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Software modeling is an integral practice for software engineers, especially as the complexity of software solutions increases. Unified Modeling Language (UML) is the industry standard for software modeling. however, it is often used incorrectly and misunderstood by novice software designers. This study is centered around understanding patterns of student proficiency of abstraction and systems thinking within a software modeling context. The participants of this study (n = 97) belonged to a systems analysis and design course that is primarily taken by second-year university students. The exam solutions to a case study from the course were evaluated for modeling proficiency. As evidence of proficiency in abstract thinking and systems thinking, we evaluated UML activity diagrams, class diagrams, and sequence diagrams and the alignment between these representations in terms of functions, structures, and behaviors. The results suggest students being proficient in modeling the functional aspects of an information system while facing some difficulty in capturing the structural and behavioral aspects of an information system. Clustering analysis revealed two groups within the sample, with one group displaying a significantly higher abstraction and systems thinking ability. Statistically significant correlations were also found between student proficiency of abstraction and their modeling proficiency in terms of functions, structures, and behaviors.

Reflective practice is becoming a more necessary skill across all disciplines and industry sectors. This study describes the reflective processes capstone engineering students engaged with as part ...

There is growing awareness of the need for mathematics and computing to quantitatively understand the complex dynamics and feedbacks in the life sciences. Although individual institutions and research groups are conducting pioneering multidisciplinary research, communication and education across fields remains a bottleneck. The opportunity is ripe for using education research principles to develop new mechanisms of cross-disciplinary training at the intersection of mathematics, computation and biology. In this paper we present a case study which describes the efforts of one computational biology lab to rapidly prototype, test, and refine a mentorship infrastructure for undergraduate research experiences in alignment with the computational apprenticeship theoretical framework. We describe the challenges, benefits, and lessons learned, as well as the utility of the computational apprenticeship framework in supporting computational/math students learning and contributing to biology, and biologists in learning computational methods. We also explore implications for undergraduate classroom instruction, and cross-disciplinary scientific communication.

This study investigates the relationship between two related computational thinking practices: data practices and computational problem-solving practices in acquiring related computational thinking practices during a first-year undergraduate engineering course. While computational thinking theory is still developing, empirical studies can help further understand how students demonstrate this knowledge and their progression in attaining the practices. Therefore, with this empirical study, the following questions are addressed. RQ1: What are the differences in students’ computational thinking practices at the beginning of an undergraduate introductory programming course? RQ 2: How do these differences correspond to the acquisition of more advanced computational thinking practices? The use of a descriptive non-experimental design that aims to understand the correlation between practices related to data and computational problem-solving is presented. A machine learning technique is employed, utilizing historical data from introductory programming for a problem-solving course with more than 1000 first-year engineering students. After identifying groups of students defining different profiles, data from posterior performance in more advanced programming topics were descriptively analyzed. This study supports the characterization of four different student profiles demonstrating differences in their performance at the beginning of the semester. From these four profiles, two of them show a subsequent differential progression besides their similarity at the beginning of the semester. In this particular case, troubleshooting and debugging appear as a relevant competency when distinguishing these two learners’ groups. These findings suggest that previous knowledge or exposure to different practices can result in different progressions of more complex computational practices, emphasizing the relevance of troubleshooting and debugging as a practice required for a successful and timely progression on the acquisition of other computational thinking practices.

This study investigates the relationship between two related computational thinking practices: data practices and computational problem-solving practices in acquiring related computational thinking practices during a first-year undergraduate engineering course. While computational thinking theory is still developing, empirical studies can help further understand how students demonstrate this knowledge and their progression in attaining the practices. Therefore, with this empirical study, the following questions are addressed. RQ1: What are the differences in students’ computational thinking practices at the beginning of an undergraduate introductory programming course? RQ 2: How do these differences correspond to the acquisition of more advanced computational thinking practices? The use of a descriptive non-experimental design that aims to understand the correlation between practices related to data and computational problem-solving is presented. A machine learning technique is employed, utilizing historical data from introductory programming for a problem-solving course with more than 1000 first-year engineering students. After identifying groups of students defining different profiles, data from posterior performance in more advanced programming topics were descriptively analyzed. This study supports the characterization of four different student profiles demonstrating differences in their performance at the beginning of the semester. From these four profiles, two of them show a subsequent differential progression besides their similarity at the beginning of the semester. In this particular case, troubleshooting and debugging appear as a relevant competency when distinguishing these two learners’ groups. These findings suggest that previous knowledge or exposure to different practices can result in different progressions of more complex computational practices, emphasizing the relevance of troubleshooting and debugging as a practice required for a successful and timely progression on the acquisition of other computational thinking practices.

