In this Experience Report, we describe a semester-long course in which university students develop original software products to teach K--12 learners ideas in artificial intelligence (AI) and machine learning (ML). The university students develop their knowledge of AI/ML, build expertise in software development, and gain skills in education research. We test the educational software tools with K--12 learners at ''AI Expos'' held at partner public schools. We teach our university students about human-subjects research and collaboratively obtain IRB approval for the school-based work. We develop pre/post surveys and conversation questions for the K--12 participants. Our students instrument their software tools to gather live interaction data from the K--12 student use. The university students complete final course papers which describe their tool design and present evidence of student understanding of AI/ML concepts based on the data they have gathered. Students of the course often continue their scholarship beyond the semester, successfully submitting their work to conferences. We are developing a growing collection of software tools to teach AI/ML that are available for use by curriculum developers. For many students, the course provides their first opportunity to create a working software product used by others; seeing others use one's system is deeply satisfying and motivational. The course also provides a full-cycle research experience, including experimental research design, data collection, analysis, and writeup; nearly all students gain their first introduction to educational research in the course. This paper presents the university course design and our insights from teaching four iterations of the course.

The AI Chef Trainer is an educational web app that introduces children to the role of data in machine learning (ML) through the engaging task of recipe recommendation. Initially, students tested the AI Chef's capabilities by selecting from a list of ingredients to see what the system recommended as possible recipes. After observing the recommendations, they contributed by adding their own recipes—each being a set of ingredients and a corresponding recipe-name—which were used to retrain the model and finally re-tested recipe suggestions. This cyclical process of testing, contributing, retraining, and post-training testing provided students with hands-on experience in how AI systems learn and adapt over time based on new data. We tested our software with middle school students. The results indicated that students recognized the importance of both data quantity and specificity in the training process. 45 of 52 students entered recipes, and 26 of the 52 tested their own recipes using the specific ingredients they entered. Students were introduced to the concept of confidence percentages via the AI recipe suggestions. Even as the primary focus was the role of data in machine learning, the AI Chef Trainer software also served as a window into students' cultural expression and personal preferences.

As Artificial Intelligence (AI) continues to integrate into more aspects of society, equipping younger generations with foundational AI knowledge becomes increasingly critical. This paper presents Word2Vec4Kids (W2V4K), an interactive application designed to familiarize middle school students with word embeddings, a key aspect of Natural Language Processing (NLP). W2V4K leverages the Word2Vec model, allowing students to explore word associations, similarity, and vector arithmetic through engaging game modes. The application was tested with 38 middle school students aged 11-14 at a Science Technology Engineering Math (STEM)-focused charter school. Data were collected on students' interactions with the application, including screen recordings, audio, and survey responses. Results demonstrated that W2V4K effectively introduces NLP concepts to students. Qualitative observations revealed high levels of engagement with students expressing excitement and curiosity about word relationships. As they progressed through the game modes, students showed increasing confidence in predicting word associations, brainstorming relevant words, and connecting the concepts to real-world applications. Quantitative data from post-interaction surveys indicated positive learning outcomes with 44.5% of students achieving perfect scores on concept-related items. Additionally, students demonstrated an ability to critically think about language representation. This study suggests that W2V4K provides an effective and engaging method for introducing NLP concepts to middle school students, contributing to the broader goal of enhancing AI literacy among younger generations.

This paper explores the evolution of artificial intelligence (AI) and machine learning (ML) educational tools and curricula for children, describing a spectrum from playful exploration to structured pedagogy. We address three research questions: (RQ1) What themes are revealed in the literature on AI/ML for children? (RQ2) In what ways do the development of AI/ML tools and curricula co-evolve? (RQ3) How does the historical context of the field frame current work on AI/ML for children? We observe a co-evolution of tools and curricula, where researchers build new tools and curriculum developers adopt tools created by others. As tools prove successful, they become widely available and are adopted by a broad range of users. Additionally, we note instances where tool creators take on the role of curriculum developer. Recognizing the need for a clear overview of AI/ML educational resources, we introduce major themes of tools, tool-forward, and curriculum-forward works. These themes are designed to help educators, researchers, and curriculum designers in discerning the fundamental nature and potential applications of resources. By distinguishing tools that are standalone from those integrated into curricula, our framework supports strategic decisions in resource adoption and development. Drawing on the field's history, we outline a continuum of tools and platforms, from constructionist elements to interactive play environments.

