Computer Science Experience Research Articles

Towards reconciling usability and usefulness of policy explanations for sequential decision-making systems.

Safefy-critical domains often employ autonomous agents which follow a sequential decision-making setup, whereby the agent follows a policy to dictate the appropriate action at each step. AI-practitioners often employ reinforcement learning algorithms to allow an agent to find the best policy. However, sequential systems often lack clear and immediate signs of wrong actions, with consequences visible only in hindsight, making it difficult to humans to understand system failure. In reinforcement learning, this is referred to as the credit assignment problem. To effectively collaborate with an autonomous system, particularly in a safety-critical setting, explanations should enable a user to better understand the policy of the agent and predict system behavior so that users are cognizant of potential failures and these failures can be diagnosed and mitigated. However, humans are diverse and have innate biases or preferences which may enhance or impair the utility of a policy explanation of a sequential agent. Therefore, in this paper, we designed and conducted human-subjects experiment to identify the factors which influence the perceived usability with the objective usefulness of policy explanations for reinforcement learning agents in a sequential setting. Our study had two factors: the modality of policy explanation shown to the user (Tree, Text, Modified Text, and Programs) and the "first impression" of the agent, i.e., whether the user saw the agent succeed or fail in the introductory calibration video. Our findings characterize a preference-performance tradeoff wherein participants perceived language-based policy explanations to be significantly more useable; however, participants were better able to objectively predict the agent's behavior when provided an explanation in the form of a decision tree. Our results demonstrate that user-specific factors, such as computer science experience (p 0.05), and situational factors, such as watching agent crash (p 0.05), can significantly impact the perception and usefulness of the explanation. This research provides key insights to alleviate prevalent issues regarding innapropriate compliance and reliance, which are exponentially more detrimental in safety-critical settings, providing a path forward for XAI developers for future work on policy-explanations.

Embracing Culturally Relevant Computational Thinking in the Preschool Classroom: Leveraging Familiar Contexts for New Learning

AbstractComputational thinking (CT) is an important twenty-first century skill that begins developing early. Recent interest in incorporating early CT experiences in early childhood education (i.e., preschool) has increased. In fact, the early years mark an important time during which initial competencies are acquired, interest and motivation begins to form, and in which children may develop a sense of belonging in STEM fields. As a result, providing children with access to robotics and computer science experiences to support CT that are also developmentally appropriate and culturally relevant is key. This paper uses the “powerful ideas” of computer science, seven developmentally appropriate CT concepts that children can learn, as a framework and explores the experiences of two (composite) teachers who participated in and co-developed a culturally relevant robotics program and the processes they undertake to support children’s CT development and learning. This paper considers practices that support the seven key powerful ideals while leveraging existing instructional routines and contexts that are already occurring in most classrooms, such as centers, small group activities, classroom environments, and read-alouds. Of note, this paper prioritizes approaches that acknowledge, center, and feature the ethnic, cultural, and linguistic backgrounds of young children and their families. Identifying affordable and accessible practices, this paper provides educators with tangible, integrated, and authentic practices to support children’s computational thinking, STEM learning, and sense of belonging.

Literacy and STEM Teachers Adapt AI Ethics Curriculum

This article examines the ways secondary computer science and English Language Arts teachers in urban, suburban, and semi-rural schools adapted a project-based AI ethics curriculum to make it better fit their local contexts. AI ethics is an urgent topic with tangible consequences for youths’ current and future lives, but one that is rarely taught in schools. Few teachers have formal training in this area as it is an emerging field even at the university level. Exploring AI ethics involves examining biases related to race, gender, and social class, a challenging task for all teachers, and an unfamiliar one for most computer science teachers. It also requires teaching technical content which falls outside the comfort zone of most humanities teachers. Although none of our partner teachers had previously taught an AI ethics project, this study demonstrates that their expertise and experience in other domains played an essential role in providing high quality instruction. Teachers designed and redesigned tasks and incorporated texts and apps to ensure the AI ethics project would adhere to district and department level requirements; they led equity-focused inquiry in a way that both protected vulnerable students and accounted for local cultures and politics; and they adjusted technical content and developed hands-on computer science experiences to better challenge and engage their students. We use Mishra and Kohler’s TPACK framework to highlight the ways teachers leveraged their own expertise in some areas, while relying on materials and support from our research team in others, to create stronger learning experiences.

Students’ expert-like attitudes in calculus and introductory computer science courses with active-learning pedagogy

ABSTRACT Background and Context Computer Science has traditionally had poor student retention, especially among women. Prior work has found that student attitudes are a key factor to retention, especially with “weedout” courses such as Calculus. Objective To determine how student attitudes towards CS 1 and Calculus change over active-learning courses with explicit real-world connections, and the implications for at-risk groups in CS, including women and students without prior experience in CS. Methods We use two validated instruments, the Computer Attitudes Survey (CAS) and the Mathematics Attitudes and Perceptions Survey (MAPS), both of which derive from the Colorado Learning Attitudes about Science Survey (CLASS), to compare student attitudes in Calculus I and CS 1. Our analysis includes 109 students for the CAS and 73 for the MAPS. Findings CS 1 students began the course with CAS scores higher than observed in prior work, and post-course CAS scores were even higher overall. Calculus students started the course with MAPS scores in line with prior work, but unlike prior work we find that overall MAPS scores did not decline during the semester. Women’s reported interest in math increased significantly during the semester. Students with prior computer science experience began CS 1 with higher overall CAS scores, while students with prior calculus experience began Calculus with similar overall MAPS scores but much higher reported interest. Implications Difference in student attitudes between the courses may show why Calculus acts a “weedout” course for Computer Science. This highlights the importance of instructors demonstrating the usefulness of Calculus.

Who participates in computer science education studies? A literature review on K-12 subjects.

Computer science education (CSEd) research within K-12 makes extensive use of empirical studies in which children participate. Insight in the demographics of these children is important for the purpose of understanding the representativeness of the populations included. This literature review studies the demographics of subjects included in K-12 CSEd studies. We have manually inspected the proceedings of three of the main international CSEd conferences: SIGCSE, ITiCSE and ICER, of five years (2014–2018), and selected all papers pertaining to K-12 CSEd experiments. This led to a sample of 134 papers describing 143 studies. We manually read these papers to determine the demographic information that was reported on, investigating the following categories: age/grade, gender, race/ethnic background, location, prior computer science experience, socio-economic status (SES), and disability. Our findings show that children from the United States, boys and children without computer science experience are included most frequently. Race and SES are frequently not reported on, and for race as well as for disabilities there appears a tendency to report these categories only when they deviate from the majority. Further, for several demographic categories different criteria are used to determine them. Finally, most studies take place within schools. These insights can be valuable to correctly interpret current knowledge from K-12 CSEd research, and furthermore can be helpful in developing standards for consistent collection and reporting of demographic information in this community.

Personal information inference from voice recordings: User awareness and privacy concerns

Abstract Through voice characteristics and manner of expression, even seemingly benign voice recordings can reveal sensitive attributes about a recorded speaker (e. g., geographical origin, health status, personality). We conducted a nationally representative survey in the UK (n = 683, 18–69 years) to investigate people’s awareness about the inferential power of voice and speech analysis. Our results show that – while awareness levels vary between different categories of inferred information – there is generally low awareness across all participant demographics, even among participants with professional experience in computer science, data mining, and IT security. For instance, only 18.7% of participants are at least somewhat aware that physical and mental health information can be inferred from voice recordings. Many participants have rarely (28.4%) or never (42.5%) even thought about the possibility of personal information being inferred from speech data. After a short educational video on the topic, participants express only moderate privacy concern. However, based on an analysis of open text responses, unconcerned reactions seem to be largely explained by knowledge gaps about possible data misuses. Watching the educational video lowered participants’ intention to use voice-enabled devices. In discussing the regulatory implications of our findings, we challenge the notion of “informed consent” to data processing. We also argue that inferences about individuals need to be legally recognized as personal data and protected accordingly.

Rural implementation of Girls' Programming Network (GPN)

A small-scale pilot study was conducted in north-west Tasmania to investigate adolescent girls’ willingness to participate in informal STEM education, through exploring their perceptions and experiences of computer science (CS), and their future aspirations. This pilot study was funded by an Inspiring Australia Public Science Event Grant, enabling the delivery of two local Girls’ Programming Network (GPN) workshops by the two Sydney-based GPN co-founders. The aim of this case study was to determine the viability of establishing an ongoing and sustainable northwest Tasmanian GPN, which would provide rural adolescent girls with opportunities to explore programming, connect them with mentors and role models, and show them what potential career opportunities exist beyond family and local contexts. Qualitative methods of data collection comprised focus group interviews and artefact elicitation with the participants, and individual interviews with the program co-founders. The results indicate that single-sex informal CS education opportunities are valued by adolescent girls, and that typical gender stereotypes can be changed due to the experience of engaging in them. The success of the pilot study indicates that establishing the GPN in a rural area to provide adolescent girls with free and fun CS experiences is worthwhile and viable.

An Interactive Jupyter Notebook for Teaching the Foundational Mathematical Basis of Compartmental Pharmacokinetics

Pharmacokinetics is often taught as two different lessons, the foundational mathematical basis and the clinical application. Due to the lack of hands-on exercises, pharmacokinetics can be difficult to teach and students report struggling with the topic. Therefore, I have developed an interactive Jupyter notebook that demonstrates the mathematical basis behind one- and two-compartment pharmacokinetic models. This notebook uses a combination of “markup” cells, which display relevant equations and text, and “code” cells, which hold Python code that executes, models, and graphs the various pharmacokinetic equations. Students benefit from exposure to this aspect of computer science because the code remains visible. With the code pre-written, students are able to see the implementations of programming in drug pharmacokinetic modeling without having to construct the code themselves. This notebook includes both the differential and algebraic versions of one- and two- compartment models. The code also generates interactive graphs where students can use sliders for each parameter (half-life, inter-compartmental distribution rates, and dose of drug) to explore the effects of the equations parameters on the output of drug concentration over time. The conversion between macro constants and micro constants is also demonstrated and explained. All software used is freely available, so students can download the notebook onto their personal computers. No computer science experience is needed to execute this tool. However, instructors can easily adapt the notebook to their particular needs, adding or removing cells as desired. I successfully used this notebook in my undergraduate upper-division biology elective, “Foundations of Pharmacology”, and in a guest lectured Master's level course on the practical application of pharmacokinetics. Overall, this tool will be useful for teaching the mathematical basis of basic pharmacokinetics while allowing students to gain useful exposure to computer data science.

An integrated, modular approach to data science education in microbiology.

We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, postsecondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has the potential to improve research and learning outcomes as well as better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated cocurricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants (TAs), postdocs, instructors, and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.

The Rollout of Computer Science Education to Every Student in New York City: A Socio-Contextual Social Network Analysis

Background CS4All is an $81 million private-public investment aimed at creating the necessary infrastructure to provide computer science experiences to all New York City public-school students by 2025. Purpose In this paper, we examine the history of the CS4All initiative and document the network of actors and their relationships in the system such that we can understand how this reform is enacted, how it might be reactive to external contexts and pressures, and how the structure and pathways of this particular social network might inform similar efforts elsewhere. Research Design To structure our analysis of CS4All, we first examine the most recent historic example of a large-scale curricular reform—Man: A Course of Study (MACOS). By reflecting on the network structure of CS4All in light of the design, enactment, and eventual failure of MACOS, we can identify potential pain points and opportunities in CS4All's 10-year effort. We conducted interviews with core members of the CS4All initiative and examined available public records to construct and analyze a social network of key CS4All stakeholders, other actors, processes, and institutions. Conclusions In our analysis of the CS4All social network, we document how well-connected individuals simultaneously mobilized government resources and grassroots enthusiasm to create the conditions necessary for the initiation of this massive curricular reform effort, and describe the current pathways in place for decision making and resource distribution. Comparing the history and structure of the CS4All initiative to Man: A Course of Study —a failed nationwide curricular reform in the 1960s—we find that CS4All's centralized decision-making process and failure to create and distribute high quality formative assessment tools may lead to challenges to adoption. However, explicit efforts to solicit input from and communicate initiative values to the large diversity of stakeholders throughout NYC, as well as the innovation of a decentralized “buffet-style” curricular approach, may put CS4All on more sure footing.

