Eye tracking reveals attention patterns in block-based programming. This lets us examine gender-linked differences as students manage the multimodal literacy of coordinating text prompts, color-coded blocks, navigation, and live output. We examined how K–12 students allocate attention while independently completing a short Scratch-like task using a mixed-methods approach. Thirty-two fourth- to sixth-grade students (19 boys, 13 girls) participated. We recorded gaze with a Tobii Spark screen-based tracker and analyzed the data in Tobii Pro Lab. For each student, we computed 187 metrics across four Areas of Interest (AOIs)—Script, Coding Blocks, Output, and Navigation—and analyzed in R 4.3.1 with multiple-comparisons correction. Fifteen metrics differed by gender (seven in Script, four in Coding Blocks, two in Output, two in Navigation). In a qualitative review of eye-tracking videos, girls tended to reread and plan in Script and checked Output later and for longer, whereas boys moved to the workspace sooner, exited Blocks faster with larger, faster eye movements, and made brief early Output checks. Because the study is ongoing, analyses are still in progress and the findings are not yet final; so far, results indicate different strategies, not ability differences.

This report presents several main collaborative design scenarios involved in designing a week-long summer camp program to engage elementary students in physiological computing. The design team consists of the research team (the authors of this report) and four elementary school teachers from a school district with a majority of historically underrepresented students in a southeastern state in the U.S. We present the goals for the co-design, followed by an account of the nature of the conversations and discussions during the synchronous co-design sessions. With the research team leading with questions, teachers contributed with abundant knowledge and experience with pedagogies to engage upper elementary-grade students, including activities that relate to students' everyday learning, scaffolding strategies to bridge potential learning gaps, and suggestions for software development and updates. These insights are expected to help enable adoptions by researchers and practitioners in collaborative design for K-12 students.

Previous research has incorporated physiological data such as heart rate and footsteps to enrich K-12 students' STEM and computing learning experiences. This qualitative study piloted a series of lessons leveraging a novel physiological computing environment with a small group of fifth-grade girls (n=5). The purpose of this study was to understand (1) how the physiological computing lesson activities promoted changes in the students' conceptual understanding of conditional logic and variables and their diverse perspectives in computing and (2) how the students approached problem-solving and what their visual attention looked like during physiological computing. We analyzed multiple sources of data, including students' artifacts, recorded classroom conversations, think-aloud verbalizations, and eye-tracking metrics data. Data analyses revealed that the girls demonstrated an improved understanding of the two computing concepts (i.e., variables and conditional logic), employed different problem-solving strategies, and encountered common challenges such as translating the task instruction to building code with the conditional block. Eye-tracking revealed that the students rarely attended to program output during their programming and debugging processes.

Decomposition is a foundational computational thinking construct that is often introduced early as students are learning computer science in the elementary grades. Although decomposition is often described in early computational activities, little research exists about how to teach and assess students’ understanding of decomposition. In this mixed-methods research study, 173 third-grade students from eight elementary school classrooms in the Midwest were taught eight lessons that integrated decomposition as well as other computational thinking practices into their mathematics instruction. They completed a computational thinking assessment after the first four lessons and again after the second four lessons. Analyses included the distribution of correct decomposition item responses, confirmatory factor analysis, and item-level error analysis. Results indicate wide variability in students’ performance on the decomposition assessment items as well as in performance on items contextualized within mathematics. This study highlights the need for additional considerations about assessing computational understanding, implications for assessment within integrated contexts, and the use of paper-and-pencil tests compared to embedded assessments.

Educational sensor-based visual programming environments (VPEs) tend to focus on directly connecting input and output sources. However, limited educational tools are designed to expose novice programmers to basic preprocessing techniques often needed to convert raw sensor data into interpretable commands. This paper presents our preliminary design process for LITI, a VPE designed to expose students to basic processing techniques used for time-series data analysis. We leveraged an iterative design process that began with focus group sessions involving local high school teachers and students. Based on educators' and students' feedback, LITI was designed to reinforce graphing and data processing concepts used in physiological computing. Afterward, we conducted usability studies with high school students in grades 10 - 12. Results from our initial user studies suggest that LITI may increase students' physiological computing self-efficacy.

Variable is a common computer science (CS) concept and is being introduced to upper elementary students in computational thinking (CT)-integrated instruction. However, there is scant empirical evidence of when and how elementary students should learn variables. For example, national computer science (CS) standards advise introducing variables in grades 3-5 and a K-8 variable learning trajectory (LT) synthesized learning goals from the literature and hypothesized four levels of thinking in working with variables. Yet, little empirical research lies behind these. This mixed methods study examined elementary students' understanding of variables. Participants were sampled from two fourth-grade classes from a Midwestern elementary school that implemented a series of CT-integrated math lessons. Students' written responses to variables assessment items were analyzed. Additionally, cognitive think-aloud interviews were conducted with nine students to elicit students' understanding while solving the variables assessment items. Our findings suggested that most students lacked a conceptual understanding of using variables to create generalized problem solutions that could work with any set of inputs. Additionally, students had difficulty with specific mechanics of using variables such as storing user input in a variable, updating variable values, and using the values stored in variables. This study underscores the need for careful design, use, and analysis of elementary CT-integrated lessons and assessments to introduce and reinforce the conceptual understanding and specific mechanics of variables for elementary students.

