This research to practice work in progress paper explores changes in teachers' knowledge of integrating computational thinking (CT) into early childhood education in a professional learning (PL) initiative. The main goal of the program is to prepare teachers to develop CT practices into early childhood learning environments by fostering several key components: recognizing the significance of CT development from early childhood, applying pedagogical practices (i.e. unplugged activities, Use-Modify-Create), identifying socially constructed roles and stereotypes surrounding toys, implications of screen and device usage in learning, and developing skills to design CT learning activities tailored for their own classroom settings. To achieve our goals, the research team designed, implemented, and evaluated an online 30-hour PL program with educators from Latin American countries. Approximately 80 teachers completed the online PL program. The participating teachers engaged with the PL program weekly by providing written journal reflections. In the final week, they were asked to design a lesson based on what they learned and implement it into their classroom. Teachers completed a pre- and post-survey surrounding their self-efficacy and technological, pedagogical, and content knowledge (TPACK) in CT. Data sources include pre- and post-surveys, teacher journal reflections, and lesson plans developed by teachers. In this paper, we analyzed their journal reflections to qualitatively develop an understanding of how teachers progressed in the PL. The lesson plans will be analyzed to identify: (1) the learning goals for the students, (2) which activities teachers preferred to implement, and (3) how they assessed student learning in CT in their classroom. Preliminary findings demonstrate the types of CT activities they implemented in their classrooms. Teachers also created lessons that met their students' needs which further provides examples of how CT is integrated within the existing curriculum surrounding student interest and culture. Many PL programs often teach about CT concepts, but do not offer a chance to implement a lesson plan and reflect on their experience. This process helps teachers develop self-efficacy for teaching CT in an elementary classroom and teachers reported that they would continue to integrate CT within their future instruction. Also, this program contributes to the reduction of the gender gap in CT, promoting equitable practices in early childhood education.

This innovative practice paper outlines a design-based research study to enhance data science literacy in students using an unplugged approach. With the rapid growth of data and its impact on daily life, there's a need for new educational methods to help students understand and use data effectively. Data science involves math, statistics, computation, AI, and machine learning, but teaching these concepts can be challenging without access to computers or the internet, potentially widening educational inequities. Inspired by unplugged computer science education, this study supports data science education in unplugged classrooms. Initial research focused on creating and testing two learning units on data visualization and summarization using innovative activities and reflection prompts. Analyzing students' work from a pilot with ten eleventh-graders in a low-income public school in a Global South country, the study aims to understand how these activities support data visualization skills and identify limitations. Future work will develop and implement lesson plans to promote data and AI literacy, aiming to make data science education more inclusive across diverse backgrounds.

In this innovative practice works-in-progress paper we designed an undergraduate/graduate engineering course that fosters learning experiences for students to develop and customize robotics for art and educational contexts. Engineering projects that offer real-world solutions provide students with a sense that they are engaging in something meaningful and see their time in school as a benefit to society. Engineering students in a robotics fundamentals course were paired with a senior art or engineering student mentor with prior experience to help develop a robotics platform for social good. Students were given basic robotics kits to create unique solutions that meet the needs of potential students in art or computer science education. We seek to capture this multidisciplinary approach that provides students with an experiential learning opportunity in engineering to design robotic systems. We report findings from the course and the students' projects. Seven groups created robots for art and three created robots for computer science education.

