This paper presents a pilot study of NaturalLanguageProcessing4All (NLP4All), a Constructionist, low-threshold, XAI learning tool designed to bring Natural Language Processing methods into high school classrooms. Specifically, NLP4All is designed to let nonprogrammers explore different corpora of text through classification activities. Together with a high school Social Studies teacher, I developed a 2-week (6-hour) learning unit focusing on analyzing tweets from political parties to explore the differences and similarities between their policy views and communication styles. In the analysis, I find that text classification shows unexplored promise as a learning activity; that students were able to draw on their prior knowledge to classify tweets; that using NLP4All to collaboratively classify tweets led to productive classroom discussions; and that while students were able to build good machine learning models for classifying tweets, their rationales often focused on identifying one party, rather than distinguishing between parties. Finally, I discuss other educational contexts where NLP andML can be productive for children, and future design features that may be worth exploring.
{"title":"NaturalLanguageProcesing4All","authors":"A. Hjorth","doi":"10.1145/3446871.3469749","DOIUrl":"https://doi.org/10.1145/3446871.3469749","url":null,"abstract":"This paper presents a pilot study of NaturalLanguageProcessing4All (NLP4All), a Constructionist, low-threshold, XAI learning tool designed to bring Natural Language Processing methods into high school classrooms. Specifically, NLP4All is designed to let nonprogrammers explore different corpora of text through classification activities. Together with a high school Social Studies teacher, I developed a 2-week (6-hour) learning unit focusing on analyzing tweets from political parties to explore the differences and similarities between their policy views and communication styles. In the analysis, I find that text classification shows unexplored promise as a learning activity; that students were able to draw on their prior knowledge to classify tweets; that using NLP4All to collaboratively classify tweets led to productive classroom discussions; and that while students were able to build good machine learning models for classifying tweets, their rationales often focused on identifying one party, rather than distinguishing between parties. Finally, I discuss other educational contexts where NLP andML can be productive for children, and future design features that may be worth exploring.","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":"129887045","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}
Amanda Buddemeyer, L. Hatley, Angela E. B. Stewart, Jaemarie Solyst, A. Ogan, Erin Walker
Dialog with a social pedagogical robot or agent is a powerful way for kids to learn [1, 5] but may limit the formation of an agentic relationship with the technology [9]. One main purpose of conversational agents is to allow the user to have a natural interaction that reduces the need to learn artificial conventions [6], but dialog systems fall short with respect to failure recovery, vocabulary diversity, remembering conversational history, and other measures [2, 3]. Further, Hill et. al. [4] found that people adapt their model of communication to match a chatbot’s in the same way they do with a child or non-native speaker. Thus, users conversing with a pedagogical agent are implicitly trained to shape their behavior to suit the technology rather than shaping the technology. For young learners, particularly among populations that have been historically excluded from technology fields, this limits agency and reinforces marginalizing power structures [9]. This project combines a conversational agent with ideas of agentic engagement to help middle-school-aged children learn computational thinking. Agentic engagement is defined as students’ constructive contribution into the flow of instruction and includes behaviors such as expressing interests, preferences, and opinions. It has been positively correlated to learning performance and motivation [7, 8]. Combined with a culturally responsive curriculum (CRC), agentic engagement may help to foster an agentic relationship with technology. Our system encourages learners to engage agentically by using programming constructs to change the agent’s vocabulary, recognizing the intent behind a user utterance (an invocation), and defining the action the agent will take to respond to an invocation. Students use computational thinking concepts such as pattern recognition, abstraction, and decomposition to convert ideas into commands for the dialog system and to understand which of their ideas can’t be programmed with the technology as presented. They learn both to personalize the system today and to see the agent as a technosocial construct that they can shape in the future. Programming can be accomplished either using Google’s Blockly visual programming tool (https://developers.google.com/blockly) or through conversation with the agent itself. The agent is embodied as a robot character, so agent actions can be verbal, physical, or both. Through social dialog with the agent, learners reflect on how computational thinking is relevant to themselves and their communities as part of a CRC, building on the work of Stewart et. al. [10]. For example, learners may be asked to reflect on the relationship between greeting behaviors and identity. After designing a greeting interaction, learners program the dialog system to achieve the greeting. Then learners may be asked to imagine how they might hypothetically enhance the dialog system to make it even more capable of implementing their preferences. In parallel to the develo
{"title":"Agentic Engagement with a Programmable Dialog System","authors":"Amanda Buddemeyer, L. Hatley, Angela E. B. Stewart, Jaemarie Solyst, A. Ogan, Erin Walker","doi":"10.1145/3446871.3469782","DOIUrl":"https://doi.org/10.1145/3446871.3469782","url":null,"abstract":"Dialog with a social pedagogical robot or agent is a powerful way for kids to learn [1, 5] but may limit the formation of an agentic relationship with the technology [9]. One main purpose of conversational agents is to allow the user to have a natural interaction that reduces the need to learn artificial conventions [6], but dialog systems fall short with respect to failure recovery, vocabulary diversity, remembering conversational history, and other measures [2, 3]. Further, Hill et. al. [4] found that people adapt their model of communication to match a chatbot’s in the same way they do with a child or non-native speaker. Thus, users conversing with a pedagogical agent are implicitly trained to shape their behavior to suit the technology rather than shaping the technology. For young learners, particularly among populations that have been historically excluded from technology fields, this limits agency and reinforces marginalizing power structures [9]. This project combines a conversational agent with ideas of agentic engagement to help middle-school-aged children learn computational thinking. Agentic engagement is defined as students’ constructive contribution into the flow of instruction and includes behaviors such as expressing interests, preferences, and opinions. It has been positively correlated to learning performance and motivation [7, 8]. Combined with a culturally responsive curriculum (CRC), agentic engagement may help to foster an agentic relationship with technology. Our system encourages learners to engage agentically by using programming constructs to change the agent’s vocabulary, recognizing the intent behind a user utterance (an invocation), and defining the action the agent will take to respond to an invocation. Students use computational thinking concepts such as pattern recognition, abstraction, and decomposition to convert ideas into commands for the dialog system and to understand which of their ideas can’t be programmed with the technology as presented. They learn both to personalize the system today and to see the agent as a technosocial construct that they can shape in the future. Programming can be accomplished either using Google’s Blockly visual programming tool (https://developers.google.com/blockly) or through conversation with the agent itself. The agent is embodied as a robot character, so agent actions can be verbal, physical, or both. Through social dialog with the agent, learners reflect on how computational thinking is relevant to themselves and their communities as part of a CRC, building on the work of Stewart et. al. [10]. For example, learners may be asked to reflect on the relationship between greeting behaviors and identity. After designing a greeting interaction, learners program the dialog system to achieve the greeting. Then learners may be asked to imagine how they might hypothetically enhance the dialog system to make it even more capable of implementing their preferences. In parallel to the develo","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"49 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":"127595905","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}
Adrian Salguero, W. Griswold, Christine Alvarado, Leo Porter
Computer science students struggle in early computing courses as evinced by high failure rates and poor retention. As such, studies have attempted to characterize the root of student struggles from many perspectives, including cognitive, meta-cognitive, and social emotional. Typically, studies have limited their inquiry to a specific perspective or a single course. This paper reports the results of a broad student experience survey conducted across several computer science courses. Through a periodic survey, students rated various cognitive, socio-emotional, external, personal, and structural barriers in terms of how much each impacted their learning throughout the term. An exploratory factor analysis of these questions revealed four factors—personal obligations, lack of sense of belonging, in-class confusion, and lack of confidence—that capture a range of possible struggles students may face. We analyzed the prevalence of these factors across courses, performance quartiles, and demographic groups broken down by gender, race/ethnicity, and matriculation status. Students in lower performance quartiles report higher stress levels on multiple factors, with statistically significant differences found between all quartiles and courses, for most factors. Moreover, students from traditionally underrepresented groups report struggling more across all four factors, suggesting that they may be facing more challenges than classmates from represented populations. Overall, these findings indicate that student struggles are associated with stresses from many areas of their lives, suggesting that future interventions should target multiple areas of stress.
{"title":"Understanding Sources of Student Struggle in Early Computer Science Courses","authors":"Adrian Salguero, W. Griswold, Christine Alvarado, Leo Porter","doi":"10.1145/3446871.3469755","DOIUrl":"https://doi.org/10.1145/3446871.3469755","url":null,"abstract":"Computer science students struggle in early computing courses as evinced by high failure rates and poor retention. As such, studies have attempted to characterize the root of student struggles from many perspectives, including cognitive, meta-cognitive, and social emotional. Typically, studies have limited their inquiry to a specific perspective or a single course. This paper reports the results of a broad student experience survey conducted across several computer science courses. Through a periodic survey, students rated various cognitive, socio-emotional, external, personal, and structural barriers in terms of how much each impacted their learning throughout the term. An exploratory factor analysis of these questions revealed four factors—personal obligations, lack of sense of belonging, in-class confusion, and lack of confidence—that capture a range of possible struggles students may face. We analyzed the prevalence of these factors across courses, performance quartiles, and demographic groups broken down by gender, race/ethnicity, and matriculation status. Students in lower performance quartiles report higher stress levels on multiple factors, with statistically significant differences found between all quartiles and courses, for most factors. Moreover, students from traditionally underrepresented groups report struggling more across all four factors, suggesting that they may be facing more challenges than classmates from represented populations. Overall, these findings indicate that student struggles are associated with stresses from many areas of their lives, suggesting that future interventions should target multiple areas of stress.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"56 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":"131072955","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}
Concerns about participation in computer science at all levels of education continue to rise, despite the substantial efforts of research, policy, and world-wide education initiatives. In this paper, which is guided by a systematic literature review, we investigate the issue of inequalities in participation by bringing a theoretical lens from the sociology of education, and particularly, Bourdieu’s theory of social reproduction. By paying particular attention to Bourdieu’s theorising of capital, habitus, and field, we first establish an alignment between Bourdieu’s theory and what is known about inequalities in computer science (CS) participation; we demonstrate how the factors affecting participation constitute capital forms that individuals possess to leverage within the computer science field, while students’ views and dispositions towards computer science and scientists are rooted in their habitus which influences their successful assimilation in computer science fields. Subsequently, by projecting the issue of inequalities in CS participation to Bourdieu’s sociological theorisations, we explain that because most interventions do not consider the issue holistically and not in formal education settings, the reported benefits do not continue in the long-term which reproduces the problem. Most interventions have indeed contributed significantly to the issue, but they have either focused on developing some aspects of computer science capital or on designing activities that, although inclusive in terms of their content and context, attempt to re-construct students’ habitus to “fit” in the already “pathologized” computer science fields. Therefore, we argue that to contribute significantly to the equity and participation issue in computer science, research and interventions should focus on restructuring the computer science field and the rules of participation, as well as on building holistically students’ computer science capital and habitus within computer science fields.
{"title":"Re-Examining Inequalities in Computer Science Participation from a Bourdieusian Sociological Perspective","authors":"Maria Kallia, Q. Cutts","doi":"10.1145/3446871.3469763","DOIUrl":"https://doi.org/10.1145/3446871.3469763","url":null,"abstract":"Concerns about participation in computer science at all levels of education continue to rise, despite the substantial efforts of research, policy, and world-wide education initiatives. In this paper, which is guided by a systematic literature review, we investigate the issue of inequalities in participation by bringing a theoretical lens from the sociology of education, and particularly, Bourdieu’s theory of social reproduction. By paying particular attention to Bourdieu’s theorising of capital, habitus, and field, we first establish an alignment between Bourdieu’s theory and what is known about inequalities in computer science (CS) participation; we demonstrate how the factors affecting participation constitute capital forms that individuals possess to leverage within the computer science field, while students’ views and dispositions towards computer science and scientists are rooted in their habitus which influences their successful assimilation in computer science fields. Subsequently, by projecting the issue of inequalities in CS participation to Bourdieu’s sociological theorisations, we explain that because most interventions do not consider the issue holistically and not in formal education settings, the reported benefits do not continue in the long-term which reproduces the problem. Most interventions have indeed contributed significantly to the issue, but they have either focused on developing some aspects of computer science capital or on designing activities that, although inclusive in terms of their content and context, attempt to re-construct students’ habitus to “fit” in the already “pathologized” computer science fields. Therefore, we argue that to contribute significantly to the equity and participation issue in computer science, research and interventions should focus on restructuring the computer science field and the rules of participation, as well as on building holistically students’ computer science capital and habitus within computer science fields.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"6 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":"116180648","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}
For over 50 years, computer scientists whose backgrounds span many academic and corporate affiliations have attempted to truncate a novice programmer’s investment into their learning that might expedite the length of time required to advance from beginner to intermediate programmer. Widely accepted innovations in programming languages that use blocks instead of text to maintain novices’ motivation and attention have replaced some conventional text-based pedagogies at the pre-college level [8]. This study aims to contribute new knowledge to the Computer Science Education (CSEd) field to empirically validate whether text or block-based languages optimally prepare high school students for success in undergraduate level CS1 (Introduction to Computer Science) courses. The research sub-focus aims to distinguish the significance of equitable preparation between students from underserved communities and their peers arriving at college from affluent areas. This study introduces a 7-week, mixed-methods inquiry aimed at entering first-year undergraduate students enrolled in CS1, exploring their prior programming knowledge and experiences that might establish a relationship among high school programming curricula and learners’ CS1 achievement.
{"title":"The Block-based, Text-based, and the CS1 Prepared","authors":"Trent Dawson","doi":"10.1145/3446871.3469777","DOIUrl":"https://doi.org/10.1145/3446871.3469777","url":null,"abstract":"For over 50 years, computer scientists whose backgrounds span many academic and corporate affiliations have attempted to truncate a novice programmer’s investment into their learning that might expedite the length of time required to advance from beginner to intermediate programmer. Widely accepted innovations in programming languages that use blocks instead of text to maintain novices’ motivation and attention have replaced some conventional text-based pedagogies at the pre-college level [8]. This study aims to contribute new knowledge to the Computer Science Education (CSEd) field to empirically validate whether text or block-based languages optimally prepare high school students for success in undergraduate level CS1 (Introduction to Computer Science) courses. The research sub-focus aims to distinguish the significance of equitable preparation between students from underserved communities and their peers arriving at college from affluent areas. This study introduces a 7-week, mixed-methods inquiry aimed at entering first-year undergraduate students enrolled in CS1, exploring their prior programming knowledge and experiences that might establish a relationship among high school programming curricula and learners’ CS1 achievement.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"138 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":"127338514","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}
This basic interpretive qualitative study investigated four students’ debugging behaviors in Zoombinis, a game-based computational thinking (CT) environment. Analysis involved deductive coding of students’ debugging behaviors using videos of students’ computer screens. The findings revealed a range of debugging behaviors and strategies. Findings also indicated that students could articulate an intermediate understanding of debugging as related to the debugging LT [7].
{"title":"Elementary Students’ Debugging Behaviors in a Game-based Environment","authors":"Wei Yan, Maya Israel, Tongxi Liu","doi":"10.1145/3446871.3469792","DOIUrl":"https://doi.org/10.1145/3446871.3469792","url":null,"abstract":"This basic interpretive qualitative study investigated four students’ debugging behaviors in Zoombinis, a game-based computational thinking (CT) environment. Analysis involved deductive coding of students’ debugging behaviors using videos of students’ computer screens. The findings revealed a range of debugging behaviors and strategies. Findings also indicated that students could articulate an intermediate understanding of debugging as related to the debugging LT [7].","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"115 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":"127346168","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}
Notional machines i.e. pedagogical devices to communicate program execution play a key role in computing classrooms, especially within introductory settings [2, 5]. From machine-generated representations to classroom learning activities, a variety of notional machines have been examined in the field of computing education research. A more recent review [2] has also noted the adoption of multiple notional machines by instructors during a course or a unit to communicate a family of interconnected, computing concepts within a learning context. Despite notional machines considered as a signature pedagogy for computing education, very few accounts are based on classroom observations–most of them draw from instructor reflections or programming interface designs [2]. Further, even fewer have been situated in the more recent contexts of computing education i.e., high school classrooms where programming environments such as physical computing have been employed to make computing concepts further accessible to novices [3]. However, what is lesser known is how teachers make these computing concepts accessible to students through notional machines. To address the gap, in Fall 2020 and Spring 2021, we conducted a two-phase study that involved: (a) co-designing notional machines with an experienced high school computing teacher in Fall 2020, and, (b) observing his classes during the 14-week electronic textiles unit within Exploring Computer Science curriculum [1] in Spring 2021. For this poster, we will share findings from a preliminary qualitative analysis of online class screen recordings (5 hours, 10 class periods) of class periods that involved discussions around programs during the unit. We answer the following questions: (a) What were the different types of notional machines implemented throughout the unit within the context of physical computing? (b) How were they related to each other and to the key computing ideas within the unit? Our video analysis so far has revealed a variety of notional machines to introduce and sustain student learning during this unit. They took the form of roleplays, metaphors, and analogies, ranging from a period-long enactment to in-the-moment explanations to better understand specific aspects of program execution such as variable definition, function calls, and conditional statements execution. From extensive code tracing to debugging specific issues to diagnosing student thinking, these notional machines provided a variety of opportunities for the teacher to move across the different levels of abstractions while explaining program execution. During the poster session, we will share qualitative details about each of these categories of notional machines with examples that highlight their key characteristics in terms of form, conceptual focus, level of abstraction, and purpose within the unit. This analysis will provide one of the first accounts of notional machines emerging from classroom observational data. More importantly
{"title":"Notional Machines in a Semester-long Introductory Physical Computing High School Unit","authors":"Gayithri Jayathirtha, Y. Kafai","doi":"10.1145/3446871.3469796","DOIUrl":"https://doi.org/10.1145/3446871.3469796","url":null,"abstract":"Notional machines i.e. pedagogical devices to communicate program execution play a key role in computing classrooms, especially within introductory settings [2, 5]. From machine-generated representations to classroom learning activities, a variety of notional machines have been examined in the field of computing education research. A more recent review [2] has also noted the adoption of multiple notional machines by instructors during a course or a unit to communicate a family of interconnected, computing concepts within a learning context. Despite notional machines considered as a signature pedagogy for computing education, very few accounts are based on classroom observations–most of them draw from instructor reflections or programming interface designs [2]. Further, even fewer have been situated in the more recent contexts of computing education i.e., high school classrooms where programming environments such as physical computing have been employed to make computing concepts further accessible to novices [3]. However, what is lesser known is how teachers make these computing concepts accessible to students through notional machines. To address the gap, in Fall 2020 and Spring 2021, we conducted a two-phase study that involved: (a) co-designing notional machines with an experienced high school computing teacher in Fall 2020, and, (b) observing his classes during the 14-week electronic textiles unit within Exploring Computer Science curriculum [1] in Spring 2021. For this poster, we will share findings from a preliminary qualitative analysis of online class screen recordings (5 hours, 10 class periods) of class periods that involved discussions around programs during the unit. We answer the following questions: (a) What were the different types of notional machines implemented throughout the unit within the context of physical computing? (b) How were they related to each other and to the key computing ideas within the unit? Our video analysis so far has revealed a variety of notional machines to introduce and sustain student learning during this unit. They took the form of roleplays, metaphors, and analogies, ranging from a period-long enactment to in-the-moment explanations to better understand specific aspects of program execution such as variable definition, function calls, and conditional statements execution. From extensive code tracing to debugging specific issues to diagnosing student thinking, these notional machines provided a variety of opportunities for the teacher to move across the different levels of abstractions while explaining program execution. During the poster session, we will share qualitative details about each of these categories of notional machines with examples that highlight their key characteristics in terms of form, conceptual focus, level of abstraction, and purpose within the unit. This analysis will provide one of the first accounts of notional machines emerging from classroom observational data. More importantly","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":"125121982","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}
With the global movement to incorporate computer science instruction into elementary education, learners are being introduced to computer science and computational thinking (CS/CT) ideas at increasingly younger ages. At these early ages, young learners are developing cognitive abilities foundational to their education. While other discipline-based education fields, such as math, science, and reading, have long studied the role of cognitive abilities, such as short-term working memory and long-term retrieval, in their respective fields, similar research in computer science education is relatively sparse. In this exploratory study, we examined the relationship between cognitive abilities and CS/CT performance of fourth-grade students (ages 9-10) who underwent either an introductory CT curriculum based on Use–>Modify–>Create or the same curriculum with additional scaffolding from the TIPP&SEE metacognitive learning strategy. Our analysis revealed performance on CT assessments to be weakly correlated with working memory and long-term retrieval, with correlations increasing as the CT concepts grew more complex. This suggests that scaffolding beyond TIPP&SEE may be needed with more complex CT concepts. We also found that when using TIPP&SEE, students scoring below average on cognitive ability tests performed as well as students in the control condition with average cognitive ability scores. These results indicate TIPP&SEE’s potential in creating more equitable computing instruction. We hope that results from this initial exploration can help encourage further study into the role of cognitive abilities in CS/CT education for young learners.
{"title":"Investigating the Role of Cognitive Abilities in Computational Thinking for Young Learners","authors":"Jean Salac, C. Thomas, C. Butler, Diana Franklin","doi":"10.1145/3446871.3469746","DOIUrl":"https://doi.org/10.1145/3446871.3469746","url":null,"abstract":"With the global movement to incorporate computer science instruction into elementary education, learners are being introduced to computer science and computational thinking (CS/CT) ideas at increasingly younger ages. At these early ages, young learners are developing cognitive abilities foundational to their education. While other discipline-based education fields, such as math, science, and reading, have long studied the role of cognitive abilities, such as short-term working memory and long-term retrieval, in their respective fields, similar research in computer science education is relatively sparse. In this exploratory study, we examined the relationship between cognitive abilities and CS/CT performance of fourth-grade students (ages 9-10) who underwent either an introductory CT curriculum based on Use–>Modify–>Create or the same curriculum with additional scaffolding from the TIPP&SEE metacognitive learning strategy. Our analysis revealed performance on CT assessments to be weakly correlated with working memory and long-term retrieval, with correlations increasing as the CT concepts grew more complex. This suggests that scaffolding beyond TIPP&SEE may be needed with more complex CT concepts. We also found that when using TIPP&SEE, students scoring below average on cognitive ability tests performed as well as students in the control condition with average cognitive ability scores. These results indicate TIPP&SEE’s potential in creating more equitable computing instruction. We hope that results from this initial exploration can help encourage further study into the role of cognitive abilities in CS/CT education for young learners.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"81 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":"126985409","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}
In recent years, research has increasingly focused on developing intelligent tutoring systems that provide data-driven support for students in need of assistance during programming assignments. One goal of such intelligent tutors is to provide students with quality interventions comparable to those human tutors would give. While most studies focused on generating different forms of on-demand support, such as next-step hints and worked examples, at any given moment during the programming assignment, there is a lack of research on why human tutors would provide different forms of proactive interventions to students in different situations. This information is critical to know to allow the intelligent programming environments to select the appropriate type of student support at the right moment. In this work, we studied human tutors’ reasons for providing interventions during two introductory programming assignments in a block-based environment. Three human tutors evaluated a sample of 86 struggling moments identified from students’ log data using a data-driven model. The human tutors specified whether and why an intervention was needed (or not) for each struggling moment. We analyzed the expert tags and their consensus discussions and extracted three main reasons that made the experts decide to intervene: “missing key components to make progress”, “using wrong or unnecessary blocks”, “misusing needed blocks”, “having critical logic errors”, “needing confirmation and next steps”, and “unclear student intention”. We use six case studies to illustrate specific student code trace examples and the tutors’ reasons for intervention. We also discuss the potential types of automatic interventions that could address these cases. Our work sheds light on when and why students might need programming interventions. These insights contribute towards improving the quality of automated, data-driven support in programming learning environments.
{"title":"You Really Need Help: Exploring Expert Reasons for Intervention During Block-based Programming Assignments","authors":"Yihuan Dong, Preya Shabrina, S. Marwan, T. Barnes","doi":"10.1145/3446871.3469764","DOIUrl":"https://doi.org/10.1145/3446871.3469764","url":null,"abstract":"In recent years, research has increasingly focused on developing intelligent tutoring systems that provide data-driven support for students in need of assistance during programming assignments. One goal of such intelligent tutors is to provide students with quality interventions comparable to those human tutors would give. While most studies focused on generating different forms of on-demand support, such as next-step hints and worked examples, at any given moment during the programming assignment, there is a lack of research on why human tutors would provide different forms of proactive interventions to students in different situations. This information is critical to know to allow the intelligent programming environments to select the appropriate type of student support at the right moment. In this work, we studied human tutors’ reasons for providing interventions during two introductory programming assignments in a block-based environment. Three human tutors evaluated a sample of 86 struggling moments identified from students’ log data using a data-driven model. The human tutors specified whether and why an intervention was needed (or not) for each struggling moment. We analyzed the expert tags and their consensus discussions and extracted three main reasons that made the experts decide to intervene: “missing key components to make progress”, “using wrong or unnecessary blocks”, “misusing needed blocks”, “having critical logic errors”, “needing confirmation and next steps”, and “unclear student intention”. We use six case studies to illustrate specific student code trace examples and the tutors’ reasons for intervention. We also discuss the potential types of automatic interventions that could address these cases. Our work sheds light on when and why students might need programming interventions. These insights contribute towards improving the quality of automated, data-driven support in programming learning environments.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"13 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":"133189647","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}
Crowdsourcing is a method of collecting services, ideas, materials or other artefacts from a relatively large and open group of people. Crowdsourcing has been used in computer science education to alleviate the teachers’ workload in creating course content, and as a learning and revision method for students through its use in educational systems. Tools that utilize crowdsourcing can act as a great way for students to further familiarize themselves with the course concepts, all while creating new content for their peers and future course iterations. In my research, I focus on investigating the effects of computing education systems that use crowdsoucing on students’ learning, and the types of quality assurance methods required to use the artefacts students produce with these tools.
{"title":"Crowdsourcing in Computer Science Education","authors":"Nea Pirttinen","doi":"10.1145/3446871.3469781","DOIUrl":"https://doi.org/10.1145/3446871.3469781","url":null,"abstract":"Crowdsourcing is a method of collecting services, ideas, materials or other artefacts from a relatively large and open group of people. Crowdsourcing has been used in computer science education to alleviate the teachers’ workload in creating course content, and as a learning and revision method for students through its use in educational systems. Tools that utilize crowdsourcing can act as a great way for students to further familiarize themselves with the course concepts, all while creating new content for their peers and future course iterations. In my research, I focus on investigating the effects of computing education systems that use crowdsoucing on students’ learning, and the types of quality assurance methods required to use the artefacts students produce with these tools.","PeriodicalId":309835,"journal":{"name":"Proceedings of the 17th ACM Conference on International Computing Education Research","volume":"11 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":"134565176","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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