Computer Science Education最新文献

{"title":"Teaching algorithms in upper secondary education: a study of teachers’ pedagogical content knowledge","authors":"Jacqueline Nijenhuis-Voogt, Durdane Bayram-Jacobs, P. Meijer, E. Barendsen","doi":"10.1080/08993408.2021.1935554","DOIUrl":"https://doi.org/10.1080/08993408.2021.1935554","url":null,"abstract":"ABSTRACT Background and Context Computing education is expanding, while the teaching of algorithms is less well studied. Objective The aim of this study was to examine teachers’ pedagogical content knowledge (PCK) for teaching algorithms. Method We conducted semi-structured interviews with seven computer science (CS) teachers in upper secondary education (students aged 15–18). The data were analyzed qualitatively. Findings We found two patterns of variation in teachers’ PCK. First, we detected variation in the teachers’ goals related to their view of algorithms: they either focused on “thinking” about the algorithm as an object, or focused on “thinking and making”, where the algorithm is also regarded as a program. Second, we found variation in teachers’ knowledge about responding to differences between students, which may be generic or topic-specific. Furthermore, our findings reveal that teachers consider class discussions to play a significant role as an instructional method for provoking reflection. Implications Our findings regarding PCK may be beneficial for the development of teacher education and professionalization activities for CS teachers.","PeriodicalId":45844,"journal":{"name":"Computer Science Education","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08993408.2021.1935554","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41840918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interdisciplinary computational thinking with music and programming: a case study on algorithmic music composition with Sonic Pi","authors":"Christopher Petrie","doi":"10.1080/08993408.2021.1935603","DOIUrl":"https://doi.org/10.1080/08993408.2021.1935603","url":null,"abstract":"ABSTRACT Background and Context Computational Thinking (CT) has been recently integrated into new and revised Digital Technologies content (DTC) in the Technology learning area of the New Zealand School Curriculum. Objective To aid this change, this research examined how CT supports learning outcomes in both music and programming with the Sonic Pi platform. Method A mixed-methods case study designed by the researcher was developed and trialled in a regular middle school classroom. The design of instruments and analysis utilised a widely used CT assessment framework at a school level. Findings Many successes and challenges not reported in the literature on how CT can support learning outcomes in music and programming were revealed. Implications Provided some significant limitations are considered, the major implication of the results suggest educators can use Sonic Pi to support many learning outcomes in music and programming for interested students.","PeriodicalId":45844,"journal":{"name":"Computer Science Education","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08993408.2021.1935603","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48803690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fostering Ph.D. aspirations among upward transfer students in computing","authors":"J. Blaney, Annie M. Wofford","doi":"10.1080/08993408.2021.1929723","DOIUrl":"https://doi.org/10.1080/08993408.2021.1929723","url":null,"abstract":"ABSTRACT Background Students who transfer from community colleges in pursuit of four-year degrees (i.e., upward transfer students) represent a diverse and talented group that is critical to advancing gender equity in STEM. However, research has not yet explored factors that promote Ph.D. aspirations among upward transfer computing students, resulting in missed opportunities to support this unique group. Objective We examine the predictors of upward transfer computing students’ Ph.D. aspirations, focusing on how these patterns might be unique for upward transfer women. Method Relying on longitudinal survey data from upward transfer students across 15 research universities, we use logistic regression with interaction terms to identify the predictors of Ph.D. aspirations. Findings We found that Ph.D. aspirations were especially frequent among upward transfer women, 14% of whom aspired to earn a Ph.D. Other results highlight the importance of faculty encouragement for graduate study in predicting all upward transfer students’ Ph.D. aspirations. Beyond the direct role of such faculty encouragement, perceptions of computing faculty uniquely predicted Ph.D. aspirations for upward transfer women. Implications Our findings provide insight into how to bolster more equitable access to faculty mentorship, support students throughout the transfer process, and create inclusive faculty policies, which impact students’ perceptions of academia.","PeriodicalId":45844,"journal":{"name":"Computer Science Education","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08993408.2021.1929723","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44946972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emotions and programming learning: systematic mapping","authors":"Mayela Coto Chotto, Sonia Mora Rivera, Beatriz Eugenia Grass, Juan Murillo-Morera","doi":"10.1080/08993408.2021.1920816","DOIUrl":"https://doi.org/10.1080/08993408.2021.1920816","url":null,"abstract":"ABSTRACT Background and context Emotions are ubiquitous in academic settings and affect learning strategies, motivation to persevere, and academic outcomes, however they have not figured prominently in research on learning to program at the university level. Objective To summarize the current knowledge available on the effect of emotions on students while they learn to program. We were interested in what emotions have been studied, what kind of tools researchers have used to measure students’ emotions, what emotions arise most frequently when students learn to program, and how researchers use this knowledge. Method A search of academic databases was performed using a search string constructed using PICo as a framework. A group of 29 studies that met the inclusion criteria were analyzed to answer a set of research questions derived from the study objective. Findings Few studies have been conducted on the influence of emotions on learning programming. Research studies do not address gender differences. Research has been oriented along three main lines (a) determining what emotions are involved in the process of learning to program; (b) determining