramatic increase in the number of student enrolments. Moreover, the ongoing COVID-19 pandemic requires institutions to radically replace the traditional way of on-site teaching, moving interaction from physical to virtual space. We report on our strategies and experience tackling these issues as part of a Haskell-based functional programming and verification course, accommodating over 2000 students in the course of two semesters. Among other things, we fostered engagement with weekly programming competitions and creative homework projects, workshops with industry partners, and collaborative pair-programming tutorials. To offer such an extensive programme to hundreds of students, we automated feedback for programming as well as inductive proof exercises. We explain and share our tools and exercises so that they can be reused by other educators.
... Soaring Enrolments The relatively young field of computer science has become one of the largest study programmes around the globe. The increase of student enrolments is dramatic [27,25] while employment of new teaching staff often lags behind. At TUM, the number of new enrolments in computer science more than doubled between 2013 and 2021 from 1110 to 2644 (an increase of 138%) while academic staff only increased from 439 to 573 (31%) [1]. ...
... Given that participants were unlikely to be familiar with the designations of routine and adaptive expertise, interview questions focused on the more immediate and mundane elements of admission processes; the skills and knowledge they believed they had obtained in each training ground; and, the teaching methods/ learning environments characteristic of their respective education programs. It is important to note that interviews were semi-structured in undergraduate CS students, the results support a recent report from the National Academies of Sciences, Engineering, and Medicine, 20 which points to a growing number of non-majors who take computing courses. In fact, 14 of the 27 undergraduate participants from this study entered the program as undecided-without a CS major in mind, and, notedly, all of these same students were initially leaning toward degrees in math, science, and/or engineering. ...
... Though scholarships can help to alleviate financial burden, funding alone is not sufficient to ensure student success [2]. In addition, many STEM fields struggle to retain students from historically marginalized populations [3]. This is especially true in computing disciplines. ...
... We couch this aim in both prior research on pre-med/health students that identifies large gaps in studies of the undergraduate pre-med/health experience (Lin et al., 2013), as well as national reports and initiatives that overlook this large portion of the STEM population (e.g., Committee on STEM Education, 2018). These gaps are made more obvious when compared with the large body of research on undergraduate STEM majors pursuing fields like physics (NRC, 2013), engineering (NRC, 2012), or computer science (NASEM, 2018). Thus, we position our research as an initial step in understanding a population that, despite its large size, is rarely studied as a unit, even though they share identity-related experiences that are likely to be of interest to educators and researchers. ...
Despite the wealth of research exploring science, technology, engineering, and mathematics (STEM) identity and career goals in both formal and informal settings, existing literature does not consider STEM identity for undergraduate students pursuing health and medical careers through STEM pathways. We address this gap by examining the STEM identity of undergraduate STEM majors on pre-med/health tracks as it compares with that of other STEM majors, thus focusing on a population that is chronically understudied in STEM education research. We surveyed 440 undergraduate STEM students enrolled in entry level STEM courses to assess their STEM identities and three identity precursors: interest , performance-competence, and recognition. Through regression analyses accounting for gender, major, and perceived home support around STEM, we found that pre-med/ health students were more likely to have higher STEM identity and recognition scores than their peers; we did not detect a significant difference for performance-competence or interest in STEM. Although there is little tracking of pre-med/health students' ultimate career attainment, the implications of our findings support a potential for sustaining pre-med/ health students while simultaneously creating pathways to other STEM pursuits for the nearly 60% of those who do not enter medical school by offering participation in experiences that affirm their STEM identities.
... Most of the abovementioned interventions and pedagogies have been found to have a positive effect on participation of youth in CS education. Some recent reports even pointed out that CS enrollment has increased in recent years, e.g., [23,70]. However, in the United States, CS education is still dominated by white male participants (see [22,71]) and the need to broaden participation still exists [72]. ...
