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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.

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.

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.

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.

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.

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.