Interdisciplinary research experience in computer science and biological sciences

Interdisciplinary research is critical to modern-day research. Today researchers should be able to work across disciplinary boundaries seemingly. However, without any practical experience, students...

Promoting Active Learning for Increasing Students’ Understanding of the Teaching Materials: A Report on Teaching Experience in Computer Science

Knowledge gap among students is one of the challenges in teaching-learning process. Active learning can be one possible way to the solution, as study shows that active learning increase students’ achievement. Knowledge transfer between peers can have its place in an active class, since group discussion is part of the class design. We have been applying active learning in our classes where students were invited to teach in small groups or in class. In this paper we share about our experience in designing and implementing active learning strategies in our classes. After witnessing students’ success through active learning, we conducted a study to gain students feedbacks about the positive impacts of the active learning to their understanding of the teaching material. Thirty-six students fill out the questionnaire in Google Form anonymously. The result shows that our class settings that employ active learning increases students’ understanding of the teaching materials and hence, students’ grade. Over 93% give positive feedback about our class design and implementation. Students also give positive feedback about our personalized teaching and personal approach during the group activity in our classes. We conclude that class designs which incorporate active learning, is beneficial for students to increase their understanding of the teaching materials.

Flipping Laboratory Sessions: An Experience in Computer Science

This paper reports our experience in flipping a second- year undergraduate course on software architecture and integration, taught in the second course of a Software Engineering degree. We compare the application of the flipped-classroom methodology with a traditional methodology. Our study encompasses two academic courses, in the years 2017 and 2018, and involves a total number of 434 students and 6 lecturers, placing this among the largest studies on flipped-classroom to date. The paper also reports on the production of the videos used with the flipped-classroom methodology, recorded by the lecturers in informal settings, and provides several lessons learned in this regard. The results of the study, backed by a solid statistical analysis of the data, demonstrate the suitability of the flipped-classroom methodology for laboratory sessions in the subject course. Among other results, our analysis concluded that students had on average 24 more minutes per session to solve in-class exercises with the flipped-classroom methodology; more than 70% of the students considered that the quantity, duration and didactic content of the videos were (very) appropriate; and 9 out of every 10 students would prefer this methodology in the laboratory sessions of future courses rather than a traditional face-to-face approach.

Exploring college students’ conceptions of learning computer science: a draw-a-picture technique study

ABSTRACT Background and context Researchers have been looking into the complexity of computer science (CS) education and tried to apply rigorous and relevant educational research methods to understand and facilitate the learning experience of students. Objective The purpose of this study was to explore college students’ conceptions of learning CS to shed light on student learning activities, feelings, contexts, and beliefs about their learning. Method Draw-a-picture technique was used as an emerging research technique in CS education. Using a modified coding checklist, we analyzed the drawings into 9 categories with 50 sub-categories. Findings College students most frequently expressed computer programming as the learning activity by illustrating syntax and semantics of programming languages in the drawings. Problem-solving constructs like decomposition and abstraction were also recorded along with other tools like diagramming techniques. Gender and prior computer science experience were analyzed as moderators. Implications The results provide a baseline for future research on the topic and preliminary evidence in using the method to discern student learning conceptions in CS.

Self-directed learners’ perceptions and experiences of learning computer science through MIT open courseware

ABSTRACT Given the rapid rise of open educational resources (OER) for informal learning pursuits impacting countless millions of people each day around the world, this study investigated the impact of the initiative that spearheaded this movement; namely, Massachusetts Institute of Technology (MIT) open courseware (OCW). More specifically, it explored self-directed learners’ perceptions of the impact of the MIT OCW, their social presences and cognitive presences while learning through MIT OCW online instructional videos. The data source of this study was learner comments regarding 23 instructional videos found in YouTube. Thematic analysis and computer-mediated discourse analysis (CMDA) were used for data analysis. The results indicated that the impact of the MIT OCW served as the first step for career development and important educational decisions of myriad learners that served to benefit MIT programmes with future applicants. In addition, YouTube comments of the self-directed learners reflected a relatively lower social presence and higher cognitive presence compared to general social media. Meanwhile, self-directed learners encountered challenges such as technology issues, non-engaging teaching methods, and social learning needs. Perhaps the future OER and OCW development will, in fact, be designed for a more interactive and engaging experience for the self-directed learners of this world.

“I like computers. I hate coding”: a portrait of two teens’ experiences

PurposeSome empirical evidence suggests that historically marginalized young people may enter introductory programming experiences with skepticism or reluctance, because of negative perceptions of the computing field. This paper aims to explore how learner identity and motivation can affect their experiences in an introductory computer science (CS) experience, particularly for young people who have some prior experience with computing. In this program, learners were asked to develop digital media artifacts about civic issues using Scratch, a block-based programming language.Design/methodology/approachThrough participant observation as a teacher and designer of the course, artifact analysis of student-generated computer programs and design journals, as well as with two follow-up 1-h interviews, the author used the qualitative method of portraiture to examine how two reluctant learners experienced a six-week introductory CS program.FindingsThese learners’ experiences illuminate the ways in which identity, community and competence can play a role in supporting learner motivation in CS education experiences.Research limitations/implicationsAs more students have multiple introductory computing encounters, educators need to take into account not only their perceptions of the computing field more broadly but also specific prior encounters with programming. Because of the chosen research approach, the research results may lack generalizability. Researchers are encouraged to explore other contexts and examples further.Practical implicationsThis portrait highlights the need for researchers and educators to take into account student motivation in the design of learning environments.Originality/valueThis portrait offers a novel examination of novice programmer experiences through the choice in method, as well as new examples of how learner identity can affect student motivation.

OPEN SOURCE IN URBAN PLANNING AND ARCHITECTURE: EXPERIENCES AND GUIDELINES FROM TRADITIONAL CULTURES, PARTICIPATORY PROCESSES AND COMPUTER SCIENCE

Open Source (OS) philosophy can cause important effects both in the design processes and in the professional role of designers. It started as a computer science phenomenon and has rapidly spread to other fields, such as architecture and urban planning. However, there are few studies reflecting about its impact, and there are not guidelines for managing its possible consequences. This article offers some insights considering three main sources: (1) current OS experiences in architecture and urban planning, (2) past traditional and participatory design strategies, and (3) works in computer science about OS. These three topics are conceptually linked using as an index some OS attributes expressed in the OS initiative definition. With that background, guidelines are finally proposed. The article demonstrates that current OS initiatives in architecture and urbanism have many similarities with past design processes and recent computer science experiences. Both subjects can be used as precedents in order to adequately incorporate and adapt OS thinking, a revolution that, depending on the managing skills, could improve the quality of the design and the designer's profession, or provoke a crisis in the current professional model.

US experience in computer science

The article analyzes the "Computer Science for All" initiative, which aims to support computer science in schools, and highlights its first results in the United States. The main features of the informatics standards for US secondary schools (CSTA K-12) are described.

A case study introducing dynamic programming in CS2

To assess students' understanding and the advantages gained in being introduced to the concepts of dynamic programming early in their computer science experience, students were introduced to dynamic programming in CS2. Two lab assignments, one homework assignment and one quiz were given following an introduction to the topic in our CS2 course, Data Structures with C++. Our results reveal that types of problems addressed were fully comprehensible and we expect this comprehension to lend itself to later success in an Algorithms and Advanced Data Structures course that explores dynamic programming in greater depth.

Supporting all learners in school-wide computational thinking: A cross-case qualitative analysis

The purpose of this study was to investigate how elementary school teachers with limited computer science experience in a high-need school integrated computational thinking into their instruction. The researchers conducted a cross-case analysis across different instructional contexts (e.g., general education classrooms, library, art) that included multiple observations and interviews over four months. Major themes included: (a) a wide range of implementation models emerged depending on teaching contexts, (b) ongoing professional development and embedded coaching resulted in increasing participation in computing education, (c) teachers and administrators viewed barriers to implementing computing from a problem solving framework, and (d) struggling learners, including students with disabilities and those living in poverty, benefitted from computing education that included scaffolding, modeling, and peer collaboration.