As generative AI increasingly integrates into society and education, the number of institutions implementing AI usage policies and offering introductory AI courses is rising. These introductory AI courses mustn't replicate the "gateway/weed-out" phenomenon observed in introductory computer science courses like CS1 and CS2. Literature in computer science education suggests that interventions such as summer camps, bridge courses, and socio-technical courses have improved the sense of belonging and retention among students from underrepresented groups, thereby broadening participation in computer science. Building on previous work to create a socio-technical curriculum for all ages and education levels, this paper presents a course for teaching introductory AI concepts that adopts a socio-technical approach, complete with weekly activities and content designed for broad access. The course has been taught as a 1-credit general education course, primarily for freshmen and first-year students from various majors, and a 3-credit course for CS majors at all levels.This paper provides a curriculum and resources to teach a socio-technical introductory AI course. This approach is important because it not only democratizes AI education across diverse student backgrounds but also equips all students with the critical socio-technical multidisciplinary perspective necessary to navigate and shape the future ethical landscape of AI technology.

Generative AI increasingly reshapes how people engage with interactive systems. It now plays a vital role in designing, studying, and refining human-centered methods that let individuals interact and collaborate with AI, strengthening their agency and control. This special issue highlights the human role in Generative AI and seeks approaches that equip diverse stakeholders across socio-technical contexts to understand, direct, and steer these systems while enabling responsible innovation. We publish in this special issue original research on new interaction techniques that integrate human input into Generative AI’s continual development, studies of interaction paradigms that support more effective human–AI collaboration, and work that deepens understanding of model capabilities. Thus, we aim to build a research community around Human-Centric GenAI that empowers people to actively shape systems in line with their values, needs, and expectations.

As AI becomes increasingly prevalent in creative domains, it is imperative to understand users’ mental models of AI in human–AI co-creation as mental models shape user experiences. Additionally, gaining insights into users’ mental models is essential for the development of human-centered co-creative AI. This article introduces a framework for exploring users’ mental models of co-creative AI. Using a large-scale study (n = 155), we explore mental models of two existing AI systems, ChatGPT and Stable Diffusion, in co-creation contexts. Participants engaged in creative tasks with both AI and completed surveys, revealing insights into mental models and their associations with demographic factors and users’ ethical stances. The results highlight the major types and patterns of mental models of AI in co-creative contexts. Findings also reveal that individuals with expertise in AI typically have Partnership-oriented mental models of co-creative AI, while those lacking AI literacy tend to have more Tool-oriented mental models. Furthermore, individuals with Partnership-oriented mental models usually have a positive ethical perspective toward anthropomorphism in AI, data collection by AI, and AI’s societal impact. Additionally, results highlight that conversational co-creative AI is generally perceived as a collaborator, whereas non-conversational AI is typically viewed as a tool.

This research full paper describes a conceptual framework for classifying the interaction design of immersive Desktop Virtual Worlds Learning Environments (DVWLEs). The increasing affordability of internet bandwidth and computer graphics processing power is making DVWLEs a promising option for next-generation online learning platforms. These environments offer advantages for widespread adoption, partic-ularly for learners and institutions lacking access to expensive Virtual Reality (VR) hardware and devices. DVWLEs facilitate immersive learning experiences through interactive simulations,$\mathbf{3D}$modeling, and real-time spatial collaboration across various engineering and computing disciplines. Unlike traditional online platforms that rely primarily on text, images, and videos, DVWLEs leverage the power of$\mathbf{3D}$spatial characteristics to create a more tangible and interactive learning environment, potentially leading to enhanced understanding, knowledge re-tention, and increased student engagement. Research suggests that two fundamental characteristics differentiate DVWLEs from conventional online platforms and significantly impact student learning: a sense of place and a sense of presence. A sense of place refers to the feeling of being located within a meaningful space, while a sense of presence conveys the feeling of being truly “there” within the virtual world. These characteristics effectively promote spatial and immersive active learning. The framework builds upon these core characteristics, categorizing student interactions within DVWLEs based on the affordances (potential actions) provided by the virtual worlds. The elements and principles of interaction design, including affordances and signifiers (visual cues), are crucial in shaping the effectiveness of DVWLEs. Studies consistently show that poorly designed interactions within DVWLEs can lead to usability issues and unsatisfactory student experiences. Using successful examples from existing DVWLEs, this framework offers educators guidelines for creating virtual classrooms. By enhancing interaction design to cultivate a strong sense of space and presence, educators can access the full potential of DVWLEs, leading to more engaging, immersive, and effective learning experiences that cater to a broad range of learners and educational disciplines.