Artificial Intelligence (AI) is growing rapidly in our society and is now apparent in our day-to-day lives. With the recent burst of interest in AI, many individuals and children may view AI as something mystic and magical. It is important to demystify and introduce to them how AI is made and works. To address this need, we developed a software application that allows children to specify the parameters used by a Reinforcement Learning (RL) algorithm. Then students experience how RL is used to train an AI model to play the game "Snake." This software tool was tested with 71 middle school-age students. Here, we describe the design of the TrainYourSnakeAI application, the approach we used to introduce the associated ideas to middle school children, and how we assessed student learning. Qualitative data collected from students are presented and discussed. We surveyed their knowledge of AI before and after using the application. In this work, our research questions were: (RQ1) How can we create an engaging tool to teach reinforcement learning? and (RQ2) Does using our application foster a stronger understanding of reinforcement learning in children? Our findings indicate that students were able to understand the functionality of reward functions and how agents can learn from the environment using the concept of RL. We found that out of the 51 students who were not previously familiar with RL, 40 were able to provide adequate descriptions of RL after using TrainYourSnakeAI.

This poster presents Teacher Education for Data Science (TEDS), a study conducted with a cohort of teachers who developed ways to incorporate new data fluency approaches to teaching and learning in multiple middle school subjects. In a four-session online professional development sequence, teachers learned how to use a web-based collaborative data visualization platform; developed a data-intensive unit for their existing middle school curriculum; implemented this unit with their students; and shared reflections with fellow teachers and the researchers. We describe how we supported the teachers in a successful co-design process. Data gathered included a pre/post survey; individual 30-minute post-interviews with each teacher; and teacher instructional artifacts. Teachers reported on student agency as they entering their data into the collaborative platform and how students found real-world import in their data. To accomplish more student fluency with data in the middle school, we recommend more such technology-supported curriculum integration approaches.

This innovative practice full paper discusses the development of an interactive tool designed to educate middle school students on the ethical considerations and societal impacts of artificial intelligence (AI). As AI technologies become more embedded in our daily lives, the younger generation must grasp the implications of algorithmic bias and its societal effects. Our tool aims to deepen this understanding by focusing on self-driving cars-a relevant and significant example of AI technology. The interactive tool incorporates three advanced image recognition models trained on diverse datasets, including traffic cones, animals, and pedestrians. Through the tool's interface, students can choose one of these models to apply in a self-driving car simulation and select different obstacles for the car to encounter, such as traffic cones, animals, and pedestrians. This hands-on simulation highlights the importance of comprehensive AI model training, showcasing how well-trained models help avoid collisions and the risks associated with encountering untrained obstacles. It engages students by demonstrating how developers' AI training decisions can significantly influence end-user experiences. Moreover, the tool emphasizes the need for diverse and representative data in building fair and robust AI systems. To assess the effectiveness of this educational tool, we conducted a two-day AI exhibit attended by 26 middle-school students from grades six to eight. The effectiveness was evaluated through posttrial questionnaires to measure the students' understanding of several key concepts: the development of resilient AI models, the societal impacts of AI, and the ethical considerations of road safety in the context of AI. The results showed that 84.6 % of the participants understood how poor training decisions could impact AI outcomes, and about 96 % recognized the necessity for diverse data.

This study investigated middle school students' learning experiences with a computer science and digital literacy (CSDL) curriculum, which was developed through the CS Pathways researcher-practitioner partnership (RPP) project. The curriculum is based on students learning computer science (CS) through creating apps that serve community and social good. Both quantitative and qualitative data were collected from students in three urban districts: 1) 330 paired pre- and post-survey responses indicating students' confidence and interest in both learning CS and creating apps for social good; 2) 343 open-ended question responses in the post-survey probing into students' perceptions on learning CS after taking the course. Whether there were gender differences emerged from both data were also examined. The results showed that students' confidence in coding and creating apps for social good significantly increased after completing the course, regardless of gender. However, their interest in pursuing CS learning remained at a low level. Further analysis showed male students reported significantly stronger interest than female students. Qualitative analysis of the open-ended responses revealed that both male and female students appreciated the collaborative learning environment and learning coding through making apps. Male students did not like certain instructional approaches that their teachers used. Female students expressed their dislike of coding in general. We applied an interest development theory to further understand these results, which suggested that we consider the trajectory of students' interest development of CS.

