Neuron Sandbox is a browser-based tool that helps middle school students grasp basic principles of neural computation. It simulates a linear threshold unit applied to binary decision problems, which students solve by adjusting the unit's threshold and/or weights. Although Neuron Sandbox provides extensive visualization aids, solving these problems is challenging for students who have not yet been exposed to algebra. We collected survey, video, and worksheet data from 21 seventh grade students in two sections of an AI elective, taught by the same teacher, that used Neuron Sandbox. We present a scaffolding strategy that proved effective at guiding these students to achieve mastery of these problems. While the amount of scaffolding required was more than we originally anticipated, by the end of the exercise students understood the computation that linear threshold units perform and were able to generalize their understanding of the worksheet’s "solve for threshold" strategy to also solve for weights.

Discrete mathematics has been a topic taught in the undergraduate computer science curriculum for decades. Most research publications that are focused on discrete mathematics focus on the teacher's perspective of the course and the students. They also suggest that students struggle in the course due to a lack of mathematical maturity or motivation without presenting much empirical evidence. This paper fills the gap in research by focusing on what students think of discrete mathematics, including the concepts that they find easier and the ones that challenge them the most. We collected survey data from 132 computing students at a large public university. We analyzed the data utilizing descriptive statistics and open coding. We found that students identify proof writing as the most difficult concept to learn due to its difference from mathematics that they are most used to. Students also suggest that their challenge is not that they find the course as a whole irrelevant, which could cause a lack of motivation, but rather that specific topics were hard to understand or connect to computer science. We discuss these findings and discuss the implications and possible future directions for research in the area of discrete mathematics in the computer science curriculum.

This research paper describes the use of natural language processing to categorize reflection responses according to their level of learning. This could help instructors in assessing the effectiveness of the prompts in encouraging meaningful reflection and students' engagement with the problem. Reflective practice is the process of using one's beliefs and prior experiences to analyze a problem; it is making meaning from experience. Answering reflection prompts has been shown to aid students in learning problem solving skills. This paper describes results of an experiment in which a pretrained bi-directional encoder representations for transformers (BERT) model was used to classify responses to reflection responses. In an earlier experiment, a method was developed for deductively coding prompts using four progressive levels of learning derived from Dewey and Moon: Noticing, Making Sense, Making Meaning, and Transformative Learning. Responses to reflection prompts designed to encourage different levels of learning were manually coded. In the experiment described in this paper, manually coded responses were used to fine tune models that classify reflection prompts for the presence of each level of learning. The models performed well on the test set, indicating substantial agreement with the assigned manually codes. The models were then used to classify responses to reflection prompts accompanying assignments in on input validation vulnerabilities in two Computer Science courses. Three reflection prompts meant to encourage successive levels of learning were given with the assignments. The models proved to be effective in classifying responses, and gave insight into the effectiveness of the reflection prompts, and gave insight into students' engagement with the material in two different classes.

With the increasing interest and enrollment in programming courses, educators must discover innovative and inclusive teaching methods to effectively cater to diverse learner needs and varying levels of prior knowledge. Introductory programming courses (CS1) can prove arduous for novices and insufficiently stimulating for those with experience, creating an educational dilemma. Striking a balance between students' expectations and engagement becomes challenging for educators, especially given the expanding pre-higher education CS exposure.

Maker-based learning and Computational Thinking (CT) have increased in popularity in formal educational settings over the past decade. Particularly, the combination of CT and making seem to hold promise for providing opportunities for students to learn and use computing concepts outside of computing courses. This paper presents findings from a two year study of the integration of computational making into 5th and 6th grade science classrooms. Students participated in computational making interventions in which they programmed Arduino microcontrollers to create scientific models of concepts that aimed to help them engage with the science content while learning CT and making skills. In this paper, we explore the differences between the desired computing learning progressions, students' performance on assessments, and perceptions of computer science to answer: To what extent are middle school students able to learn computing through computing integrated science curriculum? We observed that the programming concepts taught were largely dependent on the needs of the science and making project. Our findings suggest that while students had opportunities to learn and use programming concepts, their performance on assessments was between 15% and 78% correct for conceptual and applied questions and their programming self-efficacy and their perceptions of computer science were lower than desired. We discuss the implications of these findings and the factors that impact the integration of CT in core disciplines and the challenges this presents as we aim to use integration approaches to effectively teach computing outside of computing courses and to broaden participation in computing.

Employers in the tech industry have expectations for students' involvement outside of the classroom. One avenue where students engage with computing communities of practice is student organizations or clubs. Given the lack of empirical studies on students' involvement in clubs in computing, we designed a study that aims to understand computing undergraduate students' participation in clubs and to explore what students get out of their participation in these informal learning environments. We report findings from a multi-institutional survey-based study consisting of 673 undergraduate computing students from three universities in the United States. We found that 41% of the computing students across all years participate in at least one club related to their major. Forty-seven percent of female students participated in a club compared with thirty-eight percent of male students. Through inductive content analysis, we found that students participated in six types of clubs: computing areas, professional societies, affinity groups for underrepresented students, project-based development, Greek organizations, and other types of clubs. Overall, clubs allow students to explore computing areas, develop technical and professional skills, network with industry professionals, prepare for jobs, build a community, meet and socialize with like-minded peers, and gain motivation to sustain in computing. Our work provides empirical insights into computing students' club participation and insights to our community on the importance of types of clubs that can complement students' experiences in formal education supporting students' professional development.

Today, children of all ages interact with speech recognition systems but are largely unaware of how they work. Teaching K-12 students to investigate how these systems employ phonological, syntactic, semantic, and cultural knowledge to resolve ambiguities in the audio signal can provide them a window on complex AI decision-making and also help them appreciate the richness and complexity of human language. We describe a browser-based tool for exploring the Google Web Speech API and a series of experiments students can engage in to measure what the service knows about language and the types of biases it exhibits. Middle school students taking an introductory AI elective were able to use the tool to explore Google’s knowledge of homophones and its ability to exploit context to disambiguate them. Older students could potentially conduct more comprehensive investigations, which we lay out here. This approach to investigating the power and limitations of speech technology through carefully designed experiments can also be applied to other AI application areas, such as face detection, object recognition, machine translation, or question answering.

Robots are entering our lives and workplaces as companions and teammates. Though much research has been done on how to interact with robots, teach robots and improve task performance, an open frontier for HCI/HRI research is how to establish a working relationship with a robot in the first place. Studies that explore the early stages of human-robot interaction are an emerging area of research. Simultaneously, there is resurging interest in how human-animal interaction could inform human-robot interaction. We present a first examination of early stage human-horse interaction through the lens of human-robot interaction, thus connecting these two areas. Following Strauss’ approach, we conduct a thematic analysis of data from three sources gathered over a year of field work: observations, interviews and journal entries. We contribute design guidelines based on our analyses and findings.

Designing interactive virtual learning environments with effective and engaging design elements is crucial in enhancing learners' motivation, engagement, and learning outcomes. By prioritizing a positive user experience that curates cognitive load, interactions within virtual learning environments may effectively promote voluntary and formative engagement. This analysis focuses on investigating the affordances of having an opportunity to immediately reattempt an incorrectly answered question and additionally have access to video solutions on students' voluntary engagement with mastery learning activities. These mastery learning activities were provided in the form of quizzes through a virtual learning environment, YANTRA EDU. This application was developed to facilitate mastery learning, where learners have the opportunity to engage with the sequential practice of various concepts in an introductory programming (CS1) course.

As enrollments in CS1 courses continue to rise, it has become essential for CS educators to support students with varying learning needs and prior programming experiences. Many experts have pointed to the use of mastery-based learning (MBL), which allows students to develop proficiency by engaging in formative practice problems at their own pace. However, less is known about the characteristics of students who use and benefit from such an approach. CS educators need strong evidence for whether formative practice helps to increase aggregate learning outcomes, especially among students who could gain the most from MBL. In this paper, we are interested in exploring the characteristics of students who engage with formative learning opportunities. We analyze data from 118 students enrolled in a CS1 course who were provided with weekly optional practice quizzes that contained multiple-choice and free-response questions. We used logistic regression to analyze who actually attempted these optional quizzes and found that while gender was not significant, students who do not have prior programming experience (PPE) were more likely to use optional practice than those with PPE. We also conducted a nonparametric two-sample analysis and found that students without PPE engage with optional practice questions to a higher level than students with PPE. Our findings explore the factors that may underpin students' agency and their academic behavior and performance. These results can inform educators on how to scaffold students' learning trajectories by accounting for expected group-based behavioral patterns while utilizing MBL in large CS1 courses.

In this poster, we present the design of a browser-based Arduino programming tool, CASMM, to support computational thinking and making in science classrooms. This tool allows for unique integration of research tools, lesson planning, and scaffolding for learning computational thinking concepts and block-based programming. This poster will describe four key features of a block-based LMS: (1) reduced-scoped programming toolbox, (2) block locking, (3) lesson plans and starter code templates; and (4) low-tech code replay for researchers. Through discussion of this tool, we aim to catalyze conversations about integrating new scaffolding techniques into block-based programming environments to better support classroom use and research.

This BOF is a networking opportunity for researchers, teachers, resource developers, and state leaders involved or interested in K-12 AI Education. This BOF will serve as an informal opportunity for those who participated in the AI4K12 January 2021 State of AI Education in Your State Workshop and quarterly check-in webinars to reconnect in person. We also want to provide opportunities for newcomers working on K-12 AI Education, either through outreach or their own research, to learn about and join the community. The goal for all attendees is to talk about their work, share successes, form collaborations, and discuss issues with building the framework of implementation. Most importantly, attendees will have opportunities to connect with others with similar interests and challenges, to identify best practices, share resources, and identify next steps to help advance their work. For those who are new to the community, we want to provide opportunities to learn about K-12 AI efforts that are underway in their state and connect with their state leaders. Attendees from states with no efforts underway can learn about resources to start a state team and begin planning. The AI4K12 Initiative and the state workshop were funded by National Science Foundation award# DRL-1846073.

CS education advocates have made substantial progress toward getting states to provide universal K-12 computing education. But as computers and networking continue to drive the third industrial revolution, artificial intelligence and robotics are driving the fourth. What should states be doing now about K-12 AI education? In 2021 AI4K12.org organized The State of AI Education in Your State Workshop and held a series of follow-up meetings for state education officials and other interested parties to help plan for the introduction of AI into their state's K-12 computing education standards. Some are already well along in the work. In this lightning talk we highlight progress in several states and refer attendees to resources where they can form new professional connections and begin contributing to this effort. This work is crucial to prepare students for the economic and social disruptions anticipated to result from the AI-powered fourth industrial revolution that is already under way.

Over the past year, our AI4GA team of university faculty and middle school teachers have co-designed a middle school AI curriculum. In this poster we share how we used co-design both as a tool for collaboratively developing engaging AI activities and as a mechanism for mutual professional development. We explain our co-design process, give examples of curriculum materials provided to teachers, and showcase several teacher-created activities. We believe this approach to curriculum development centers the lived experiences of teachers and leverages the knowledge and expertise of university researchers to create high quality and engaging AI learning experiences for K-12 students.

This poster will explain a debugging intervention that centered around debugging in Scratch. The debugging intervention focuses on teaching 4th and 5th-grade students' computational skills that assist with debugging (code reading, writing, tracing, and prediction) and the debugging process (finding and fixing bugs). This debugging intervention taught 49 students (ages 9 and 10) how to read, write, trace, and debug in Scratch 3.0. These skills are essential for students to become efficient programmers and debuggers.

This experience report describes and evaluates the introduction of Hire Thy Gator technical interview preparation activities in a Data Structures and Algorithms (DSA) course. Our intervention included a panel on internship experiences, a role-play interview demonstration, two participatory mock interview preparation exercises where students interviewed each other first using self-selected peers and second through random pair-ups, and graded short programming problems. We (1) explain the logistics and rationale for embedding these activities, (2) describe the lessons learned and evolution of the activities beyond the intervention semester, and (3) evaluate the impact of these activities on students. We report data from 257 students who participated in our intervention and 106 students who were a part of a control group. Students found that our activities promoted awareness of the recruitment process, allowed them to self-evaluate their strengths and weaknesses, and prepared them for technical interviews. Quantitatively, the intervention cohort reported a higher average normalized confidence gain (0.42) than the control group (0.36) indicating that our activities can aid in building students' confidence.

Computing students are expected to contribute to large unfamiliar codebases as they transition from university to industry settings. While computing courses provide students ample opportunities to write code independently or utilize abstract functionalities from standard libraries, students have fewer opportunities to read or extend codebases written by other programmers. This paper presents the logistics, affordances, and empirical evaluation of a novel instructional strategy, Build Programming, which is designed to promote code reading and extension in CS courses. In this strategy, a student (1) solves a programming problem, (2) is assigned a new codebase from a peer who solved the same problem, and (3) is asked to extend the assigned codebase to solve another problem. This allows a student to understand and extend an authentic codebase that is situated in a familiar context. In this paper, we shed light on the logistics of operationalizing this strategy in the context of an undergraduate Data Structures and Algorithms course (N=206). We also describe the affordances of this strategy through student experiences and evaluate the efficacy of one of these affordances, improving code quality through source code analysis. Most students (91%) proposed continuing Build Programming and students' code quality significantly improved after our strategy. Our findings underscore the benefits of Build Programming, and we hope that more instructors incorporate it in CS courses.

The AI4GA project is developing a nine-week elective course called Living and Working with Artificial Intelligence and piloting it in several Georgia middle schools. Since we aspire to educate all students about AI, the course addresses a wide range of student abilities, levels of academic preparedness, and prior computing experience, and leaves room for teachers to adapt the material to their own students' needs and interests. The course content is primarily focused on unplugged activities and online demonstration programs. We also provide small programming projects using AI tools as an option for teachers to incorporate. In this poster we describe lessons learned from initial pilot offerings by five teachers who taught 12 sections of the course totaling 299 students. We present evidence that middle school students can successfully engage with substantive technical content about Artificial Intelligence.

Internships provide opportunities for computing students to self-evaluate their interests and develop authentic technical and professional skills that are critical to a career in computing-related industries. However, it is a cause for concern that only 60% of computing students participate in an internship before graduation. Our work aims to identify the factors which are associated with the likelihood of a student's participation in an internship. To identify these factors, we designed a cross-sectional study at a large public university in the United States. 518 computing undergraduate students completed our survey, and we used a quantitative approach to model a student's ability to secure internships. Using a logistic regression model, we found that (1) year in school, (2) household income (a proxy for socioeconomic status), (3) involvement in activities outside the curriculum, and (4) lower identity diffusion scores (i.e., low exploration and low commitment) are significantly associated with a student's participation in an internship. Our findings confirm prior work which showed that factors outside the curriculum are at play for students' internship participation. Further, we add to the computing education research literature the unexplored relationship between computing students' identity formation and participation in internships.

This paper presents the design of a browser-based Arduino programming tool and learning management system (LMS), CASMM, that offers end-to-end support for learners utilizing Chromebooks in a classroom environment. This tool aims to support learners through the entire process of coding and using Arduinos in group projects at scale in formal classrooms. The novelty of this tool and its discussion for the VL/HCC community lies in the design and customization of this tool to meet real world constraints of formal classrooms. In addition, it encourages expansion of who we consider users and requires inclusion of where and how learning takes place to truly support human-centered development of programming tools. In this paper, we shift the focus from individual users to multiple groups of student users and 1–3 teachers/mentors in a classroom environment. In particular, this paper aims to make explicit the unique needs of teachers and students who may have limited technology expertise both in coding and using Arduinos in formal classroom environments, the human and technological constraints of a formal classroom and features we've designed into CASMM to address these needs. Through this paper, we aim to spark discussion about the human-centered requirements of these users and how tools that support learners end-to-end in the development process may be necessary to truly provide accessible programming languages and environments for a wide range of novices (i.e., students, classroom teachers, and college mentors/volunteers).

This paper discusses applications of the Social Constructivist learning approach in the design of virtual reality (VR) games to promote empathy in children. Early Childhood Development (ECD) research provides guidelines for engaging children in empathy development activities (i.e., learning through interactions, reflective activities, role-taking, dialogical inquiry), which are grounded in Social Constructivist Learning Theory. VR researchers suggest the affordances of VR technologies to create immersive learning experiences for empathy development in children, but this research is still at its early stages. This research aimed to explore ways to engage children in empathetic interactions with VR characters based on social constructivist principles. We developed a VR Empathy Game and conducted a qualitative study with 14 children (6-9 years old). Based on the thematic analysis, we found gender differences between the gameplay experiences of girls and boys. Girls were more interested in interacting with VR characters and, as a result, spent more time than boys asking them questions, listening to them, and using the role-taking features to explore the game world from the characters’ perspective. We suggest a follow-up research study exploring ways to better scaffold empathetic experiences for boys.

Technical interviews have predominantly been used by companies to recruit students for software-related and other computing jobs. Since the content of the interviews has an overlap with Data Structures and Algorithms (DSA), we introduced a mock interview activity to promote students' awareness of the technical interview process and build students' confidence in problem-solving in a DSA course. In this short paper, we (1) describe the logistics for embedding such an intervention, and (2) explain the affordances and opportunities for improvement of the activity through student perspectives. Students were explained the technical interview process and asked to interview each other twice during the semester on coding problems. Students received the intervention positively describing that the activity helped them to understand the technical interview process, prepared them for future interviews, built their confidence to secure a job, and supported them in knowing their strengths and weaknesses. Opportunities for improving the activity include providing interview questions explicitly, offering an alternate activity for students who are not interested in computing careers, and reducing the length requirement for interviews. Given students' positive reception of the intervention, we recommend that instructors adopt these mock interview exercises in computing courses to improve students' access to professional development opportunities.

Dual-modality blocks-text programming environments have shown promise in helping students learn programming and computational thinking. These environments link blocks-based visualizations to text-based representations, which are more typical of production languages. Since prior work shows that some students who learn in dual-modality environments outperform those who learn in text on assessments, we sought to understand specifically how students use dual-modality environments and what support these environments provide to the learning process. We analyzed survey responses and tool logs collected during a study at a large public university in a CS1 course (N=425). We found that students from all prior programming experience backgrounds made use of the ability to visualize code structures by using blocks. Students with prior experience in blocks or no prior experience said they felt the dual-modality instruction helped them understand code structure and meaning. As students progressed through the class, we found that they made more use of the blocks mode's reference palettes than to its drag-and-drop facilities or mode-switching features. By identifying how students interact with dual-modality tools and how they impact student understanding, this work provides guidance for classroom instructors.

In this paper we present findings on a pedagogical approach we designed to enhance students' understanding of Functional Programming, in which they were required to create two video-tutorials. The first video-tutorial assignment asked the students to develop explanations of Functional Programming concepts. The second video-tutorial required them to explain their solutions while completing coding exercises using Haskell. We present a detailed description of the activities, their evaluation, and their impact on students' learning, motivation, and performance. Our findings suggest that the use of a student-created video-tutorial approach can be effective for increasing students' understanding, performance, and engagement on Functional Programming assessments. This suggests that using student-created video tutorials may be a promising strategy to implement in other computing courses.

In college-level introductory computer science courses, students traditionally learn to program using text-based languages which are common in industry and research. This approach means that learners must concurrently master both syntax and semantics. Blocks-based programming environments have become commonplace in introductory computing courses in K-12 schools and some colleges in part to simplify syntax challenges. However, there is evidence that students may face difficulty moving to text-based programming environments when starting with blocks-based environments. Bi-directional dual-modality programming environments provide multiple representations of programming language constructs (in both blocks and text) and allow students to transition between them freely. Prior work has shown that some students who use dual-modality environments to transition from blocks to text have more positive views of text programming compared to students who move directly from blocks to text languages, but it is not yet known if there is any impact on learning. To investigate the impact on learning, we conducted a study at a large public university across two semesters in a CS1 course (N=673). We found that students performed better on typical course exams when they were taught using dual-modality representations in lecture and were provided dual-modality tools. The results of our work support the conclusion that dual-modality instruction can help students learn computational concepts in early college computer science coursework.

Computing curricula are finding their way into many elementary and middle school students' classrooms and after-school learning experiences. As more curricula are developed, there is a need to understand how they align with the Rich et al. learning trajectories (LTs) for sequencing, repetition, and conditionals [3] in terms of curricula design and student learning outcomes. This poster examines how LTs map to the 2018-2019 Girls Who Code (GWC) curricula implemented in a local GWC coding club. This poster shows how student learning under the 2018-2019 GWC curriculum maps to paths within the beginning and intermediate levels of the Rich et al. LTs for sequencing, repetition, and conditionals [3]. We also discuss challenges in evaluating several LT learning goals and potential uses of LTs as diagnostic tools to identify student misconceptions.

Industry internships offer CS students an opportunity to gain authentic disciplinary experiences, evaluate self-interests, and secure future employment. However, little is empirically known about CS students' participation in industry internships and the preparation process used to successfully securing an internship. This paper presents findings from our multi-institutional study aimed at understanding the participation of CS students in industry internships as well as analyzing the differences between students who intern and those who do not. We surveyed 536 CS undergraduate students across three universities in the United States and analyzed the quantitative data using descriptive and inferential statistical methods. We used thematic analysis on the open-ended survey responses. Overall, we found that 40% of students participate in at least one internship. Demographically, equal proportions of males and females interned. However, we observed that students who have higher socioeconomic status were more likely to intern. Academically, there were no significant differences between students who intern and those who do not. However, through thematic analysis, we found differences regarding students' preparation process. Interns explicitly prepared to secure internship positions by practicing interview questions and dedicating time to career preparation. Students who do not intern were less involved in the application process or relied on coursework for securing internships. Quantitative results from the survey corroborated our qualitative findings that factors outside of coursework are influencing students' ability to secure industry internships.

The ubiquity of AI in society means the time is ripe to consider what educated 21st century digital citizens should know about this subject. In May 2018, the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) formed a joint working group to develop national guidelines for teaching AI to K-12 students. Inspired by CSTA's national standards for K-12 computing education, the AI for K-12 guidelines will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The AI for K-12 working group is also creating an online resource directory where teachers can find AI- related videos, demos, software, and activity descriptions they can incorporate into their lesson plans. This blue sky talk invites the AI research community to reflect on the big ideas in AI that every K-12 student should know, and how we should communicate with the public about advances in AI and their future impact on society. It is a call to action for more AI researchers to become AI educators, creating resources that help teachers and students understand our work.

Previous research in K-5 CS education has focused on improving students' engagement in programming using visual block-based environments like Scratch. However, little is known about how elementary school students' reason about programs. We define computational reasoning as the ability to read, write, trace and debug programs and predict program behavior. Recently, computing education researchers have become interested in exploring how elementary school students build their computational reasoning abilities. This poster presents results from a study which analyzed the role of explicit instruction in the form of 'laws of computation' in cultivating elementary school (4th and 5th graders) students' ability to reason about programs using Microsoft Kodu Game Lab. We used pretests to record students' default models of reasoning about programs and then used posttests to measure the effectiveness of intervention by noting students' reasoning responses on a similar program. Our findings indicate that by default students reason sequentially about program execution which can be incorrect in situations like parallel rule execution. We also found that the use of explicit instruction in the form of 'laws' is helpful for students to refine their understanding of program execution and to improve their reasoning ability.

This poster presents findings on a pedagogical approach we designed to enhance undergraduate computing students' understanding of functional programming (FP) through student-created video-tutorials. Students created two video tutorials. The first video-tutorial assignment asked the students to develop explanations of FP concepts (theoretical understanding of FP). The second video-tutorial required students to explain their solutions to programming exercises using Haskell (application of FP). Our findings suggest that the use of student-created video-tutorial can be effective for increasing students' understanding and application of functional programming.

Academic institutions play a crucial role in the development of students' professional identities. However, we have limited knowledge of how computing professional identity develops. This paper aims to understand how CS undergraduate students develop their professional identity through analyzing students' reflection on their career goals, experiences in CS degree programs, and engagement in professional development. We present findings from qualitative analysis of 14 semi-structured interviews with CS undergraduate students in the United States. We found that CS undergraduates form their computing professional identity typically between Years 2-3 of their degree programs. We identified several reasons students are committed to a computing profession: intrinsic factors (e.g., interest and perception of ability), and discipline-specific factors (e.g. utility and growth). We also found several factors that shape their professional identity: coursework, informal activities like hackathons, and professional development activities including internships and conferences. These findings suggest that the development of computing professional identity is not limited to students' involvement in the academic degree programs but the engagement they have with the broader computing community.

As CS expands into more K-12 classrooms and children become familiar with computational thinking, advances in AI pose new challenges for CS educators. Children now enjoy conversing with AI-powered agents such as Alexa and Siri, while their parents worry about the imminent arrival of autonomous robots and self-driving cars. As AI technologies become more prominent in our lives, we need to consider what every child should know about AI. This BOF provides a timely opportunity to introduce CS educators and researchers to several AI for K-12 efforts, including available curricula, tools, and resources. Attendees will discuss how AI can best be incorporated into the K-12 CS curriculum, the tools/resources that will be needed to support students and teachers learning about AI, and how AI education might impact their own work. This BOF is complementary to the SIGCSE 2019 Special Session: AI for K-12 Guidelines Initiative that introduces the current draft of our 'Big Ideas in AI." Further information about the initiative and resources is available at http://ai4k12.org.