In response to the need to broaden participation in computer science, we designed a summer camp to teach middle-school-aged youth to code apps with MIT App Inventor. For the past four summers, we have observed significant gains in youth's interest and self-efficacy in computer science, after attending our camps. The majority of these youth, however, were youth from our local community. To provide equal access across the state and secure more diversity, we were interested in examining the effect of the camp on a broader population of youth. Thus, we partnered with an outreach program to reach and test our camps on youth from low-income high-poverty areas in the Intermountain West. During the summer of 2019, we conducted two sets of camps: locally advertised app camps that attracted youth from our local community and a second set of camps as part of a larger outreach program for youth from low-income high-poverty areas. The camps for both populations followed the same design of personnel, camp activities, structure, and curriculum. However, the background of the participants was slightly different. Using survey data, we found that the local sample experienced significant gains in both self-efficacy and interest, while the outreach group only reported significant gains in self-efficacy after attending the camp. However, the qualitative data collected from the outreach participants indicated that they had a positive experience both with the camp and their mentors. In this article, we discuss the camp design and findings in relation to strategies for broadening participation in Computer Science education.
... As observed in a 2017 US National Academies Report, "A wide range of jobs in virtually all sectors demand computing skills to an unprecedented extent. And every academic discipline finds itself incorporating computing into its research and educational mission" (National Academies of Sciences, Engineering, and Medicine, et al. 2018). More recently, the collective influence of the Internet of Things (IoT), 'big' data, accessible cloud computing, and advances in artificial intelligence have been presented as a driver for digital transformation (Siebel 2019). ...
Computer science has experienced dramatic growth and diversification over the last twenty years. Towards a current understanding of the structure of this discipline, we analyze a large sample of the computer science literature from the DBLP database. For insight on the features of this cohort and the relationship within its components, we have constructed article level clusters based on either direct citations or co-citations, and reconciled them with major and minor subject categories in the All Science Journal Classification. We describe complementary insights from clustering by direct citation and co-citation, and both point to the increase in computer science publications and their scope. Our analysis reveals cross-category clusters, some that interact with external fields, such as the biological sciences, while others remain inward looking. Overall, we document an increase in computer science publications and their scope.
Interdisciplinarity has been touted as a means to recruit more racially and gender diverse students to computing. In this explanatory sequential mixed-methods study, we investigated demographic characteristics among a sample of undergraduate students pursuing interdisciplinary computing major and minor combinations at 15 institutions in the United States who completed a survey at the end of their introductory course. Descriptive analyses of responses to this survey of introductory computing students revealed that enrollment in interdisciplinary combinations was limited and did not appear to disproportionately attract women or Black/African American, Latine, Indigenous, and Multiracial students. We then conducted a directed content analysis of departmental websites to examine the language and policies that may preclude or encourage students to pursue interdisciplinary computing major and minor combinations. Findings revealed that departmental offerings of such programs were limited, and, among those that did offer such programs, communication about their goals and requirements was often lacking. Implications for research and practice, especially as they pertain to efforts to broaden participation in computing, are discussed.
As the need for skilled computer programmers increases each year globally, so does the need for learning environments that serve the novice programmer. This literature review aims to contribute to the field of computer science education by highlighting successful practices reported in the literature and describing those practices through the lens of instructional design. As the demand for programmers increases, the success of novice programmers remains stagnant. By reviewing research-based instructional practices through the lens of instructional design, instructional designers, researchers, and CS, instructors can make purposeful design decisions in the future that help to meet the needs of the growing number of novice programmers. This chapter highlights the importance of instructional design theories and learning methods in meeting the diverse needs of computer science learners.
Despite continued efforts to further the participation of women in Computer Science (CS), progress has been limited during the past decades. Recent efforts have been focused on recruitment and retention, with a notable gap in exploring the impact of admissions processes on diversity and inclusion. Through an extensive literature review, contextual analysis of public admissions data from 40 universities across four regions around the world, and qualitative and quantitative analysis on surveys and interviews, we explored the role of admissions in enhancing diversity and inclusion in CS undergraduate programs. Our findings highlight the role of financials, the possible positive effects of explicit advocacy for diversity and inclusion, and the imperative to cultivate a more welcoming and inclusive culture in CS programs.
Artificial intelligence (AI) in programming teaching is something that still has to be explored, since in this area assessment tools that allow grading the students work are the most common ones, but there are not many tools aimed toward providing feedback to the students in the process of creating their program. In this work a small sized AI-based intelligent tutor that answers students programming questions and provides them with examples is presented. AI is becoming more and more popular as time passes, allowing to perform tasks automatically in a way that could not be done before. From predictions to customization, AI is being used in many areas, not being educational environments outside this situation. AI is being used in educational settings to customize contents or to provide personalized feedback to the students, among others. In this scenario, The tool has been tested by university students at the Universidad Rey Juan Carlos during the course Programming Fundamentals in the first course of their Biomedical Engineering degree to evaluate if it helps the students in the process of learning programming skills. One of the main goals was to provide guidance to the students without needing the instructor to be physically by their side. Even if the tool is still in its preliminary phase, it helped the students with their questions, providing accurate answers and examples. The students were able to use the intelligent tutor easily and they thought that it could be a useful tool to use in other courses.
