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Infrared (IR) technologies have been universally acknowledged as a valuable pedagogical tool for exploring novel and abstract scientific subjects in science education. This study explores the roles of IR images played in middle school students' Evidence-based Reasoning (EBR) process in support of the understanding of the heat radiation process. Specifically, we implement image processing algorithms explicitly for the visual artifacts mentioned in students' descriptions of the radiation phenomenon to obtain the numeric representations of their corresponding features. Meanwhile, the quality of those descriptions is further coded with the guidance of the EBR framework for indicating students' understanding levels of the phenomenon. Finally, the associations between the numerical image features and the quality of descriptions are analyzed to examine the effectiveness of the IR visual artifacts in helping students understand the heat radiation process. The analytical results found that the image features are further positively correlated with the quality of the descriptions generated by students for the heat radiation. The results further suggest the IR images have the potential of driving students to think proactively and explore detailed procedural changes in learning the heat radiation process. Finally, our study calls for the integration of interdisciplinary instructional approaches in science education to reduce students' cognitive load and guide learning attention, for example, incorporating visualization and relevant processing approaches to present and analyze the otherwise invisible abstract process to help students make sense related knowledge more easily.

Learning analytics, referring to the measurement, collection, analysis, and reporting of data about learners and their contexts in order to optimize learning and the environments in which it occurs, is proving to be a powerful approach for understanding and improving science learning. However, few studies focused on leveraging learning analytics to assess hands-on laboratory skills in K-12 science classrooms. This study demonstrated the feasibility of gauging laboratory skills based on students' process data logged by a mobile augmented reality (AR) application for conducting science experiments. Students can use the mobile AR technology to investigate a variety of science phenomena that involve concepts central to physics understanding. Seventy-two students from a suburban middle school in the Northeastern United States participated in this study. They conducted experiments in pairs. Mining process data using Bayesian networks showed that most students who participated in this study demonstrated some degree of proficiency in laboratory skills. Also, findings indicated a positive correlation between laboratory skills and conceptual learning. The results suggested that learning analytics provides a possible solution to measure hands-on laboratory learning in real-time and at scale.

Engineering design plays an important role in education. However, due to its open nature and complexity, providing timely support to students has been challenging using the traditional assessment methods. This study takes an initial step to employ learning analytics to build performance prediction models to help struggling students. It allows instructors to offer in-time intervention and support for these at-risk students. Specifically, we develop a task model to characterize the engineering design process so that the data features can be associated with the abstract engineering design phases. A two-stage feature selection method is proposed to address the data sparsity and high dimensionality problems. Then, instead of relying on probability-based algorithms such as Bayesian Networks to represent the task model, this study used the Radial Basis Function based Support Vector Machines for prediction to identify the struggling students. Next, we employ an extra-tree classification method to rank the importance of these features. Teachers can integrate the feature importance ranking with the abstract task model to diagnose students' problems for scaffolding design. The results show that the proposed approach can outperform the baseline models as well as providing actionable insights for teachers to provide personalized and timely feedback to students. Implications of this study for research and practice are then discussed.

Research on self-regulated learning (SRL) in engineering design is growing. While SRL is an effective way of learning, however, not all learners can regulate themselves successfully. There is a lack of research regarding how student characteristics, such as science knowledge and design knowledge, interact with SRL. Adapting the SRL theory in the field of engineering design, this study proposes a research model to examine the mediation and causal relationships among science knowledge, design knowledge, and SRL activities (i.e. observation, formulation, reformulation, analysis, evaluation). Partial least squares modeling was utilized to examine how the science and design knowledge of 108 ninth-grade participants interacted with their SRL activities in the process of performing an engineering task. Results reveal that prior science and design knowledge positively predict SRL activities. They also show that reformulation and analysis are the two SRL activities that can lead to an improvement in post science and design knowledge, but excessive observation can hinder post design knowledge. These results have important implications for the construction of learning environments to support SRL based on students' prior knowledge levels.