This study implements a design-based research approach to design and evaluate different scaffolding strategies for supporting student learning as well as promoting student agency within a computational science course. The course introduces computational methods and tools in the context of disciplinary problems for materials science and engineering students. Initial course offerings suggested that students were overwhelmed by the interdisciplinary nature of the course. Therefore, the research team evaluated different scaffolding strategies for supporting students’ learning, and how those may have provided students with agency to self-scaffold when needed. Three rounds of data collection included 17 students who participated in individual semi-structured interviews to explore how they used (or not) different scaffolds. Five of the participants were recruited for the first iteration; six of them were recruited in the second iteration, and six more in the third one. The iterative process allowed us to adapt the scaffolding procedures for the third iteration from the data collected in iterations 1 and 2. The purpose of this study is to understand how students used different scaffolds, and what implementation strategies were effective according to student uses of these scaffolds in the context of computational science. The results suggest that students developed agency to self-scaffold when needed, as they benefited from multiple scaffolds at different steps of the problem-solving process. Moreover, providing worked examples without engaging students in their active exploration can be ineffective, but this engagement can be achieved using written explanations. Additional support may be needed at an early stage of skill development, so students have an idea of how to validate their model.

Contribution: Knowledge of students’ Habits of Mind in a signal processing course, and a method for education research. The method identifies factors that may influence students’ performance, but are not evident when analyzing agglomerated data; it is an alternative to the traditional case study method as it derives the cases after applying a clustering approach. Background: Habits of Mind refer to mathematical, logical, and attitudinal modes of thinking required for students of science, mathematics, technology, and engineering to become effective problem solvers, capable of transferring such modes to new contexts. These are particularly relevant in a signal processing course in which students must learn to address engineering problems using tools and techniques previously acquired in an abstract context (mathematics). Research Questions: 1) What are the different Habits of Mind patterns exhibited by the students? 2) Are some of these patterns associated with differences in course grades? Methodology: A qualitative method is combined with random signal modeling and machine learning. Students’ work is first annotated manually based on a custom-built rubric of Habits of Mind. Data is modeled and clustered to obtain statistically significant patterns of Habits of Mind corresponding to divisions of the students into groups. Findings: The data obtained suggests that the student group is inhomogeneous in terms of their Habits of Mind, and this inhomogeneity is associated with grade differences. In particular, the course grade is found to be dependent on inhomogeneity based on at least two Habits of Mind: 1) computation and estimation and 2) values and attitudes.

There has been an increased emphasis on designing integrated STEM learning environments for K-12 students that facilitate seamless learning of disciplinary concepts infused with science inquiry, engineering design, mathematical reasoning, and technological skills. However, there is limited prior research investigating how to facilitate such integrated STEM learning in formal classrooms that go beyond a simple combination of the different subject areas and instead enable teaching and learning of disciplinary concepts infused with scientific inquiry, engineering design, mathematical reasoning, and 21st century technological skills. In this paper, we investigate the affordances of using an educational Computer Aided Design tool, Energy 3D, and corresponding curricular materials to support such integrated STEM learning anchored in the engineering design process. We present an exploratory case study that was conducted in a middle school in the US, where a project-based learning approach was followed and students were asked to design a low-cost energy-efficient home within a given budget using the Energy3D CAD tool. Findings indicate that students learned to engage in the design process and demonstrated practices of idea fluency and systematic experimentation; practices usually representative of informed designers. During the design process while analyzing the problem space, generating ideas, and evaluating solutions, they developed better understanding of the relationships between variables and underlying science concepts, used various mathematical analysis tools and graphical representations embedded in the available technology to inform their engineering design decisions. The learning environment using Energy3D afforded formative feedback to help students understand relationship between variables, provided converging evidences using multiple analytical tools, and enabled visual problem decomposition using suboptimal model to engage students in integrated STEM learning. This study provides a platform for future research investigating the effectiveness of educational CAD tools and curricular scaffolds designed specifically for K-12 students for supporting integrated STEM learning anchored in the design process.