Men’s significant underrepresentation in U.S. colleges has attracted widespread attention. As of 2021, men make up only 40.5% of the college enrollments. Given the proven economic and social benefits of a college degree, men’s lack of a college degree is projected to generate detrimental effects to the U.S. economy and society, as well as bring missed opportunities for individuals. We review the gender composition at various degree levels, discuss possible impacts of men’s underrepresentation, and analyze underlying reasons of men’s low college enrollment. We also review strategies that institutions use to attract more male applicants. Despite women’s overrepresentation in college enrollment, efforts to promote gender equality should continue, as females are still underrepresented in high-paying and high-ranking positions, in both academic and corporate settings.
男性在美国大学占比不足（underrepresentation）的现象引起了广泛关注。截至2021年，男性学生仅占大学入学人数的40.5%。鉴于大学学位的经济和社会效益已经得到证明，男性中大学学位的缺少预计会对美国的经济与社会产生不利影响，同时也会导致个人错失各种机会。我们回顾了不同学位等级的性别构成，讨论男性占比不足带来的可能影响，并分析男性低入学率的潜在原因。我们还回顾了各院校用以吸引更多男性申请者的策略。尽管女性在大学入学人数中占比过高，但是促进性别平等的努力仍应继续；因为在学术和企业环境中，女性在高薪和高级职位的比例仍然不足。
The main aim of this thesis is to investigate the inequalities in the academic outcomes of Italian students. This dissertation is divided into four chapters in an attempt to adhere to a chronological order. Each chapter will deal with a different step in university careers and with a different type of inequality (socioeconomic, geographical, and gender inequalities), and different approaches will be used to answer various questions.
The need for software developers continues to grow, while students’ engagement, attrition, achievements, and diversity in novice students’ first programming classes (CS1) remain active global research concerns. It appears that conventional programming pedagogy involving mainly lectures and labs does not meet the learning needs of some CS1 students. Meanwhile, because of the ease of initiating novices from diverse backgrounds and interests, Scratch, a constructionist block-based programming environment, has become a popular staple in K-12 programming classes. Seymour Papert’s constructionism theory posits that students learn not just by spoon-feeding them with knowledge but especially as they are given the freedom to develop and share artefacts of interest with their peers. Scratch is now used in higher education, with limited and mixed impacts on CS1 students. In this PhD research project, conducted in two phases, the pilot and main studies, lasting two academic sessions, we employed a pretest-posttest non-equivalent control group design. The research aimed to compare CS1 students’ achievement between those in the conventional and constructionist programming classes. We also sought to investigate whether backgrounds such as academic level, programming, visual arts, age, and gender moderate CS1 achievements. In both studies, purposive sampling was employed to select polytechnics from two central Nigerian states, and selected schools were randomly assigned to the experimental and control groups. We then employed Coarsened Exact Matching algorithm to generate adequate treatment cases from both studies used in the analysis, two samples from each study. The results from the t-test and ANCOVA analysis of research data from both studies revealed a consistent pattern: CS1 students in the constructionist Scratch class outperformed their counterparts in the conventional class, although the impact was moderate. This implies that employing Scratch following constructionist pedagogy may be more engaging, leading to learning gains for college students, especially for those from disadvantaged backgrounds with no programming experience.
Department chairs play a key role in efforts to diversify higher education, particularly in fields like computer science that face long-standing gender and racial/ethnic gaps. This study considers the role of computer science department chairs in guiding broadening participation efforts and how they make sense of external dynamics that influence their efforts.
Objective
This study seeks to better understand the external dynamics that shaped diversity efforts within their departments and how chairs positioned themselves in relation to those dynamics.
Method
Using the Inclusive Excellence Model as a conceptual framework this longitudinal qualitative study draws on 29 interviews conducted over two years with 18 computer science department chairs from 15 research universities across the United States.
Findings
Chairs described actively grappling with and leveraging external dynamics across three dimensions: systemic, political, and bureaucratic/structural. While existing literature on department chairs often frame external dynamics as unidirectionally impacting departmental efforts, we found that chairs saw their role as agentive.