Video analysis tools such as Tracker are used to study "mechanical motion" captured by photography. One can also imagine a similar tool for tracking "thermal motion" captured by thermography. Since its introduction to physics education, thermal imaging has been used to visualize phenomena that are invisible to the naked eye and teach a variety of physics concepts across different educational settings. But thermal cameras are still scarce in schools. Hence, videos recorded using thermal cameras such as those featured in "YouTube Physics" are suggested as alternatives. The downside is that students do not have interaction opportunities beyond playing those videos.

Augmented reality (AR) has the potential to fundamentally transform science education by making learning of abstract science ideas tangible and engaging. However, little is known about how students interacted with AR technologies and how these interactions may affect learning performance in science laboratories. This study examined high school students' navigation patterns and science learning with a mobile AR technology, developed by the research team, in laboratory settings. The AR technology allows students to conduct hands-on laboratory experiments and interactively explore various science phenomena covering biology, chemistry, and physics concepts. In this study, seventy ninth-grade students carried out science laboratory experiments in pairs to learn thermodynamics. Our cluster analysis identified two groups of students, which differed significantly in navigation length and breadth. The two groups demonstrated unique navigation patterns that revealed students' various ways of observing, describing, exploring, and evaluating science phenomena. These navigation patterns were associated with learning performance as measured by scores on lab reports. The results suggested the need for providing access to multiple representations and different types of interactions with these representations to support effective science learning as well as designing representations and connections between representations to cultivate scientific reasoning skills and nuanced understanding of scientific processes.

During the COVID-19 pandemic, many students lost opportunities to explore science in labs due to school closures. Remote labs provide a possible solution to mitigate this loss. However, most remote labs to date are based on a somehow centralized model in which experts design and conduct certain types of experiments in well-equipped facilities, with a few options of manipulation provided to remote users. In this article, we propose a distributed framework, dubbed remote labs 2.0, that offers the flexibility needed to build an open platform to support educators to create, operate, and share their own remote labs. Similar to the transformation of the Web from 1.0 to 2.0, remote labs 2.0 can greatly enrich experimental science on the Internet by allowing users to choose and contribute their subjects and topics. As a reference implementation, we developed a platform branded as Telelab. In collaboration with a high school chemistry teacher, we conducted remote chemical reaction experiments on the Telelab platform with two online classes. Pre/posttest results showed that these high school students attained significant gains (t(26) = 8.76, p < 0.00001) in evidence-based reasoning abilities. Student surveys revealed the following three key affordances of Telelab: 1) live experiments; 2) scientific instruments; and 3) social interactions. All 31 respondents were engaged by one or more of these affordances. Students' behaviors were characterized by analyzing their interaction data logged by the platform. These findings suggest that appropriate applications of remote labs 2.0 in distance education can, to some extent, reproduce critical effects of their local counterparts on promoting science learning.

Background: With the increasing popularity of distance education, how to engage students in online inquiry-based laboratories remains challenging for science teachers. Current remote labs mostly adopt a centralized model with limited flexibility left for teachers' just-in-time instruction based on students' real-time science practices. Objectives: The goal of this research is to investigate the impact of a non-centralized remote lab on students' cognitive and behavioural engagement. Methods: A mixed-methods design was adopted. Participants were the high school students enrolled in two virtual chemistry classes. Remote labs 2.0, branded as Telelab, supports a non-centralized model of remote inquiry that can enact more interactive hands-on labs anywhere, anytime. Teleinquiry Instructional Model was used to guide the curriculum design. Students' clickstreams logs and instruction timestamps were analysed and visualized. Multiple regression analysis was used to determine whether engagement levels influence their conceptual learning. Behavioural engagement patterns were corroborated with survey responses. Results and Conclusions: We found approximate synchronizations between student-teacher-lab interactions in the heatmap. The guided inquiry enabled by Telelab facilitates real-time communications between instructors and students. Students' conceptual learning is found to be impacted by varying engagement levels. Students' behavioural engagement patterns can be visualized and fed to instructors to inform learning progress and enact just-in-time instruction. Implications: Telelab offers a model of remote labs 2.0 that can be easily customized to live stream hands-on teleinquiry. It enhances engagement and gives participants a sense of telepresence. Providing a customizable teleinquiry curriculum for practitioners may better prepare them to teach inquiry-based laboratories online.

Understanding the design process may reveal when and where resources should be focused and how engineers can better use tools, methods, and techniques to enhance the quality of designs and creative performance. The literature suggests the importance of iterative evaluation and reformulation in the engineering design process. The current study collected 111 high school students' logs of designing an energy-saving house in Energy3D, a computer-aided design environment. Using a cross-lag model, we investigated the reciprocal relationship between students' evaluation and reformulation behaviors and how these behaviors influence students' design performance at the early, middle, and final design stages. The results suggest that there is a positive predictive relationship between students' evaluation and reformulation process; reformulation positively predicts design performance and mediates the relationship between evaluation and design performance across time. These results provide empirical evidence of the importance of iterative evaluation and reformulation in the design process and implications for teachers and system designers to support students' design.

Digital sensors allow people to collect a large quantity of data in chemistry experiments. Using infrared thermography as an example, we show that this kind of data, in conjunction with videos that stream the chemical phenomena under observation from a vantage point, can be used to construct digital twins of experiments to support science education on the cloud in a visual and interactive fashion. Through digital twins, a significant part of laboratory experiences such as observation, analysis, and discussion can be delivered on a large scale. Thus, the technology can potentially broaden participation in experimental chemistry, especially for students and teachers in underserved communities who may lack the expertise, equipment, and supplies needed to conduct certain experiments. With a cloud platform that enables anyone to store, process, and disseminate experimental data via digital twins, our work also serves as an example to illuminate how the movement of open science, which is largely driven by data sharing, may be powered by technology to amplify its impacts on chemistry education.

In this paper, we demonstrate how machine learning could be used to quickly assess a student's multimodal representational thinking. Multimodal representational thinking is the complex construct that encodes how students form conceptual, perceptual, graphical, or mathematical symbols in their mind. The augmented reality (AR) technology is adopted to diversify student's representations. The AR technology utilized a low-cost, high-resolution thermal camera attached to a smartphone which allows students to explore the unseen world of thermodynamics. Ninth-grade students (N = 314) engaged in a prediction-observation-explanation (POE) inquiry cycle scaffolded to leverage the augmented observation provided by the aforementioned device. The objective is to investigate how machine learning could expedite the automated assessment of multimodal representational thinking of heat energy. Two automated text classification methods were adopted to decode different mental representations students used to explain their haptic perception, thermal imaging, and graph data collected in the lab. Since current automated assessment in science education rarely considers multilabel classification, we resorted to the help of the state-of-the-art deep learning technique-bidirectional encoder representations from transformers (BERT). The BERT model classified open-ended responses into appropriate categories with higher precision than the traditional machine learning method. The satisfactory accuracy of deep learning in assigning multiple labels is revolutionary in processing qualitative data. The complex student construct, such as multimodal representational thinking, is rarely mutually exclusive. The study avails a convenient technique to analyze qualitative data that does not satisfy the mutual-exclusiveness assumption. Implications and future studies are discussed.

Integrating engineering design into K-12 curricula is increasingly important as engineering has been incorporated into many STEM education standards. However, the ill-structured and open-ended nature of engineering design makes it difficult for an instructor to keep track of the design processes of all students simultaneously and provide personalized feedback on a timely basis. This study proposes a Bayesian network model to dynamically and automatically assess students' engagement with engineering design tasks and to support formative feedback. Specifically, we applied a Bayesian network to 111 ninth-grade students' process data logged by a computer-aided design software program that students used to solve an engineering design challenge. Evidence was extracted from the log files and fed into the Bayesian network to perform inferential reasoning and provide a barometer of their performance in the form of posterior probabilities. Results showed that the Bayesian network model was competent at predicting a student's task performance. It performed well in both identifying students of a particular group (recall) and ensuring identified students were correctly labeled (precision). This study also suggests that Bayesian networks can be used to pinpoint a student's strengths and weaknesses for applying relevant science knowledge to engineering design tasks. Future work of implementing this tool within the computer-aided design software will provide instructors a powerful tool to facilitate engineering design through automatically generating personalized feedback to students in real time.

This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels. Although the classroom orchestration of the particular learning design was customised for specific audiences and contexts, findings from this study suggest that the core components of the learning design, such as content, assessment, and pedagogy, and their alignment among them, resulted in students' learning. Specifically, results from a pre-post science assessment suggest that the three student groups arrived at similar understanding post-intervention levels, along with a significant aggregate growth in their scientific understanding. Regarding design performance, students in different groups demonstrated different levels of success in meeting design constraints. The findings also suggest that students' success rate in meeting the design constraints directly influenced their final design performance, where middle-school students had better performance than students in the other groups. That is, across the board, students increased their conceptual understanding of heat transfer, Earth, and solar science and were able to produce feasible designs. Implications of the study include how learning experiences with engineering and science simulations should be designed so that teachers can adopt and adapt materials for their specific audiences, contexts, and settings.

Telelab, an implementation of remote labs 2.0, is open-source and freely available for non-commercial use. The platform consists of sensors that collect data, smartphone apps that transmit data and videos from anywhere, and a web app that provides a user interface to view, analyze, and control remote experiments. Based on this platform, any science teacher can broadcast their own experiments to any number of students, who will not only observe the experiments unfold over a livestream of images but also receive real-time sensor data for independent analyses. The platform also allows teachers to record experiments along with sensor data and upload them to a repository, so students who miss the live sessions can also catch up by working with these prerecorded experiments. As a video- and data-streaming platform, Telelab allows teachers to conduct experiments in the same way they would in a physical classroom under normal circumstances, which paves the way for a comfortable transition to remote labs. In this article, the authors demonstrate how Telelab can assist remote teaching with three common examples. For all the examples, a low-cost infrared (IR) thermal camera is used as a high-throughput sensor to collect large quantities of temperature data in real time.

First-year engineering students are often introduced to the engineering design process through project-based learning situated in a concrete design context. Design contexts like mechanical engineering are commonly used, but students and teachers may need more options. In this article, we show how sustainable building design can serve as an alternative for students of diverse backgrounds and with various interests. The proposed Net Zero Energy Challenge is an engineering design project in which students practice the full engineering design cycle to create a virtual house that generates renewable energy on-site, with the goal to achieve net zero energy consumption. Such a design challenge is made possible by Aladdin, an integrated tool that supports building design, simulation, and analysis within a single package. A pilot study of the Net Zero Energy Challenge at a university in Mid-Atlantic United States suggests that around half of the students were able to achieve the design goal. [ABSTRACT FROM AUTHOR]

Infrared (IR) thermography renders invisible infrared radiation with intuitive coloration in images and videos taken of objects, reactions, and processes. Educators can take advantage of this technology to extend students' sensory perception of chemical reactions or processes that absorb or release heat in rich detail. In theory, IR thermography can be applied essentially universally for such analysis given that any change in thermal energy must result in, or from, the change of potential energy due to the interactions among atoms, molecules, and photons. Through the use of IR thermography, students can visualize otherwise invisible evidence of what is occurring on the molecular level in a variety of chemical process such as evaporative cooling, phase change, dissolution, titration, and enzymatic reactions. While not new, IR cameras are rapidly becoming affordable with models that connect easily with smartphones and tablets. The price decrease has opened the door for large-scale implementation in the chemistry education laboratory. We report here several laboratory activities and best practices that will facilitate the exploration of specific chemistry concepts through the use of infrared thermography, as well as integration of this technique into existing general chemistry laboratory courses.

Although reflection is a key behaviour of expert designers, it is often a challenging task for new designers. In addition, research on the reflectivity of student designers is limited. The purpose of this study is twofold: (1) to identify the levels of reflectivity while designing, and (2) to study the relationship between reflectivity and conceptions of informed design. We collected data from high school students engaged in an engineering design project. We developed a coding protocol to score levels of reflectivity in student reflections at three levels (low, medium, and high), and used the "conceptions of design test" to assess changes in student understanding of design activities. Using Wilcoxon signed-rank tests, we determined if students tended to select more 'key' design activities and fewer 'distractors' within each reflection group. We also performed McNemar's tests to determine which specific design activities were important within each reflection group after the design project. The results show moderately reflective students had higher gains in understanding of informed design activities compared to those with high or low reflectivity. Results also indicate that different design activities became important for students within each of the three reflective groups. Implications from this research indicate that groups of students experience changing conceptions of design in different ways. An understanding of what students deem important while designing would better allow teachers to encourage behaviours that are like those of informed designers.

As solar energy becomes increasingly affordable, many schools are considering installing new solar power systems. In this article, the authors present Solarize Your School, an engineering project that gives students the opportunity to design and evaluate solar power solutions for their own schools. This STEM project requires students to learn and apply skills and practices related to solar energy and photovoltaic technology concepts, such as architectural measurement and modeling techniques, graphical interpretation and data analysis, budgeting and investing, scientific inquiry and engineering design, and collaboration and communication. Solarize Your School can be incorporated into environmental science, physical science, and engineering courses, and can be adapted to fit any curriculum scope and time frame. The authors suggest a 10-day sequence of learning activities.

This paper investigates iteration and fixation in design by mining digital footprints left by designers. High school students used computer-aided design software to create buildings in an urban area, with the goal of applying passive solar design techniques to ensure optimal solar gains of the buildings throughout a year. Students were required to complete three different designs. Fine-grained data including design actions, intermediate artifacts, and reflection notes were logged. Computational analytics programs were developed to mine the logs through three indicators: (a) frequency of the action of using energy analysis tools; (b) solar performance of the final designs; and (c) difference in solar performance between the prototype and final designs. Triangulating results from the indicators suggests three types of iteration--efficacious, inadequate and ineffective. Over half of the participants were detected as being efficacious iterative during the first design and becoming more and more fixated toward the end of the project, which resonates with previous findings on fixation effect among college students and professional designers. Overall the results demonstrate the power of applying computational analytics to investigate complex design processes. Findings from this work shed light on how to quantitatively assess and research student performance and processes during design projects.

Many pedagogical innovations aim to integrate engineering design and science learning. However, students frequently show little attempt or have difficulties in connecting their design projects with the underlying science. Drawing upon the Cultural-Historical Activity Theory, we argue that the design tools available in a learning environment implicitly shape knowledge development as they mediate students' design actions. To explore the roles of tools in design-science integrated learning environments, this study investigated how secondary students' tool-mediated design actions were linked with their science learning in a tool-rich design environment with minimal explicit guidance. Eighty-three ninth-grade students completed an energy-efficient home design challenge in a simulated environment for engineering design supported by rich design tools. Results showed that students substantially improved their knowledge as a result of designing with the tools. Further, their learning gains were positively associated with three types of design actions--representation, analysis, and reflection--measured by the cumulative counts of relevant computer logs. In addition, these design actions were linked with learning gains in ways that were consistent with their theoretical impacts on knowledge development. These findings suggest that, instead of being passive components in a learning environment, tools considerably shape design processes, and learning paths. As such, tools offer possibilities to help bridge the design-science gap.

Background: Design and science inquiry are intertwined during engineering practice. In this study, we examined the relationship between design behaviors and scientific explanations. Data on student design processes were collected as students engaged in a project on designing energy-efficient buildings on a blank square city block surrounded by existing buildings using a computer-aided design program, Energy3D, with built-in solar energy simulation capabilities. We used criterion sampling to select two highly reflective students among 63 high school students. Results: The main data sources were design replays (automatic playback of student design sequences within the CAD software) and electronic notes taken by the students. We identified evidence of informed design such as problem framing, idea fluency, and balancing benefits and trade-offs. Opportunities for meaningful science learning through engineering design occurred when students attempted to balance design benefits and trade-offs. Conclusions: The results suggest that design projects used in classrooms should emphasize trade-off analysis and include time and resources for supporting trade-off decisions through experimentation and reflection. Future research should explore ways to visualize patterns of design behavior based on large samples of students.