This full research paper presents the development of the Career Development Supports (CDS) instrument and established evidence of validity for utilizing the CDS in assessing engineering students' sources of career development within work-integrated learning (work-integrated learning) programs. Mentors in work-integrated learning programs (e.g., Co-op programs, internships, and workforce development programs) play a crucial role in developing students' informal and formal networking and promoting their persistence in the field. We propose the CDS survey, a name and resource generator that assesses how mentors support students' self-efficacy, outcome expectations and interest and goal development adapted from Lent's Social Cognitive Career Theory. We distributed the survey to two work-integrated learning programs over the course of a year: a large Co-op program at an R1 institution and a Defense funded microelectronics workforce development program. In total, 497 students completed the survey. We performed confirmatory factor analysis and examined data fit holistically with multiple absolute and relative fit indices. Our findings provide evidence that the CDS instrument can be used to assess work-integrated learning programs engineering students' sources of career development. Specifically, based on the confirmatory factor analysis, the CDS survey can be used to measure students in work-integrated learning programs sources of self-efficacy, career outcome expectations and interest and goal development. Researchers, faculty, and program managers can use the Career Development Supports instrument to understand the supports that students are not receiving and inform the supports, resources, and opportunities they provide to students.

This innovative practice full paper presents a novel ROI metric for engineering and technician work force development (WFD) programs along with complementary key performance indices (KPI's) derived from business financial measures. As many workforce development programs are funded by taxpayers, quantifying outcomes in terms of economic benefit is crucial for justifying their existence. Moreover, Pareto frontiers (boundaries of optimal solutions) are found that reveal inherent trade-offs between: (a) value to funding agencies; (b) value to students; and (c) cost effectiveness. These trade-offs depend not only on program performance but also on the chosen WFD approach. Practical application of the ROI metric to a leading, cross-university, multi-discipline, national WFD effort in microelectronics engineering that is funded by the US Department of Defense is included as a reference case. The utility of the metric for identifying tangible paths to higher ROI is shown for this program. Moreover, its ROI, cost, and student value are compared to alternate WFD approaches of scholarship and internship programs using Pareto frontiers. While the scope of this work is WFD programs in undergraduate and graduate education, the approach is even more general, which could point to further research in this area.

This work-in-progress research paper presents research that describes the impact of integrating microelectronic curriculum into first-year engineering courses. This study is a component of the larger SCALE (Scalable Asymmetric Lifestyle Engagement) project, aimed at developing a robust microelectronics workforce. The Semiconductor Industry Association predicts that by 2030, the United States (US) will face a shortfall of 67,000 semiconductor professionals, 80% in technical roles. This growing demand for microelectronics professionals in the US is both driven and supported by the CHIPS and Science Act of 2022, which provides significant funding to enhance domestic semiconductor production. Therefore, addressing the critical gap in the workforce has become imperative, setting the stage for targeted educational initiatives. This study examines the effectiveness of two interventions that incorporated microelectronic activities into engineering curricula, measuring their impact on student motivation, interest, and perceived transformational experiences towards microelectronic industries using a retrospective pre-test survey. We aim to answer the question: How have students' motivation, interest, awareness, and transformative experiences towards microelectronics shifted from before to after the intervention? Our preliminary findings suggest that the interventions significantly enhance students' interest, motivation, and awareness in microelectronics, while also fostering a positive transformation in their perceptions. This shift underscores the value of educational interventions in microelectronics, highlighting their potential to contribute to the development of a skilled microelectronics workforce. Furthermore, by providing empirical evidence on the benefits of practical, hands-on training, this study extends the existing body of literature, emphasizing the importance of integrating microelectronics into the early stages of engineering education.

This Work In Progress research investigates sketching and visualization experts' perspectives on the definitions and alignment of object assembly sketching exercises with experience from their professional practice. Learning to sketch is a key skill for developing strong visualization and spatial reasoning skills, as well as communication, representation, idea generation, and idea fluency during engineering design. However, manual sketching has largely been replaced by computer graphics tools in undergraduate engineering classrooms. The expert feedback of architecture, civil engineering, and mechanical engineering instructors are reported on relative importance of eight sketching skills, as well as grading practices and discipline-specific practices. Experts generally valued shape quality metrics over line quality, and suggested new interpretations of rubric levels and criteria. We discuss recommended changes to the rubric and exercises.

