Lauren E. Margulieux, Paul Denny, Kathryn Cunningham, Michael Deutsch, Benjamin R. Shapiro
For many decades, educational communities, including computing education, have debated the value of telling students what they need to know (i.e., direct instruction) compared to guiding them to construct knowledge themselves (i.e., constructivism). Comparisons of these two instructional approaches have inconsistent results. Direct instruction can be more efficient for short-term performance but worse for retention and transfer. Constructivism can produce better retention and transfer, but this outcome is unreliable. To contribute to this debate, we propose a new theory to better explain these research results. Our theory, multiple conceptions theory, states that learners develop better conceptual knowledge when they are guided to compare multiple conceptions of a concept during instruction. To examine the validity of this theory, we used this lens to evaluate the literature for eight instructional techniques that guide learners to compare multiple conceptions, four from direct instruction (i.e., test-enhanced learning, erroneous examples, analogical reasoning, and refutation texts) and four from constructivism (i.e., productive failure, ambitious pedagogy, problem-based learning, and inquiry learning). We specifically searched for variations in the techniques that made them more or less successful, the mechanisms responsible, and how those mechanisms promote conceptual knowledge, which is critical for retention and transfer. To make the paper directly applicable to education, we propose instructional design principles based on the mechanisms that we identified. Moreover, we illustrate the theory by examining instructional techniques commonly used in computing education that compare multiple conceptions. Finally, we propose ways in which this theory can advance our instruction in computing and how computing education researchers can advance this general education theory.
{"title":"When Wrong is Right: The Instructional Power of Multiple Conceptions","authors":"Lauren E. Margulieux, Paul Denny, Kathryn Cunningham, Michael Deutsch, Benjamin R. Shapiro","doi":"10.1145/3446871.3469750","DOIUrl":"https://doi.org/10.1145/3446871.3469750","url":null,"abstract":"For many decades, educational communities, including computing education, have debated the value of telling students what they need to know (i.e., direct instruction) compared to guiding them to construct knowledge themselves (i.e., constructivism). Comparisons of these two instructional approaches have inconsistent results. Direct instruction can be more efficient for short-term performance but worse for retention and transfer. Constructivism can produce better retention and transfer, but this outcome is unreliable. To contribute to this debate, we propose a new theory to better explain these research results. Our theory, multiple conceptions theory, states that learners develop better conceptual knowledge when they are guided to compare multiple conceptions of a concept during instruction. To examine the validity of this theory, we used this lens to evaluate the literature for eight instructional techniques that guide learners to compare multiple conceptions, four from direct instruction (i.e., test-enhanced learning, erroneous examples, analogical reasoning, and refutation texts) and four from constructivism (i.e., productive failure, ambitious pedagogy, problem-based learning, and inquiry learning). We specifically searched for variations in the techniques that made them more or less successful, the mechanisms responsible, and how those mechanisms promote conceptual knowledge, which is critical for retention and transfer. To make the paper directly applicable to education, we propose instructional design principles based on the mechanisms that we identified. Moreover, we illustrate the theory by examining instructional techniques commonly used in computing education that compare multiple conceptions. Finally, we propose ways in which this theory can advance our instruction in computing and how computing education researchers can advance this general education theory.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"47 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":"128810448","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}
Colin M. Gray, S. Chivukula, Kassandra Melkey, Rhea Manocha
In conjunction with the increasing ubiquity of technology, computing educators have identified the need for pedagogical engagement with ethical awareness and moral reasoning. Typical approaches to incorporating ethics in computing curricula have focused primarily on abstract methods, principles, or paradigms of ethical reasoning, with relatively little focus on examining and developing students’ pragmatic awareness of ethics as grounded in their everyday work practices. In this paper, we identify and describe computing students’ negotiation of values as they engage in authentic design problems through a lab protocol study. We collected data from four groups of three students each, with each group including participants from either undergraduate User Experience Design students, Industrial Engineering students, or a mix of both. We used a thematic analysis approach to identify the roles that students took on to address the design prompt. Through our analysis, we found that the students took on a variety of “dark” roles that resulted in manipulation of the user and prioritization of stakeholder needs over user needs, with a focus either on building solutions or building rationale for design decisions. We found these roles to actively propagate through design discourses, impacting other designers in ways that frequently reinforced unethical decision making. Even when students were aware of ethical concerns based on their educational training, this awareness did not consistently result in ethically-sound decisions. These findings indicate the need for additional ethical supports to inform everyday computing practice, including means of actively identifying and balancing negative societal impacts of design decisions. The roles we have identified may productively support the development of pragmatically-focused ethical training in computing education, while adding more precision to future analysis of computing student discourses and outputs.