This research-to-practice paper presents a curriculum, “AI Literacy for All,” to promote an interdisciplinary under-standing of AI, its socio-technical implications, and its practical applications for all levels of education. With the rapid evolution of artificial intelligence (AI), there is a need for AI literacy that goes beyond the traditional AI education curriculum. AI literacy has been conceptualized in various ways, including public literacy, competency building for designers, conceptual understanding of AI concepts, and domain-specific upskilling. Most of these conceptualizations were established before the public release of Generative AI (Gen-AI) tools such as ChatGPT. AI education has focused on the principles and applications of AI through a technical lens that emphasizes the mastery of AI principles, the mathematical foundations underlying these technologies, and the programming and mathematical skills necessary to implement AI solutions. The non-technical component of AI literacy has often been limited to social and ethical implications, privacy and security issues, or the experience of interacting with AI. In AI Literacy for all, we emphasize a balanced curriculum that includes technical as well as non-technical learning outcomes to enable a conceptual understanding and critical evaluation of AI technologies in an interdisciplinary socio-technical context. The paper presents four pillars of AI literacy: understanding the scope and technical dimensions of AI, learning how to interact with Gen-AI in an informed and responsible way, the socio-technical issues of ethical and responsible AI, and the social and future implications of AI. While it is important to include all learning outcomes for AI education in a Computer Science major, the learning outcomes can be adjusted for other learning contexts, including, non-CS majors, high school summer camps, the adult workforce, and the public. This paper advocates for a shift in AI literacy education to offer a more interdisciplinary socio-technical approach as a pathway to broaden participation in AI. This approach not only broadens students' perspectives but also prepares them to think critically about integrating AI into their future professional and personal lives.

In a human-AI co-creation, AI not only categorizes, evaluates and interprets data but also generates new content and interacts with humans. As co-creative AI is a form of intelligent technology that directly involves humans, it is critical to anticipate and address ethical issues during all design stages. The open-ended nature of human-AI interactions in co-creation poses many challenges for designing ethical co-creative AI systems. Researchers have been exploring ethical issues associated with autonomous AI in recent years, but ethics in human-AI co-creativity is a relatively new research area. In order to design human-centered ethical AI, it is important to understand the perspectives, expectations, and ethical concerns of potential users. In this paper, we present a study with 18 participants to explore several ethical dilemmas and challenges in human-AI co-creation from the perspective of potential users using a design fiction (DF) methodology. DF is a speculative research method that depicts a new concept or technology through stories as an intangible prototype. We present the findings from the study as potential users’ perspectives, stances, and expectations around ethical challenges in human-AI co-creativity as a basis for designing human-centered ethical AI partners for human-AI co-creation.

This work-in-progress paper presents a study that sheds light on the concerns that students may not develop sufficient programming skills and as a result, be less competent with the use of ChatGPT. The potential benefits for students are significant: Access to ChatGPT increases the ability for students to work constructively on their own schedule. The ease of use of ChatGPT may engage students who might otherwise hesitate in asking for support. Before these tools can be meaningfully introduced into a course, work must be done to study the impact of these AI tools on a student's ability to learn. In this study, participants are recruited from introductory Java programming courses at a large public university in the United States. This paper presents preliminary findings from a mixed method study design that consists of a pre-task assessment quiz; and a programming task in one of three conditions: (1) with no external help, (2) with the help of an AI chatbot, or (3) with the help of a generative AI tool like GitHub Copilot; followed by a post-task assessment and an interview on their experience and perceptions of the tools. Our preliminary findings describe our data collection, thematic analysis of the students' prompts and chatGPT responses, and a summary of the experience for 3 students. Our findings demonstrate a range of students' attitudes and behaviors towards chatGPT that provides insight for future research and plans for incorporating such AI tools in a course.

This full paper presents the Collaborative Active Learning and Inclusiveness (CALI) inventory, and an analytical model using the CALI inventory, demographic data, mindset surveys, and knowledge mastery assessment, to explore relationships between classroom climate and student experiences. The CALI inventory enables the investigation of the impact of the student experience in an active learning classroom by distinguishing the factors that characterize the structure, social learning, and inclusive practices. The Structure Index includes components related to course setup, organization, assessment, grading, and communications. The Sociality Index includes components related to opportunities for students to interact with each other. The Inclusiveness Index includes components related to how the instructor communicates a sense of belonging to the students through a growth mindset and inclusive policies and practices. A CS Mindset Instrument was developed based on research that measured students' self-efficacy by evaluating the extent of variation in their self-perceived ability to accomplish a task, sense of belonging in computing, and professional identity development. Demographic data is collected that allows for an analysis using an intersectional lens to acknowledge the complexity of social and cultural contexts. The knowledge and mastery assessments capture changes in competency through pre-post mastery quizzes. The combination of CALI with other instruments, including those that characterize student mindset, identity, and levels of mastery, enables investigation of how various practices of inclusive and collaborative active learning have differential effects on students with different identities in computer science.