We consider the problem of predicting link formation in Social Learning Networks (SLN), a type of social network that forms when people learn from one another through structured interactions. While link prediction has been studied for general types of social networks, the evolution of SLNs over their lifetimes coupled with their dependence on which topics are being discussed presents new challenges for this type of network. To address these challenges, we develop a series of autonomous link prediction methodologies that utilize spatial and time-evolving network architectures to pass network state between space and time periods, and that models over three types of SLN features updated in each period: neighborhood-based (e.g., resource allocation), path-based (e.g., shortest path), and post-based (e.g., topic similarity). Through evaluation on six real-world datasets from Massive Open Online Course (MOOC) discussion forums and from Purdue University, we find that our method obtains substantial improvements over Bayesian models, linear classifiers, and graph neural networks, with AUCs typically above 0.91 and reaching 0.99 depending on the dataset. Our feature importance analysis shows that while neighborhood and path-based features contribute the most to the results, post-based features add additional information that may not always be relevant for link prediction. The code and four of the datasets used in this work are available athttps://github.com/Jess-jpg-txt/sln-learning.

Traditional learning-based approaches to student modeling generalize poorly to underrepresented student groups due to biases in data availability. In this paper, we propose a methodology for predicting student performance from their online learning activities that optimizes inference accuracy over different demographic groups such as race and gender. Building upon recent foundations in federated learning, in our approach, personalized models for individual student subgroups are derived from a global model aggregated across all student models via meta-gradient updates that account for subgroup heterogeneity. To learn better representations of student activity, we augment our approach with a self-supervised behavioral pretraining methodology that leverages multiple modalities of student behavior (e.g., visits to lecture videos and participation on forums), and include a neural network attention mechanism in the model aggregation stage. Through experiments on three real-world datasets from online courses, we demonstrate that our approach obtains substantial improvements over existing student modeling baselines in predicting student learning outcomes for all subgroups. Visual analysis of the resulting student embeddings confirm that our personalization methodology indeed identifies different activity patterns within different subgroups, consistent with its stronger inference ability compared with the baselines.

The broader field of engineering education has for decades emphasized the need to broaden engineering participation. However, there have been few conversations about the role of assessment in engineering education as both gatekeeper and talent finder in the process. While national and international policies may communicate value for diverse perspectives in engineering, how those divergent ways of thinking and perspectives show on engineering classroom assessments is less clear. During this special session, our goal is to engage participants in a rich and difficult conversation about how fairness and equity for diverse learners can be represented in engineering assessment practices. We will present a framework for culturally responsive assessment applied to engineering context and engage participants in answering how can assessments capture the strengths that diverse students bring into engineering.

This Research Work In Progress Paper examines empirical evidence on the impacts of feedback from an intelligent tutoring software on sketching skill development. Sketching is a vital skill for engineering design, but sketching is only taught limitedly in engineering education. Teaching sketching usually involves one-on-one feedback which limits its application in large classrooms. To meet the demands of feedback for sketching instruction, SketchTivity was developed as an intelligent tutoring software. SketchTivity provides immediate personalized feedback on sketching freehand practice. The current study examines the effectiveness of the feedback of SketchTivity by comparing students practicing with the feedback and without. Students were evaluated on their motivation for practicing sketching, the development of their skills, and their perceptions of the software. This work in progress paper examines preliminary analysis in all three of these areas.

This full research paper presents the exploratory factor analysis (EFA) results for the Professional Skill Opportunities survey (PSO) we designed to measure undergraduate engineering students’ opportunities to develop and practice important nontechnical professional skills. We use Dall’alba’s "ways of being" as the theoretical framework for the survey development and generated construct definitions based on past literature, expert review, and cognitive think-aloud interviews. We administered the survey in an engineering class at the beginning of the Spring 2022 semester. After comparing the three EFA models based on goodness-of-fit indices and model interpretability aligned to the theoretical model, the researchers selected a five-factor model. The EFA result and literature on leadership and teamwork showed these two skills are highly interrelated and could be combined into one construct to stress the "sharedness" of leadership responsibilities in teams. The result allowed our team to refine our item pool, revise construct definitions, and generate new items. In future work, we will administer the revised PSO survey to the same population at the end of the same semester as further validation. We also plan to explore the relationship between professional skill development opportunities and students’ social support. We hope the PSO survey can provide educators and institutions a means to offer scaffoldings and more opportunities for professional skill development and better prepare students for the engineering workforce.

