There is little empirical research related to how elementary students develop computational thinking (CT) and how they apply CT in problem-solving. To address this gap in knowledge, this study made use of learning trajectories (LTs; hypothesized learning goals, progressions, and activities) in CT concept areas such as sequence, repetition, conditionals, and decomposition to better understand students’ CT. This study implemented eight math-CT integrated lessons aligned to U.S. national mathematics education standards and the LTs with third- and fourth-grade students. This basic interpretive qualitative study aimed at gaining a deeper understanding of elementary students’ CT by having students express and articulate their CT in cognitive interviews. Participants’ (n= 22) CT articulation was examined usinga prioricodes translated verbatim from the learning goals in the LTs and was mapped to the learning goals in the LTs. Results revealed a range of students’ CT in problem-solving, such as using precise and complete problem-solving instructions, recognizing repeating patterns, and decomposing arithmetic problems. By collecting empirical data on how students expressed and articulated their CT, this study makes theoretical contributions by generating initial empirical evidence to support the hypothesized learning goals and progressions in the LTs. This article also discusses the implications for integrated CT instruction and assessments at the elementary level.

Computational Thinking (CT ), entailing both domain-general and domain-specific skills, is a competency fundamental to computing education and beyond. However, as a cross-domain competency, appropriate assessment design and method remain equivocal. Indeed, the majority of the existing assessments have a predominant focus on measuring programming proficiency and neglecting other contexts in which CT can also be manifested. To broaden the promotion and practice of CT, it is necessary to integrate diverse problem types and item formats using a competency-based assessment method to measure CT. Taking a psychometric approach, this article evaluates a novel computer-based assessment of CT competency, Computational Thinking Challenge. The assessment was administered to 119 British upper secondary school students ( M = 16.11; SD = 1.19) with a range of prior programming experiences. Results from several reliability analyses, a convergent validity analysis, and a Rasch analysis, provided evidence to support the quality of the assessment. Taken together, the study demonstrated the feasibility to expand from traditional assessment methods to integrating multiple contexts, problem types, and item formats in measuring CT competency in a comprehensive manner.

Computer science is a fast-growing field in today’s digitized age, and working in this industry often requires creativity and innovative thought. An issue within computer science education, however, is that large introductory programming courses often involve little opportunity for creative thinking within coursework. The undergraduate introductory programming course (CS1) is notorious for its poor student performance and retention rates across multiple institutions. Integrating opportunities for creative thinking may help combat this issue by adding a personal touch to course content, which could allow beginner CS students to better relate to the abstract world of programming. Research on the role of creativity in computer science education (CSE) is an interesting area with a lot of room for exploration due to the complexity of the phenomenon of creativity as well as the CSE research field being fairly new compared to some other education fields where this topic has been more closely explored.To contribute to this area of research, this article provides a literature review exploring the concept of creativity as relevant to computer science education and CS1 in particular. Based on the review of the literature, we conclude creativity is an essential component to computer science, and the type of creativity that computer science requires is in fact, a teachable skill through the use of various tools and strategies. These strategies include the integration of open-ended assignments, large collaborative projects, learning by teaching, multimedia projects, small creative computational exercises, game development projects, digitally produced art, robotics, digital story-telling, music manipulation, and project-based learning. Research on each of these strategies and their effects on student experiences within CS1 is discussed in this review. Last, six main components of creativity-enhancing activities are identified based on the studies about incorporating creativity into CS1. These components are as follows: Collaboration, Relevance, Autonomy, Ownership, Hands-On Learning, and Visual Feedback. The purpose of this article is to contribute to computer science educators’ understanding of how creativity is best understood in the context of computer science education and explore practical applications of creativity theory in CS1 classrooms. This is an important collection of information for restructuring aspects of future introductory programming courses in creative, innovative ways that benefit student learning.