There is little empirical research related to how elementary students develop computational thinking (CT) and how they apply CT in problem-solving. To address this gap in knowledge, this study made use of learning trajectories (LTs; hypothesized learning goals, progressions, and activities) in CT concept areas such as sequence, repetition, conditionals, and decomposition to better understand students’ CT. This study implemented eight math-CT integrated lessons aligned to U.S. national mathematics education standards and the LTs with third- and fourth-grade students. This basic interpretive qualitative study aimed at gaining a deeper understanding of elementary students’ CT by having students express and articulate their CT in cognitive interviews. Participants’ ( n = 22) CT articulation was examined using a priori codes translated verbatim from the learning goals in the LTs and was mapped to the learning goals in the LTs. Results revealed a range of students’ CT in problem-solving, such as using precise and complete problem-solving instructions, recognizing repeating patterns, and decomposing arithmetic problems. By collecting empirical data on how students expressed and articulated their CT, this study makes theoretical contributions by generating initial empirical evidence to support the hypothesized learning goals and progressions in the LTs. This article also discusses the implications for integrated CT instruction and assessments at the elementary level.

Developing computational thinking (CT) assessment methods appropriate for elementary students is attracting growing attention as CT research in elementary education progresses. To review the current elementary CT assessments for potential gaps, and seek additional methodologies to expand our understanding of CT, an integrative literature review of 75 research papers was performed in two phases. In Phase One, we conducted a critical analysis of existing elementary CT assessment studies. Key results include: 1) Artifact analysis, CT assessment items, and interviews are the most common methods utilized to assess CT in elementary grades; 2) Existing CT assessments primarily focus on students' computational artifacts and performance on CT tests; however, strategies to study students' thought processes during CT problem-solving are limited and under-utilized. Guided by the results of phase one, along with the theoretical perspective that connected CT to visual processing ability, in phase two we performed a survey of literature in the area of understanding cognitive processes through eye-tracking (i.e., visual attention) and think-aloud methodologies (i.e., verbalization). We focused on eye-tracking and think-aloud methodologies as these have been used to understand students' cognitive processes during problem-solving in other areas. Based on these findings, we proposed that in addition to current established methodologies, eye-tracking with the think-aloud technique can provide new insights into students' CT.

Debugging has been an expanding topic in K-12 computer science (CS) education research. However, few studies have focused on in-depth analysis of elementary students' debugging in block-based visual programming environments. Thus, using the video analysis technique, this basic interpretive qualitative study aimed to explore what debugging behaviors students exhibited and how these debugging behaviors mapped with an existing K-8 debugging learning trajectory (LT). Findings revealed five types of debugging behaviors and four primary challenges. These debugging behaviors mapped to five consensus goals in the K-8 debugging learning trajectory. Future research will focus on students' efficiency in using debugging strategies and understanding of debugging.

This study aimed to understand elementary students' challenges and interactions in computational thinking-integrated botany through robotics activities. Data was collected from screen-casting videos and analyzed using Collaborative Computing Observation Instrument (CCOI), a web-based analysis instrument with nodes and paths that classify and specify students' computing experience. The results revealed that all participants engaged in independent work for most of the time, with short interactions on 1) general computer technology issues; 2) software navigating issues; 3) questions about academic content; 4) computing discussion with the instructor; 5) informing the instructor about task accomplishment. The findings of this study will provide important insights to CS researchers, educators, and elementary teachers regarding CT-integration research and practice.

For K-8 computer science (CS) education to continue to expand, it is essential that we understand how students develop and demonstrate computational thinking (CT). One approach to gaining this insight is by having students articulate their understanding of CT through cognitive interviews. This study presents findings of a cognitive interview study with 13 fourth-grade students (who had previously engaged in integrated CT and mathematics instruction) working on CT assessment items. The items assessed four CT concepts: sequence, repetition, conditionals, and decomposition. This study analyzed students\textquotesingle articulated understanding of the four CT concepts and the correspondence between that understanding and hypothesized learning trajectories (LTs). We found that 1) all students articulated an understanding of sequence that matched the intermediate level of the Sequence LT; 2) a majority of students\textquotesingle responses demonstrated the level of understanding that the repetition and decomposition items were designed to solicit (8 of 9 responses were correct for repetition and 4 of 6 were correct for decomposition); and 3) less than half of students\textquotesingle responses articulated an understanding of conditionals that was intended by the items (4 of 9 responses were correct). The results also suggested questioning the directional relationships of two statements in the existing Conditionals LT. For example, unlike the LT, this study revealed that students could understand "A conditional connects a condition to an outcome'' before "A condition is something that can be true or false.''