Lijun Ni, Tom McKlin, Han Hao, Jake Baskin, Jason Bohrer, Yan Tian
Motivation: Recent efforts to expand K-12 computer science education highlight the great need for well-prepared computer science (CS) teachers. Teacher identity theory offers a particular conceptual lens for us to understand computer science teacher preparation and professional development. The emerging literature suggests that teacher identity is central to sustaining motivation, efficacy, job satisfaction, and commitment, and these attributes are crucial in determining teacher retention. While the benefits associated with a strong sense of teacher identity are great, teachers face unique challenges and tensions in developing their professional identity for teaching computer science. Objectives: This exploratory study attempts to operationalize computer science teacher identity through discussing the potential domains, proposing and testing a quantitative instrument for assessing computer science teachers’ professional identity. Method: We first discussed the potential domains of computer science teacher identity based on recent teacher identity literature and considerations on some unique challenges for computer science teachers. Then we proposed the computer science teacher identity scale, which was piloted through a national K-12 computer science teacher survey with 3,540 completed responses. The survey results were analyzed with a series of factor analyses to test the internal structure of the computer science teacher identity scale. Results: Our analyses reveal a four-factor solution for the computer science teacher identity scale, which is composed of CS teaching commitment, CS pedagogical confidence, confidence to engage students, and sense of community/belonging. There were significant differences among the teachers with different computer science teaching experiences. In general, teachers with more computer science teaching experience had higher computer science teacher identity scores on all four factors. Discussion: The four-factor model along with a large national dataset invites a deeper analysis of the data and can provide important benchmarks. Such an instrument can be used to explore developmental patterns in computer science teacher identity, and function as a pedagogical tool to provoke discussion and reflection among teachers about their professional development. This study may also contribute to understanding computer science teachers’ professional development needs and inform efforts to prepare, develop, and retain computer science teachers.
{"title":"Understanding Professional Identity of Computer Science Teachers: Design of the Computer Science Teacher Identity Survey","authors":"Lijun Ni, Tom McKlin, Han Hao, Jake Baskin, Jason Bohrer, Yan Tian","doi":"10.1145/3446871.3469766","DOIUrl":"https://doi.org/10.1145/3446871.3469766","url":null,"abstract":"Motivation: Recent efforts to expand K-12 computer science education highlight the great need for well-prepared computer science (CS) teachers. Teacher identity theory offers a particular conceptual lens for us to understand computer science teacher preparation and professional development. The emerging literature suggests that teacher identity is central to sustaining motivation, efficacy, job satisfaction, and commitment, and these attributes are crucial in determining teacher retention. While the benefits associated with a strong sense of teacher identity are great, teachers face unique challenges and tensions in developing their professional identity for teaching computer science. Objectives: This exploratory study attempts to operationalize computer science teacher identity through discussing the potential domains, proposing and testing a quantitative instrument for assessing computer science teachers’ professional identity. Method: We first discussed the potential domains of computer science teacher identity based on recent teacher identity literature and considerations on some unique challenges for computer science teachers. Then we proposed the computer science teacher identity scale, which was piloted through a national K-12 computer science teacher survey with 3,540 completed responses. The survey results were analyzed with a series of factor analyses to test the internal structure of the computer science teacher identity scale. Results: Our analyses reveal a four-factor solution for the computer science teacher identity scale, which is composed of CS teaching commitment, CS pedagogical confidence, confidence to engage students, and sense of community/belonging. There were significant differences among the teachers with different computer science teaching experiences. In general, teachers with more computer science teaching experience had higher computer science teacher identity scores on all four factors. Discussion: The four-factor model along with a large national dataset invites a deeper analysis of the data and can provide important benchmarks. Such an instrument can be used to explore developmental patterns in computer science teacher identity, and function as a pedagogical tool to provoke discussion and reflection among teachers about their professional development. This study may also contribute to understanding computer science teachers’ professional development needs and inform efforts to prepare, develop, and retain computer science teachers.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133005532","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}