the behavioral patterns of novice students and how from this behavior their emotions can be identified; and (c) developing intelligent systems that respond adaptively to students’ emotions. Implications The results provide an important foundation for researchers and instructors who are willing to recognize that learning to program generates strong emotions in students and that these emotions influence their learning and academic performance.","PeriodicalId":45844,"journal":{"name":"Computer Science Education","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08993408.2021.1920816","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48859033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Teaching computational thinking to exceptional learners: lessons from two inclusive classrooms","authors":"Yenda Prado, S. Jacob, M. Warschauer","doi":"10.1080/08993408.2021.1914459","DOIUrl":"https://doi.org/10.1080/08993408.2021.1914459","url":null,"abstract":"ABSTRACT Background and Context Computational Thinking (CT) is a skill all students should learn. This requires using inclusive approaches to teach CT to a wide spectrum of students. However, strategies for teaching CT to students with exceptionalities are not well studied. Objective This study draws on lessons learned in two fourth-grade classrooms – one an inclusive general education classroom including students with and without disabilities, the other an inclusive GATE classroom including students with and without giftedness – to illustrate how CT frameworks can inform inclusive CS instruction. Method A comparative case study design integrating content analysis and first and second cycle coding of data was used to analyze teachers’ instructional strategies using a CT framework. Data included transcriptions of audio-recorded classroom lessons, field notes, and conversations with teachers and students. Findings While each teacher used different strategies, both were effective in developing students’ CT. Explicit instruction provided students receiving special education services with needed structure for the complex tasks inherent to computing. Peer feedback facilitated independent computational practice opportunities for students receiving GATE. Implications This study highlights how inclusive instructional practices can be assessed using a CT framework and leveraged to maximize learning and access to CT curricula for learners with exceptionalities.","PeriodicalId":45844,"journal":{"name":"Computer Science Education","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08993408.2021.1914459","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47739894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computerized adaptive assessment of understanding of programming concepts in primary school children","authors":"S. Hogenboom, F. Hermans, H.L.J. van der Maas","doi":"10.1080/08993408.2021.1914461","DOIUrl":"https://doi.org/10.1080/08993408.2021.1914461","url":null,"abstract":"ABSTRACT Background and Context Valid assessment of understanding of programming concepts in primary school children is essential to implement and improve programming education. Objective: We developed and validated the Computerized Adaptive Programming Concepts Test (CAPCT) with a novel application of Item Response Theory. The CAPCT is a web-based and resource-efficient adaptive assessment of 4489 questions measuring: the understanding of basic sequences, loops, conditions (if & if-else statements), debugging, multiple agents, procedures, and the ability to generalize to a new syntax. Method: Data was collected through an existing online adaptive practice and monitoring system called Math Garden. We collected 14 million responses from 93,341 Dutch children (ages 4 - 13). Findings: The CAPCT demonstrated good psychometric qualities because 75% of the variance in question difficulty was explained by differences in item characteristics. The CAPCT demonstrated robustness against adding new participants and adding new items. Differences in player ability (i.e., understanding of CS concepts) were due to differences in age, gender, the number of items played, and prior mathematical ability. Implications: The CAPCT may be used by teachers to identify the level of programming concept understanding of their pupils, while researchers may use the CAPCT to construct and validate effective teaching resources.","PeriodicalId":45844,"journal":{"name":"Computer Science Education","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08993408.2021.1914461","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44361951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing computational thinking: an overview of the field","authors":"David Weintrop, Daisy W. Rutstein, M. Bienkowski, S. McGee","doi":"10.1080/08993408.2021.1918380","DOIUrl":"https://doi.org/10.1080/08993408.2021.1918380","url":null,"abstract":"The last decade has seen rapid growth in the presence of computational thinking (CT) in educational contexts. Those working to advance CT argue that the concepts and skills associated with CT are essential to succeed in an increasingly computational world. As a result of these efforts, CT has a growing presence in K-12 classrooms and beyond. This can be seen in the inclusion of CT in disciplinary standards (e.g. the Next Generation Science Standards and Common Core Math identifying CT as a core practice), as well as national curricular efforts (e.g. the United Kingdom’s national computing curriculum seeks to have students “develop and apply their analytic, problem-solving, design, and computational thinking skills”). Just as CT has a growing presence in formal education, it can also be seen in informal contexts through the growth of computing camps, after-school and library CT programming, and a growing array of toys designed to engage youth with CT. The contemporary discussion around CT began with Wing’s (2006) article, where she argued “to reading, writing, and arithmetic, we should add computational thinking to every child’s analytical ability” (p. 33). However, the conceptual origins have a much longer history, dating back to early work on the Logo programming language and Papert’s insights on the potential of computing as a powerful context for learning (1980). In response to Wing’s article, much effort has been dedicated to trying to define what constitutes CT and where the boundaries of the construct lie. While the community has yet to settle on a single unified definition, there is general consensus that CT includes foundational computing concepts such as abstraction and algorithms, as well as