Contribution: This paper adds to existing literature on teaching basic concepts of electricity using computer-based instruction; findings suggest that students can develop an accurate understanding of electric circuits when they generate multiple and complementary representations that build toward computational models. Background: Several studies have explored the efficacy of computer-based, multi-representational teaching of electric circuits for novice learners. Existing research has found that instructional use of computational models that move from abstract to concrete representations can foster students’ comprehension of electric circuit concepts, but other features of effective instruction using computational models need further investigation. Research Questions: 1) Is there a correlation between students’ representational fluency and their ability to reason qualitatively on electric circuits? and 2) Is the quality of student-generated computational representations correlated to their conceptual understanding of electric circuits? Methodology: The study comprised two cases in which 51 sophomore-engineering students completed a voluntary assignment designed to assess their representational fluency and conceptual understanding of electric circuits. Qualitative insights from the first case informed the design of a scoring rubric that served as both the assessment and the data collection instrument. Findings: The results suggest that a multi-representational approach aimed at the construction of computational models can foster conceptual understanding of electric circuits. The number and quality of students’ representations showed a positive correlation with their conceptual understanding. In particular, the quality of the computational representations was found to be highly, and significantly, correlated with the correctness of students’ answers to qualitative reasoning questions.

Much can be learned from the vast work on the use of computer simulations for inquiry learning for the integration of modeling and simulation practices in engineering education. This special issue presents six manuscripts that take steps toward evidence-based teaching and learning practices. These six studies present learning designs that align learning objectives, with evidence of the learning, and pedagogy. Here we highlight the main contributions from each paper individually, but also themes identified across all of them. These themes include (a) approaches for modeling-and-simulation-centric course design; (b) teaching practices and pedagogies for modeling and simulation implementation; and (c) evidence of learning with and about modeling and simulation practices. We conclude our introduction by highlighting desirable characteristics of studies that report on the effectiveness of modeling and simulation in engineering education, and with that we provide some recommendations for improving the scholarship of teaching and learning in this field.

This paper studies electrical engineering learners’ preferences for learning methods with various degrees of activity. Less active learning methods such as homework and peer reviews are investigated, as well as a newly introduced very active (constructive) learning method called “slectures,” and some others. The results suggest that graduate students’ perception of the usefulness of the activity increases with its level of activity. For undergraduate students, an increased perception of the usefulness of the activity was observed for lightly active but structured learning methods. Group-based analysis focusing on two types of learners, defined as “instructor-dependent” and “instructor-independent” according to their perception of the usefulness of the classroom lectures, was also performed. The results suggest that instructor-independent learners may benefit more from active learning methods than instructor-dependent learners. For example, instructor-independent undergraduate learners were found to perceive the homework assignment as being more useful than the lectures. Such a preference was not seen in the average group data. In fact, no learning method was found to be perceived as more useful than the lectures, on average. Thus this paper illustrates the pertinence of group-based data analysis.

Computational tools and methods have permeated multiple science and engineering disciplines, because they enable scientists and engineers to process large amounts of data, represent abstract phenomena, and to model and simulate complex concepts. In order to prepare future engineers with the ability to use computational tools in the context of their disciplines, some universities have started to integrate these tools within core courses. This paper evaluates the effect of introducing three computational modules within a thermodynamics course on student disciplinary learning and self-beliefs about computation. The results suggest that using worked examples paired to computer simulations to implement these modules have a positive effect on (1) student disciplinary learning, (2) student perceived ability to do scientific computing, and (3) student perceived ability to do computer programming. These effects were identified regardless of the students’ prior experiences with computer programming.

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 Innovative Practice Full Paper details a design-based research approach for implementing computational learning activities in a first-year engineering physics course. This study contributes to the growing body of research on computation in engineering education by introducing computational concepts and activities during a physics laboratory class. Drawing from Experiential Learning Theory and using an adapted version of the Use-Modify-Create framework for teaching computational thinking, a series of lab activities was designed that combined physical lab experiments with computational modeling using custom-built VPython simulations. Data was collected from the labs in the form of (1) responses to lab activity worksheets, (2) code modified and/or generated by the students during lab activities, (3) in-code comments provided by the students during the activities. A qualitative thematic analysis was used to analyze students’ learning benefits and any challenges faced during the activities. While results show a number of learning benefits, an observed homogeneity of student responses to the questions on the lab handouts point to a set of potential limitations within the activities themselves that warranted further examination. Insight gained from this analysis process is presented as a set of four design principles that will inform future implementations of the hybrid course design.