Professional development is critical for preparing undergraduate CS students for their future careers. Industry internships offer students pathways for professional development. However, little is empirically known about the impact industry-based internships have on CS students' career paths as well as the effectiveness of CS degree programs in preparing students for these professional development opportunities. In this paper, we present a thematic analysis of open-ended survey responses of 40 CS undergraduate students in the US who participated in an internship. This study aimed to understand the impact that professional internships have on: CS students' career goals, students' perceptions of the gaps between academia and industry, and students' strategies for professional success. We found four themes that describe the impact of internships on CS students. Internships (1) strengthened students' commitment to CS degrees and careers; (2) encouraged exploration of CS careers and industries; (3) promoted personal/professional growth; and (4) developed awareness of professional expectations. We also analyzed students' perception of the curriculum's effectiveness and found that students were strategically working to improve their technical skills outside of coursework to secure employment. These findings have the potential to retain students in computing and reduce the gaps between academia and industry, thereby increasing CS students' competitiveness in the workforce.

Encouraging Reflection in Support of Learning Data Structures Cheryl Resch, University of Florida Christina Gardner-McCune, University of Florida Contact: [email protected] Our research looks at using reflection in teaching Data Structures and Algorithms (DSA) to promote meaningful learning. This poster examines the differences in reflections produced by two different programming assignments in DSA at the University of Florida. In the first assignment, students were asked to implement a line editor using a linked list and in the reflection prompt students were asked to reflect on the use of the assigned data structure and what they would do differently if they could do the assignment again. In this assignment, the dominant reflection was a simple recounting of material taught in class. In 15% of the reflections, students identified an implementation improvement that could be used. In 25% of the reflections, students identified non-technical issues such as time management when reflecting on what they would do differently if they had to do the assignment again. In the second assignment, students were asked to code Google's page-rank algorithm and were allowed to choose a graph implementation. They were asked to reflect on their implementation and what they would do differently if they could do the assignment again. In this assignment, the dominant reflection, ?%, was about the appropriateness of the chosen graph implementation compared to other implementations. In their reflection on what they would do differently, only 12% of the reflections mentioned non-technical issues. This data suggests that giving students an opportunity to decide the implementation approach and providing more specific reflection prompts produces more technical reflections. DOI: HTTPS://DOI.ORG/10.1145/3287324.3293802

Text languages are perceived by many computer science students as difficult, intimidating, and/or tedious in nature. Conversely, blocks-based environments are perceived as approachable, but many students see them as inauthentic. Bidirectional hybrid environments provide textual and blocks-based representations of the same code, thereby offering students the opportunity to seamlessly transition between representations to build a conceptual bridge between blocks and text. However, it is not known how use of hybrid environments impacts perceptions of programming. To investigate, we conducted a study in a public middle school with six classes (n=129). We found that students who used hybrid environments perceived text more positively than those who moved directly from blocks to text. The results of this research suggest that hybrid programming environments can help to transition students from blocks to text-based programming while minimizing negative perceptions of programming.

This paper presents the design of Blocks4DS, a block-based environment for students to learn data structures. As a proof-of-concept, we designed custom blocks to allow students to build and visualize Binary Search Trees (BST). Blocks4DS is built on Blockly and uses vis.js to provide visualizations of the binary search tree and its operations. This paper describes the results from an initial evaluation of usability and student learning.

Understanding undergraduate students’ academic, professional and social experiences in computer science (CS) degree programs is critical to retaining students in these programs. This paper presents findings from an exploratory study aimed at empirically investigating the academic, social, and professional experiences that influence CS students to consider switching out of their major. We surveyed 96 CS undergraduate students at the University of Florida and examined their experiences during their degree program. The data were categorically analyzed to identify factors that influenced students to consider switching out of a CS major. We found that students who considered switching out of a CS major experienced gender biases in the classroom, had negative or neutral satisfaction with computing courses, and felt that the assignments and projects were not relevant to the coursework. We also found that females were twice as likely to consider leaving a CS major as compared to males. Several factors significantly affected female students: perception of the presence of gender biases in the classroom, not receiving timely feedback, negative satisfaction in coursework, and negative team experiences. We conclude by discussing these findings in light of retention theories and literature.

Over the last decade, CS Education researchers have developed different curricula, resources, and strategies to foster computer science learning in K-12 education. However, there is a lack of research about how elementary school students develop the ability to reason about programs. Reasoning about programs consists of a student's ability to read, write, debug, trace, and predict program behavior. This paper presents results from a think-aloud study of fourth and fifth grade students learning to program in Kodu. The goal of this study was to track students' understanding of how Kodu interprets and executes rules of a program. To understand students' reasoning of program execution, we explicitly taught them the Laws of Kodu computation which govern the decision making and execution process of Kodu rules. We collected students' responses on pre- and post-assessments, and we conducted think-aloud interviews with students where students explained their answers to assessment questions. We found that explicitly teaching students how Kodu rules are interpreted significantly improved their ability to understand the execution of programs and to explain program behavior. The results of this study provide insight into how elementary school students reason about simple programs, and how this ability can be scaffolded.

The computing and STEM industries face challenges in attracting people to fill expanding needs. The literature shows that computing preconceptions shape interest in and impact decisions of whether or not to enter computing disciplines, especially for women and underrepresented minorities. In this study, our research questions focused on how perceptions of programming in elementary and middle school students varied based on prior programming experience. We examined the programming constructs they found challenging. Our study was in the context of a week-long summer camp dedicated to Scratch-based game development. We conducted semi-structured interviews at the beginning, middle, and end of the weeklong program with 28 students who agreed to participate. During the interviews, we asked students about their perceptions of programming in general and which programming constructs they found easy and/or hard. We found that all students perceived programming as a means of creating artifacts, but that students with prior programming experience went deeper by associating programming with process and function. We also characterize the specific Scratch programming constructs that beginning versus experienced children perceive as easy and/or hard. These findings will help experts and educators better understand how children think about programming and how experience changes these perceptions over time. These findings also have implications on the design of curricula and instructional resources to address difficulties children face while learning to program.

As CS + X courses become more common, it is important for us to investigate ways to leverage interdisciplinary learning tools to expand the types of experiences available to students. This paper discusses our experiences introducing CS undergraduates to basic Brain-Computer Interface (BCI) concepts using NeuroBlock. Neuroblock is a visual programming environment that allows users to build applications driven by near-real-time neurophysiological (i.e., brainwaves) data. Brainwave data is captured using a commercial-grade BCI device. Students use brainwave data from the BCI device to create interactive hybrid-BCI applications (e.g., games) featuring objects that respond to students' affective states (e.g. engagement, relaxation, and attention) and keyboard events. In this paper, we describe NeuroBlock, three example activities, and results from an exploratory empirical study that suggests exposure to NeuroBlock increased students' confidence in their ability to develop applications that leverage neurophysiological signals. NeuroBlock and the discussed activities have the potential to supplement future CS + X courses by providing students hands-on experiences with emerging physiological devices.

Understanding the development of professional identity in Computer Science (CS) undergraduate students can help better evaluate CS degree program's effectiveness in preparing students for their career goals. This poster presents findings from a study where we surveyed 105 CS undergraduate students about their self-perceptions of technical competencies in their chosen CS professions, the mechanisms to develop their competencies, as well as their motivations behind attaining these skill sets. Preliminary analysis of this data indicates that most CS students (93.3%), identified themselves into 7 different computing professions including Software Engineering (81.0%), Web Development (37.1%), User Experience (19.0%), and Computer Security (12.4%). They indicated using multiple mechanisms to develop their technical competencies including coursework, internship/professional experience, and research. Motivations behind their learning included self-interest as well as industry demands. We analyzed the relationships between students' skill proficiencies, motivations, and mechanisms for learning and found differences between novice, intermediate, and advanced learners. We found that students who self-assessed their proficiencies as novices in their chosen professional identity had a single mechanism for learning, most commonly coursework preparation. On the other hand, students who rated themselves as intermediate or advanced learners had multiple mechanisms and motivational factors behind attaining their skill set. These findings are important for better understanding students' learning needs and career aspirations. CS departments can use this information for better aligning their degree programs to student goals and creating pathways that ensure the development of CS students professional identity.

Understanding professional goals and identities of undergraduate Computer Science (CS) students is critical for curriculum decisions, workforce development, and retention programs. This paper aims to explore the ways in which undergraduate CS students describe their professional goals and identities, and gauge how these goals and identities vary across gender and academic standing. This paper is part of a larger study aimed at understanding how students form their professional goals and identities. In the study presented in this paper, we surveyed 109 CS undergraduate students and interviewed 14 CS undergraduate students across gender and academic standing. The data were qualitatively analyzed using inductive coding and thematic analysis. Our findings indicate that most students identify themselves professionally as software development professionals, various specialized CS professionals, and by their majors. We also found that both male and female students were interested in becoming entrepreneurs, and females were more likely to have professional goals to move into management. This paper contributes to the fields' growing knowledge of undergraduate students' professional goals and professional identities. This knowledge can help CS departments to better align their degree programs, curriculum, and specialization tracks with student goals. Such an alignment has the potential to increase retention in the major as well as prepare students to be competitive in the workforce.

In light of our field/s progress in making programming accessible to novices, we contemplate an even more ambitious goal: make AI accessible to all. The Cozmo robot by Anki is revolutionizing consumer and educational robotics through built-in computer vision and artificial intelligence algorithms. Calypso is a scaffolded robot programming environment for Cozmo inspired by Microsoft/s Kodu Game Lab. Calypso allows novices to program with advanced features such as visual recognition of objects and faces, simultaneous localization and mapping (SLAM), landmark-based navigation, and speech input. Like Kodu, Calypso emphasizes rule-based programming with high-level primitives such as "see", "hear", "move toward", and "grab", and it uses an Xbox game controller as its primary interface. User testing of Calypso has shown that children as young as eight can easily use it to program Cozmo.

We believe teaching elementary school students to reason about programs is as important as teaching them to write programs. To facilitate development of this skill in young children one must choose a developmentally appropriate domain. Microsoft's Kodu Game Lab is a pattern-matching rule-based language whose semantics is significantly different than Scratch or Python. We chose Kodu because one can write non-trivial programs in two to four lines, and analyzing these programs is within the abilities of a typical 8 year old. Reasoning about programs requires students to understand the structure of code. The approach we're advocating is analogous to sentence diagramming, where one starts with a sequence of words and develops a representation of their syntactic and semantic relationships. One can similarly analyze Kodu programs by characterizing rules and recognizing relationships between rules. In this poster we describe "couplets", an analysis technique that reveals the presence within a program of an important Kodu design pattern called Pursue and Consume. Using this technique leads to accurate predictions about program behavior, and uncovers bugs if the pattern is not fully realized. As part of a study of 40 third graders who were learning Kodu, we provided brief instruction in the couplets technique. We found that they were able to apply couplets to 3-4 line programs and answer prediction questions with a roughly 85% success rate. Our results demonstrate that elementary school children can learn to reason abstractly about programs if given the right mental tools.

Block-based programming environments have increased students' interest in computer science (CS). Research suggests that block-based programming environments have positively impacted students' retention, effectiveness, efficiency, engagement, attitudes, and perceptions towards computing. We know that when novice programmers are learning to program in block-based programming environments, they need to understand the components of these environments, how to apply programming concepts, and how to create artifacts. However, few studies have been done to understand the impacts that usability of block-based programming environments may have on students' programming. In this poster, we present results from a two-part study designed to understand the impact that usability of the programming environment has on novice programmers when learning to program in Scratch. Our findings indicate that usability challenges may affect students' ability to navigate and create programs within block-based programming environments.

Researchers and educators have designed curricula and resources for introductory programming environments such as Scratch, App Inventor, and Kodu to foster computational thinking in K-12. This paper is an empirical study of the effectiveness and usefulness of tiles and flashcards developed for Microsoft Kodu Game Lab to support students in learning how to program and develop games. In particular, we investigated the impact of physical manipulatives on 3rd -- 5th grade students' ability to understand, recognize, construct, and use game programming design patterns. We found that the students who used physical manipulatives performed well in rule construction, whereas the students who engaged more with the rule editor of the programming environment had better mental simulation of the rules and understanding of the concepts.

Tools like MIT App Inventor and Scratch are designed to help students develop programming and computational thinking skills by allowing them to use their interest and personal experiences to create meaningful artifacts. However, students often need additional help in translating their ideas into functional programs because they lack understanding of how to map the visual aspects of their projects to programming constructs and understanding of how to develop appropriate algorithms that bring their ideas to life. To address this issue, we created a Computational Thinking App Design Mat (App Design Mat) to scaffolds students' CT skill development in the context of creating a mobile application with MIT APP Inventor 2. The App Design Mat fosters student engagement in computational thinking through four areas of the mat: Problem Decomposition, Pattern Abstraction, Pattern Recognition, and Algorithm Design. In this poster will describe the design and results from the use of the App Design Mat with 80 eighth grade students. Our results suggest that most students understood the purpose of using the App Design Mat, used the App Design Mat effectively, and used some aspects of the App Design Mat in developing their final mobile app project.

Reading, tracing, and explaining the behavior of code are strongly correlated with the ability to write code effectively. To investigate program understanding in young children, we introduced two groups of third graders to Microsoft's Kodu Game Lab; the second group was also given four semantic "Laws of Kodu" to better scaffold their reasoning and discourage some common misconceptions. Explicitly teaching semantics proved helpful with one type of misconception but not with others. During each session, students were asked to predict the behavior of short Kodu programs. We found different styles of student reasoning (analytical and analogical) that may correspond to distinct neo-Piagetian stages of development as described by Teague and Lister (2014). Kodu reasoning problems appear to be a promising tool for assessing computational thinking in young programmers.

In recent years, block-based programming languages have been employed as learning tools to help students starting out with programming. How we design the layout of the available blocks likely impacts the success of the student. In this study, we compare student performance in three conditions consisting of different layouts of block categories in a block-based language: a grouping based on computer science (CS) concepts, a grouping based on block functionality, and one with no groupings. We measured task completion time, quality of the final code product, and perceived system usability. We found that although time and quality did not differ across conditions, the students in the functionality condition reported higher usability scores than the students in the CS concepts condition. These results can inform how we design block-based interfaces to improve learner satisfaction without affecting their performance.

This article explores how computer games can be developed to engage young children in critical discussions of cultural awareness and empathy in or out of classrooms. In order to answer the question "How can we expose children to different cultures and help them develop cultural awareness and empathy?" we are building a computer game for young children (5 to 7 years old) based on a Russian traditional fairy tale "The Magic Swan Geese." As a framework for designing the game, we draw on research on computer game design, early childhood development, and fairy tales literature. Through creating a pilot game we will test the feasibility of translating the theory of the design framework for into practice. This paper presents the design framework, the design of the game, and data from this on-going research and development project.

Mental simulation is an important skill for program understanding and prediction of program behavior. Assessing students' ability to mentally simulate program execution can be challenging in graphical programming environments and on paper-based assessments. This poster presents the iterative design and refinement process for assessing students' ability to mentally simulate and predict code behavior using a novel introductory computational thinking curriculum for Microsoft's Kodu Game Lab. We present an analysis of question prompts and student responses from data collected from three rising 3rd - 6th graders where the curriculum was implemented. Analysis of student responses suggest that this type of question can be used to identify misconceptions and misinterpretation of instructions. Finally, we present recommendations for question prompt design to foster better student simulation of program execution.

This paper introduces reasoning about lawful behavior as an important computational thinking skill and provides examples from a novel introductory programming curriculum using Microsoft's Kodu Game Lab. We present an analysis of assessment data showing that rising 5th and 6th graders can understand the lawfulness of Kodu programs. We also discuss some misconceptions students may develop about Kodu, their causes, and potential remedies.