This work-in-progress research paper presents the preliminary results of a study exploring the effectiveness of using computational notebooks to enhance student learning in a Finite Element Analysis (FEA) course for undergraduate Mechanical Engineering students. Our previous work has shown that students often face difficulties in grasping abstract concepts from mechanics of materials while simultaneously learning computational modeling. However, students recognized several advantages of using MATLAB for FEA compared to manual calculations, including significant time savings, increased efficiency, and reduced errors. Nevertheless, they also faced challenges, including a steep learning curve for MATLAB and concerns about how this limitation hinders their conceptual understanding. Despite these drawbacks, they recognized the importance and value of developing computational skills for their future careers. In this study, we extended the scaffolds and changed the sequence of activities to address the challenges the students faced in the previous iteration of our work. Specifically, we provided worked examples that students needed to use, self-explain, and modify before they engaged in programming from scratch. Also, after developing a basic understanding of how to implement FEA in MATLAB, the students used ChatGPT to generate a code that would do the same task. This activity required them to evaluate and refine automatically generated MATLAB code, as ChatGPT may provide alternative solutions that might not always work correctly. We explore three main topics to understand student experiences with and perceptions of this approach: (1) the value of using computational methods compared to manual completion for FEA; (2) the challenges and support of using MATLAB for FEA; and (3) the effectiveness of simulation tools to learn FEA. The goal of this project is two-fold: (1) supporting student learning of intricate phenomena explored in mechanics of materials, like distribution of stress, and stiffness, and (2) fostering essential computational thinking skills through practical disciplinary coding experience. By implementing these elements, the study anticipates a substantial improvement in students' understanding of FEA principles and their ability to translate them into solutions for real-world engineering challenges.

This work-in-progress research paper reflects on findings from a program evaluation of a nationwide professional development (PD) initiative designed to enhance computational thinking (CT) skills among teachers in Colombia's public schools. Evaluating CT education programs presents challenges due to the limited research on the validity and reliability of data collection tools. In addition, evaluation of PD programs in CT often overlooks affective factors and fails to align with program outcomes beyond the training context, leading to insufficient evidence of their effectiveness. This study seeks to identify the factors that influence the effective implementation of CT teaching practices and contribute to the body of knowledge of professional development programs. Using a sequential mixed-methods approach, we first conducted pre-post surveys to assess changes in teachers' knowledge, followed by realist-based focus group discussions to gain deeper insights into participants' experiences and the factors influencing classroom implementation. The primary contribution of this paper is providing insights into the underlying mechanisms driving changes in teachers' practices after participating in a CT-focused PD program in a middle-income country. Preliminary results indicate significant improvements in teachers' CT knowledge post-intervention. Qualitative findings highlight the importance of teachers' confidence in their CT knowledge and the need for comprehensive school support for effective CT integration.

This innovative practice work-in-progress paper explores the outcomes of a Machine Learning module taught to undergraduate students from different majors with various levels of programming backgrounds. Our approach differs from most machine learning for undergraduate courses, which usually teach about machine learning on a language-centric code-first basis. However, while not everyone needs to deploy a model in production, we should open the black box so they understand what they consume and help build through their data and feedback. Our results will contribute to the growing field of machine learning education as we explore students conceptual understanding about Machine Learning and its algorithms. Machine learning is relatively ubiquitous in many different fields and contexts. There are new developments that include machine learning algorithms that students interact with every day. From the auto-complete feature in their cellphones to the content they are shown while browsing the internet, machine learning algorithms shape the way students experience the world. Therefore, students must critically understand this technology and its implications for their lives. This study focuses on a four-week course module that included learning activities designed for students to develop their intuition regarding the benefits of implementing machine learning before moving to a programming language. Students recognize commercial uses of machine learning algorithms, apply the concepts in a visual programming environment, and reflect on the benefits and limitations of the algorithms. This work-in-progress paper presents the work of 13 participants who completed at least one of the following learning activities. The participants completed reflection activities that represented retrieval practice opportunities to consolidate their learning and a final project to demonstrate their understanding of machine learning and how it may be implemented. We analyzed the qualitative data of student work to describe how the suggested progression supports learning about machine learning.