Artificial intelligence (AI) and machine learning (ML) have a deepening impact in our world. For empowered citizenship and career readiness, elementary and middle school students need to understand these technologies. This poster reports on five original interactive AI and ML software exhibits tested by 125 elementary and middle school students aged 7 to 14 years. Four themes emerged: Students recognized that AI and ML systems can process data from cameras (perception); they saw that these systems responded to their training input (trust); they appreciated the practical import of AI/ML systems (affective and cognitive attitudes); and students were introduced to models and modes (specialization).

Machine learning (ML) systems are increasingly in use in society. For young learners to be informed citizens and have full career potential it is important for them to understand these concepts. To support this learning, we created "ChemAIstry,'' an interactive software tool for children which demonstrates training and classification in machine learning. Students select which everyday items are safe to bring into a chemistry lab (e.g., a lab coat is safe; pizza is not). These selections serve as training input for a decision tree classifier. After training, students see how the trained model performs in classifying new objects. ChemAIstry was tested with 40 students aged 7 to 14 years at a public K?8 school. The software captured student selections during training. We analyzed these interactions to yield a "Correspondence Score,'' a measure of student understanding of the classification task. We screen-recorded student use of the software and audio-recorded our conversations with them during this use. Our analysis of these data indicates that students were able to understand the concept of model training, including that items were subsequently classified based on their training input. More than half of the student trials indicated that students correctly understood the task. This suggests ChemAIstry was effective in introducing students to these ideas in machine learning. We recommend continued development of related tools for curriculum integration of AI in K-8 education.

Data science and machine learning should not only be research areas for scientists and researchers but should also be accessible and understandable to the general audience. Enabling students to understand the details behind the technology will support them in becoming aware consumers and encourage them to become active participants. In this paper, we present instructional materials developed for introducing students to two key machine learning algorithms: decision trees and k-nearest neighbors. The materials were tested in a middle school's afterschool artificial intelligence program with four participating students aged 12 to 13. A combination of hands-on activities, innovative technology, and intuitive examples facilitated student learning. With hand-drawn decision trees and penguin species classifications, students used the algorithms to solve problems and anticipate other possible applications. We present the technology used, curriculum materials developed, and classroom structure. Following the guidelines from AI4K12 and introducing foundational machine learning algorithms, we hope to foster student interest in STEM fields.

In this poster we present "DoodleIt,'' an interactive web application that performs sketch recognition and an afterschool curriculum that teaches students the key concepts of convolutional neural network (CNN). With DoodleIt, students make simple line drawings on a canvas area and a previously-trained CNN identifies the object drawn. The application visualizes the different layers that are involved in the process of CNN, including a display of kernels, the resulting feature maps, and the percentage of match at output neurons. We used DoodleIt as a part of 18-hour curriculum to introduce students to artificial intelligence, machine learning, and data science. Our findings indicate that students were able to understand the functionality of the kernels and feature maps involved in the CNN to perform rudimentary image recognition.

This study examined student learning outcomes from a culturally responsive middle school computer science (CS) curriculum. The curriculum is based on students creating mobile apps serving community and social good. Two sets of data were collected from 294 students in three urban districts: (1) pre- and post- survey responses on their attitudes toward learning CS and creating culturally responsive apps; (2) the apps created by those students. The analyses of student apps indicated that students were able to create basic apps that connected with their personal interests, life experiences, class community, and the larger society. Paired sample t-tests of pre- and post- survey results indicated that students were significantly more confident in coding and creating community-focused apps after completing the course, regardless of gender and race. However, their interest in solving coding problems and continuing to learn CS decreased afterward. Analyses of students' attitudes by gender, grade, and race showed significant differences among some of those groups. Seventh grade students rated more positively on their attitudes than eighth graders. Students of different racial groups indicated significantly different attitudes, especially the Southeast Asian and African American groups. Male students also reported stronger confidence and interest and more positive attitudes overall than female students.