This Research Work-In-Progress reports the implementation of an Object Assembly Test for sketching skills in an undergraduate mechanical engineering graphics course. Sketching is essential for generating and refining ideas, and for communication among team members. Design thinking is supported through sketching as a means of translating between internal and external representations, and creating shared representations of collaborative thinking. While many spatial tests exist in engineering education, these tests have not directly used sketching or tested sketching skill. The Object Assembly Test is used to evaluate sketching skills on 3-dimensional mental imagery and mental rotation tasks in 1- and 2-point perspective. We describe revisions to the Object Assembly Test skills and grading rubric since its pilot test, and implement the test in an undergraduate mechanical engineering course for further validation. We summarize inter-rater reliability for each sketching exercise and for each grading metric for a sample of sketches, with discussion of score use and interpretation.

Feedback is a critical element of student-instructor interaction: it provides a direct manner for students to learn from mistakes. However, with student to teacher ratios growing rapidly, challenges arise for instructors to provide quality feedback to individual students. While significant efforts have been directed at automating feedback generation, relatively little attention has been given to underlying feedback characteristics. We develop a methodology for analyzing instructor-provided feedback and determining how it correlates with changes in student grades using data from online higher education engineering classrooms. Specifically, we featurize written feedback on individual assignments using Natural Language Processing (NLP) techniques including sentiment analysis, bigram splitting, and Named Entity Recognition (NER) to quantify post-, sentence-, and word-dependent attributes of grader writing. We demonstrate that student grade improvement can be well approximated by a multivariate linear model with average fits across course sections between 67% and 83%. We determine several statistically significant contributors to and detractors from student success contained in instructor feedback. For example, our results reveal that inclusion of student name is significantly correlated with an improvement in post-feedback grades, as is inclusion of specific assignment-related keywords. Finally, we discuss how this methodology can be incorporated into educational technology systems to make recommendations for feedback content from observed student behavior.

We study the problem of predicting student knowledge acquisition in online courses from clickstream behavior. Motivated by the proliferation of eLearning lecture delivery, we specifically focus on student in-video activity in lectures videos, which consist of content and in-video quizzes. Our methodology for predicting in-video quiz performance is based on three key ideas we develop. First, we model students’ clicking behavior via time-series learning architectures operating on raw event data, rather than defining hand-crafted features as in existing approaches that may lose important information embedded within the click sequences. Second, we develop a self-supervised clickstream pre-training to learn informative representations of clickstream events that can initialize the prediction model effectively. Third, we propose a clustering guided meta-learning-based training that optimizes the prediction model to exploit clusters of frequent patterns in student clickstream sequences. Through experiments on three real-world datasets, we demonstrate that our method obtains substantial improvements over two base-line models in predicting students’ in-video quiz performance. Further, we validate the importance of the pre-training and meta-learning components of our framework through ablation studies. Finally, we show how our methodology reveals insights on video-watching behavior associated with knowledge acquisition for useful learning analytics.

Sketchtivity is a stylus-based intelligent tutoring system that can help instructors automatically provide feedback to their students, saving them the time and effort of providing personalized feedback themselves. The system uses a generic evaluation of perspective, direction, and accuracy to give students feedback on the quality of their sketches. If instructors want to personalize the metrics, the system would require them to provide multiple sets of samples. Therefore, instructors may use instructional team members such as teaching and graduate teaching assistants to provide feedback on the required samples. Compared to that of assistants, the feedback they produce might vary due to expertise and create noise in the training data. To address this problem, we implement a deep neural network that leverages learning to reweight algorithms. The data collected by the instructor from undergraduate and graduate-level rectilinear perspectives sketching is considered the validated sample. In this study, we analyzed the training size requirement for a Multi-Layer Perceptron (MLP) to accurately predict whether or not a stroke was a perspective stroke. We observed that the training data required to predict stroke accuracy is small. In addition, the performance of the algorithm in terms of accuracy was good even under extreme conditions such as having highly unbalanced data and having a small valid set of data. The results from the study support the use of these types of algorithms for future system personalizing to support scalable feedback systems in education.

This research WIP contributes to understanding the relationship between learning engagement in Learning Management System (LMS) and outcomes in an online course. In large engineering courses, it is challenging for instructors to identify who is engaging with course materials at a level necessary to be successful in terms of course outcomes. The purpose of this research WIP study is two-fold: (1) to develop metrics for quantifying learner engagement in online courses, and (2) to explore the relationship between engagement and student success. Our research question is: How does learning engagement relate to course outcomes? We modeled learner engagement on a course level using the following features: number of views per content object, total time spent in the platform, percentage of the course accessed by the learners, percentage of feedback read, and number of attempts per quiz. We used the data collected by the LMS in a large first-year engineering course. We obtained data in Fall 2020, the first semester that many traditional universities were forced mostly or entirely online. After calculating the proposed metrics, we used a linear mixed model to analyze the effect of engagement on learning outcomes. Our linear mixed model shows that all engagement metrics are positively related to the final grade. However, the results also indicate that the relationship between engagement and learning outcomes is not linear; more complex modeling is needed to further explore this relationship.

This research Work In Progress systematically reviews the current literature on sketching assessment in engineering education. Sketching is an integral part of the engineering curriculum for conceptual understanding, communication, and design. Sketching enables designers to offload, view, share, and test their ideas. In addition, sketching serves as a tool to increase students' spatial reasoning skills, which is critical to retention and success in engineering. Due to its impact, sketching has been studied in a variety of ways and settings and there are a wide array of methods for assessing sketching. Researchers often assess sketching skill through expert judgment, and when actual sketches are assessed, there are many different metrics that are used. This study is a systematic literature review of sketching assessment exploring applications, cognitive dimensions, and metrics. Databases namely Engineering Village, APA PsycInfo, and Education Source were searched for finding relevant literature related to sketching assessment. Data collection criteria included papers at the high school and college level in engineering, design, architecture, and art. In this paper, our search strings and summary of the final literature sample at the abstract level in terms of publication sources, year, and reviewer decisions are presented. Future directions include continuation of content analysis at the full paper level and assigning quality rankings. The end goal of the project is to provide the design and education communities with a succinct recommendation on sketching assessment to unify efforts in sketching research across the literature.

This research-to-practice WIP (Work In Progress) presents the design and assessment of online Model-Based Systems Engineering (MBSE) modules for practicing engineers using social constructivism as a theoretical framework. Despite many advantages of MBSE, experts in this field are still scarce in the current engineering workforce. To address this need, an online module that will be deployed in the summer of 2020 targets practicing engineers as its learners to equip them with MBSE-related knowledge and skills. In industry, teams working on MBSE-related projects usually collaborate across multidisciplinary units. Therefore, social interaction plays an integral role in MBSE training programs. To understand how group interaction could foster learning in online engineering modules, we apply social constructivism as a theoretical framework to engage learners in meaningful interactions and facilitate the acquisition and application of knowledge. The modules will utilize deep-level, student-centered, small-group discussions, and peer review between student groups as forms of social learning in authentic engineering assignments. The assessment of the modules will focus on the effectiveness of social learning in promoting the mastery and application of content knowledge.

This research Work-In-Progress presents a survey of advanced learners' motivation in highly-technical advanced engineering MOOCs. Advanced engineering courses as MOOCs are increasingly prevalent in graduate and professional learning and are gaining importance for credentialing and accredited degrees. However, these courses are open to the public as well as formal learners, making it difficult to generalize experiences for all. To understand what prevents advanced learners from reaching their goals and to better support them in meeting goals, there is a need for more sensitive tools to measure motivation. We have revised the Expectancy-Value-Cost scale and examined its functioning on pilot data to begin looking at validity evidence for using the revised scale with professional engineers. We analyze motivations, intentions, and course ratings of learners from two online MOOCs who are enrolled in formal degree programs at a four-year institution, learners completing a MOOC master's degree, and independent learners. We also perform bivariate correlation of motivation items to test the performance of the instrument. Preliminary results show high motivation for all learner groups, but greater differences between independent and formal learners, with formal learners reporting a wider range of costs.

This Research Work in Progress Paper describes the initial development and validation for using a new assessment instrument to understand how the research experiences of engineering Ph.D. students shape them as future professionals. Little is understood about how engineering Ph.D. students are prepared for professional practice through their research experiences, yet this is of growing concern to the engineering education community. This paper describes how assessment items were developed and the collection of initial validity evidence through a structured think-aloud protocol where engineering Ph.D. students read and interpreted the items, verbalizing their thinking as they completed the assessment. Two rounds of think-alouds were completed with engineering Ph.D. students. The first round was conducted with seven students and resulted in modification of twenty-eight of the thirty assessment questions, including minor word changes to twenty-two questions and relatively major rewrites to six questions. The second round was conducted with five students and resulted in modification of twelve of the thirty assessment questions, with only minor word changes to the questions. The evidence collected shows that the vast majority of the assessment questions were understood as intended, and the assessment is ready for pilot data collection through a survey.

This research paper presents the results of an explorative study to identify what concepts beginning students find challenging regarding semiconductors physics, diodes and transistors at an introductory electric circuits course. In order to prepare students for practically-important and application-relevant circuits and systems, there is a need to properly introduce semiconductors and solid-state electronics early in the electrical/computer engineering curriculum. Such concepts are not traditionally covered in the very first circuits course in most electrical/computer engineering programs. The purpose of this paper is to explore students' level of understanding of basic semiconductor physics, diodes, transistors and simple circuits that utilize such devices. To address the research purpose, we utilize a Design-Based Research (DBR) methodology. Design-Based Research is an iterative process where new theory is developed through applying research and theory to a specific educational problem, developing conjectures about the relationship between variables, testing and then revising educational intervention based on findings and then retesting. We analyze students' final exam scores (n=99) to determine which topics were most challenging and then qualitatively analyze students' work to explore common errors. As more Electrical and Computer Engineering (ECE) programs look to modernize their introductory courses to include topics of semiconductor physics and devices, this research can inform instructional and curricular interventions.

Advanced engineering Massive Open Online Courses (MOOCs) aim to teach highly technical content to learners around the world at a much greater scale than the traditional classroom. Unlike the latter setting, it is common for many MOOC learners to initiate learning after the official start date, or even after the course ends. This archive-mode learning allows students an open-ended period for course completion. However, little research on MOOCs focuses on how such learners utilize these resources. To address this gap, the present study seeks to compare live, late, and archive learners' intentions, interests, and behaviors in accessing the course materials in a highly technical engineering MOOC. Utilizing the Kruskal-Wallis test, we find that live learners desire to achieve a passing grade at the onset of the course, while archive learners are less concerned about grades. Also, learners who work full-time tend to enroll late, while full-time students tend to enroll as live or archive learners. Most importantly, while similar classes of behaviors are found in all groups, a significantly greater proportion of archive-mode learners complete the course than live or especially late-enrolling learners. These results suggest that relaxing or removing time constraints may significantly improve learning outcomes in MOOCs.

Massive Open Online Courses (MOOCs) attract learners with different learner intentions, background knowledge, and skills as compared to traditional, closed enrollment settings. This level of diversity in learners introduces new factors that impact student persistence and engagement. Previous research has analyzed MOOC learners who are either fully engaged in a course in its entirety or at least consistently accessing course materials. Sporadic users, those who access course content randomly, have not been studied as in depth, even though they comprise the largest number of learners in a highly advanced engineering MOOC. In this study, we use clickstream data and pre-survey data to understand sporadic learners in a highly advanced nanoelectronics MOOC. We used a MOOC on nanotechnology with a total enrolment of close of 10,000 users offered for a duration of 8-weeks. We identified that academic preparedness, learner intentions, and expected time commitment could be used to predict sporadic users. Finally, an effect size analysis was performed.

Despite the remarkable expansion of massive open online courses (MOOCs) over the last decade, the extent to which they achieve their potential as a “disruptive force” in education is questionable, with limited evidence of true impact. There is a wide range of desired outcomes from both learners themselves and those that invest in MOOCs. Therefore, to truly evaluate the effectiveness of a MOOC, it is necessary to understand the needs and goals of all stakeholder groups involved. While some studies have explored these factors for students who take MOOCs and the institutions offering MOOCs, little research has investigated what the instructors who choose to teach advanced science, technology, engineering, and mathematics (STEM) MOOCs hope to gain from the experience. In order to inform this component of a contextual evaluation framework of MOOCs, this study seeks to answer the question, “What factors drive teaching a MOOC?” in the context of advanced STEM courses. This study explores instructor perspectives through semi-structured interviews with 14 instructors of advanced STEM MOOCs who were identified through purposive sampling. Following transcription, we performed thematic analysis to identify underlying themes. A set of nine themes fell into two main categories: personal factors and situational factors. While the expression of these themes varied from one instructor to the next, the consistent presence of each theme suggests the value of incorporating the themes into a framework for evaluating the effectiveness of a MOOC.