Human-AI co-creativity involves both humans and AI collaborating on a shared creative product as partners. In a creative collaboration, interaction dynamics, such as turn-taking, contribution type, and communication, are the driving forces of the co-creative process. Therefore the interaction model is a critical and essential component for effective co-creative systems. There is relatively little research about interaction design in the co-creativity field, which is reflected in a lack of focus on interaction design in many existing co-creative systems. The primary focus of co-creativity research has been on the abilities of the AI. This article focuses on the importance of interaction design in co-creative systems with the development of the Co-Creative Framework for Interaction design (COFI) that describes the broad scope of possibilities for interaction design in co-creative systems. Researchers can use COFI for modeling interaction in co-creative systems by exploring alternatives in this design space of interaction. COFI can also be beneficial while investigating and interpreting the interaction design of existing co-creative systems. We coded a dataset of existing 92 co-creative systems using COFI and analyzed the data to show how COFI provides a basis to categorize the interaction models of existing co-creative systems. We identify opportunities to shift the focus of interaction models in co-creativity to enable more communication between the user and AI leading to human-AI partnerships.

Human-AI co-creativity involves humans and AI collaborating on a shared creative product as partners. In a creative collaboration, communication is an essential component among collaborators. In many existing co-creative systems, users can communicate with the AI, usually using buttons or sliders. Typically, the AI in co-creative systems cannot communicate back to humans, limiting their potential to be perceived as partners rather than just a tool. This paper presents a study with 38 participants to explore the impact of two interaction designs, with and without AI-to-human communication, on user engagement, collaborative experience and user perception of a co-creative AI. The study involves user interaction with two prototypes of a co-creative system that contributes sketches as design inspirations during a design task. The results show improved collaborative experience and user engagement with the system incorporating AI-to-human communication. Users perceive co-creative AI as more reliable, personal, and intelligent when the AI communicates to users. The findings can be used to design effective co-creative systems, and the insights can be transferred to other fields involving human-AI interaction and collaboration.

This full paper presents CALI 1.0: an inventory to capture the various components of collaborative active learning and inclusive practices in introductory CS courses, as a basis for studying their impact on a student’s ability to engage with course material and be successful in the course. Collaborative active learning is defined as a pedagogy where learners work with one another on activities that involve actively engaging with the course material through discussions, problem-solving, case studies, role plays, and other methods. CALI consists of three separate indices: Structure, Sociality, and Inclusiveness. The Structure Index includes components related to course setup, organization, assessment, grading, and communications. The Sociality Index includes components related to opportunities for students to interact with each other. The Inclusiveness Index includes components related to how the instructor communicates a sense of belonging to the students through a growth mindset and inclusive policies and practices. In this paper, we present the inventory and results from a focus group of faculty to gauge responses to using this inventory to capture the teaching components of their courses. We present CALI as a tool that can be used to study how teaching practices, e.g. inclusive and collaborative active learning pedagogies, impact course experiences for students in different demographic groups as well as a reflective tool for use by faculty while designing their courses.

The design of online learning environments has a fundamental and immediate impact on how students experience learning in these environments and particularly on the students’ satisfaction and learning engagement. The COVID-19 pandemic has resulted in students participating in online learning on a large scale and forcing educational institutions to adopt various communication and virtual teaching environments: this has provided an opportunity to study and compare different online learning environments. To understand the design performance of the currently available virtual learning environments, they should be considered and evaluated from the students’ point of view. Identifying their pros and cons gives designers a better idea of how to enhance the design of virtual learning environments and provide a positive educational experience for students. The two most broadly used types of virtual learning environments are video conferencing and virtual worlds. These two mediums provide different affordances for students to experience and interact with the learning materials as well as to interact with the instructor and other students. This paper reports on a study comparing the use of Zoom as an example of video conferencing and Gather.Town as an example of a virtual world in online learning during the COVID-19 pandemic. The study is based on students’ responses to an online survey asking them to compare the two environments based on their experience in their courses. The participants were CS undergraduate students exposed to both environments and experiences with both platforms. A qualitative analysis of the data collected showed that students realized certain values from the affordances offered by the virtual world environment that facilitated collaborative learning with peers and instructors and made the learning experience more effective and engaging.