This work-in-progress paper will explore the effectiveness of topic modeling to support the analysis of Colombian teachers' conceptions of computational thinking (in Spanish) in an online professional development program. Computational thinking has become a form of literacy as it can help individuals to solve problems. Consequently, governments and bodies of accreditation worldwide have supported educational initiatives, primarily at the K-12 level. However, curricular changes are not enough. Teachers need to be prepared, so they develop the content knowledge associated with computational thinking concepts, practices, and applications in the classroom. To contribute to professional development opportunities geared toward the development of computational thinking pedagogical content knowledge, the Colombian National Academy of Exact, Physics, and Natural Sciences and the Global Center for Equitable Computer Science Education implemented an open online professional development program for Latin American early childhood and elementary educators. More than 100 teachers enrolled in a six-week online professional development program to integrate computational thinking activities from early childhood education. The program included two modules focused on conceptual understanding of computational thinking in early childhood and four more modules where the participants adapted, designed, implemented learning activities, and reflected on what happened during the implementation. As part of the participants' weekly interactions, the program included a Jamboard space, where the teachers answered a set of guiding questions, just like a discussion forum, but as a post-it wall, where they could access all their peers' contributions and questions in a single space.

In this work-in-progress, we explore the use of prompt engineering and image generation to elicit student understanding of different concepts discussed in structural engineering courses. In the context of image generation, prompt engineering refers to the process of providing a text or image prompt to a machine learning model to generate an image that meets certain criteria. The quality of the prompt can significantly impact the quality of the generated image. Hence, prompt engineering is an important part of the image generation process. By providing a well-crafted prompt that accurately describes the desired image, the generative model will more likely produce an image that meets the desired criteria. In the context of this study, students created prompts that were converted into images using Text-to-image Generative Models. When adequately prompted, these images are meant to visually describe their understanding of the concepts we have discussed during traditional lectures. The participants were presented with their own image as well as others' images to elicit both agreements or concerns at two levels: their understanding of the concepts and their understanding of the importance of prompting in the foreseeable future of AI-based systems. Hitherto, we have tried to use the generation of images using natural language for answering these research questions: i) for students, to which extent the crafting prompts may elicit thinking about the structural engineering phenomena? and ii) for instructors, to which extent these represent the level of understanding of the students on these topics?

This work-in-progress paper introduces an innovative approach that uses computational notebooks to teach Finite Element Analysis (FEA) in a Mechanical Engineering undergraduate course to aid the understanding of complex phenomena in Mechanics of Materials, and enhance students' computational thinking skills. Research indicates that students, irrespective of their educational level, face difficulties in grasping fundamental concepts in mechanics of materials. These challenges arise from the inherent complexity of concepts like stress, strain, torsion, and buckling, which are difficult to observe, hindering comprehension. Therefore, this work aims to leverage the synergy between mechanics of materials and computational principles to actively engage students in advanced topics such as structural strength, failure of structures, and sensitivity analysis, through the use of computational notebooks. To evaluate the effectiveness of this approach, we first asked students to analyze truss structures using hand calculations following the discrete stiffness method within a Finite Element Analysis framework. Subsequently, we implemented the same method as a simulation tool in a MATLAB Computational Notebook. Finally, we asked the students to reflect on: (1) the value of using computational methods to approach Finite Element Analysis when compared to hand calculations; (2) the difficulties they faced when implementing the activities in the MATLAB Computational Notebook; (3) the support they required to successfully complete these activities; and (4) the effectiveness of the simulation tool in understanding the effect of forces and stress distributions in structures. We anticipate that computational notebooks will provide an ideal platform for sharing lessons and tutorials, enhancing student engagement, and promoting active learning. Students have access to the complete source code, allowing them to develop computational skills. Early exposure to coding, modeling, and simulation techniques is crucial in preparing students for the computational demands of modern engineering workplaces.

This work-in-progress paper is part of a project that aims to analyze how the implementation of different instructional designs through complete and incomplete examples influence the cognitive load that students perceive when learning statistics and computer programming. For this first stage, we conducted a pilot study aimed at conducting a differentiated measurement of cognitive loads through secondary tasks and a subjective scale. The pilot study consisted of three moments. The first one focused on measuring their working memory capacity and their prior knowledge of statistics. Then, students worked on a learning activity focused on using Chi-square and Pearson correlation to conduct inferential analyses. The study included two different instructional designs in the form of computational notebooks. Design 1 consisted of correct and incomplete examples, while Design 2 included correct, incomplete, and incorrect examples. Simultaneously, students had to attend to a secondary task, which consisted of responding to an auditory stimulus, after which they had to press a key on their keyboard. Finally, students completed a naïve rating scale of cognitive loads involved in the task. The results highlight the important role of prior knowledge in different instructional designs and how it connects to students' perceived cognitive effort. Likewise, the differences in the instructional designs influenced students' cognitive loads, suggesting a differentiated measure of extraneous loads. Using incorrect examples in learning statistics and programming may affect students' perceived difficulty without resulting in a better learning outcome.