Massive Open Online Courses (MOOCs) offer the ability to educate large numbers of diverse learners who might not have access, time, or the financial resources necessary for more formal coursework. While some studies have focused primarily on understanding MOOC learners purely through their access rates to course materials, others have sought to understand learners through surveys. We combined these two sources of data to address two research questions: (1) What are the patterns of user behavior in an advanced, technical MOOC? and (2) What are the characteristics of fully engaged learners? By analyzing clickstream and pre-survey data for a nanotechnology-related MOOC, we identified differences and similarities between fully engaged learners and other groups. The lack of strong indicators to predict fully engaged learners suggests a need for improved data from pre-course surveys.

Highly technical STEM MOOCs have recently become widely available, but little is known about the motivations of the various groups of learners participating. In this work, we perform a detailed survey of 1,624 learners to examine their motivations in detail. These learners exhibited overall high levels of intrinsic motivation, but varied in their extrinsic motivation, according to their current position as students, workers, or unemployed individuals. Students generally reported the highest levels of extrinsic motivation compared to other groups (p<;0.001). The results from this analysis indicate that additional factors about learners in each group, such as their course participation and performance, should be examined in future work to help better understand the various needs of those enrolling in highly technical STEM MOOCs.

Information literacy, the ability and processes of information gathering and application, is of paramount importance to engineers, engineering design, and engineering decision-making. Building on prior qualitative research, this paper presents the development and initial validation study of a self-directed information literacy assessment for engineering and technology students (n = 366). Internal consistency was found to be high, (α = 0.895). Exploratory factor analysis results provide evidence of structural aspects of validity and support for scoring structure. In addition, areas were identified for future development of the items and instrument.

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.

Engineers must be adept in finding, evaluating, and using information in order to create quality designs. Previous research has found first year engineering students frequently do not use sufficient sources to support their design decisions. To further understand what strategies engineering students use in seeking, evaluating, and using information, we interviewed 21 engineering students at a large research university. Results show that while there is variation between students in each group, higher level engineering students reported use of broader and more complex search strategies to assist in finding information for completing projects. In addition, higher level students discussed applying information they found to better understand their design problem, whereas lower level students discussed the application of information less frequently. These results provide insight into how students conceive of the role of information in solving problems at different stages of their educational careers, and can be used to inform teaching and learning in engineering classrooms. These research findings will be used to help inform the development of assessment tools to provide more quantitative evidence of actual skill levels and students' perception of their skills.

Educational interventions in first-year engineering programs can positively affect students' awareness of nanotechnology by introducing students to basic nanotechnology concepts and motivating them to follow nanotechnology-related career paths. The research question examined in this study is: What are the differences in exposure, awareness, and motivation between students in classrooms where a mathematical modeling activity is implemented and where a mathematical modeling activity and a simulation design project are implemented? In a pre/posttest quasi-experimental design, first-year engineering course sections were split into experimental and control groups. The experimental course sections (6 sections n=496) implemented a mathematical modeling activity and a simulation design project related to nanotechnology. The control course sections (8 sections n=703) implemented only the mathematical modeling activity. Results show that when implemented together, the simulation design project and the mathematical modeling activity are more effective than only the mathematical modeling activity in terms of increasing student awareness, exposure and motivation related to nanotechnology. Although the change in motivation is statistically significant, the effect size is low. Therefore, further research for uncovering factors linked to motivation is necessary.

The concepts of size and scale are fundamental to nanotechnology education but can be difficult for beginning undergraduate students to grasp. It is important to develop curricular interventions to increase students' conceptual understanding of size and scale, before moving on to more advanced subject matter. The purpose of this paper is to present the initial steps in the development of the Nano Size and Scale Instrument (NSSI), an assessment of students' understanding of size and scale. Ten items were developed for the NSSI. The NSSI was administered to 118 first year engineering students and studied using Classical Test Theory. Results suggest that six of the ten items show appropriate difficulty and discrimination. Five of the items were of moderate difficulty and students' mean scores overall were low (4.97 out of 10), indicating that students in this study found size and scale concepts difficult. Potential modifications to the NSSI are discussed.