{"title":"Understanding “Dark” Design Roles in Computing Education","authors":"Colin M. Gray, S. Chivukula, Kassandra Melkey, Rhea Manocha","doi":"10.1145/3446871.3469754","DOIUrl":"https://doi.org/10.1145/3446871.3469754","url":null,"abstract":"In conjunction with the increasing ubiquity of technology, computing educators have identified the need for pedagogical engagement with ethical awareness and moral reasoning. Typical approaches to incorporating ethics in computing curricula have focused primarily on abstract methods, principles, or paradigms of ethical reasoning, with relatively little focus on examining and developing students’ pragmatic awareness of ethics as grounded in their everyday work practices. In this paper, we identify and describe computing students’ negotiation of values as they engage in authentic design problems through a lab protocol study. We collected data from four groups of three students each, with each group including participants from either undergraduate User Experience Design students, Industrial Engineering students, or a mix of both. We used a thematic analysis approach to identify the roles that students took on to address the design prompt. Through our analysis, we found that the students took on a variety of “dark” roles that resulted in manipulation of the user and prioritization of stakeholder needs over user needs, with a focus either on building solutions or building rationale for design decisions. We found these roles to actively propagate through design discourses, impacting other designers in ways that frequently reinforced unethical decision making. Even when students were aware of ethical concerns based on their educational training, this awareness did not consistently result in ethically-sound decisions. These findings indicate the need for additional ethical supports to inform everyday computing practice, including means of actively identifying and balancing negative societal impacts of design decisions. The roles we have identified may productively support the development of pragmatically-focused ethical training in computing education, while adding more precision to future analysis of computing student discourses and outputs.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"8 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":"129805171","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}
Team-based projects are increasingly used within software engineering education because they can teach valuable communication and collaboration skills to help prepare students for professional software engineering positions. However, team-based projects are not without their downsides: in particular, poor communication or a lack of participation can endanger the success of the project. We propose identifying metrics and building a predictive model to help instructors detect when teams are facing harmful dynamics, and evaluations to assess the metrics and their impact on teams in undergraduate software engineering courses.
{"title":"Towards Better Support for Undergraduate Software Engineering Teams","authors":"Kai Presler-Marshall","doi":"10.1145/3446871.3469773","DOIUrl":"https://doi.org/10.1145/3446871.3469773","url":null,"abstract":"Team-based projects are increasingly used within software engineering education because they can teach valuable communication and collaboration skills to help prepare students for professional software engineering positions. However, team-based projects are not without their downsides: in particular, poor communication or a lack of participation can endanger the success of the project. We propose identifying metrics and building a predictive model to help instructors detect when teams are facing harmful dynamics, and evaluations to assess the metrics and their impact on teams in undergraduate software engineering courses.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"48 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":"122594872","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}
Fangqing He, Yumih Chang, Yinmiao Li, Mingnan Du, Qianyi Chen
Traditional methods of learning conditional statements follow lecture-based instruction, which focuses on teaching concepts and definitions. While this traditional method of instruction is the dominant teaching style in Computer Science (CS) education, it has also been identified to be one of the least effective methods for teaching and engaging students [3]. The existing limitations of traditional methods include low engagement, disconnection from the real-world applications, a lack of valuable artifacts to evaluate the learning outcomes. To improve the learning methods of conditional statements stated above, we asked the following two questions: RQ1: Can storytelling logic, an early development logic, be a viable tool that translates the concept of condition followed by a consequence to the logic of conditional statements? RQ2: In what approaches can we apply the isomorphic relationship between the two logics to the learning of conditional statements? The research project proposes a multi-thread storytelling method that mimics the logic of conditional statements. As students develop new storylines, they create new conditions and the consequences under the conditions. Through active involvement in the storyline design, development, and presentation, students will be able to grasp the concept of conditional statements by applying the theoretical programming knowledge to daily life situations embedded within the story. In addition, the interactive scenes created by students are ideal artifacts for learning outcome evaluation. Based on the computational thinking phases proposed by Repenning et al. [2] which is composed of Problem Formulation (Abstraction), Solution Expression (Automation) and Solution execution and evaluation (Analysis), we design a