computing practices such as problem decomposition and debugging (Grover & Pea, 2013; Shute et al., 2017). As the dust started to settle from early debates around the scope and nature of CT, a growing number of research projects sought to design CT learning experiences. Spurred in part by an increase in funding for educational projects at the intersection of computing and other disciplines, a space in which CT is particularly well-suited to contribute, the last decade has seen tremendous growth in curricula, learning environments, and innovations around CT education (Tang et al., 2020). In the wake of this growth, this special issue seeks to respond to a question of growing importance: How do we assess computational thinking? This is not a straightforward question to answer as several aspects of CT make it challenging to assess. For example, there is a wide variety of methods by which CT is taught and contexts in which students learn CT. While some schools offer stand-alone CT learning experiences, other schools may try to integrate CT within current subject matters. Further, as discussed above, CT is a relatively ill-defined construct, thus, different assessments may focus on slightly different dimensions of CT. Collectively, this produces a landsca","PeriodicalId":45844,"journal":{"name":"Computer Science Education","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08993408.2021.1918380","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44300297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How do students develop computational thinking? Assessing early programmers in a maze-based online game","authors":"M. Guenaga, A. Eguíluz, P. Garaizar, J. Gibaja","doi":"10.1080/08993408.2021.1903248","DOIUrl":"https://doi.org/10.1080/08993408.2021.1903248","url":null,"abstract":"ABSTRACT Background and Context: Despite many initiatives to develop Computational Thinking (CT), not much is known about how early programmers develop CT and how we can assess their learning. Objective: Determine if the analysis of students’ interactions with an online platform allows understanding the development of CT, how we can convert data collected into valuable insights, and the aspects that should be considered in platforms design. Method: We developed an online platform with a fine-grained log–recording system. We analysed the data collected from 1004 students (ages 8-14) to understand the difficulties they face. We explain our platform and the tools to process and filter the interaction logs. We calculate additional indicators that provide useful information about student’s behaviour. Findings: Age and gender have shown to influence on CT learning. Generating additional indicators from basic interaction data provide valuable insights. We provide a list of recommendations for developing more effective programming learning platforms.","PeriodicalId":45844,"journal":{"name":"Computer Science Education","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08993408.2021.1903248","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46949311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and validation of learning trajectory-based assessments for computational thinking in upper elementary grades","authors":"Brian D. Gane, Maya Israel, Noor Elagha, Wei Yan, Feiya Luo, J. Pellegrino","doi":"10.1080/08993408.2021.1874221","DOIUrl":"https://doi.org/10.1080/08993408.2021.1874221","url":null,"abstract":"ABSTRACT Background & Context: We describe the rationale, design, and initial validation of computational thinking (CT) assessments to pair with curricular lessons that integrate fractions and CT. Objective: We used cognitive models of CT (learning trajectories; LTs) to design assessments and obtained evidence to support a validity argument Method: We used the LTs and evidence-centered design to develop assessments that 144 Grade 3 and Grade 4 elementary students completed following the integrated instruction. We analyzed data using multiple psychometric approaches. Findings: The design approach and data analysis suggest that the assessments are well-suited to evaluate students’ CT knowledge, skills and abilities across multiple LTs. Implications: We show how to use LTs to design assessments that can yield valid inferences about students’ CT competencies; these methods can be adopted and extended by others to create additional assessments that can advance computer science education.","PeriodicalId":45844,"journal":{"name":"Computer Science Education","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08993408.2021.1874221","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47877736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-Computer Pair Programming: Exploring a Feedback Intervention to improve Collaborative Talk in Elementary Students","authors":"Zarifa Zakaria, Jessica Vandenberg, Jennifer L. Tsan, D. Boulden, Collin Lynch, K. Boyer, E. Wiebe","doi":"10.1080/08993408.2021.1877987","DOIUrl":"https://doi.org/10.1080/08993408.2021.1877987","url":null,"abstract":"ABSTRACT Background and Context Researchers and practitioners have begun to incorporate collaboration in programming because of its reported instructional and professional benefits. However, younger students need guidance on how to collaborate in environments that require substantial interpersonal interaction and negotiation. Previous research indicates that feedback fosters students’ productive collaboration. Objective This study employs an intervention to explore the role instructor-directed feedback plays on elementary students’ dyadic collaboration during 2-computer pair programming. Method We used a multi-study design, collecting video data on students’ dyadic collaboration. Study 1 qualitatively explored dyadic collaboration by coding video transcripts of four dyads which guided the design of Study 2 that examined conversation of six dyads using MANOVA and non-parametric tests. Findings Result from Study 2 showed that students receiving feedback used productive conversation categories significantly higher than the control condition in the sample group considered. Results are discussed in terms of group differences in specific conversation categories. Implications Our study highlights ways to support students in pair programming contexts so that they can maximize the benefits afforded through these experiences.","PeriodicalId":45844,"journal":{"name":"Computer Science Education","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08993408.2021.1877987","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44948658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}