Implications
As efforts to broaden participation in computing (BPC) continue to expand, we argue that chairs must recognize and harness the complex and interconnected external forces that influence their work in order to fully actualize their role as transformative leaders.
Surveillance capitalism is what happened when US democracy stood down. Two decades later, it fails any reasonable test of responsible global stewardship of digital information and communications. The abdication of the world’s information spaces to surveillance capitalism has become the meta-crisis of every republic because it obstructs solutions to all other crises. The surveillance capitalist giants–Google, Apple, Facebook, Amazon, Microsoft, and their ecosystems–now constitute a sweeping political-economic institutional order that exerts oligopolistic control over most digital information and communication spaces, systems, and processes.
The commodification of human behavior operationalized in the secret massive-scale extraction of human-generated data is the foundation of surveillance capitalism’s two-decade arc of institutional development. However, when revenue derives from commodification of the human, the classic economic equation is scrambled. Imperative economic operations entail accretions of governance functions and impose substantial social harms. Concentration of economic power produces collateral concentrations of governance and social powers. Oligopoly in the economic realm shades into oligarchy in the societal realm. Society’s ability to respond to these developments is thwarted by category errors. Governance incursions and social harms such as control over AI or rampant disinformation are too frequently seen as distinct crises and siloed, each with its own specialists and prescriptions, rather than understood as organic effects of causal economic operations.
In contrast, this paper explores surveillance capitalism as a unified field of institutional development. Its four already visible stages of development are examined through a two-decade lens on expanding economic operations and their societal effects, including extraction and the wholesale destruction of privacy, the consequences of blindness-by-design in human-to-human communications, the rise of AI dominance and epistemic inequality, novel achievements in remote behavioral actuation such as the Trump 2016 campaign, and Apple-Google’s leverage of digital infrastructure control to subjugate democratic governments desperate to fight a pandemic. Structurally, each stage creates the conditions and constructs the scaffolding for the next, and each builds on what went before. Substantively, each stage is characterized by three vectors of accomplishment: novel economic operations, governance carve-outs, and fresh social harms. These three dimensions weave together across time in a unified architecture of institutional development. Later-stage harms are revealed as effects of the foundational-stage economic operations required for commodification of the human.
Surveillance capitalism’s development is understood in the context of a larger contest with the democratic order—the only competing institutional order that poses an existential threat. The democratic order retains the legitimate authority to contradict, interrupt, and abolish surveillance capitalism’s foundational operations. Its unique advantages include the ability to inspire action and the necessary power to make, impose, and enforce the rule of law. While the liberal democracies have begun to engage with the challenges of regulating today’s privately owned information spaces, I argue that regulation of institutionalized processes that are innately catastrophic for democratic societies cannot produce desired outcomes. The unified field perspective suggests that effective democratic contradiction aimed at eliminating later-stage harms, such as “disinformation,” depends upon the abolition and reinvention of the early-stage economic operations that operationalize the commodification of the human, the source from which such harms originate.
The clash of institutional orders is a death match over the politics of knowledge in the digital century. Surveillance capitalism’s antidemocratic economic imperatives produce a zero-sum dynamic in which the deepening order of surveillance capitalism propagates democratic disorder and deinstitutionalization. Without new public institutions, charters of rights, and legal frameworks purpose-built for a democratic digital century, citizens march naked, easy prey for all who steal and hunt with human data. Only one of these contesting orders will emerge with the authority and power to rule, while the other will drift into deinstitutionalization, its functions absorbed by the victor. Will these contradictions ultimately defeat surveillance capitalism, or will democracy suffer the greater injury? It is possible to have surveillance capitalism, and it is possible to have a democracy. It is not possible to have both.
Computationally intensive science (CIS)‐related disciplines and careers are predicted to be among the cutting‐edge fields and high‐demand jobs. Such disciplines and careers demand expertise in both traditional scientific knowledge and computer science (CS)‐related understanding and skills. Preparing educational systems for developing interest and abilities for such career paths require a new set of research tools. In this study, we developed and validated a multidimensional instrument called computationally intensive science career interests (CISCI) to measure middle school students' interests in CIS careers. We also explored several predictors of students' interests in such careers and examined the impact of a 4‐day online synchronous scientific computational modeling activity on students' career interests. A total of 934 Indonesian middle school students (aged 11–14) participated in this study. A combination of the classical test theory and item response theory approaches was used to validate the CISCI instrument. Multiple linear regression tests were run to identify the predictors of students' career interests, and paired‐sample t‐tests were used to examine a scientific computational modeling activity's impact on students' career interests. The results revealed that CISCI was a psychometrically valid and reliable instrument to measure students' career interests. We also found that science and CS attitudes, computational thinking, and prior experience in CS‐related activities were significant predictors of students' career interests. In addition, computational modeling activity significantly influenced the frequency of students discussing such career paths with their parents. This study underscores the importance of engaging students in CS‐integrated science learning activities to help develop their interests in CIS jobs.