learning system that allows students to learn conditional statements in the context of multi-thread storytelling. A 4-day workshop was developed and hosted to assess the proposed learning system with 6 children aged 11-12 years old. Based on the classic fairy tale Little Red Riding Hood, a story world is built up in the classroom by integrating stage design and sensory-based physical interactions. An immersive and interactive story with a tragic ending is intentionally built by the instructors and students are invited to experience the instructor-developed storyline. Sensor-based choice-making interactions are embedded in the experience to imply that the choices students make will directly impact the outcome of the story. Students are asked to recall moments that different choices can be made to save the main character, design sensor-based interactions that allow opportunities to make choices, create new storylines, code with conditional statements, and present to the class.Based on Brennan and Resnick’s “three approaches to assessing the development of computational thinking in young people” [1], we evaluated students’ learning outcomes through 1) codes written by students to exam the implementation of
{"title":"Programmable Little Red: A Multi-thread Immersive and Interactive Storytelling Approach to Learning Conditional Statements","authors":"Fangqing He, Yumih Chang, Yinmiao Li, Mingnan Du, Qianyi Chen","doi":"10.1145/3446871.3469797","DOIUrl":"https://doi.org/10.1145/3446871.3469797","url":null,"abstract":"Traditional methods of learning conditional statements follow lecture-based instruction, which focuses on teaching concepts and definitions. While this traditional method of instruction is the dominant teaching style in Computer Science (CS) education, it has also been identified to be one of the least effective methods for teaching and engaging students [3]. The existing limitations of traditional methods include low engagement, disconnection from the real-world applications, a lack of valuable artifacts to evaluate the learning outcomes. To improve the learning methods of conditional statements stated above, we asked the following two questions: RQ1: Can storytelling logic, an early development logic, be a viable tool that translates the concept of condition followed by a consequence to the logic of conditional statements? RQ2: In what approaches can we apply the isomorphic relationship between the two logics to the learning of conditional statements? The research project proposes a multi-thread storytelling method that mimics the logic of conditional statements. As students develop new storylines, they create new conditions and the consequences under the conditions. Through active involvement in the storyline design, development, and presentation, students will be able to grasp the concept of conditional statements by applying the theoretical programming knowledge to daily life situations embedded within the story. In addition, the interactive scenes created by students are ideal artifacts for learning outcome evaluation. Based on the computational thinking phases proposed by Repenning et al. [2] which is composed of Problem Formulation (Abstraction), Solution Expression (Automation) and Solution execution and evaluation (Analysis), we design a learning system that allows students to learn conditional statements in the context of multi-thread storytelling. A 4-day workshop was developed and hosted to assess the proposed learning system with 6 children aged 11-12 years old. Based on the classic fairy tale Little Red Riding Hood, a story world is built up in the classroom by integrating stage design and sensory-based physical interactions. An immersive and interactive story with a tragic ending is intentionally built by the instructors and students are invited to experience the instructor-developed storyline. Sensor-based choice-making interactions are embedded in the experience to imply that the choices students make will directly impact the outcome of the story. Students are asked to recall moments that different choices can be made to save the main character, design sensor-based interactions that allow opportunities to make choices, create new storylines, code with conditional statements, and present to the class.Based on Brennan and Resnick’s “three approaches to assessing the development of computational thinking in young people” [1], we evaluated students’ learning outcomes through 1) codes written by students to exam the implementation of","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"28 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":"126840374","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}
Computing education’s booming enrollment exacerbates inclusion challenges ranging from tools that do not support diverse learners to instructors not being aware of unique challenges that students of minoritized groups face. While data often perpetuates inequities in many contexts, it could also serve to support equity-related goals if properly contextualized. To understand how data could support equitable learning, I explore how affording information and agency supports students’ self-directed learning of Python programming, how contextualizing psychometric data on test bias with curriculum designers’ domain expertise could support equitable curriculum improvements, and how contextualizing student feedback with demographic information and peer perspectives could help instructors become aware of challenges that students from minoritized groups face while preserving student privacy and well-being. By studying how students, curriculum designers, and teachers interpreted and used data relating to experiences learning computing, I contribute techniques that contextualize equity-oriented interpretations and uses of data with stakeholders’ domain expertise.