This Research to practice work in progress paper explores teachers' experiences about a professional development program focused on discipline-based computational thinking (CT). The goal of the program is to integrate computational practices into disciplinary learning environments at the K-12 level in Colombia. To promote this integration and to explore teachers' conceptions and possible scenarios to integrate CT in this context, the research team designed, implemented, and assessed a 20-hour professional development program with 21 teachers from public middle and high schools in Antioquia, Colombia. The program introduced the concepts and practices of CT and discussed why these are relevant for students to learn. A use-modify-create instructional sequence was used as the pedagogical approach to scaffold participants' learning process. At the end of the workshop, the teachers completed a survey regarding their experience in the program. Preliminary results indicate that the participating teachers increased their knowledge and interest to integrate computational thinking practices into their disciplinary learning environments.

This Work in Progress, Innovative Practice paper examines the implementation and preliminary results of a flipped classroom strategy in a computational modeling undergraduate course at a [nationality] midsize university. Previous work has discussed the potential of flipped classrooms to leverage active learning through the use of videos and other computer-based tools that encourage students to explore course content autonomously. This study explores how the tools and tactics used in the course, namely out-of-class readings and Jupyter notebooks, can be effective to engage students and foster learning. To that aim, the final study will compare the changes in perceptions and performance of two classes (Fall 2019 and Spring 2020) before and after students take the course. The perceptions part of the instrument focuses on students' self-efficacy and interest in programming. On the other hand, the performance part of the instrument asks students to explain the purpose of simple programs by examining the code. Preliminary results suggest that the flipped format implemented increases students' self-efficacy regarding programming tasks, particularly within students already interested in this computational modeling. These results align well with preliminary performance results, which suggest that the strategy implemented prompts more students to try solving the programming problems and provide explanations, yet not always complete or correct.

This Research to Practice Full Paper explores students' self-explanations in the context of programming. Specifically, this paper explores the use of in-code comments as an approach to support students' learning process and development of abstraction skills in an introductory programming course at the undergraduate level. Computer programming is a difficult skill to learn by novices due to the complexity of multiple elements interacting with each other to produce a specific outcome. Providing worked-examples paired with an engaging pedagogical practice has demonstrated to be an effective strategy for novices to start learning complex topics such as computer programming. One of the strategies that can support the introduction of worked examples is the use of self-explanation activities. In the context of programming, using in-code comments as a way for students to self-explain programming code can support the integration of worked-examples to scaffold their learning process. In this study, students wrote comments to explain how worked examples that were provided completed a specific task. Their comments were scored using an assessment rubric to provide detailed feedback about regarding the quality of their comments and their understanding of the code beyond the line by line execution. The goal of this study is to explore whether students with prior exposure to computer programming generate better self-explanations, and the effect that the quality of the written explanations and students' prior programming experiences have on student overall performance in the introductory programming course. The implications of this study will contribute to a better understanding of effective practices to incorporate worked examples and self-explanation activities in the form of in-code comments for introductory programming courses.

This paper proposes a special session on the use of computational methods for analyzing educational data. Computation has permeated all disciplines because it provides unique opportunities to represent knowledge and understand complex phenomena. In education, disciplines such as learning analytics and educational data mining have emerged to better understand educational phenomena. This special session will discuss three different approaches to use computational methods to analyze qualitative educational data. After the discussion, the participants will be able to implement these methods using R programming, while reflecting on how they can use these methods in their own context.