Teaching programming is a challenging activity nowadays, especially in introductory programming courses, which are typically massively attended. Writing functional programs is a cognitive skill, which many students, novices in programming, find it difficult to master. It is equally challenging to assess this ability of students. Research has shown that students need to learn how to read and understand, before they can learn to write programs. Code tracing questions are a suitable way to assess the knowledge of semantics of programming constructs (understanding what programs do). However, when designing these types of questions, teachers need to be aware of the complexity of the code and must always try to create questions with code snippets of same or similar complexity, especially when preparing different versions of the same test (for large student groups). In this paper we propose a new model for smart automatic generation of code tracing questions. The model uses a suitable source code metric to measure the code complexity, and to enable generation of questions containing code snippets of consistent complexity. We also introduce CodeCPP: a newly developed Moodle plugin, which is an implementation of the proposed model. The evaluation of CodeCPP, through a year‐long use of the system in teaching programming as well as feedback from selected teachers with many years of experience in teaching programming courses, shows positive and promising results. The system utilization confirms that the application of the proposed model enables a fair and objective knowledge assessment. Furthermore, the feedback provided by teachers and students is highly positive.
Technology should be accessible and inclusive, so designers should learn to consider the needs of different users. Toward this end, we created the theoretically-grounded CIDER assumption elicitation technique, an educational analytical design evaluation method to teach inclusive design skills. CIDER ( Critique , Imagine , Design , Expand , Repeat ) helps designers recognize and respond to bias using the critical lens of assumptions about users . Through an eleven-week mixed-method case study in an interaction design course with 40 undergraduate students and follow-up interviews, we found that activities based on the CIDER technique may have helped students identify increasingly many types of design bias over time and reflect on their unconscious biases about users. The activities also had lasting impacts, encouraging some students to adopt more inclusive approaches in subsequent design work. We discuss the implications of these findings, namely that educational techniques like CIDER can help designers learn to create equitable technology designs.
As computing courses become larger, students of minoritized groups continue to disproportionately face challenges that hinder their academic and professional success (e.g. implicit bias, microaggressions, lack of resources, assumptions of preparatory privilege). This can impact career aspirations and sense of belonging in computing communities. Instructors have the power to make immediate changes to support more equitable learning, but they are often unaware of students' challenges. To help both instructors and students understand the inequities in their classes, we developed StudentAmp, an interactive system that uses student feedback and self-reported demographic information (e.g. gender, ethnicity, disability, educational background) to show challenges and how they affect students differently. To help instructors make sense of feedback, StudentAmp ranks challenges by student-perceived disruptiveness. We conducted formative evaluations with five large college computing courses (150 - 750 students) being taught remotely during the COVID-19 pandemic. We found that students shared challenges beyond the scope of the course, perceived sharing information about who they were as useful but potentially dangerous, and that teaching teams were able to use this information to consider the positionality of students sharing challenges. Our findings relate to a central design tension of supporting equity by sharing contextualized information about students while also ensuring their privacy and well-being.
Context. Computing Education Research (CER) is critical to help the computing education community and policy makers support the increasing population of students who need to learn computing skills for future careers. For a community to systematically advance knowledge about a topic, the members must be able to understand published work thoroughly enough to perform replications, conduct meta-analyses, and build theories. There is a need to understand whether published research allows the CER community to systematically advance knowledge and build theories.
Objectives. The goal of this study is to characterize the reporting of empiricism in Computing Education Research literature by identifying whether publications include content necessary for researchers to perform replications, meta-analyses, and theory building. We answer three research questions related to this goal: (RQ1) What percentage of papers in CER venues have some form of empirical evaluation? (RQ2) Of the papers that have empirical evaluation, what are the characteristics of the empirical evaluation? (RQ3) Of the papers that have empirical evaluation, do they follow norms (both for inclusion and for labeling of information needed for replication, meta-analysis, and, eventually, theory-building) for reporting empirical work?