{"title":"Interpretations and Uses of Data for Equity in Computing Education","authors":"Benjamin Xie","doi":"10.1145/3446871.3469780","DOIUrl":"https://doi.org/10.1145/3446871.3469780","url":null,"abstract":"Computing education’s booming enrollment exacerbates inclusion challenges ranging from tools that do not support diverse learners to instructors not being aware of unique challenges that students of minoritized groups face. While data often perpetuates inequities in many contexts, it could also serve to support equity-related goals if properly contextualized. To understand how data could support equitable learning, I explore how affording information and agency supports students’ self-directed learning of Python programming, how contextualizing psychometric data on test bias with curriculum designers’ domain expertise could support equitable curriculum improvements, and how contextualizing student feedback with demographic information and peer perspectives could help instructors become aware of challenges that students from minoritized groups face while preserving student privacy and well-being. By studying how students, curriculum designers, and teachers interpreted and used data relating to experiences learning computing, I contribute techniques that contextualize equity-oriented interpretations and uses of data with stakeholders’ domain expertise.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"362 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":"121648407","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}
James Finnie-Ansley, Paul Denny, Andrew Luxton-Reilly
When a student reads a programming problem statement, something has to happen; that something could be abject confusion, the beginnings of a search for a solution, or a well-formed understanding of what the problem is asking and how to solve it. Barring abject confusion, several theories explain the differences between these responses all revolving around the existence or non-existence of a problem schema – some mental concept or knowledge structure which encodes what it is to be a particular type of problem which gets solved in a particular type of way. Learners often lack appropriate schemata to call upon when solving problems, instead resorting to generic problem-solving techniques. Not only is this an inefficient method of solving problems, it can even inhibit the development of schemata. In line with constructivist theories of learning, effective teaching should build on the existing knowledge of learners; to do so, we must understand the nature of what they know – what do their schemata, as undeveloped as they may be, ‘look like’ and what concepts do they have about problems? In this paper, we explore the categories students identify when sorting simple algorithmic computing problem statements and the language they use to describe those categories. We conduct an interpretivist study involving a card sorting exercise, in which 35 computing students across four years of tertiary-level study grouped problem statements into categories they identified as meaningful, followed up with semi-structured interviews. Results of qualitative analysis revealed several students do demonstrate productive knowledge for identifying and reasoning about common tasks such as filtering, mapping, aggregating, and searching; however, this knowledge is fragile and concrete, and does not demonstrate the existence of pre-established problem schemata or abstract knowledge of algorithmic patterns. One implication of this work is that instruction may benefit from a more explicit focus on patterns and plans, and an established language with which students can communicate and reason about them.
{"title":"A Semblance of Similarity: Student Categorisation of Simple Algorithmic Problem Statements","authors":"James Finnie-Ansley, Paul Denny, Andrew Luxton-Reilly","doi":"10.1145/3446871.3469745","DOIUrl":"https://doi.org/10.1145/3446871.3469745","url":null,"abstract":"When a student reads a programming problem statement, something has to happen; that something could be abject confusion, the beginnings of a search for a solution, or a well-formed understanding of what the problem is asking and how to solve it. Barring abject confusion, several theories explain the differences between these responses all revolving around the existence or non-existence of a problem schema – some mental concept or knowledge structure which encodes what it is to be a particular type of problem which gets solved in a particular type of way. Learners often lack appropriate schemata to call upon when solving problems, instead resorting to generic problem-solving techniques. Not only is this an inefficient method of solving problems, it can even inhibit the development of schemata. In line with constructivist theories of learning, effective teaching should build on the existing knowledge of learners; to do so, we must understand the nature of what they know – what do their schemata, as undeveloped as they may be, ‘look like’ and what concepts do they have about problems? In this paper, we explore the categories students identify when sorting simple algorithmic computing problem statements and the language they use to describe those categories. We conduct an interpretivist study involving a card sorting exercise, in which 35 computing students across four years of tertiary-level study grouped problem statements into categories they identified as meaningful, followed up with semi-structured interviews. Results of qualitative analysis revealed several students do demonstrate productive knowledge for identifying and reasoning about common tasks such as filtering, mapping, aggregating, and searching; however, this knowledge is fragile and concrete, and does not demonstrate the existence of pre-established problem schemata or abstract knowledge of algorithmic patterns. One implication of this work is that instruction may benefit from a more explicit focus on patterns and plans, and an established language with which students can communicate and reason about them.