This full research paper studies the correlation of self-efficacy in computer science as well as learning and social skills with students' academic performance and their emotions in collaborative learning environments. Self-efficacy is an essential part of social cognitive theory and provides the foundation for analyzing human thoughts, motivations, and actions. Studies show that students' successful performance and accomplishment are directly affected by the level of self-efficacy. Therefore, analyzing self-efficacy in engineering education is important since it can impact the learning process in academic settings as well as provide a metric to track for improvement. Social cognitive theories also emphasize that students' interaction with each other affects their learning process and how they perform in educational settings. In previous work [5], we analyzed students' conversations in low-stake teams in an introductory programming course (CS1) and observed a strong positive correlation between students' positive emotions while interacting with each other with their performance in the course. In this study, we focus on the correlation of self-efficacy with learner's emotion and performance. We measure students' self-efficacy with a standard instrument called “Student Attitudes Toward STEM (S-STEM) Survey”. For this purpose, we asked the participants to self-report on a 5-point Likert-scaled survey including 20 questions. These 20 questions are grouped into 2 main categories of computer science and learning/social skills. Students' emotions were extracted from their speeches in teams by applying natural language processing (NLP) methods. The result of data analysis shows a statistically significant correlation between overall self-efficacy and performance in the course and positive emotions during the teamwork. We further investigate which category of self-efficacy questions most correlate with students' performance. The result shows self-efficacy in interpersonal skills and learning ability most impact students' performance.

To address the increasing enrollment in Computer Science (CS), face-to-face (F2F) courses are often redesigned to online courses. For the success of a large-enrollment online CS class, it is important to design it with effective strategies that can enhance student learning and engagement. With this aim, we developed an online, third year web-development course, and redesigned its existing F2F version. This study focuses on examining the course strategies utilized in the two versions of of the course, and how students' learning and engagement vary between these two offerings. In Fall 2019, we conducted experimental research with pre- and post-tests to collect data from these two sections. The participants of the study include 51 out of 103 online students, and 38 out of 72 F2F students. We identified course strategies that were more valuable to one group versus the other, and both the sections. In terms of student learning, we found that there was no significant difference between these sections. Behavioral engagement measures depicted how the online and F2F students engaged within their courses, and how it is related to students' learning. We also found how the level of engagement varied in the online course according to the engagement factors that include performance, emotions, skills, and participation. The results of this study will inform online, F2F, and hybrid course design of upper-level CS courses.

In this project, we explore how academic integrity and teaching computer ethics have evolved within the SIGCSE community over the past 50 years. We apply Natural Language Processing (NLP) techniques to identify the topics in the records in 50 years of SIGCSE Symposia. We extract records that include these two themes and apply a temporal model identifying periods with an increase in the number of records that include these themes and a significant increase in 2017-2018.

We have established a research-practice partnership (RPP) to build a computer science (CS) and computational thinking (CT)-focused STEM ecosystem at two middle schools. Creating such an ecosystem to broaden student participation in computing through an RPP approach involves all stakeholders in the research process. Borrowing upon visual participatory research methods, we developed a graphic research instrument to engage teachers in the research process and elicit their perspectives on strategies for building the ecosystem. This experience report describes our research methodology across two distinct cases to demonstrate the utility of this drawing activity as an investigative and partnership development tool. The contribution is in offering a flexible approach to other university-based RPP teams that enables a synergistic partnership development tool and data collection instrument that can be tailored to a variety of RPP contexts, facilitating more productive and equitable ways of engaging stakeholders in the research process. We describe our project contexts and share results from the pilot study with practitioner-members of our RPP teams. We discuss two cases to highlight the contribution this approach made to the development of our partnerships.

The paper is part of the Difference Makers Track of FIE 2020.Many institutions of higher learning are in the process of defining and implementing Data Science programs. The emerging field is revolutionizing scientific discovery and many industries. Broad availability of data sets and emerging analytical techniques for their processing are changing our economies and societies. While there is a broad agreement about underlying principles in this new discipline, defining data science as a pedagogically independent discipline has proven to be a challenge.In this paper, we present the university-wide effort that led to the creation of a new undergraduate interdisciplinary Data Science program. In particular, we point to the critical role of inclusive pedagogical design and broadening participation criteria in program development. Current and projected job market data clearly indicate that data science programs need to expand traditional STEM workforce and attract students with varying backgrounds and degrees or preparation. Such a focus has led to a broad agreement across our campus regarding the new program and its curriculum.