This full innovative practice paper presents a study on the effectiveness of a computer science bridge program at a mid-sized regional public university in the Northeast United States. The program was designed for incoming first-year full-time students pursuing a degree in computer science (CS) at the university. The structure of this program differs from others with its leadership consisting of undergraduate students of varying seniority. It also features an emphasis on building a strong sense of community and seeks to inspire creativity in CS through lesson plans that are complementary with what participants learn in the university’s degree program.This study investigates the outcomes of the program after three iterations. To quantify its impact on participants, retention rates of program participants are compared with those of students who were invited to participate in the program but declined. Positive effects stemming from near-peer mentoring and the creation of a lasting digital support network for program participants are also analyzed.The researchers expected the data collected to reflect a successful impact from students’ participation in the program. Chi-square testing on the collected retention data from the first two cohorts revealed a statistically significant result for one cohort.The third iteration of the program resulted in a highly active online community of freshmen that has been supported by students of higher seniority throughout participants’ first academic school year.Based on these findings, we urge all institutions seeking to support a diverse body of students in their STEM pathways to implement summer bridge programs. We recommend engaging existing undergraduate students in developing and leading such programs, and focusing on building a community and on-going support network.

In this Research-to-Practice Full Paper, we report on CS Pathways, a middle school computer science (CS) curriculum developed as part of a researcher-practitioner partnership among two public universities and three urban school districts in the Northeast USA. The curriculum serves middle school students to develop apps for social impact. The partnership focuses on bridging the gap between STEM (Science, Technology, Engineering, and Mathematics) and community and gender gap within STEM. The project is based on Culturally Responsive Pedagogy (CRP), which includes the importance of recognizing students’ culture in all facets of learning. The project employs a researcher-practitioner partnership (RPP) model, which recognizes that transformational change can occur in educational ecosystems that connect research, policy, practice, and community work.The project curriculum was collaboratively developed by CS researchers, teacher-practitioners, and school administrators. Middle school students develop their own apps that support socially relevant activities in their communities. Using the RPP process, continuous feedback from researchers, teacher-practitioners, and students shaped the curriculum design. Key feedback was collected via one-on-one meetings with the teacher-practitioners, which bridged the visions and knowledge among different groups of the project partners.The curriculum includes the areas of computing and society, digital tools and collaboration, computing systems, and computational thinking. The curriculum helps students develop a critical consciousness of the role they can play in affecting their communities through computing, and empower them to move beyond simply learning to code [1]. The curriculum strives to demonstrate how to integrate computing across middle school subjects in a culturally-responsive way and spread a powerful message of Computer Science for All. This paper advocates for the need for culturally-responsive computing, describes how it is integrated into teachers’ instruction, and presents the CS Pathways curriculum design.

In this research full paper, we examine students’ improvement in spatial visualization skills when using MYR (short for "My Reality"), a browser-based, cloud-hosted programming environment for beginning through advanced programmers to create immersive, three-dimensional virtual reality scenes.The research literature suggests that there is correlation between students’ spatial abilities and their success in programming. In this study, we conducted a three-week (six hour) virtual after-school program which introduced high school students with different programming backgrounds to coding in MYR. During the program, students learned the basics of MYR, introductory CS topics, and completed individual coding projects, creating an original MYR scene.A study examined changes in students’ spatial reasoning as a result of this intervention. The students’ performance in spatial skills was measured using the Revised Purdue Spatial Visualization Test: Visualization of Rotations (Revised PSVT:R) [1]. Students completed this instrument using a pre/post survey design. We analyzed the impact of the intervention using a paired samples T-test. We further developed a rubric for analyzing the sophistication of students’ MYR code and applied it to evaluating the programming expertise of our study participants.The program was hosted twice with two different groups of high school students. Most showed interest in MYR programming and expressed their creativity and learned skills in their original project. With the first group of students, we found increases in their spatial visualization performance after the intervention with MYR, though statistical significance was not reached. The second group of students had higher baseline prior experience in computing and spatial visualization skills; these students did not further increase in their spatial visualization skill.The analysis showed that the MYR has a potential to improve spatial skills and engage students’ interest in computing. We recommend that MYR and related computational environments be further studied and made available to students.

