Functional reasoning in diagnostic problem solving

This work is one facet of an integrated approach to diagnostic problem solving for aircraft and space systems currently under development. The authors are applying a method of modeling and reasoning about deep knowledge based on a functional viewpoint. The approach recognizes a level of device understanding which is intermediate between a compiled level of typical Expert Systems, and a deep level at which large-scale device behavior is derived from known properties of device structure and component behavior. At this intermediate functional level, a device is modeled in three steps. First, a component decomposition of the device is defined. Second, the functionality of each device/subdevice is abstractly identified. Third, the state sequences which implement each function are specified. Given a functional representation and a set of initial conditions, the functional reasoner acts as a consequence finder. The output of the consequence finder can be utilized in diagnostic problem solving. The paper also discussed ways in which this functional approach may find application in the aerospace field

Integrating vendors into cooperative design practices

This paper describes a new approach to cooperative design using distributed, off-the-shelf design components. The ultimate goal is to enable assemblers to rapidly design their products and perform simulations using parts that are offered by a global network of suppliers. The obvious way to realise this goal would be to transfer desired component models to the client computer. However, in order to protect proprietary data, manufacturers are reluctant to share their design models without non-disclosure agreements, which can take in the order of months to put in place. Due to bandwidth limitations, it is also impractical to keep the models at the manufacturer site and do simulations by simple message passing. To deal with these impediments in e-commerce the modular distributed modelling (MDM) methodology is leveraged, which enables transfer of component models while hiding proprietary implementation details. MDM methodology with routine design (RD) methods are augmented to realise a platform (RD-MDM) that enables automatic selection of secured off-the-shelf design components over the Internet, integration of these components in an assembly, running simulations for design testing and publishing the approved product model as a secured MDM agent. This paper demonstrates the capabilities of the RD-MDM platform on a fuel cell-battery hybrid vehicle design example.Publisher's VersionAuthor Post Prin

Work in Progress: The RICA Project: Rich, Immediate Critique of Antipatterns in Student Code

Rich, relevant, and immediate student feedback is a core ingredient supporting effective student learning. Feedback is particularly important for introductory computing courses where novice programmers are still learning the basic syntax and semantics of a programming language. Our project is aimed at detecting poor solutions to common problems, termed antipatterns, in student code and providing feedback that guides the student to better solutions. This paper discusses the first year of the project, specifically, the development of a Code Critiquer to detect antipatterns in student code and generate appropriate feedback. This important first step sets-up the project to advance knowledge about novice antipatterns and their detection. The use of these antipatterns and code critiquers in future classroom interventions will help the project improve our understanding of student learning, retention, and self-efficacy

WIP: Longitudinal outcomes of a requirement for student-owned laptop computers across a college of engineering

This WIP focusses on one component of our updated first year engineering program (FYEP), a student-owned laptop requirement. Requiring students to bring laptops will enable all students to practice the skills learned during their first-year engineering classes. Other engineering instructors will also be able to require students to bring and use laptops in their courses. Infusing the use of laptops into coursework throughout our engineering curriculums should positively affect computational problem solving and develop a mindset of ubiquitous computing. In this paper, we outline a longitudinal study in which we plan to assess the impact of the updated first year engineering program and in particular the laptop requirement on computational competencies and attitudes

Framework for Developing Intelligent tutoring Systems Incorporating Reusability

The need for effective tutoring and training is mounting, especially in industry and engineering fields, which demand the learning of complex tasks and knowledge. Intelligent tutoring systems are being employed for this purpose, thus creating a need for cost-effective means of developing tutoring systems. We discuss a novel approach to developing an Intelligent Tutoring System shell that can generate tutoring systems for a wide range of domains. Our focus is to develop an ITS shell framework for the class of Generic Task expert systems. We describe the development of an ITS for an existing expert system, which serves as an evaluation test-bed for our approach

Work in Progress: Utilizing the MUSIC Instrument to Gauge Progress in First-Year Engineering Students