This full research paper studies affective states in students' verbal conversations in an introductory Computer Science class (CS1) as they work in teams and discuss course content. Research on the cognitive process suggests that social constructs are an essential part of the learning process. This highlights the importance of teamwork in engineering education. Besides cognitive and social constructs, performance evaluation methods are key components of successful team experience. However, measuring students' individual performance in low-stake teams is a challenge since the main goal of these teams is social construction of knowledge rather than final artifact production. On the other hand, in low-stake teams the small contribution of teamwork to students' grade might cause students not to collaborate as expected. We study affective metrics of sentiment and subjectivity in collaborative conversations in low-stake teams to identify the correlation between students' affective states and their performance in CS1 course. The novelty of this research is its focus on students' verbal conversations in class and how to identify and operationalize affect as a metric that is related to individual performance. We record students' conversation during low-stake teamwork in multiple sessions throughout the semester. By applying Natural Language Processing (NLP) algorithms, sentiment classes and subjectivity scores are extracted from their speech. The result of this study shows a positive correlation between students' performance and their positive sentiment as well as the level of subjectivity in speech. The outcome of this research has the potential to serve as a performance predictor in earlier stages of the semester to provide timely feedback to students and enables instructors to make interventions that can lead to student success.

The creative sketching partner (CSP) is a proof of concept intelligent interface to inspire designers while sketching in response to a specified design task. With this interactive system we are studying the effect of an AI model of visual and conceptual similarity for selecting the Al's sketch response as an inspiration to the current state of the user's sketch. Specifically, we are interested in the user's behavior and response to an AI partner when engaged in a design task. By developing deep learning models of the sketches from a large-scale dataset, the user can control the amount of visual and conceptual similarity of the AI response when requesting inspiration from the CSP. We conducted a study with 50 design students to examine the participants' interaction behavior and their self reports. The participants' behavior maps into clusters that are co-related with three types of design creativity: combinatorial, exploratory, and transformational. Our findings demonstrate that the tool can facilitate ideation and overcome design fixation. In addition, analysis suggests that inspiration related to conceptual similarity is more associated with transformational creativity and inspiration related to visual similarity occurs more frequently during the detailed stages of design and is more prevalent with combinatorial creativity.

This project is a Research to Practice Partnership (RPP) between two middle schools and two universities. It focuses on investigating problems and on identifying solutions around increasing participation and interest in computer science (CS). We aim to do this by identifying, experimenting with, and fine-tuning methods to help students develop computational thinking (CT) skills. The research employs a STEM ecosystem model, which facilitates a support structure that aims to mitigate barriers and impact students as they progress in STEM areas. While this RPP is still a work in progress, we present data from the first year of our collaboration with one of the middle schools. While the research questions guiding this RPP are intended to be iterative and revised annually, year one data provides perspectives on (1) barriers to developing a STEM ecosystem that supports CS/CT for every student through integration into science, math, and language arts courses, (2) the factors or interventions needed for the development of a CS/CT focused ecosystem that supports everyone in the school, (3) the indicators of success for a CS/CT focused STEM ecosystem in a school, and (4) how the ecosystem prepares and engages all students for CS/CT work in high school. Year one data is discussed in terms of the STEM ecosystem framework and in how it will guide the next steps in this partnership. This project contributes to the understanding of how to prepare future generations for participation in a workforce where knowledge of the foundations of CS/CT is integral to success.

An organization with high undergraduate enrollment, the College of Computing and Informatics atUNC Charlotte is undergoing a sustainable pedagogical shift. Our Graduate Teaching Assistants (GTAs) being a crucial part to sustain this change, are also facing a shift in their climate. We aim to present measurement to gauge our GTAs' climate under this pedagogical climate shift. For this purpose, we have analyzed 184 survey responses from GTAs and developed three constructs:Self-Competence, GTA-to-Faculty Relations andCommunity Belonging. Exploratory Factor Analysis was used to identify the underlying factors, exhibiting 13 items retaining to these three constructs with a Cronbach's alpha of 0.94. This measurement shows that with engaged classroom practices, we are also fostering an engaged climate for our GTAs.

This paper presents and evaluates a new method for inspiring creativity in a co-creative design system. The method uses a computational model of aconceptual shift based on clustering of deep features from a database of sketches. The co-creative sketching tool maps a user's sketch to a sketch of a distinct category that has high, medium, or low visual and semantic similarity. We hypothesize that the degree of similarity between the user's and the system's sketches is associated with a range of cognitive models of creativity in a design context. We report on the findings of an empirical study that analyzes different design scenarios in which the user sketches in response to a proposed conceptual shift. The findings show that how similar the computational agent's sketch is to the user's original sketch is related to the presence of three types of design creativity in the user's response: combinatorial, exploratory, and transformational.