Methods. We conducted a systematic literature review of the 2014 and 2015 proceedings or issues of five CER venues: Technical Symposium on Computer Science Education (SIGCSE TS), International Symposium on Computing Education Research (ICER), Conference on Innovation and Technology in Computer Science Education (ITiCSE), ACM Transactions on Computing Education (TOCE), and Computer Science Education (CSE). We developed and applied the CER Empiricism Assessment Rubric to the 427 papers accepted and published at these venues over 2014 and 2015. Two people evaluated each paper using the Base Rubric for characterizing the paper. An individual person applied the other rubrics to characterize the norms of reporting, as appropriate for the paper type. Any discrepancies or questions were discussed between multiple reviewers to resolve.
Results. We found that over 80% of papers accepted across all five venues had some form of empirical evaluation. Quantitative evaluation methods were the most frequently reported. Papers most frequently reported results on interventions around pedagogical techniques, curriculum, community, or tools. There was a split in papers that had some type of comparison between an intervention and some other dataset or baseline. Most papers reported related work, following the expectations for doing so in the SIGCSE and CER community. However, many papers were lacking properly reported research objectives, goals, research questions, or hypotheses; description of participants; study design; data collection; and threats to validity. These results align with prior surveys of the CER literature.
Conclusions. CER authors are contributing empirical results to the literature; however, not all norms for reporting are met. We encourage authors to provide clear, labeled details about their work so readers can use the study methodologies and results for replications and meta-analyses. As our community grows, our reporting of CER should mature to help establish computing education theory to support the next generation of computing learners.
Although undergraduate (UG) computer science (CS) programs are increasingly engaged in diversification efforts, this work is rarely critically informed or assessed. We conducted a qualitative secondary analysis of interviews with 55 campus leaders at four U.S. institutions of higher education, to examine how diversity initiatives broadened the participation of undergraduate Students of Color (SoC) in CS majors. We found that racial equity work happened predominantly in three types of counterspaces—professional conferences, campus identity centers, and student organizations—that explicitly centered and affirmed students’ racialized identities, provided community, expanded students’ professional and academic networks, and supported students to navigate the racial exclusion prevalent in their classes, the department, and in industry. However, our framework of diversity ideology exposed how counterspaces and their leaders of Color operated with little to no support from computing departments, were peripherally positioned outside of broader diversity efforts, and were ultimately inhibited from transforming the dominant culture in CS that privileged whiteness. We argue that studying and amplifying the intentional, impactful labor performed by counterspaces is necessary to address persistent racial disparities in computing.
Computing Education Research (CER) is critical for supporting the increasing number of students who need to learn computing skills. To systematically advance knowledge, publications must be clear enough to support replications, meta-analyses, and theory-building. The goal of this study is to characterize the reporting of empiricism in CER literature by identifying whether publications include information to support replications, meta-analyses, and theory building. The research questions are: RQ1) What percentage of papers in CER venues have empirical evaluation? RQ2) What are the characteristics of the empirical evaluation? RQ3) Do the papers with empirical evaluation follow reporting norms (both for inclusion and for labeling of key information)? We conducted an SLR of 427 papers published during 2014 and 2015 in five CER venues: SIGCSE TS, ICER, ITiCSE, TOCE, and CSE. We developed and applied the CER Empiricism Assessment Rubric. Over 80% of papers had some form of empirical evaluation. Quantitative evaluation methods were the most frequent. Papers most frequently reported results on interventions around pedagogical techniques, curriculum, community, or tools. There was a split in papers that had some type of comparison between an intervention and some other data set or baseline. Many papers lacked properly reported research objectives, goals, research questions, or hypotheses, description of participants, study design, data collection, and threats to validity. CER authors are contributing empirical results to the literature; however, not all norms for reporting are met. We encourage authors to provide clear, labeled details about their work so readers can use the methodologies and results for replications and meta-analyses. As our community grows, our reporting of CER should mature to help establish computing education theory to support the next generation of computing learners.
Automatic software plagiarism detection tools are widely used in educational settings to ensure that submitted work was not copied. These tools have grown in use together with the rise in enrollments in computer science programs and the widespread availability of code on-line. Educators rely on the robustness of plagiarism detection tools; the working assumption is that the effort required to evade detection is as high as that required to actually do the assigned work. This paper shows this is not the case. It presents an entirely automatic program transformation approach, Mossad, that defeats popular software plagiarism detection tools. Mossad comprises a framework that couples techniques inspired by genetic programming with domain-specific knowledge to effectively undermine plagiarism detectors. Mossad is effective at defeating four plagiarism detectors, including Moss and JPlag. Mossad is both fast and effective: it can, in minutes, generate modified versions of programs that are likely to escape detection. More insidiously, because of its non-deterministic approach, Mossad can, from a single program, generate dozens of variants, which are classified as no more suspicious than legitimate assignments. A detailed study of Mossad across a corpus of real student assignments demonstrates its efficacy at evading detection. A user study shows that graduate student assistants consistently rate Mossad-generated code as just as readable as authentic student code. This work motivates the need for both research on more robust plagiarism detection tools and greater integration of naturally plagiarism-resistant methodologies like code review into computer science education.
The Black Stork (Ciconia nigra L.) has a special protection status. In Latvia this species is protected, most often with a microreserve. Considering the fact that the black stork is a forest core species, the landscape of which is significant at least 3 km from the breeding site and the fact that active forestry is allowed outside the microreserves, a situation in which the habitat in the sense of the landscape has lost its role in protecting the species may arise. The aim of the study is to identify potential changes and impacts of Ciconia nigra L. habitats landscape on representative indicators, taking into account the current forestry intensity. The following work objectives are (1) perform general analysis of the habitats landscape in area of microreserves that are identified over the last 5 years (n=32), (2) modelling the future landscape scenario for the next 100 years, using LANDIS-II model taking into account the current forest management, and (3) determined habitat landscape indicators and their impacts by comparing the results of Fragstats to randomly selected plots (n = 40). In the 6x6 km landscape of the Ciconia nigra L. habitat were found that birch, pines, spruce stands with black alder are most common, grouping habitats according to tree species societies in the DCA ordination. On average, the eligible stands for habitats were found to be 18% of the total landscape area, which is half the size of the non-eligible stands(36%). The average distance of potential metapopulation is estimated to be 28 m with average area of 20 ha. Were found that the existing forest management regime situation will lead to a statistically significant reduction in suitable stock areas over time, despite no significant changes in their diversity indexes. A number of potential metapopulation metrics have been identified to change statistically, but no significant change in diversity. The analysis of habitats by the random sample did not identify any significant impacts of species, landscape or class metrics on the existence of the habitat. Areas of potential metapopulation were found to be less fragmented in randomly selected plots. It is identified that in the habitats of the black stork are statistically significantly more ditches and forest paths in the plots of general length and more spruce and birch stands under the age of 90 years.
Investigating student knowledge transfer and metacognitive activities at college CS departments and at coding bootcamps.
We describe the deployment of an imperfect NLP-based automatic short answer grading system on an exam in a large-enrollment introductory college course. We characterize this deployment as both high stakes (the questions were on an mid-term exam worth 10% of students’ final grade) and high transparency (the question was graded interactively during the computer-based exam and correct solutions were shown to students that could be compared to their answer). We study two techniques designed to mitigate the potential student dissatisfaction resulting from students incorrectly not granted credit by the imperfect AI grader. We find (1) that providing multiple attempts can eliminate first-attempt false negatives at the cost of additional false positives, and (2) that students not granted credit from the algorithm cannot reliably determine if their answer was mis-scored.
Given growing interest in computing fields, as well as a longstanding gender gap in computer science, this study used nationwide survey data on college students during 4 decades to: (a) document trends in aspirations to major in computer science among undergraduate women and men; (b) explore the characteristics of women and men who choose to major in computer science and how this population has evolved over time; and (c) identify the key determinants of the gender gap in the selection of computer science majors during the past 4 decades. The data included 8 million students attending 1,225 baccalaureate-granting institutions from 1971 to 2011, with selected-year multivariate analyses of 18,830 computer science majors (and 904,307 students from all other majors). The results revealed heavy fluctuations in students’ interest in computer science from 1971 to 2011, with trends highlighting a significant downturn between the late 1990s and 2011 as well as a persistent, sizeable underrepresentation of women across all years. The study also showed that while some of the traditional explanations for the gender gap in computer science held true, there have been distinctive shifts in who pursues computer science and why some students may be particularly interested in or dissuaded from the major.