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"40 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":"117333245","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 Only 27% of computer and mathematical scientists in the United States and 18% of IT specialists in Europe are women. The under-representation of women in the field of Computer Science is, among other things, influenced by stereotypes of computer scientists. These stereotypes include being male, asocial and having an (obsessive) interest in computers. Even though stereotypical beliefs can develop at an early age, research on children’s stereotypes of computer scientists is sparse and inconclusive. Objectives Stereotypes we hold can be implicit or unconscious beliefs, or explicit or conscious beliefs. In this study, we focus on children’s implicit and explicit stereotypes regarding computer scientists’ gender, social skills and interests. We also study whether explaining what a computer scientist does affects these stereotypes. Method We study the implicit stereotypes through the reduced-length Child Implicit Association Test and the explicit stereotypes through self-reported absolute and relative Likert scale questions. We gathered data on 564 children between the age of 7 and 18 who were visiting a science museum. The participants in the experiment group (n=352) watch a video of either a man or woman explaining what a computer scientist does at the start of the study. Results We found weak implicit stereotypical beliefs on computer scientists’ social skills and moderate implicit stereotypical beliefs on computer scientists’ interests. We also found explicit stereotypes on computer scientists’ gender, social skills and interests. Measuring the effects of the intervention, we found significant differences between the control and experiment group in their explicit stereotypes on computer scientists’ social skills. Discussion The amount of scientific work on children’s stereotypes regarding computer scientists is still limited. Applying the reduced-length Child Implicit Association Test to measure children’s stereotypes on computer scientists has, to our knowledge, not been done before. Understanding children’s stereotypes and how to tackle them contributes to closing the gender gap in Computer Science.
{"title":"Children’s Implicit and Explicit Stereotypes on the Gender, Social Skills, and Interests of a Computer Scientist","authors":"S. D. Wit, F. Hermans, Efthimia Aivaloglou","doi":"10.1145/3446871.3469753","DOIUrl":"https://doi.org/10.1145/3446871.3469753","url":null,"abstract":"Motivation Only 27% of computer and mathematical scientists in the United States and 18% of IT specialists in Europe are women. The under-representation of women in the field of Computer Science is, among other things, influenced by stereotypes of computer scientists. These stereotypes include being male, asocial and having an (obsessive) interest in computers. Even though stereotypical beliefs can develop at an early age, research on children’s stereotypes of computer scientists is sparse and inconclusive. Objectives Stereotypes we hold can be implicit or unconscious beliefs, or explicit or conscious beliefs. In this study, we focus on children’s implicit and explicit stereotypes regarding computer scientists’ gender, social skills and interests. We also study whether explaining what a computer scientist does affects these stereotypes. Method We study the implicit stereotypes through the reduced-length Child Implicit Association Test and the explicit stereotypes through self-reported absolute and relative Likert scale questions. We gathered data on 564 children between the age of 7 and 18 who were visiting a science museum. The participants in the experiment group (n=352) watch a video of either a man or woman explaining what a computer scientist does at the start of the study. Results We found weak implicit stereotypical beliefs on computer scientists’ social skills and moderate implicit stereotypical beliefs on computer scientists’ interests. We also found explicit stereotypes on computer scientists’ gender, social skills and interests. Measuring the effects of the intervention, we found significant differences between the control and experiment group in their explicit stereotypes on computer scientists’ social skills. Discussion The amount of scientific work on children’s stereotypes regarding computer scientists is still limited. Applying the reduced-length Child Implicit Association Test to measure children’s stereotypes on computer scientists has, to our knowledge, not been done before. Understanding children’s stereotypes and how to tackle them contributes to closing the gender gap in Computer Science.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"57 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":"123010750","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}
Program writing is difficult to teach, learn, and assess. One challenge is a lack of theory or understanding about what program writing is. My dissertation will address this challenge by applying theories from natural language (NL) writing to try and understand program writing as a cognitive process. By analyzing keystroke logs collected during program writing, I plan to identify similarities with NL writing, potential diagnostic information, and how program writing changes as students become more proficient.