This innovative practice work-in-progress paper presents measurement and analysis of a comprehensive model of pedagogical change in the computing college of a large, urban university in the United States. Our approach to change has two major thrusts: (1) a model for systemic change in the culture and climate of computing education, and (2) pedagogical patterns that transform students who enter undergraduate computing programs from people with an interest in computing to people who identify as computing professionals. We collected a total of 940 student survey responses across four semesters. Survey items gauged students’ connection to their peers and their profession and were used to create a composite measure of peer learning and professional identity. Exploratory Factor Analysis indicates an internally consistent factor structure composed of 18 items with a Cronbach’s alpha of 0.91. In this paper, we present the methodology for measurement and analysis, describe our rationale in an engaged computing education context, and discuss how studying the constructs of peer learning and professional identity contributes to the knowledge base of the computing education community.

Our natural tendency to be curious is increasingly important now that we are exposed to vast amounts of information. We often cope with this overload by focusing on the familiar: information that matches our expectations. In this paper we present a framework for interactive serendipitous information discovery based on a computational model of surprise. This framework delivers information that users were not actively looking for, but which will be valuable to their unexpressed needs. We hypothesize that users will be surprised when presented with information that violates the expectations predicted by our model of them. This surprise model is balanced by a value component which ensures that the information is relevant to the user. Within this framework we have implemented two surprise models, one based on association mining and the other on topic modeling approaches. We evaluate these two models with thirty users in the context of online health news recommendation. Positive user feedback was obtained for both of the computational models of surprise compared to a baseline random method. This research contributes to the understanding of serendipity and how to "engineer" serendipity that is favored by users.

This paper describes and compares two categories of pedagogical design patterns that have emerged from CS education practice: lecture-based design patterns and active learning design patterns. Pedagogical design patterns provide faculty with combinations of generalized descriptions of problems and solutions that occur in teaching and learning. The benefit of forming design patterns is the codification of successful practice that can be reused in multiple scenarios and draw on the creativity of the instructor for defining the details relevant to the course and the students. Design patterns have been represented in many formats since Alexander’s initial design pattern model highlighting different aspects of what is important in each domain in which the patterns are created and used. This paper analyzes design patterns emerging from recent developments in lecture-based pedagogy and active learning in CS education. Traditional lectures in computer science, engineering, and other STEM disciplines are being reconsidered due to research that shows that students are less likely to learn while listening and more likely to learn while actively engaged. Design patterns that address problems and provide potential solutions to traditional lectures in computer science education have been published that provide solutions to engage students during the lecture. The pedagogy of flipped classrooms and active learning have recently been adopted by many faculty in Computer Science leading to emerging design patterns for active learning. We compare how previously published lecture-based patterns and our active learning patterns address similar problems with different solutions to engaging students. We show how an object-based structure for pedagogical design patterns can provide additional information about the problems and the solutions addressed by the patterns that are more easily indexed and combined.

This Research-to-Practice Work-in-Progress Paper presents a midway report on a change initiative underway in the College of Computing and Informatics (CCI) at UNC Charlotte. Comprised of approximately 100 faculty members and nearing 2,000 undergraduate majors, CCI was awarded a 5-year, $2 million grant from the National Science Foundation in 2015 to revolutionize computer science education at the collegiate level. This initiative, which seeks to simultaneously achieve both pedagogical and organizational change, is built upon a foundation of educational innovation through engaged teaching practices. To sustain educational innovation in CCI beyond the funded scope of the project, pedagogical change is thus strategically embedded in CCI's organizational structure through a 3-stage model of faculty adoption, redesigned student course evaluations, and realigned values in the reappointment, promotion, and tenure process. By recognizing that sustained organizational change takes root when there is concurrent buy-in from organizational members—both bottom-up and top-down— this reform initiative seeks to embed two-pronged change not only via educational innovation (pedagogical change), but also in the day-to-day practices, policies, and physical environment of the College itself (organizational change).

Broadening participation in computing education for K-12 students requires all levels of the education system to collaborate on five critical needs: 1) creating diverse student opportunities and access to CS activities; 2) empowering highly qualified, highly skilled CS teachers; 3) developing relevant and accessible CS courses and curricula; 4) establishing CS policy and leadership at the local and state levels; and 5) engaging communities and industry partners in broadening participation in CS.