One of the Grand Challenges in Engineering Education is to engage students in their own learning. Student engagement is widely seen as a necessary component driving the success of active learning methodologies. The Music Model of academic motivation was developed as a means to make the human motivation literature accessible to instructors interested in improving courses to increase student motivation and engagement. The model has a reliable and validated survey instrument that assesses 5 components of academic motivation. The model has been applied in two contexts relevant to our current project: in course design and improvement to assess the impact of changes on student motivation and learning, and second, it is used to examine students\u27 motivational perceptions and their relationship to other learning-related constructs. MUSIC has been used in K12 through higher education, and across a variety of fields.In this Work in Progress report, we had two purposes: First, we sought to test the use of the Music Model in an engineering course, since little research has been conducted in engineering courses to date. Second, we sought set the stage for developing a community of practice focused on student engagement with a common and straightforward assessment methodology for the first-year engineering community. Our broad goal is thus to leverage the MUSIC components as one metric for gauging improvement of student engagement for our own first-year engineering program, then eventually a community wide tool for first-year engineering programs broadly. The MUSIC scale inventory data (n=221) was collected electronically in 3 sections of a first-year engineering course at a mid-western technological university. A confirmatory factor analysis replicated the 5-factor MUSIC Model. An ANOVA revealed no differences in student motivations between our three-course sections. This result validates our ability to offer similar experiences across sections and instructors within our first-year course. Multiple comparisons between factor scores demonstrated significantly higher motivation reported on both the caring and success factors as compared to the others. In addition, the interest motivation factor was significantly lower than all other factors. These findings demonstrate the utility of the Music Model within engineering education. We discuss future research to develop a process for instructors to understand the results and make formative decisions for future course iterations. Further, we suggest future research re-establishing the link between the various motivational factors and educational outcomes such as GPA, course grades, retention in STEM, etc. We propose that global events, such as the pandemic, may have resulted in changes in students\u27 priorities regarding education, thereby altering previous findings regarding the importance of specific motivational factors on educational outcomes

Student Preference: ONLINE or Face-To-Face Instruction in a Year of COVID-19

This full paper, in the research to practice category, focuses on student preferences for online versus face-to-face instruction. Spring Semester, 2020 started as usual but proved to be anything but usual. Instead, in a seven-day turnaround, the first-year engineering program at Michigan Technological University moved from a face-to-face, highly interactive studio environment to a remote/synchronous environment. At the end of the semester, our University and many others across the United States conducted a short survey of undergraduate students on their preference of face-to-face versus online instruction. Results showed a strong preference for face-to-face instruction. However, to adequately consider the extensive ranges of approach in both umbrella terms (\u27face-to-face instruction\u27 and \u27online instruction\u27), we need to unpack the surface results. This paper reports on a short survey given to second-semester students in our College of Engineering, First-Year Engineering Program, and students in the first-year course in Systems Engineering. The survey sought to gather student preferences for two variations of our instructional models in current use in our first-year program: (a) remote/synchronous instruction versus (b) a hybrid environment that included face-to-face instruction with mandatory masking and social distancing. Results showed that students, at worst, held preferences that were generally not statistically different in terms of preferences. The several exceptions that did show significance showed numerical differences that were not of practical importance, with one exception. The core takeaway from our study is that determining student preferences for \u27face-to-face instruction\u27 versus \u27distance learning\u27 needs to be unpacked to enable students to register reasoned judgments and set the stage for meaningful results

Tendencies towards DEEP or SURFACE learning for participants taking a large massive open online course (MOOC)

In this report we will describe our first steps in understanding the characteristics of individuals enrolling and completing MOOCs. The learner characteristic we focus on is the deep versus shallow learning dimension. We will use the revised two-factor study process questionnaire of Biggs in our study [1]. To our knowledge, there is no comparable research either reported in the literature or currently under way. Our focus is on Learning How to Learn (LHTL), currently the most heavily subscribed course on the Coursera platform. The last offering of LHTL, completed in January, 2015, attracted just under a quarter million learners. In the fourth and final week of the course, the R-SPQ-2F survey instrument was made available to all students on the LHTL site. Approximately 1,600 students completed the survey. We believe our research to be of interest widely because of the confluence in our research of (a) MOOCs, (b) the deep versus surface learning dimension, and (c) a methodology that can lead to better understanding of MOOCs