This poster shares our experience of engaging middle school teachers in a collaborative design of a computer science and digital literacy (CSDL) curriculum through a researcher and practitioner partnership (RPP) among two public universities and three urban school districts in the Northeast USA. The project used the co-design approach to facilitate curriculum development and foster professional learning. In this poster, we introduce the co-design process, the developed curriculum, and teachers' professional learning experiences. Preliminary results indicate that the co-design approach supplemented with one-one-on coaching has not only facilitated the curriculum development but also fostered professional learning and collective capacity building for CS education.

This pilot study explores the impact of the CS Pathways professional development (PD) program on the teachers' self-efficacy in teaching a middle school computer science and digital literacy (CSDL) curriculum. The main goal of the study is to investigate the attributes that describe the teachers' self-efficacy after their first-year participation in the PD. A total of 19 middle school teachers from two states, NY and MA, attended the CS Pathway PD program and completed the end-of-year survey pertaining to self-efficacy in CSDL; more than half accepted the interview to help further understand their perceptions (n=10). Principal Component Analysis (PCA) is applied to study the attributes of the teachers' self-efficacy. The preliminary results capture teachers' self-efficacy patterns, which inform the PD and indicate its effectiveness and challenges.

In this study, an instrument was designed to measure social factors which might predict student success (n=95). The instrument was administered to computer science undergraduates at a public research university in the Northeast USA. The instrument was a survey which asked a series of questions regarding different social factors such as living situation, experience in computer science prior to university, and use of university resources. The results were then compared to the students' self-reported department GPA with the goal of determining which factors predict student success. We find that students who: (1) live within 15 minutes from campus, (2) receive financial support from their family, or (3) were not responsible for physically or financially caring for their family are more likely to have a department GPA at or above a 3.0. There were other social factors examined that did not have significant correlations with GPA. These results contribute to the body of literature on how social factors impact student success and can guide interventions that may increase student success.

This paper presents an experience report from an NSF-funded researcher-practitioner partnership (RPP) project. Based on a collaboration among two public research universities and three urban school districts in the Northeast USA, the goal of the project is to establish an institutionalized middle school computer science curriculum in the districts. The CS curriculum incorporates digital literacy skills as an integral aspect of learning computer science, and is based on students developing mobile apps that provide social and community good. Here, we share our professional learning process during the project's first year, which had been developed iteratively and dynamically adjusted to a remote format in response to exigencies of Spring 2020. The paper includes analysis of three data sets from teacher-participants: (1) their questions about the nature of the project, which we categorized into three levels: project, district and teacher levels. These questions bridge the visions and knowledge among different groups of the project partners; (2) analysis of semi-structured interview conversations with more than half of the teacher-participants; and (3) teacher survey responses. Our findings include two recommendations: that RPP projects elicit teacher questions to illuminate the three levels identified, and use strategies that engage teachers in designing a professional learning process for teaching computer science.

The Use-Modify-Create progression (UMC) was conceptualized in 2011 after comparing the productive integration of computational thinking across National Science Foundation-funded Innovative Technology Experiences for Students and Teachers (NSF ITEST) programs. Since that time, UMC has been widely promoted as a means to scaffold student learning of computational thinking (CT) while enabling personalization and allowing for creative adaptations of pre-existing computational artifacts. In addition to UMC's continued application, it has recently been utilized to scaffold student learning in topics as diverse as machine learning, e-textiles, and computer programming. UMC has also been applied to instructional goals other than "supporting students in becoming creators of computational artifacts." This panel will re-examine the UMC progression and refine our understanding of when its use is suitable, and when not, and share findings on evaluations and extensions to UMC that are productive in new and different contexts.

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.

MYR is a browser-based, educational platform built to spark student interest in computer science by allowing users to write code that generates three-dimensional, animated scenes in virtual reality. The interface consists of two primary components: (1) an integrated editor, which leverages the MYR API and the A-Frame entity-component-system, and (2) a real-time renderer that displays the corresponding scene. The scenes, which vary in complexity, are viewable using virtual reality headsets, smartphones, and any device that supports a web browser. By providing access to the specific domain of virtual reality to students, the system aims to make computer science concepts tangible for novice programmers. The MYR development team conducted pilot tests with middle school students in order to collect feedback from this audience. The larger goal of the project is to develop MYR as a research tool to gain insight into computing students' success, motivation, and confidence in learning computing.