This paper proposed a workshop to introduce the use of computational tools and methods to analyze educational data. The workshop will demonstrate three different contexts in which these tools can be used to visualize and characterize patterns within educational data, and validate them using statistical techniques. Participants in this workshop will have the opportunity to learn how to implement these methods using R programming language.

Classroom instruction in the 21st century needs to incorporate innovative, research-based pedagogies. The engineering classroom is currently experiencing a shift towards more active learning activities due to both advances in educational research, and advances in technologies that enable practices such as the flipped classroom model. Given that course transformation is a gradual process that begins at the level of the instructor, educators need access to the essential tools and training in order to introduce these changes into the curricula. This paper introduces a course re-design based on Self-Determination Theory and Constructivism; and outlines effectively implemented active learning strategies using the flipped classroom model. The data were collected from a Numerical Analysis course, which is an important course across several engineering disciplines at Universidad EAFIT. This course enables engineering students to solve complex problems using mathematical and computational methods. This paper describes the implementation of an online active learning platform called “Numérico Interactivo” for two related engineering courses: Numerical Analysis (NA) and Numerical Processes (NP). The platform was available to all students, but only NA implemented it using a flipped classroom model. NP made the platform available as an optional course tool. Informed by SDT principles, “Numérico Interactivo” includes a variety of instructional materials such as explanations, examples, frequently asked questions (FAQ), self-assessment tools, and evaluation. This study compares the two courses in terms of: (1) students' perceptions about the instructional materials of the course; (2) students' use of the platform; and (3) students' perceived usefulness of the different elements within the platform. Results suggest that students in the NA course found the classroom sessions and the homework assignments more useful as compared to the students enrolled in the NP course. In addition, in the NA course students used the platform more often for class preparation and to study before each module. The way in which the platform was implemented in NA also increased student motivation in the course. Overall, the results suggest that “Numérico Interactivo” is useful to implement course re-designs into engineering and computing education courses, but such tools need to be guided by active learning practices so that students can fully benefit from them.

This article presents two case studies aimed at exploring the use of self-explanations in the context of computational science and engineering (CSE) education. The self-explanations were elicited as students’ in-code comments of a set of worked-examples, and the cases involved two different approaches to CSE education: glass box and black box. The glass-box approach corresponds to a programming course for materials science and engineering students that focuses on introducing programming concepts while solving disciplinary problems. The black-box approach involves the introduction of Python-based computational tools within a thermodynamics course to represent disciplinary phenomena. Two semesters of data collection for each case study allowed us to identify the effect of using in-code comments as a self-explanation strategy on students’ engagement with the worked-examples and students’ perceptions of these activities within each context. The results suggest that the use of in-code comments as a self-explanation strategy increased students’ awareness of the worked-examples while engaging with them. The students’ perceived uses of the in-code commenting activities include: understanding the example, making a connection between the programming code and the disciplinary problem, and becoming familiar with the programming language syntax, among others.

Design is recognized as a key engineering activity, and engineering is a fundamental part of science education at the K-12 level. However, it is difficult to assess student designs when the range of “correct” answers is wide. Feedback in the form of assessment helps students learn from a design activity and can direct students along the pathway of improvement. The purpose of this paper is to develop an assessment protocol to measure solution quality taking into account both objective and subjective design criteria (e.g. measurements of cost/energy used along with aesthetics). Three protocols are developed by analyzing and comparing 109 high school students' design solutions to a zero-energy home design task. Lessons learned from the first two approaches informed the third approach that highlights the importance of balancing trade-offs. Results suggest the Trade-off Value approach provides an intuitive and accurate way to understand how well a designer has balanced both complementary and competing design criteria. These results can be used as feedback to support a systems design process and to evaluate the relationship between design quality and design behavior.