Meredith Pearce, Braedon McConnell, Allison Bolton, A. Tartaro
ACM Reference Format: Meredith Pearce, Braedon McConnell, Allison Bolton, and Andrea Tartaro. 2021. Theories of Participation: A Literature Review of Women’s Participation in Computer Science. In Proceedings of the 17th ACM Conference on International Computing Education Research (ICER 2021), August 16–19, 2021, Virtual Event, USA. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3446871.3469786
{"title":"Theories of Participation: A Literature Review of Women’s Participation in Computer Science","authors":"Meredith Pearce, Braedon McConnell, Allison Bolton, A. Tartaro","doi":"10.1145/3446871.3469786","DOIUrl":"https://doi.org/10.1145/3446871.3469786","url":null,"abstract":"ACM Reference Format: Meredith Pearce, Braedon McConnell, Allison Bolton, and Andrea Tartaro. 2021. Theories of Participation: A Literature Review of Women’s Participation in Computer Science. In Proceedings of the 17th ACM Conference on International Computing Education Research (ICER 2021), August 16–19, 2021, Virtual Event, USA. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3446871.3469786","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116492825","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}
K-12 computing teachers need guidance on how to best implement student-centered practices for teaching programming, particularly in block-based programming environments (BBPEs) [10]. One way to provide such guidance to teachers is through Integrated Development Environment (IDE)-based learning analytics [4], which involves collecting data on students’ interactions with IDEs, translating them into meaningful information about students’ learning processes, and designing interventions that are grounded in such data. Most of the work on IDE-based learning analytics have focused on university-level introductory computer science courses (e.g., BlueJ [6]) that taught text-based programming languages. Specific to BBPEs, there has been significant analytics work with iSnap [7], which focused on offering intelligent tutoring to students based on their actions within the IDE. Little work, however, has been done on IDE-based learning analytics in Scratch [9]; prior work on Scratch learning analytics used clickstream data to characterize students’ programming abilities, which fails to fully capture students’ learning processes [3]. For K-12 teachers teaching with Scratch, collecting and analyzing data beyond clickstream (e.g., ProgSnap2 [8]) can provide more insight on student programming behaviors and how students learn to program with Scratch. Insight provided by a richer set of Scratch learning process data could empower teachers to design classroom interventions (e.g., feedback, scaffolds) to proactively respond to student needs; the use of Scratch learning analytics to inform the design of classroom interventions has not been thoroughly explored in IDE-based learning analytics [4]. We have started to address the need for capturing students’ learning processes in Scratch by adapting the ProgSnap2 standards to reconstruct states of students’ Scratch projects over time and capture patterns of tinkering behaviors among novice programmers [5]. A key aspect we want to improve in our prior work is the use of theory—particularly theory developed in CS Education contexts—to ground the analyses of novice Scratch programmers’ programming behaviors, and which can be used to guide the designs of interventions that support programming tasks in Scratch. We will do this by adapting and applying an existing multi-faceted SOLO taxonomy of programming skills [1, 2] to the processing and analysis of data on students’ interactions within Scratch. For example, we will look at whether and how patterns of Scratch programming behaviors reflect certain programming skill levels within the taxonomy. This will enable us to gauge students’ performance levels for various skills involved in Scratch programming and how students evolve in those skills. We will examine correlations between levels within the taxonomy and programming behaviors found in our Scratch programming process data. We will also use student interviews and surveys on students’ approaches to their solutions as supporti
{"title":"Integrating the Analytics of Student Interaction Data Within Scratch with a Programming Skills Taxonomy","authors":"F. Castro, Minji Kong","doi":"10.1145/3446871.3469788","DOIUrl":"https://doi.org/10.1145/3446871.3469788","url":null,"abstract":"K-12 computing teachers need guidance on how to best implement student-centered practices for teaching programming, particularly in block-based programming environments (BBPEs) [10]. One way to provide such guidance to teachers is through Integrated Development Environment (IDE)-based learning analytics [4], which involves collecting data on students’ interactions with IDEs, translating them into meaningful information about students’ learning processes, and designing interventions that are grounded in such data. Most of the work on IDE-based learning analytics have focused on university-level introductory computer science courses (e.g., BlueJ [6]) that taught text-based programming languages. Specific to BBPEs, there has been significant analytics work with iSnap [7], which focused on offering intelligent tutoring to students based on their actions within the IDE. Little work, however, has been done on IDE-based learning analytics in Scratch [9]; prior work on Scratch learning analytics used clickstream data to