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.

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 K-12 students about artificial intelligence. Inspired by CSTA's national standards for K-12 computing education, the - AI for K-12 guidelines (ai4k12.org) will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The working group is also creating an online resource directory where teachers can find AI-related videos, demo software, and activity descriptions they can incorporate into their lesson plans. The goal of this session is to raise the SIGCSE community's awareness of the initiative, its deliverables, and outcomes, and to foster a community-wide conversation about AI education in K-12. This initiative parallels other recent initiatives in K-12 AI education and community-wide initiatives and discussions around CS For All and CS in K-12. This Special Session is aimed toward K-12 CS educators, researchers, and curriculum and tool designers.

A computer science faculty member and a philosophy faculty member collaborated in the development of a one-week introduction to ethics which was integrated into a traditional AI course. The goals were to: (1) encourage students to think about the moral complexities involved in developing accident algorithms for autonomous vehicles, (2) identify what issues need to be addressed in order to develop a satisfactory solution to the moral issues surrounding these algorithms, and (3) and to offer students an example of how computer scientists and ethicists must work together to solve a complex technical and moral problems. The course module introduced Utilitarianism and engaged students in considering the classic "Trolley Problem," which has gained contemporary relevance with the emergence of autonomous vehicles. Students used this introduction to ethics in thinking through the implications of their final projects. Results from the module indicate that students gained some fluency with Utilitarianism, including a strong understanding of the Trolley Problem. This short paper argues for the need of providing students with instruction in ethics in AI course. Given the strong alignment between AI's decision-theoretic approaches and Utilitarianism, we highlight the difficulty of encouraging AI students to challenge these assumptions.

Student success rates in introductory computer science courses at colleges and universities across worldwide are scandalously low - 30% to 50% of students fail a first-semester course. At our university, over the past ten semesters, 40.6% of our students failed our first-semester computer science course. A survey (204 computer science students) was administered at the beginning of Fall 2016 to measure two hypothesized constructs: one on prior engagement in activities (such as summer camp, jobs) related to computer science, and another on prior experience in computer science topics. The prior experience bank consisted of several yes/no questions asking about familiarity with specific topics found in a first-year computer science course (e.g. globals, arrays, conditionals). The survey data was matched with the final course grades. Results revealed that the prior experience variables could be construed as a construct, but this was not the case with the prior engagement variables. We discovered a statistically significant relation between prior experience and course grade, with more experience predicting higher grades. Except ethnicity, other variables such as gender and transfer status were not found to be significant. This study emphasizes the need to consider the prior knowledge of students in building introductory computer science curricula, such as creating multiple tracks with students self-selecting into higher or lower prior-experience cohorts.

Assessing students' learning of concepts in programming is an essential part of teaching computer science. We developed the Tablet Game, an embedded assessment that measures students' skill in identifying programming structures used to create various behaviors in MIT App Inventor. The assessment was implemented as an app for Android devices. Students conducted an activity in the app, and identified which code-blocks would create those behaviors. Students' responses were transmitted to our custom data-collection server. In two five-day app development summer camps held with middle school students, students completed the same Tablet Game assessment on day 1 and day 5. Students also completed pre/post surveys which gathered ethnographic data and asked about interest levels in computer science and prior programming experience. Using data from 44 students with pre/post assessments matched to surveys, our results indicated that (1) students with high self-reported prior experience in App Inventor outperformed students with low prior experience on the Tablet Game pre-test, indicating that the assessment measures programming skill and (2) students with low prior experience achieved equivalent results as the high prior experience cohort in the post-test, indicating that the camp was successful in imparting programming skills. Both of these results are statistically significant. Further, (3) there were no statistically significant differences in gender composition of the two experience cohorts, indicating that the camp was equally accessible to girls and boys.