In this Work In Progress in the Research Category we present a qualitative study that explores how computing instruction experiences may mediate the progression of social and cognitive factors associated with career development in community college engineering students. The rapidly evolving technological landscape has created an increased need for an engineering workforce with computing skills. Traditionally, policymakers, educators and researchers have focused on K-12 and four-year secondary institutions as the primary arena for addressing our workforce development needs. However, community colleges, which enroll over one-third of U.S. undergraduates, represent an important, and not yet fully utilized resource for training future engineers. However, there is a dearth of engineering education research that seeks to critically understand the experiences of community college engineering students in learning computational skills. To address this gap, our study includes semi-structured interviews with first- and second-year students enrolled in an engineering program at a public two-year institution in the midwestern United States. Our work is informed by a social cognitive career theory (SCCT) perspective from which we examine how engineering course learning experiences involving integrated computing instruction influence students’ self-efficacy beliefs and outcome expectations. In alignment with SCCT, our analysis will aim to identify personal attributes and external environmental factors that mediate students’ differential responses to computational instruction. This pilot study is the first part of a larger research effort directed towards learning how to support students as they navigate the community college pathway to engineering.

This Work in Progress paper in the Research category studies undergraduate students use of higher order metacognitive skills while engaging in computational modeling engineering research. Policymakers, universities and other key stakeholders have promoted ‘authentic learning experiences’ including undergraduate research experiences as a vehicle for the cognitive and metacognitive development of undergraduates. Indeed, undergraduate research experiences in STEM fields have been demonstrated to have positive effects including enhanced technical skills development and greater persistence in STEM degrees. However, specific research is needed to understand how metacognitive skills are engaged during undergraduate research experiences. Further, work on undergraduate research experiences has typically focused on science research, and relatively little work has been done to understand student development during research experiences in engineering and computational fields. To address this gap, we have initiated a qualitative study of undergraduate researchers within a computational modeling engineering research group at a large midwestern university. Here, we share preliminary results from semi-structured interviews of undergraduate student researchers and their supervisors. The research and implications of our work are grounded within a cognitive apprenticeship framework and will provide insight into how best to support the cognitive and metacognitive development of undergraduate engineering researchers.

Engineering students, as compared to computing-related majors, are not traditionally introduced to computing in the context of authentic learning experiences, i.e., real-world applications within their discipline. This paper identifies the impact of computation delivered by authentic learning experiences in the form of anchored instruction on students' self-beliefs and their capacity to leverage computation to acquire disciplinary concepts in subsequent computationally-based engineering coursework. This case study included 130 students with different programing preparation (authentic or traditional), who were exposed to computational learning modules. Control-Value Theory of Achievement Emotions is the conceptual framework that guided the evaluation of this investigation. Measures included student self-beliefs such as control and value appraisals, and their relationship with academic performance. Results suggest that programming preparation presented in an authentic engineering context provides an important foundation that goes beyond increasing students' control self-beliefs. This preparation seems to effectively enable students to leverage computational practices for the purpose of acquiring disciplinary concepts. Implications for teaching relate to the integration of computation sooner, more often and within a disciplinary context in the undergraduate engineering curriculum. Implications for learning relate to fostering engineering computational literacy guided by anchored instruction to support disciplinary problem solving. Students exposed an 'authentic' programming course, increased their control appraisals.Students exposed to the computational modules reported increased control appraisals.Most students increased their disciplinary knowledge when using the configuring approach.Students exposed the 'authentic' course benefitted from the programming and configuring approach.

In this paper we present an iterative research process to integrate worked examples for introductory programming learning activities. Learning how to program involves many cognitive processes that may result in a high cognitive load. The use of worked examples has been described as a relevant approach to reduce student cognitive load in complex tasks. Learning materials were designed based on instructional principles of worked examples and were used for a freshman programming course. Moreover, the learning materials were refined after each iteration based on student feedback. The results showed that novice students benefited more than experienced students when exposed to the worked examples. In addition, encouraging students to carry out an elaborated self-explanation of their coded solutions may be a relevant learning strategy when implementing worked examples pedagogy.

This paper presents a literature review on the integration of laboratories into undergraduate engineering education. The literature review starts with a brief discussion about historical and current trends of how laboratories were (are) integrated into the engineering curriculum. This initial review indicates that although highly important to the education of engineers, laboratories often are not used to their full potential. To identify the possible reasons for that situation, the authors synthesize the scarce literature on faculty attitudes toward the integration of laboratories into undergraduate engineering education, and identify factors that could be associated with instructional decisions and approaches to teaching laboratory classes. The findings indicate that faculty decisions regarding laboratory education are shaped by a myriad of factors, including beliefs, knowledge, institutional factors such as departmental values and reward systems, and external factors such as funding agencies and professional societies. Finally, the authors discuss implications of these findings for the design of professional development programs that would foster the effective use of laboratory classes in engineering education.

Bioinformatics education can be broadly defined as the teaching and learning of the use of computer and information technology, along with mathematical and statistical analysis for gathering, storing, analyzing, interpreting, and integrating data to solve biological problems. The recent surge of genomics, proteomics, and structural biology in the potential advancement of research and development in complex biomedical systems has created a need for an educated workforce in bioinformatics. However, effectively integrating bioinformatics education through formal and informal educational settings has been a challenge due in part to its cross-disciplinary nature. In this article, we seek to provide an overview of the state of bioinformatics education. This article identifies: 1) current approaches of bioinformatics education at the undergraduate and graduate levels; 2) the most common concepts and skills being taught in bioinformatics education; 3) pedagogical approaches and methods of delivery for conveying bioinformatics concepts and skills; and 4) assessment results on the impact of these programs, approaches, and methods in students’ attitudes or learning. Based on these findings, it is our goal to describe the landscape of scholarly work in this area and, as a result, identify opportunities and challenges in bioinformatics education.

This article investigates the effectiveness of a course employing a discipline-based computing approach. The research questions driving this study were: (1) Can experiences with discipline-based computing promote students’ acquisition and application of foundational computing concepts and procedures? (2) How do students perceive and experience the integration of discipline-based computing as relevant to their future career goals? (3) How do students perceive the structure of the class as useful and engaging for their learning? We used qualitative and quantitative research methods to approach the research questions. The population studied was 20 engineering undergraduates from Johns Hopkins University. Results of this study suggest that students performed proficiently in applying computing methods, procedures, and concepts to the solution of well-structured engineering problems. Results also suggest that student self-perceptions of their overall computing abilities and their abilities to specifically solve engineering problems shifted from low to high confidence. Students consistently found the course to be important and useful for their studies and their future careers. They also found the course to be of very high quality and identified the instructors and the teaching and feedback methods employed as very useful for their learning. Finally, students also described the course as very challenging compared with other courses in their own department and at the university in general.

Background The computational simulations used by the instructors in this study were originally developed for use as research tools by subject-matter experts and then incorporated in advanced undergraduate and graduate courses in engineering and science. Although some research has been done on students' learning with these computational simulations, less progress has been made toward understanding instructors' goals, or affordances, for incorporating these simulations in their teaching. Purpose (Hypothesis) To identify how computational simulations can be effectively used in teaching and learning environments, this study examined instructors' rationale for using these simulations as learning tools. The study was based on the following research question: What were the intended learning outcomes that guided the instructors' use of computational simulations as learning tools? Design/Method This study used qualitative methods based on the theoretical framework of phenomenography. Openended interviews were conducted with 14 instructors teaching undergraduate and graduate courses in science and engineering who were not familiar with the research literature on beneficial ways of using simulations for learning and instruction. Results Analysis revealed an outcome space consisting of eight qualitatively different categories that detailed ways in which the engineering and science instructors in this study conceptualized the use of simulation tools with learning activities into existing courses they taught. Conclusions The outcome space of instructors' goals for using computational simulations is consistent with the recommendations found in the literature based on studies of the use of simulations in more restricted research settings.

To meet the urgent need for a well-prepared CS&E workforce, we must better understand how students perceive the field.