As the field of computing education grows and matures, it has become essential to unite computing education and higher education research. Educational research has highlighted that how students study is crucial to their learning progress, and study behaviors have been found to play an important role in students’ academic success. This article presents the main results of a systematic literature review intended to determine what we know about the study behaviors of computing students and the role of educational design in shaping them. A taxonomy of study behaviors was developed and used to clarify and classify the definitions of study behavior, process, strategies, habits, and tactics as well as to identify their relationship to the educational context. The literature search resulted in 107 included papers, which were analyzed according to defined criteria and variables. The review of study behavior terminology found that the same terms are used to describe substantially different study behaviors, and the lack of standard terminology makes it difficult to compare findings from different papers. Furthermore, it was more common for papers to use study behaviors to explain other aspects of students rather than exploring and understanding them. Additionally, the results revealed a tendency to focus on specific educational contexts, predominantly introductory programming courses. Although computing education as a field is well equipped to expand the knowledge about both study behaviors and their connection to the educational context, the lack of common terminology and theories limits the impact. The taxonomy of study behaviors in computing education proposed in this article can contribute to contextualizing the research in such a way that researchers and educators across institutional borders can compare and utilize results. Last, the article outlines some areas for future research and recommendations for practice.

The necessity for a steady STEM workforce has prompted academia to develop strategies to encourage people of diverse backgrounds to enter the STEM fields. A bridge program, also known as a conversion program, offers alternative pathways for individuals who have no prior computing education to receive the education that can help in developing their careers or acquiring a graduate-level degree in the computer science fields. This mixed-methods study consisted of two parts. First, an online post-baccalaureate bridge program was evaluated, with a focus on students’ performance. Factors for analysis included gender, prior major, and the length of the program, any or all of which might play a role in students’ unsuccessful attempts to complete the program. The results indicated that female students have a higher tendency to not complete the program. However, female students who completed the program and enrolled in a graduate school have as much potential to do well in the MS program as their male cohorts do. The second part of the study comprised a survey of students who completed or did not complete the program and interviews with women students. Grounded in self-determination theory (SDT), the results showed that strategies are needed to enhance women students’ perceived competence and relatedness in the program.

College-educated women in the workforce are discovering a latent interest in and aptitude for computing motivated by the prevalence of computing as an integral part of jobs in many fields as well as continued headlines about the number of unfilled, highly paid computing jobs. One of these women's choices for retraining are the so-called coding boot camps that teach programming skills through intensive multi-week courses. This article reports on a qualitative research study focused on the Silicon Valley area of California. We used social cognitive career theory (SCCT) to investigate the larger context surrounding women entering computing professions through boot camp learning sites, including: the environment of a booming technology workforce, boot camps as learning settings , the characteristics of women who attend boot camps, how retraining at a coding boot camp influence women's computing self-efficacy and outcome expectations , and the performance attainments of women at boot camps. Interview data was collected from 14 women who had attended boot camps—first before graduating from the boot camp and again after six months in the workforce. To contextualize the computing ecosystem, we conducted single interviews with 6 boot camp organizers/curriculum developers, 7 industry hiring managers, and 14 university computer science faculty. To provide a contrast with women at boot camps, we interviewed 5 women who majored in computer science at the university and with 17 men who had attended boot camps. Structural data coding and analysis was done focused on the SCCT mechanisms of environment, person inputs, learning experiences, self-efficacy, outcome expectations, and performance attainments. Findings here demonstrate that training at a boot camp can be the catalyst for college-educated women to attain computing jobs and careers, although these entry-level jobs may be a compromise to the goal of a software development job and are unlikely to lead to a job at large, well-known, established technology companies.