This paper explores how participants constructed and re-constructed their relationship with an interactive art installation. The piece, textit{the willful marionette}, was developed in collaboration between artists and researchers. It explores the dynamics of non-verbal communication by building (from a scanned image of a human figure) a stringed marionette that responds to human movement. The intent was to challenge participants' expectations about communication, intelligence, emotion and the social role of the body. We show that participants' descriptions of their interactions vary along two axes: whether they or the marionette was perceived as leading the interaction, and whether they constructed a social or a technical mindset. We explore these differences using semi-structured interviews and a mix of qualitative and quantitative methods. We then present some implications for the design of both goal-directed and expressive embodied intelligent systems. textit{the willful marionette} has since been acquired by the Smithsonian American Art Museum as an example of cutting-edge art that deals with contemporary issues of machine intelligence and the social role of our bodies.

This paper presents a cognitive model of goal formulation in designing that is triggered by surprise. Cognitive system approaches to design synthesis focus on generating alternative designs in response to design goals or requirements. Few existing systems provide models for how goals change during designing, a hallmark of creative design in humans. In this paper we present models of surprise and reformulation as metacognitive processes that transform design goals in order to explore surprising regions of a design search space. The model provides a system with specific goals for exploratory behaviour, whereas previous systems have modelled exploration and novelty-seeking abstractly. We use observed designs to construct a probabilistic model that represents expectations about the design domain, and then reason about the unexpectedness of new designs with that model. We implement our model in the domain of culinary creativity, and demonstrate how the cognitive behaviors of surprise and problem reformulation can be incorporated into design reasoning.

The Connected Learner is a re-orientation of undergraduate computing education that focuses on connecting students to peers, the profession, and purpose. The College of Computing and Informatics at UNC Charlotte—comprised of three departments with an undergraduate enrollment of approximately 1,500 students—is a large research institution in an urban setting with a diverse student population. Within this unique context, the project aim is to build a sustainable practice of educational innovation across the undergraduate computing curriculum by increasing faculty awareness of teaching innovations, resources for pedagogical change, and support for teaching practices that engage students. The vision is to create an active learning environment that transforms the student entering the computing undergraduate program from a person with an interest in computing to a person with an affinity identity [1] as a computing professional through these ongoing connections. We employ a systems theory of change for the Connected Learner based on two foundational concepts: flipped classrooms and engagement theory. To achieve related goals of improving student retention and time to graduation, Connected Learner teaching strategies are being integrated across the undergraduate curriculum with an initial focus on introductory gateway courses. Change is occurring via infrastructure supports to sustain learning practices across our college through faculty development initiatives (hiring, training, mentoring, and incentives) that are designed to inform faculty about engagement pedagogies, motivate faculty to adopt these practices, and shift pedagogical attitudes. During the first year of the five-year project, quantitative and qualitative data from students and faculty was collected and analyzed. This data provides a baseline of student attitudes and performance as well as a baseline of faculty attitudes and teaching practices. In this paper, we present the background, context, organizational structure, and research questions for the project. Findings from Year 1 are discussed, including academic outcomes, project milestones, and student attitudes towards new teaching practices. Long-term project goals and expectations are presented.

Video usage in educational environments has increased significantly with the expansion of online learning opportunities. However, current video-based applications used for learning are designed for passive consumption of video content and do not support small-group collaboration. Specifically, they lack interaction affordances for active and collaborative learning through video materials. In this paper, we present the affordances and user experience of a web-based video application called Video Collaboratory that is designed with multiple affordances for group-based learning around video content. We surveyed students and analyzed the use of the Video Collaboratory in an HCI course. Students found the Video Collaboratory helpful because they could learn from their peers. Students also used the application beyond the activities required for credit.

Computer science instructors are incorporating Massive Open Online Courses (MOOCs), and MOOC material, into their campus courses. The closed instance is the dominant model being followed, in which MOOC material (e.g., in AI, in Programming Languages, in Software Engineering) is ported to a protected LMS so that campus students can use the MOOC video and other material (e.g., assessments) and infrastructure (e.g., autograders) in a "private" manner. In an embedded instance, campus students take a live MOOC as one component of their on-campus course. Another model, albeit nascent, is to exploit self-paced open online courses so that campus cohorts can move through a course when a campus instructor wants, and at a pace the instructor wants, but still availing campus students to the advantages of interacting with global students (and vice versa). The BoF session will allow participants to talk about MOOC-based blended models, form a group interested to archiving experience, synthesizing best practices, creating shared learning-object repositories (e.g., of active learning, in-class activities), and to discuss issues of policy (e.g., FERPA).