Engineering design is an iterative process that supports the solution of problems by applying scientific knowledge to make informed decisions. Assessing different levels of expertise in experimentation is a difficult task since these are not usually visible as part of a student's final design solution. The purpose of this research is to investigate and characterize students' experimentation strategies while working on a design challenge. We conducted a concurrent think-aloud to capture students' thinking while they were working on a design challenge using an educational computer-aided design (CAD) software. We showed how the design replays generated from the log files collected from the CAD software can be used to represent students' experimentation strategies and how these representations can be validated by the data collected from the think-aloud. Our preliminary results show that technology-based assessment by the educational CAD tool allows us to identify the differences between different experimentation strategies and that the result of this assessment is supported by the result obtained from the concurrent think-aloud. Implications of this work would be relevant to engineering educators and researchers who are interested in understanding and assessing students' experimentation strategies in engineering design.

Engineering design is a complex activity for students to undertake and for instructors to assess. This research uses large learner data sets collected through automatic, unobtrusive logging of student actions in a CAD platform to address this difficulty in observing design behavior. We used a computer-aided design software that captured student design activities to investigate patterns of student design behaviors that are associated with idea fluency. We show how micro-level process data can be used to validate observations made from viewing the student design process through design replays. Students who engaged in high idea fluency showed evidence of fluency in both process data and design replays. Similar patterns were observed for low idea fluency students. There is great potential to investigate student design learning through system-collected data. Yet, how to justify the inferences made about students based on their process data is largely unexplored. Our results demonstrate how traditional forms of assessment data can be used to validate inferences made by process data. Implications of this work would be highly relevant to engineering educators as well as researchers who are interested in understanding the relationship between learner analytics and student learning.

Advances in computing contribute to science and engineering discovery, innovation, and education by facilitating representations, processing, storage, analysis, simulation, and visualization of unprecedented amounts of experimental and observational data to address problems that affect health, energy, environment, security, and quality of life. In spite of the emerging importance of the role of computing in engineering, a well-recognized shortage of scientists and engineers who are adequately prepared to take advantage of, or contribute to, such highly interdisciplinary, highly computational scientific challenges is evident. This exploratory study identifies how computation is integrated in the engineering disciplines at the undergraduate level. The research question is: How engineering professors integrate computation as part of their disciplinary undergraduate courses? This study reports anonymous survey responses of thirty-nine engineering and engineering technology faculty members who identified themselves as integrating computation as part of their undergraduate courses. Results indicate that most of the faculty members used computation for the solution of complex calculations, for conducting simulations, and for design purposes. Further research is required in order to identify and validate appropriated pedagogical practices to integrate computation as part of disciplinary courses.

This paper describes the outcomes of a three-day teacher professional development workshop aimed at introducing concepts, principles and practices of computational thinking. The guiding research question for this study was: How teachers implement the backwards design process embodying elements of CS Principles (i.e., computational thinking big ideas and computational thinking practices) in the context of their classrooms? The participants of this study included 15 elementary, high school and college level teachers who are also graduate students from a master program in engineering. As part of the workshop participants developed a learning activity that included a set of learning objectives, the design of computational thinking related activities considering appropriate pedagogical strategies, and the integration of mechanisms to evaluate students' performance. Here we describe (a) how participants embodied the CS Principles in the design of learning activities to be integrated into their classrooms, (b) how they used the backwards design process as a tool to implement elements of the CS Principles and (c) what is teachers' performance in integrating CS Principles to the design of learning activities as evidenced by their peer evaluations. Finally, we propose the use of backwards design process together with the CS Principles as a framework for the design of computing learning activities and the development of teacher professional development programs in computing education.

TAG, in Spanish: Tecnología, Aprendizaje y Gestión (Technology, Learning and Management) is an ubiquitous learning model that aims becoming a point of reference for higher education institutions within their transformation processes in educational innovation. The model is built on three dimensions: Technology, Learning and Management, which are assessed through the identification of categories and properties, and their associated metrics and indicators to determine the levels of ubiquity in a higher education institution, enabling the organizacional diagnosis and the design of strategies to increment the ubiquity level. This paper introduces the conceptual guidelines that enables the definition of the main properties for each one of the TAG dimensions.