Peer review is the standard process within academia for maintaining publication quality, but it is also widely employed in other settings, such as education and industry, for improving work quality and for generating actionable feedback to content authors. For example, in the software industry peer review of program source code—or peer code review—is a key technique for detecting bugs and maintaining coding standards. In a programming education context, although peer code review offers potential benefits to both code reviewers and code authors, individuals are typically less experienced, which presents a number of challenges. Some of these challenges are similar to those reported in the educational literature on peer review in other academic disciplines, but reviewing code presents unique difficulties. Better understanding these challenges and the conditions under which code review can be taught and implemented successfully in computer science courses is of value to the computing education community. In this work, we conduct a systematic review of the literature on peer code review in higher education to examine instructor motivations for conducting peer code review activities, how such activities have been implemented in practice, and the primary benefits and difficulties that have been reported. We initially identified 187 potential studies and analyzed 51 empirical studies pertinent to our goals. We report the most commonly cited benefits (e.g., the development of programming-related skills) and barriers (e.g., low student engagement), and we identify a wide variety of tools that have been used to facilitate the peer code review process. While we argue that more empirical work is needed to validate currently reported results related to learning outcomes, there is also a clear need to address the challenges around student motivation, which we believe could be an important avenue for future research.

The language and concepts used by curriculum designers are not always interpreted by children as designers intended. This can be problematic when researchers use self-reported survey instruments in concert with curricula, which often rely on the implicit belief that students’ understanding aligns with their own. We report on our refinement of a validated survey to measure upper elementary students’ attitudes and perspectives about computer science (CS), using an iterative, design-based research approach informed by educational and psychological cognitive interview processes. We interviewed six groups of students over three iterations of the instrument on their understanding of CS concepts and attitudes toward coding. Our findings indicated that students could not explain the terms computer programs nor computer science as expected. Furthermore, they struggled to understand how coding may support their learning in other domains. These results may guide the development of appropriate CS-related survey instruments and curricular materials for K–6 students.

This article provides a survey of methods and paradigms for teaching Computer Networks (CN). Since the theoretical concepts are rather abstract in this subject, and students often find them too technical and difficult to understand, many authors attempt to answer the question on how to improve students’ motivation and interest for the complex teaching material of CN. In this work, we follow a rigorous paper collection methodology and extract a large number of previous studies that relate to the stated research questions. Also, we find that there is no review article in the current literature that would provide a clear systematization or a guided study on this topic. Hence, this work provides a literature overview by binding all the previously used methods for teaching CN in one place, and brings contribution by classifying the existing approaches into four basic classes: methods based on using visualization objects such as network simulators, multimedia applications, packet-tracing tools or visual analogies; methods based on using the virtualization techniques; methods precipitating active learning paradigm and methods based on the practical hands-on laboratory exercises. Moreover, the research in this article goes beyond the proposed classification. The classes of methods and tools are compared and contrasted based on the findings from the literature. Methods are evaluated by a detailed cross-comparison based on their advantages, disadvantages and challenges in the perspective of both teachers and students. The review is additionally strengthened by comparing the educational effectiveness of the classified methods. We examine, classify, and contrast the usual approaches used in teaching CN, provide useful insights on how appropriate they are in achieving specific educational goals and determine the future research directions.

The number of computer science (CS) courses has been dramatically expanding in U.S. high schools (HS). In comparison with well-established courses in mathematics and science, little is known about how the decisions made by HS CS teachers regarding how and what to teach impact student performance later in introductory college CS courses. Drawing on a large sample of 2,871 introductory college CS students at 115 U.S. institutions who had taken a CS course in HS, we examined the topic coverage and prevailing instructional methods in the HS course and investigated how these experiences influenced student performance in college CS. Controlling for differences in student background, we find two predictors of higher grades in college CS: greater frequency of coding-related activities in HS (programming, debugging, studying algorithms) and lower frequency of “non-coding” computer use (e.g., data analysis, computer security). Interaction models revealed a more complex story. Coding-related activity more heavily benefited students who did not have coding help available at home. In the 28% of college CS courses in which instructors employed innovative pedagogies, students with higher ACT or SAT mathematics scores had a greater advantage than in traditionally taught courses. Finally, in the innovative college courses, students whose HS CS exams had typically included testing on vocabulary did worse than students whose exams had not included such tests.