The basic principles of a flipped classroom teaching method are to deliver content outside of the class and to move active learning into the classroom. There are many strategies for delivering the content online, such as having instructors prepare online lectures, wrapping the course around a MOOC, and curating online videos from various sources. There are also many strategies for including active learning in the classroom that go beyond providing programming labs, and can include various forms of peer instruction. In this paper we describe our experiences flipping four different computer science classes across multiple semesters over two years. This breadth of experience with classroom flipping has enabled us to compare strategies and approaches and develop an understanding of which approaches appear to work under which circumstances. We discuss how we structured out-of-class preparatory work, how we created or sourced online videos, how we used active learning activities in-class to scaffold skills development and identify students' misconceptions, and how we structured teams for in class activities. This paper contributes a set of flexible strategies to consider for provision of curricular content out-of-class, structuring students' preparatory work, applying active learning of skills and concepts, and leveraging social interaction and peer instruction for CS education. We present the impact of our approaches based upon leading indicators of course evaluations and student surveys. We discuss lessons learned and students' responses to our strategies.

This paper introduces the concept of mobile collective creativity in Mobile Augmented Reality (MAR), with a focus on the interaction and contribution of the audience in digital exhibitions. This paper identifies key dimensions of mobile collective creativity and develops a framework to characterise existing and design new MAR artworks. Through case studies we show that the notable characteristics of MAR artworks are Tangible Manipulation with Expressive Representation towards Enhanced Intelligence. Our collective creativity framework contributes to a better understanding of mobile collective creativity as well as proposes design guidelines for this novel kind of creativity.

Most studies on tangible user interfaces for the tabletop design systems are being undertaken from a technology viewpoint. Although there have been studies that focus on the development of new interactive environments employing tangible user interfaces for designers, there is a lack of evaluation with respect to designers' spatial cognition. In this research we study the effects of tangible user interfaces on designers' spatial cognition to provide empirical evidence for the anecdotal views of the effect of tangible user interfaces. To highlight the expected changes in spatial cognition while using tangible user interfaces, we compared designers using a tangible user interface on a tabletop system with 3D blocks to designers using a graphical user interface on a desktop computer with a mouse and keyboard. The ways in which designers use the two different interfaces for 3D design were examined using a protocol analysis method. The result reveals that designers using 3D blocks perceived more spatial relationships among multiple objects and spaces and discovered new visuo-spatial features when revisiting their design configurations. The designers using the tangible interfaces spent more time in relocating objects to different locations to test the moves, and interacted with the external representation through large body movements implying an immersion in the design model. These two physical actions assist in designers' spatial cognition by reducing cognitive load in mental visual reasoning. Further, designers using the tangible interfaces spent more time in restructuring the design problem by introducing new functional issues as design requirements and produced more discontinuities to the design processes, which provides opportunity for reflection and modification of the design. Therefore this research shows that tangible user interfaces changes designers' spatial cognition, and the changes of the spatial cognition are associated with creative design processes.

Tabletop systems including novel user interfaces provide an effective platform for design collaboration. This research studied the effects of a tabletop system with tangible user interfaces on designers' spatial cognition and design communication in collaborative design. The empirical results reveal that the use of TUIs changed designers' spatial cognition, and that these changes affected the design process by increasing characteristics associated with creative design processes.

Agents are systems capable of perceiving their environment through sensors, reasoning about their sensory input using some characteristic reasoning process and acting in their environment using effectors. When one or more agents control the objects that comprise a 3D virtual world, the result is a dynamic, adaptive environment that changes in response to users' actions. We have experimented with three different agent models for this purpose: a swarm model, a cognitive model and a motivated agent model. Each of these models differs in the complexity of its implementation and can thus be used to produce dynamic virtual environments of differing behavioural complexity. This paper introduces a schema for characterising the implementation and behavioural complexity of agent models for dynamic virtual environments. We apply this schema to the agent models we have studied to reveal their advantages and disadvantages and identify directions for future work.

Wayfinding is a cognitive element of navigation that allows people to plan and form strategies prior to executing them. Wayfinding in large scale virtual environments is a complex task and even more so in dynamic virtual worlds. In these dynamic worlds everything, including the objects, the paths, and the landmarks, may be created, deleted, and moved at will. We propose a wayfinding tool using swarm creatures to aid users in such dynamic environments. The tool produces dynamic trails leading to desired destinations and generates teleport/warp gates. These are created as a consequence of swarm creatures exploring dynamic worlds. In this paper, we describe the swarm algorithms developed to create such a tool to generate wayfinding aids in dynamic virtual worlds.