{"title":"Towards an Understanding of Program Writing as a Cognitive Process: Analysis of Keystroke Logs","authors":"Matt J. Davidson","doi":"10.1145/3446871.3469774","DOIUrl":"https://doi.org/10.1145/3446871.3469774","url":null,"abstract":"Program writing is difficult to teach, learn, and assess. One challenge is a lack of theory or understanding about what program writing is. My dissertation will address this challenge by applying theories from natural language (NL) writing to try and understand program writing as a cognitive process. By analyzing keystroke logs collected during program writing, I plan to identify similarities with NL writing, potential diagnostic information, and how program writing changes as students become more proficient.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"300 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":"116526503","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}
Gregor Große-Bölting, Dietrich Gerstenberger, Lara Gildehaus, A. Mühling, Carsten Schulte
The identity or self-concept of computer scientists has received increasing attention in the computing education research (CER) literature in recent years. Identity is often considered relevant both for initially choosing a path of study and subsequent retention. It is therefore also considered highly relevant for the questions of how to reduce drop-out rates and broadening participation of currently underrepresented groups in computing in higher education. However, as more and more students have eligible or mandatory computing education in their K-12 years, identity may become relevant in this area as well. In this article, we analyze the use and development of identity in the CER literature with a focus on K-12 education. To do so, we undertook a systematic literature review that identified appropriate publications through both a traditional database search (ACM DL, IEEE Xplore, SpringerLink, ScienceDirect, DBLP, and Google Scholar) as well as an additional forward and backward snowballing process. In total, 31 papers from the years 1997-2020 were identified that address identity in the K-12 CS context. We summarize key research findings from these articles and develop a category system that demonstrate how and why identity is used in CER in the K-12 context. Our findings suggest that the use of identity in K-12 research needs to be thought of in fundamentally different ways than for higher education. Alongside, we provide evidence that the underlying theory is less fragmented than often claimed and highlight potentials arising from greater networking and discussion of identity research in (K-12) CER.
{"title":"Identity in K-12 Computer Education Research: A Systematic Literature Review","authors":"Gregor Große-Bölting, Dietrich Gerstenberger, Lara Gildehaus, A. Mühling, Carsten Schulte","doi":"10.1145/3446871.3469757","DOIUrl":"https://doi.org/10.1145/3446871.3469757","url":null,"abstract":"The identity or self-concept of computer scientists has received increasing attention in the computing education research (CER) literature in recent years. Identity is often considered relevant both for initially choosing a path of study and subsequent retention. It is therefore also considered highly relevant for the questions of how to reduce drop-out rates and broadening participation of currently underrepresented groups in computing in higher education. However, as more and more students have eligible or mandatory computing education in their K-12 years, identity may become relevant in this area as well. In this article, we analyze the use and development of identity in the CER literature with a focus on K-12 education. To do so, we undertook a systematic literature review that identified appropriate publications through both a traditional database search (ACM DL, IEEE Xplore, SpringerLink, ScienceDirect, DBLP, and Google Scholar) as well as an additional forward and backward snowballing process. In total, 31 papers from the years 1997-2020 were identified that address identity in the K-12 CS context. We summarize key research findings from these articles and develop a category system that demonstrate how and why identity is used in CER in the K-12 context. Our findings suggest that the use of identity in K-12 research needs to be thought of in fundamentally different ways than for higher education. Alongside, we provide evidence that the underlying theory is less fragmented than often claimed and highlight potentials arising from greater networking and discussion of identity research in (K-12) CER.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"8 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":"126597983","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}
Sophia Krause-Levy, W. Griswold, Leo Porter, Christine Alvarado
Students’ sense of belonging has been found to be connected to student retention in higher education. In computing education, prior studies suggest that a hostile culture and a feeling of non-belonging can lead women, Black, Latinx, Native American, and Pacific Islander students to drop out of the computing field at a disproportionately high rate. Yet, we know relatively little about how computing students’ sense of belonging presents and evolves (if at all) through their college courses, particularly in courses beyond the introductory level, and little is known about how sense of belonging impacts student outcomes in computing. In an extension of a previous study, we examined students’ sense of belonging in six early undergraduate computer science courses across three consecutive quarters at a large research-intensive institution in North America. We found that women and first generation students have a lower incoming sense of belonging across all courses. When exploring sense of belonging’s tie to student outcomes we found that lower sense of belonging was correlated with negative course outcomes in terms of pass rates and course performance. We also found that it is less tied to student performance as students get further into the CS curriculum. Surprisingly, there was no indication that sense of belonging is predictive of retention in terms of persistence to the next CS course outside of the first course in our two-course CS1 sequence.
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