Background: The newly formed discipline of engineering education is addressing the need to (a) enhance STEM education for precollege students and (b) identify optimum ways to introduce engineering content starting, perhaps, from the early ages. Introducing engineering at the Prekindergarten through 12th grade (PreK-12) education level requires significant changes in teacher preparation and support. It highlights the need for developing developmentally appropriate content knowledge and pedagogical methods, thus revealing the challenges of preparing teachers to incorporate this type of knowledge base into their practice. Although professional development offered by universities, school districts, and other educational entities provides the primary source of formal learning tools for teachers, an increasing number of teachers also utilize other informal web-based resources regularly. This paper examines available PreK-12 engineering web resources offered by entities formally related to education in seven different languages, namely Arabic, Chinese (Mandarin), English, French, Greek, Korean, and Spanish. Results: Findings showed how different educational systems designated different attention to the entry level ages when introducing engineering content. Differences in the terminology used to identify STEM resources for introducing these topics also became apparent. Similarities suggest that a large number of resources available on the web were originating in all researched languages. However, the developmental appropriateness and content validity of many of these resources remained questionable in many cases. In general, there was a plethora of isolated activities and lesson plans but significantly fewer complete engineering curricula available to teachers on the web. Conclusions: This study presents a comparative analysis of the international landscape of PreK-12 engineering education and provides guidelines and samples of state-of-the art resources in each language for teachers interested in introducing their students to engineering as a reliable starting point towards PreK-12 engineering information gathering.

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.

Previous studies indicated that misconceptions related to heat transfer, fluid mechanics, and thermodynamics, persist among engineering juniors and seniors even after they completed college-level courses in these subjects. Researchers have proposed an innovative instructional approach, the ontological schema training method, which helps students develop appropriate schemas or conceptual frameworks for learning difficult science concepts. Three online training modules were designed to help engineering students develop appropriate schemas in heat transfer, diffusion and microfluidics. The effectiveness of these modules was examined with two different student populations from two different universities (US and Hispanic). At each institution, participants were assigned randomly to a control or experimental group. The treatment for each group at both institutions was exactly the same. Preliminary results indicated a mixed effectiveness of the training modules among these populations.

Declining interest in computing programs nationwide presents a threat to America's security and limits potential for innovation across all domains. One way to address this problem is to remove misconceptions held by the nation's youth about computing, including information about how it positively impacts many subjects and showing them that applying computing can be fun and rewarding. One program at a Midwestern university accomplished this goal through a week-long, residential, summer camp for high school students to educate them about career opportunities and possibilities for people with Information Technology skills. Participants completed a variety of hands-on activities daily, along with listening to work experiences of computing professionals. Feedback collected from the student participants showed that in addition t o raising awareness about computing opportunities, the program increased youth interest in IT, prompted many to enroll in computing/engineering courses, and improved their performance in school. This paper shares details about the program and participant feedback to make a case for offering similar programs to correct the knowledge people have about computing.

Technological advances in cyberinfrastructure have paved the way for research grade computational simulation tools, such as those available on nanoHUB.org. Even though benefits have been acknowledged for incorporating these tools into teaching and learning environments, difficulties have also been identified. To address some of these difficulties researchers have emphasized that inquiry learning with simulations, in order to be successful, needs adequate but not intrusive scaffolding. As a response to this need, nanoHUB.org affiliated faculty have proposed tool-based curricula to be used for training 21st century engineers in the nanoelectronics field. Motivated and informed by our previous work and related literature on inquiry learning with simulation, a transparency and scaffolding framework is proposed to be integrated into existing nanoHUB tool-based curricula.

We present an online learning tool called SugarAid version 0.2 to assist in the education of students of science, technology, engineering, and mathematics (STEM). We have used the tool in both mechanical and electrical engineering courses with postive results, such as improved written exam scores. SugarAid may be used online at nanoHUB.org with remote computation; i.e. all that is required to use SugarAid is any device with internet browsing capability. The tool is intended to replace or complement course homework, and to provide custom review material that adapts to each student's learning curve. The tool prepares students for in-class examinations by providing timed exercises, and allows the students and instructor know which ABET (Accreditation Board of Engineering and Technology) concepts are being addressed. Immediate grading provides instant feedback to both instructor and students. SugarAid may be configured to display image files, function plots, multiple choice questions, detailed solutions, etc. We have implemented a weakness function in SugarAid that works by remembering exercises answered incorrectly and tests for the retention of such exercises at a later date. Reference material such as lecture notes and data tables may be displayed in SugarAid. The latest version allows students to modify exercises to explore what-if scenarios by, say, replacing a resistor with a capacitor in a circuit. In this paper we describe SugarAid and examine various metrics including a comparison of exam scores by students that did, and did not, use the tool. The exam results, usage data, and survey suggests that SugarAid has a positive impact on students' performance.