Proceedings of the 22nd Koli Calling International Conference on Computing Education Research最新文献

{"title":"Finding Significant p in Coffee or Tea: Mildly Distasteful","authors":"Sami Sarsa, Arto Hellas, Juho Leinonen","doi":"10.1145/3564721.3565953","DOIUrl":"https://doi.org/10.1145/3564721.3565953","url":null,"abstract":"Students’ preferences have an impact on their behavior, and behaviors can in turn affect student performance. Earlier work has found that students who tend to work earlier in the course or curse more in their source code tend to perform better. But could other types of preferences also affect student performance? In this work, we examine the relationship between student preferences such as preferring coffee over tea, and students’ performance in the course. Our results suggest that certain preferences are related to better overall performance in the course, but only for certain cohorts of students. Indeed, this work provides an example of how easy it is to find statistically significant correlations in educational settings.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124557772","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":"On Supervising Master’s Theses in Industry Context","authors":"H. Jaakkola, T. Mikkonen, Kari Systä","doi":"10.1145/3564721.3564743","DOIUrl":"https://doi.org/10.1145/3564721.3564743","url":null,"abstract":"In software engineering, students easily find internships in companies while still studying. To combine their studies and employment, many of them seek to compose their final theses in an industry context, for the benefit of the employer as well as to simplify their context switching between job and studies. This can put the student between a rock and a hard place, as on one hand the employer has certain expectations in terms of working for the company, whereas the supervising professor needs to follow the university guidelines. An additional aspect worth considering is the university as an administrative home for the thesis and owner of the thesis process. In this paper, we study how the different stakeholders – the student, the supervising professor, and the company – should act for the best possible results, so that the company problem gets solved, and the results can be reported in accordance with the best academic practices. The research builds on authors’ collective supervision experience, covering more than 1000 theses (mainly master’s level) and close to a sum of hundred years. The thesis has been mainly supervised in two universities, with the clear majority executed in this setup, but there are also several exceptions where the thesis has been eventually accepted in some other university. The results are expressed in the form of anti-patterns, which consist of a definition of symptoms of a problem, its root causes, and proposals to salvage the situation in a practical fashion.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125716896","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":"An Analysis of Tutors’ Adoption of Explicit Instructional Strategies in an Introductory Programming Course","authors":"Olivier Goletti, K. Mens, F. Hermans","doi":"10.1145/3564721.3565951","DOIUrl":"https://doi.org/10.1145/3564721.3565951","url":null,"abstract":"In this paper we analyse in detail how tutors of an undergraduate-level introductory programming course use two explicit instructional strategies in practice with their students. The two strategies they used were an explicit tracing strategy and a subgoal learning strategy. We explored what triggered their use of these strategies, how faithfully they followed the proposed strategies, and how they adapted it in practice to their classroom setting. We rely on literature on fidelity of implementation to assess tutors’ adoption of these strategies. The tracing strategy was much more used and with higher fidelity than the subgoal learning strategy. Tutors adopted both strategies with adaptations, simplifications and even combining them. From our observations we draw good and bad practices on the adoption of such explicit instructional strategies for future generations of tutors.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"2010 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127345457","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":"Categorizing Research on Identity in Undergraduate Computing Education","authors":"A. Kapoor, Christina Gardner-Mccune","doi":"10.1145/3564721.3565948","DOIUrl":"https://doi.org/10.1145/3564721.3565948","url":null,"abstract":"Researchers in education have explored identity constructs to solve a variety of problems such as improving retention, ameliorating diversity and inclusion, fostering learning, and gauging decision-making. However, literature in social sciences describes identity research as often fragmented, with researchers often building their work on siloed factions in identity literature. This paper aims to build a categorization model for classifying types of papers on identity in computing education research (CER). We categorized 55 papers that either investigated identity formation of students in computing undergraduate degree programs or suggested relationships of other constructs to identity using a systematic literature review. We first explored trends in the types of papers with respect to their demographics and then categorized the papers based on semantics and contributions using inductive content analysis. We observed a growing interest in identity over the last five years. The types of papers on identity in CER fell into two themes: identity-centric studies and non-identity centric studies. These themes included six categories of papers that described identity, assessed identity formation, measured identity construct, studied the influence of identity on a factor, implied another construct as identity, and inferred relationships of other constructs to identity. We shed light on our categorization scheme, provide a framework for positioning future research, and discuss opportunities for future work on identity in computing. Our model can support researchers to position their work or find appropriate literature when investigating work related to identity in computing at the undergraduate level.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"244 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133491869","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":"Towards Open Natural Language Feedback Generation for Novice Programmers using Large Language Models","authors":"Charles Koutcheme","doi":"10.1145/3564721.3565955","DOIUrl":"https://doi.org/10.1145/3564721.3565955","url":null,"abstract":"Automated feedback on programming exercises has traditionally focused on correctness of submitted exercises. The correctness has been inferred, for example, based on a set of unit tests. Recent advances in the area of providing feedback have suggested relying on large language models for building feedback. In this poster, we present an approach for automatically constructed formative feedback, written in natural language, that builds on two streams of research: (1) automatic program repair, and (2) automatically generating descriptions of programs. Building on combining these two streams, we propose a new approach for constructing written formative feedback on programming exercise submissions.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133289474","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":"The Impact of Solving Adaptive Parsons Problems with Common and Uncommon Solutions","authors":"Carl Haynes-Magyar, B. Ericson","doi":"10.1145/3564721.3564736","DOIUrl":"https://doi.org/10.1145/3564721.3564736","url":null,"abstract":"Traditional introductory computer programming practice such as code-tracing and code-writing can be time-intensive, frustrating, and decrease students’ engagement and motivation. Parsons problems, which require learners to place mixed-up code blocks in the correct order, usually improve problem-solving efficiency, lower cognitive load, and most undergraduates find them useful for learning how to program. Parsons problems can also be adaptive—meaning the difficulty of a problem is based on a learner’s performance. To become proficient at computer programming, it is critical for novice learners to be explicitly taught how to recognize and apply programming patterns/solutions. But how do we help them to acquire this knowledge efficiently and effectively? Our prior research revealed that an adaptive Parsons problem with an uncommon solution was not significantly more efficient to solve than writing the equivalent code. Interestingly, 77% of the students used the unusual Parsons problem solution to later solve an equivalent write-code problem. Hence, we hypothesized that changing the unusual Parsons problem solution to the most common student-written solution would make that problem significantly more efficient to solve. To test our hypothesis, we conducted a mixed within-between-subjects experiment with 95 undergraduates. The results confirmed our hypothesis and its inverse. Students were significantly more efficient at solving the modified Parsons problem (made with a common solution) than writing the equivalent code. Students were not significantly more efficient at solving a different Parsons problem with an uncommon solution. We also explored the impact on cognitive load ratings for each problem type. There was a significant difference in cognitive load ratings for students who solved the modified Parsons problem first versus those who wrote the equivalent code first. To understand how students solve Parsons problems and the impact of changing the adaptation process, we also report on three think-aloud observations with undergraduates. Results revealed that some students could benefit from help with self-regulated learning (planning), more explanation of distractors, and that there were no new problems due to modifications of the adaptation process. Our findings have implications for how to automatically generate and sequence adaptive Parsons problems.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114668952","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 Gender, Ethnicity, and Public Presentation Shape Coding Perseverance after Hackathons","authors":"Emilia Gan, Benjamin Mako Hill, T. Menezes","doi":"10.1145/3564721.3564727","DOIUrl":"https://doi.org/10.1145/3564721.3564727","url":null,"abstract":"Hackathon-style coding events are a popular and promising approach to broadening participation in computer science and engineering. We present a quantitative analysis of self-reported perseverance in coding after hackathon-style events for 4,703 hackathon participants run by the nonprofit organization CodeDay. Drawing from previous work on broadening participation in computing, we test hypotheses that seek to answer three questions about whether and how hackathon-style coding events support continued engagement in computing among young people: (1) Are participants from underrepresented groups as likely to continue to engage in coding after attending a hackathon-style event? (2) Are participants more likely to continue to code after hackathon-style events if they attend events with demographically similar peers? (3) Are participants more likely to continue to code after a hackathon-style event if they present their work? In line with many studies of broadening participation, we find that members of underrepresented groups are less likely to report continuing to engage in programming 10 weeks after hackathon-style events. However, we find that these participants are more likely to report continuing to code when a larger proportion of attendees at their event share their gender or ethnicity. We also find that membership in underrepresented groups is associated with a greater likelihood of continued engagement when participants present their work to others at the end of events. Our work contributes to the literature on both education and broadening participation in computing by outlining several conditions under which hackathon-style events may be effective in promoting continued engagement among underrepresented young people.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129194537","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":"Examining the Use of Computational Thinking Skills When Solving Bebras Tasks","authors":"Imke De Jong, Bo Sichterman, J. Jeuring","doi":"10.1145/3564721.3565957","DOIUrl":"https://doi.org/10.1145/3564721.3565957","url":null,"abstract":"Computational thinking (CT) is considered an essential problem-solving skill in the 21st century, and is receiving attention on different educational levels. To promote and assess students’ CT skills, so-called ’Bebras tasks’ (i.e. small tasks for problem solving in informatics) are created by experts in the field of CT. There has not been empirical research to determine to what extent and how CT skills are used while solving these tasks, however. This qualitative study bridges this gap by using the think-aloud method to examine the use of CT skills when solving Bebras. The results of this study can serve as a validation for the use of CT skills in solving Bebras tasks. This poster abstract introduces the background and setup of the study.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"66 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113964178","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":"High School Students’ Sense-making of Artificial Intelligence and Machine Learning","authors":"Can Tatar, Duncan Culbreth, Shiyan Jiang, C. Rosé, J. Chao, Rebecca Ellis, Shan Jiang, Kenia Wiedemann","doi":"10.1145/3564721.3565958","DOIUrl":"https://doi.org/10.1145/3564721.3565958","url":null,"abstract":"This paper presents high school students’ sense-making of Artificial Intelligence (AI) and Machine Learning (ML) before and after they participated in a three-week technology-enhanced AI curriculum experience. We analyzed students’ pre-and-post assessment responses, responses to activity-specific questions, and classroom video recordings to explore their understanding of AI and ML. Our analysis revealed that students’ AI and ML conceptions shifted from media-informed concepts and presuppositions to more structured and process-oriented understandings after they completed AI curriculum modules. Our ongoing and future work aims to develop a deeper understanding of how students’ sense-making of AI and ML is influenced by their prior knowledge, experiences, and perspectives as well as the curriculum activities, tasks, and resources.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114400344","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":"Why Students Drop Computing Science: Using Models of Motivation to Understand Student Attrition and Retention","authors":"M. Barr, Maria Kallia","doi":"10.1145/3564721.3564733","DOIUrl":"https://doi.org/10.1145/3564721.3564733","url":null,"abstract":"Computing science (CS) classrooms, whether at school or university level, provide a useful context for examining disparities in participation: particular groups – especially females – remain under-represented. Among the factors that influence retention in CS are those associated with motivation. In this study, we investigate why students drop CS by drawing on two motivation models: the expectancy–value model developed by Eccles, Wigfield, and colleagues, and Marsh’s internal/external frame of reference model. Through a survey of 32 undergraduate students who dropped CS, we identify and discuss the factors that affected their decision to do so. We highlight the interplay between components of both models, revealing how utility value, cost, and students’ internal/external comparisons influenced their decision to drop the subject. We find that comparisons with peers, social concerns, perceived subject difficulty, and issues of attainment associated with self-concept all play a more significant role in female students’ decision to drop CS.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124529106","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}