The Computer Science Teachers Association (CSTA) is an ACM-affiliated member organization of more than 26,000 educators and supporters of K-12 computer science. CSTA supports local communities of educators and partners through more than 70 local chapters, made up of educators, administrators, college faculty, and industry supporters, which meet regularly for networking and professional development. In addition, CSTA provides valuable resources to teachers and CS advocates, such as the newly revised K-12 CS Standards, research reports, a member listserv, a bimonthly newsletter, and recorded sessions from the annual conference. This session will provide a brief overview of CSTA, its chapters, and available resources for teachers. College faculty who are interested in connecting with local K-12 teachers will be encouraged to network and explore mutually beneficial partnerships. An open forum will follow for discussing CSTA's mission, possible initiatives, and benefits to K-12 and college educators. The major goals of this Birds-of-a-Feather session are:

MIT App Inventor is widely used to introduce students to programming and building mobile apps. In this workshop, we will introduce AppVis, an extension to App Inventor that allows users to create apps that publish data to iSENSE (isenseproject.org), a web-based system for collaborating with data and visualizations. Using AppVis, apps can also retrieve data from iSENSE and display visualizations in the app. This workshop will provide a hands-on introduction to App Inventor, AppVis, and iSENSE. You will build our demo apps, including jump counter, survey, and map-marking. We will have conversations about how to introduce AppVis to your non-majors courses, intro-CS courses, and interdisciplinary teaching. Prior experience with App Inventor is helpful, but not necessary.

Recent work has demonstrated the power of providing relevant, meaningful contexts for computer science to broaden participation. In this poster, we present the design and evaluation of a one-week App Inventor summer camp for middle school students with an explicit focus on addressing local community needs. We recruited community partners to present their organization's work to students, and then we supported students in developing apps that would support these organizations' missions. Students successfully developed apps that were designed to address community issues. Students developed apps on topics ranging from local farmers' markets, healthy food and nutrition apps, invasive species, local trees, to providing users with information about recycling. At the end of the workshop, we conducted an in-depth interview study to examine its impact on students' attitudes and perceptions toward computer science, and supplemented this with results from project evaluation. Our results indicate that students had positive experience in learning, creating and sharing real apps for solving community problems. Focusing on local community needs can also help to motivate students' interest not only in creating apps as well as in learning more about computer science. After the camp, students became more confident in creating apps as well as in using apps to solve community problems, and the camps were successful in welcoming a diverse set of students into computing.

Computational Thinking Through Mobile Computing is an NSF-funded project for introducing students to computational thinking through creating mobile apps. In this hands-on workshop, which is targeted at undergraduate and secondary school computer science teachers, participants will develop Android apps using MIT App Inventor 2. This is a new version of the visual blocks-based programming environment with additional language features (e.g., local variables) and browser-based blocks editing. The workshop will also present pedagogical materials (lessons, tutorials, assignments), evaluation materials (blocks-based quizzes, surveys, project rubrics), and student projects. All of the pedagogical materials presented in the workshop, as well as all of the materials used by the workshop presenters in their individual courses, are posted on the Web and are available to everyone under a Creative Commons license. A laptop is required for this workshop. Each participant will be provided with an Android mobile device to use during the workshop. Participants who have their own Android phones or tablets can use them if they choose. This workshop is based upon work supported by the National Science Foundation under Grant Numbers 1225680, 1225719, 1225745, 1225976, and 1226216.

The CS Principles Project is an NSF-funded initiative to develop a breadth-first advanced placement (AP) course in computer science. App Inventor is a visual, blocks-based programming language that makes sophisticated computing concepts accessible to a broad range of students. This hands-on workshop, aimed at high school and undergraduate teachers, will introduce participants to lessons, homework exercises, project assignments, and assessment materials (quizzes, grading rubrics) that can be used in an App Inventor-based CS0 course. Participants will develop simple Android apps, using devices provided by the workshop, and will use them in the context of lessons and assignments that fit within the CS Principles framework. A laptop is required. For further details see: http://is.gd/sigcse2013appinv.

No abstract available.

This project studied the manner in which 7th and 8th grade students approach design problems, focusing on testing and iteration behaviors. Students were asked to solve design problems and create generalized processes for solving them. Observations of the students were analyzed using degree of design success with length and speed of iterations. The time spent exploring the problem before starting testing and iteration was the most significant factor to the success of the student's design. Future work exploring this property of design is needed to understand the causes of it.

