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

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

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

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

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

Systems of power - such as structural racism, a system that upholds the ideology of white supremacy - are invisible and permeate our everyday lives and un/intentionally into our research. As these systems influence what counts as 'successful' work, CSEd researchers often have to assimilate ('whiten') themselves and their work into dominant ideals that uphold these structures. This workshop brings together aspiring and early-career CSEd researchers to critically examine and unpack the impacts of systems of power on ourselves and our research practices. Such examination is necessary to strive towards a critical consciousness in the field. We investigate critical questions like the following: What are systems of power? Who has the right to participate? Who has the right to exclude? We will use group-based activities that center historicities and 'outsider' knowledge to examine CSEd research practices and theories to answer these questions. We will draft a set of guidelines and recommendations available to the broader CSEd research community from the discussions and activities.

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

This study explored the relationships between eye tracking and traditional usability testing data in the context of analyzing the usability of Algebra Nation™, an online system for learning mathematics used by hundreds of thousands of students. Thirty-five undergraduate students (20 females) completed seven usability tasks in the Algebra Nation™ online learning environment. The participants were asked to log in, select an instructor for the instructional video, post a question on the collaborative wall, search for an explanation of a mathematics concept on the wall, find information relating to Karma Points (an incentive for engagement and learning), and watch two instructional videos of varied content difficulty. Participants’ eye movements (fixations and saccades) were simultaneously recorded by an eye tracker. Usability testing software was used to capture all participants’ interactions with the system, task completion time, and task difficulty ratings. Upon finishing the usability tasks, participants completed the System Usability Scale. Important relationships were identified between the eye movement metrics and traditional usability testing metrics such as task difficulty rating and completion time. Eye tracking data were investigated quantitatively using aggregated fixation maps, and qualitative examination was performed on video replay of participants’ fixation behavior. Augmenting the traditional usability testing methods, eye movement analysis provided additional insights regarding revisions to the interface elements associated with these usability tasks.

In recent years, the US has begun scaling up efforts to increase access to CS in K-12 classrooms and many teachers are turning to block-based programming environments to minimize the syntax and conceptual challenges students encounter in text-based languages. Block-based programming environments, such as Scratch and App Inventor, are currently being used by millions of students in and outside of classroom. We know that when novice programmers are learning to program in block-based programming environments, they need to understand the components of these environments, how to apply programming concepts, and how to create artifacts. However, we still do not know how are students' learning these components or what learning challenges they face that hinder their future participation in CS. In addition, the mental effort/cognitive workload students bear while learning programming constructs is still an open question. The goal of my dissertation research is to leverage advances in Electroencephalography (EEG) research to explore how students learn CS concepts, write programs, and complete programming tasks in block-based and hybrid programming environments and understand the relationship between cognitive load and their learning.

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

Block-based programming environments such as Scratch and App Inventor are currently being used by millions of students around the world. Although block-based programming environments were created to make computing concepts simpler for students to understand and visualize, students continue to struggle with understanding some basic Computer Science (CS) concepts [1]. My research focuses on understanding how students interact and learn computing concepts in block-based programming environments from a cognitive perspective. In particular, I am using neurophysiological devices, electroencephalography (EEG), to measures students' mental efforts that is attention and perception during programming tasks.

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

A research study measuring perceptions of computing held by Hispanic high school students has been conducted using two visual programming interfaces. Undergraduates conducted regular weekend classes using both Alice and App Inventor lessons to provide computing instruction to the students enrolled in an enrichment program. The goal of this research was to identify and measure high school student perceptions of computing after being introduced computing using drag-drop programming interfaces. The results of this work demonstrates how student interest in computing increases once exposed to computing, but the interest increment is not enough for them to major in a computing area, highlighting the importance of ongoing engagement in computing throughout the high school years.