characterize students’ programming abilities, which fails to fully capture students’ learning processes [3]. For K-12 teachers teaching with Scratch, collecting and analyzing data beyond clickstream (e.g., ProgSnap2 [8]) can provide more insight on student programming behaviors and how students learn to program with Scratch. Insight provided by a richer set of Scratch learning process data could empower teachers to design classroom interventions (e.g., feedback, scaffolds) to proactively respond to student needs; the use of Scratch learning analytics to inform the design of classroom interventions has not been thoroughly explored in IDE-based learning analytics [4]. We have started to address the need for capturing students’ learning processes in Scratch by adapting the ProgSnap2 standards to reconstruct states of students’ Scratch projects over time and capture patterns of tinkering behaviors among novice programmers [5]. A key aspect we want to improve in our prior work is the use of theory—particularly theory developed in CS Education contexts—to ground the analyses of novice Scratch programmers’ programming behaviors, and which can be used to guide the designs of interventions that support programming tasks in Scratch. We will do this by adapting and applying an existing multi-faceted SOLO taxonomy of programming skills [1, 2] to the processing and analysis of data on students’ interactions within Scratch. For example, we will look at whether and how patterns of Scratch programming behaviors reflect certain programming skill levels within the taxonomy. This will enable us to gauge students’ performance levels for various skills involved in Scratch programming and how students evolve in those skills. We will examine correlations between levels within the taxonomy and programming behaviors found in our Scratch programming process data. We will also use student interviews and surveys on students’ approaches to their solutions as supporti","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124215461","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}
Software Engineering (SE) education presents many challenges, especially related to hands-on activities that aim to improve practical skills and competencies to solve real-world problems. Some new Learning Approaches, such as Flipped Classroom (FC), emerged and can improve learning processes in this scenario. This doctoral work explores the Flipped Classrooms ideas, identifies challenges, and proposes a tool for reducing them, thus contributing to improved software engineering teaching research. Furthermore, the study aims to combine Adaptive Learning and Flipped Classroom approaches to offer study guides personalization and positively impact SE students’ learning during outside classroom studies.
{"title":"An Authoring Tool to Support Flipped Classroom in Software Engineering Teaching","authors":"N. Veras, L. Rocha, Windson Viana","doi":"10.1145/3446871.3469798","DOIUrl":"https://doi.org/10.1145/3446871.3469798","url":null,"abstract":"Software Engineering (SE) education presents many challenges, especially related to hands-on activities that aim to improve practical skills and competencies to solve real-world problems. Some new Learning Approaches, such as Flipped Classroom (FC), emerged and can improve learning processes in this scenario. This doctoral work explores the Flipped Classrooms ideas, identifies challenges, and proposes a tool for reducing them, thus contributing to improved software engineering teaching research. Furthermore, the study aims to combine Adaptive Learning and Flipped Classroom approaches to offer study guides personalization and positively impact SE students’ learning during outside classroom studies.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"63 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123310017","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}
Motivation. “Adversarial thinking” (at) is viewed as a central idea in cybersecurity. We believe a similar idea carries over into other critical areas as well, such as understanding the perils of social networks and machine learning. Objectives. What kinds of at can we expect of early post-secondary computing students? In particular, can they meaningfully analyze computing systems that are well beyond their technical ken? Is their analysis limited to only a social or only a technical space? Method. In an introductory post-secondary course, we study student responses to questions designed to exercise at, broadly defined. To do this we develop a rubric that provides insight into desirable content. Results. We find that these students are fairly strong at at. They are regularly able to adopt an adversarial or empathetic viewpoint and analyze quite sophisticated systems. Most of all, they can meaningfully do so (a) outside an explicit cybersecurity context, (b) even from an introductory level, and (c) well before they understand well the key technologies under evaluation. On the other hand, we also find several instances where students do not explore systems as much as they could, and fail to reference other material they know, which could be evidence of lack of transfer. In addition, our rubric would benefit from refinement that would enable a more sophisticated analysis of student responses. Discussion. Our work provides a baseline evaluation of what we can expect from students. It suggests that at can be introduced early in the curriculum, and in contexts outside computer security.
{"title":"Early Post-Secondary Student Performance of Adversarial Thinking","authors":"Nick Young, S. Krishnamurthi","doi":"10.1145/3446871.3469743","DOIUrl":"https://doi.org/10.1145/3446871.3469743","url":null,"abstract":"Motivation. “Adversarial thinking” (at) is viewed as a central idea in cybersecurity. We believe a similar idea carries over into other critical areas as well, such as understanding the perils of social networks and machine learning. Objectives. What kinds of at can we expect of early post-secondary computing students? In particular, can they meaningfully analyze computing systems that are well beyond their technical ken? Is their analysis limited to only a social or only a technical space? Method. In an introductory post-secondary course, we study student responses to questions designed to exercise at, broadly defined. To do this we develop a rubric that provides insight into desirable content. Results. We find that these students are fairly strong at at. They are regularly able to adopt an adversarial or empathetic viewpoint and analyze quite sophisticated systems. Most of all, they can meaningfully do so (a) outside an explicit cybersecurity context, (b) even from an introductory level, and (c) well before they understand well the key technologies under evaluation. On the other hand, we also find several instances where students do not explore systems as much as they could, and fail to reference other material they know, which could be evidence of lack of transfer. In addition, our rubric would benefit from refinement that would enable a more sophisticated analysis of student responses. Discussion. Our work provides a baseline evaluation of what we can expect from students. It suggests that at can be introduced early in the curriculum, and in contexts outside computer security.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132209937","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}
Albina Zavgorodniaia, Artturi Tilanterä, A. Korhonen, O. Seppälä, Arto Hellas, Juha Sorva
The modality effect in multimedia learning suggests that pictures are best accompanied by audio explanations rather than text, but this finding has not been replicated in computing education. We investigate which instructional modality works best as an accompaniment for algorithm visualizations. In a randomized controlled trial, learners were split into three conditions who viewed an instructional video on Dijkstra’s algorithm, with diagrams accompanied by audio, text, or both. We find neither a modality effect in favor of the audio condition nor a verbal redundancy effect in favor of using only a single modality rather than both. Taken together with earlier research, our findings suggest that the modality effect is difficult to apply reliably and computing educators should not rush to integrate audio into visualizations in expectation of the effect. We discuss theoretical viewpoints that future research should attend to; these include alternative part-explanations of the modality effect and attention-based models of working memory, among others.
{"title":"Algorithm Visualization and the Elusive Modality Effect","authors":"Albina Zavgorodniaia, Artturi Tilanterä, A. Korhonen, O. Seppälä, Arto Hellas, Juha Sorva","doi":"10.1145/3446871.3469747","DOIUrl":"https://doi.org/10.1145/3446871.3469747","url":null,"abstract":"The modality effect in multimedia learning suggests that pictures are best accompanied by audio explanations rather than text, but this finding has not been replicated in computing education. We investigate which instructional modality works best as an accompaniment for algorithm visualizations. In a randomized controlled trial, learners were split into three conditions who viewed an instructional video on Dijkstra’s algorithm, with diagrams accompanied by audio, text, or both. We find neither a modality effect in favor of the audio condition nor a verbal redundancy effect in favor of using only a single modality rather than both. Taken together with earlier research, our findings suggest that the modality effect is difficult to apply reliably and computing educators should not rush to integrate audio into visualizations in expectation of the effect. We discuss theoretical viewpoints that future research should attend to; these include alternative part-explanations of the modality effect and attention-based models of working memory, among others.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130314440","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}
Erdogan Kaya, Anna Newley, Ezgi Yesilyurt, Hasan Deniz
For the last decade, there has been a continuous increase in the number of research attempts in K-12 computer science education, especially with the Obama administration's CSforAll initiative, the push from the Code.org, the release of Computer Science Teachers Association (CSTA) standards, and adoption of K-12 computer science standards by several states (e.g., Virginia, New York, and Nevada). The K-12 Computer Science Framework, along with the CSTA's computer science standards play a fundamental role in designing the pre-college computer science curriculum and teaching in computing labs. However, without emphasizing the characteristics of computer science, also known as the Nature of Computer Science (NOCS), it might be difficult to create a computationally literate society that will make informed decisions on computing-based issues. One can see with a critical eye that K-12 Computer Science Framework and CSTA standards include the NOCS aspects implicitly without using the term. Particularly, NOCS refers to the epistemological beliefs pertaining to computing. The focus on NOCS as a curricular component and instructional goal in pre-college computer science education may aid in creating computationally literate citizens in the United States and abroad; however, there is no consensus on the K-12 relevant NOCS aspects yet. Additionally, to the best of the authors’ knowledge, there is no valid and reliable instrument that can assess learners’ NOCS views. The goal of this work-in-progress manuscript is two-fold: (1) to examine the representations of the NOCS aspects in the national computer science reform documents including the K-12 Computer Science Framework and the CSTA computer science standards, and (2) to develop a valid and reliable open-ended NOCS instrument to assess learners’ NOCS views which is recommended to be used in conjunction with interviews. In other words, the K-12 Computer Science Framework and CSTA computer science standards will be analyzed for the inclusion and coverage of the NOCS aspects which will guide the prospective NOCS instrument development.
{"title":"Nature of Computer Science: Identification of K-12 Accessible Nature of Computer Science Tenets and Development of an Open-Ended Nature of Computer Science Instrument","authors":"Erdogan Kaya, Anna Newley, Ezgi Yesilyurt, Hasan Deniz","doi":"10.1145/3446871.3469784","DOIUrl":"https://doi.org/10.1145/3446871.3469784","url":null,"abstract":"For the last decade, there has been a continuous increase in the number of research attempts in K-12 computer science education, especially with the Obama administration's CSforAll initiative, the push from the Code.org, the release of Computer Science Teachers Association (CSTA) standards, and adoption of K-12 computer science standards by several states (e.g., Virginia, New York, and Nevada). The K-12 Computer Science Framework, along with the CSTA's computer science standards play a fundamental role in designing the pre-college computer science curriculum and teaching in computing labs. However, without emphasizing the characteristics of computer science, also known as the Nature of Computer Science (NOCS), it might be difficult to create a computationally literate society that will make informed decisions on computing-based issues. One can see with a critical eye that K-12 Computer Science Framework and CSTA standards include the NOCS aspects implicitly without using the term. Particularly, NOCS refers to the epistemological beliefs pertaining to computing. The focus on NOCS as a curricular component and instructional goal in pre-college computer science education may aid in creating computationally literate citizens in the United States and abroad; however, there is no consensus on the K-12 relevant NOCS aspects yet. Additionally, to the best of the authors’ knowledge, there is no valid and reliable instrument that can assess learners’ NOCS views. The goal of this work-in-progress manuscript is two-fold: (1) to examine the representations of the NOCS aspects in the national computer science reform documents including the K-12 Computer Science Framework and the CSTA computer science standards, and (2) to develop a valid and reliable open-ended NOCS instrument to assess learners’ NOCS views which is recommended to be used in conjunction with interviews. In other words, the K-12 Computer Science Framework and CSTA computer science standards will be analyzed for the inclusion and coverage of the NOCS aspects which will guide the prospective NOCS instrument development.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123559003","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}
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