The authors created a general education undergraduate course, Tangible Interaction Design. We describe our learning goals, the course structure, "Tiddles" (in-class exercises that promote creativity), and three student final projects. The paper contributes to the literature on teaching interaction design by describing what's achievable with undergraduates at a public university in a general education context.

As part of a larger team developing collaborations between computing and the arts, the co-authors created a general education undergraduate course, Tangible Interaction Design. We briefly describe the course, "Tiddles" (in-class exercises that promote creativity), and three exemplar student final projects. We conclude with observations about creativity in the arts and in engineering.

A hands-on embedded computing project is introduced into an undergraduate social sciences course. In the pilot module, nine student teams created working prototypes, using the technology to address social, ecological and ethical issues. The teams included freshman to senior level computer science majors, other technical majors, and non-technical students. Most students became highly engaged in the activity, developed exciting ideas, and reported improved learning in the social sciences.

Traditionally, the topics of compiler construction and language processing have been taught as an elective course in Computer Science curricula. As such, students may graduate with little understanding or experience with the useful techniques embodied in modern compiler construction.In this paper, we present the design of Chirp, a language specification and compiler implementation. As a language, Chirp is based on Java/C syntax conventions and is matched with the stack-based virtual machine that is built into the simple yet versatile Handy Cricket educational robot controller. As a compiler, the Chirp design is a series of Java components. These modules demonstrate key compiler construction techniques including lexing, parsing, intermediate representation, semantic analysis, error handling and code generation.We have designed a 6-week teaching module to be integrated into an intermediate-level undergraduate programming class. In the module, students will incrementally build the Chirp compiler, culminating with code generation for the Cricket controller. They will test their work on both physical Cricket-based robots and a web-based Cricket simulator. The Chirp system and our pedagogical design provides a realistic and engaging environment to teach compilers in undergraduate core programming courses.

This paper describes "Empowering Minds", a collaboration between the MIT Media Laboratory, St. Patrick's College in Dublin and Irish primary school teachers. In this project, we are introducing LEGO Mindstorms technology into Irish primary schools, along with a framework for teacher professional development that centrally recognizes teachers' passions and interests in bringing about pedagogical change.

In many educational settings, manipulative materials (such as Cuisenaire Rods and Pattern Blocks) play an important role in children's learning, enabling children to explore mathematical and scientific concepts (such as number and shape) through direct manipulation of physical objects.Our group at de MJT Media Lab has developed a new generation of "digital manipulatives"-computationallyenhanced versions of traditional children's toys.These new manipulatives enable children to explore a new set of concepts (im particular, "systems concepts" such as feedback and emergence) that have previously been considered "too advanced" for children to learn.In this paper, we discuss four of our digital manipulatives-computationallyaugmented versions of blocks, beads, balls, and badges.

Article Free Access Share on Meme tags and community mirrors: moving from conferences to collaboration Authors: Richard Borovoy MIT Media Laboratory, 20 Ames Street, Cambridge, MA MIT Media Laboratory, 20 Ames Street, Cambridge, MAView Profile , Fred Martin MIT Media Laboratory, 20 Ames Street, Cambridge, MA MIT Media Laboratory, 20 Ames Street, Cambridge, MAView Profile , Sunil Vemuri MIT Media Laboratory, 20 Ames Street, Cambridge, MA MIT Media Laboratory, 20 Ames Street, Cambridge, MAView Profile , Mitchel Resnick MIT Media Laboratory, 20 Ames Street, Cambridge, MA MIT Media Laboratory, 20 Ames Street, Cambridge, MAView Profile , Brian Silverman MIT Media Laboratory, 20 Ames Street, Cambridge, MA MIT Media Laboratory, 20 Ames Street, Cambridge, MAView Profile , Chris Hancock MIT Media Laboratory, 20 Ames Street, Cambridge, MA MIT Media Laboratory, 20 Ames Street, Cambridge, MAView Profile Authors Info & Claims CSCW '98: Proceedings of the 1998 ACM conference on Computer supported cooperative workNovember 1998 Pages 159–168https://doi.org/10.1145/289444.289490Online:01 November 1998Publication History 112citation991DownloadsMetricsTotal Citations112Total Downloads991Last 12 Months46Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF