Educational literature has long supported strong correlations between student motivation and academic success. STEM literature has more recently shown mechatronic experiences to have positive impacts on these constructs, albeit limited empirical grounding. Therefore, the purpose of this study was to conduct a pilot experiment to empirically quantify differences in undergraduate student motivation and academic success in a mechatronic vs. a non-mechatronic experience, as well as examine the correlation between student motivation and academic success in both groups. We used a quasiexperimental, non-equivalent control vs. treatment design to collect n = 84 responses from multiple sections of a single undergraduate course. The multivariate dependent variable of student motivation was measured using the Motivated Strategies for Learning Questionnaire’s motivational orientation items. Our multivariate dependent variable of academic success was based on final course grades, final project scores, and quiz scores. Using ANCOVA and differences of proportions, we found no statistical difference in motivational orientation—specifically value choices and expectancy beliefs—in the mechatronic vs. non-mechatronic experience. In contrast, statistically significant differences in project scores and final course grades were observed in the mechatronic experience group. Additionally, we found no significant correlation between student motivation and academic success. These results indicated that students in the mechatronic experience, while earning significantly higher grades, did not exhibit different levels of motivation, leading to no association between student motivation and academic success. Even so, future research is needed to further understand the nuanced dynamics of motivational orientation within a mechatronic experience.

Few elementary teachers have experience with implementing engineering into the classroom. While engineering professional development opportunities for inservice teachers are becoming more numerous, engineering education is rarely required or even offered in elementary teacher-preparation programs (O'Brien et al. 2014). To prepare future elementary teachers to teach engineering, a collaborative partnership was formed between professors in Iowa State University's College of Engineering (CoE) and School of Education (SoE). The partnership included teacher education faculty in science and mathematics education and three engineering faculty who provided perspectives on content, knowledge, and skills foundational to engineering. Members of the partnership worked together to co-plan and co-implement engineering experiences across a teacher education program. These experiences included building engineering content knowledge through a Saturday short course, inclusion of engineering in methods courses, and a summer workshop that preceded a partnership with an engineering graduate student. This article describes the Saturday short course provided to prospective elementary teachers by three members of the engineering faculty and two from the teacher education faculty.

Mentoring is a critical aspect of graduate education, but few studies have explored the mentoring behaviors that specifically contribute to effective mentoring, making it difficult to train mentors to behave in a way that optimally supports students’ development. The Effective Mentoring of Student Researchers Scale (EMSRS) was developed to identify graduate advisors’ key mentoring behaviors. The survey instrument was distributed to graduate students at a large Midwestern university, items were finalized using exploratory factor analysis (principal axis factoring with promax rotation), and confirmatory factor analysis was conducted to confirm the factor structure. We identified four components of effective mentoring, as follows: Engaged, Positive, Professional, and Present. Implications for training new mentors and improving the mentoring of graduate students are discussed.

Using an observational methodology, we studied the time and cost associated with developing and piloting a mechatronic experience in a first-year undergraduate engineering technology course. Our exploratory study included a sample size of 48 students across two sections of an existing course and analyzed the categories of capital, support staff, and instructor time and cost. Our capital purchases totaled ~$5,000, or ~$104 per student. Analyzing the capital verse capacity (class size) of our study, we found it to follow the chemical process industry’s common 0.6 economies of scale model. In contrast, support staff and instructor time and cost were not proportional to class capacity, but were primarily driven by the discrete stepped requirement of one teaching assistant per 50 students. Finally, setting our capital, support staff, and instructor costs as a function of class size, we projected a ~$4,000 per semester total cost, with a step size of ~$450 at each additional increment of 50 students.

Many experiences in engineering education boast positive gains to students’ learning and achievement. However, current literature is less clear on the economic costs associated with these efforts, or methods for performing said analyses. To address this gap, we proposed a structured approach to analyzing the incremental costs associated with an experience in engineering education. This method was modeled after those found in medicine and early childhood education. We illustrated our methodology using marginal (above baseline) time and cost ingredients that were collected during the development, pilot, and steady-state phases of a mechatronic experience in a first-year undergraduate engineering technology course. Specifically, our method included descriptive analysis, Pareto analysis, and cost per capacity estimate analysis, the latter of which has received limited discussion in current cost analysis literature. The purpose of our illustrated explanation was to provide a clear method for incremental cost analyses of experiences in engineering education.We found that the development, pilot, and steady-state phases cost just over $17.1k (approximately $12.4k for personnel and approximately $4.7k for equipment), based on 2015 US$ and an enrollment capacity of 121 students. Cost vs. capacity scaled at a factor of – 0.64 (y = 3,121x–0.64, R2 = 0.99), which was within the 95% interval for personnel and capital commonly observed in the chemical processing industry. Based on a four-year operational life and a range of 20–400 students per year, we estimated per seat total costs to range from roughly $70–$470, with our mechatronic experience averaging just under $150 per seat. Notably, the development phase cost, as well as the robot chassis and microcontroller capital cost were the primary cost terms of this intervention.

The purpose of this study was to understand the how’s, why’s, and what’s behind students’ motivational orientation in a first-year engineering technology course, following a mechatronic project. To accomplish this, we implemented an eight-week treatment that required 61 students to design and integrate a software program to control an electro-mechanical robotic system. Using non-parametric quantitative analyses of pre-/post-survey responses we found that students’ median motivational orientation score,on the Motivated Strategies for Learning Questionnaire, was significantly lower (Mdndiff= -0.34; W = 1360; p-value = 0.0111) following the mechatronic project (i.e., they were less motivated to engage in the learning process following the project). However, when asked directly,a significantly larger proportion of students reported that it was motivating(= 0.90; p-value < 0.010). To clarify these divergent results, we used a mix of text-mining algorithms and word stem frequency analyses to examine open-ended student responses. From this we discovered the word stems work*, project*, learn*, program*, want*, see*, motiv*, androbot*to be the most prevalent used for “why” the mechatronic project was motivating;the word stems work*, code*, get*, motiv*, robot*, see*, project*, want*, and complet* were the most commonly reported for “what” motivated students. From this we start to uncover the “why’s” and “what’s” behind students’ motivation: namely, that the visual and physical aspects of the mechatronic project were motivating to them.

For nearly a decade, our institution has used multiple-linear-regressions models to predict student success campus-wide. Over the past three years, we worked to refine the success prediction models to the college of engineering (COE) students in particular, and to explore the use of classification and regression tree (CART) approaches to doing the prediction (e.g., Authors, 2016). In a parallel effort, our institution has contracted with an academic analytics company to do a retrospective analysis of student performance in every course as the university in relation to graduation rates. Here, we report on recent work we have done to make synergistic use of the results from the COE CART model and the academic analytics. Specifically, we have been able to examine student performance (i.e., grades) in core “success marker” courses as a function of the risk-grouping into which the CART model places them. We are now using this information to inform our advising. We provide details on these efforts, and on the opportunities and challenges provided by data-driven approaches to enhancing student success.

In the 2013-14 academic year, we embarked on an effort to flip two engineering courses in our department – a year-1 problem solving and programming course (Y1PS), and a year-3 numerical methods course (Y3NM). Initially, the Y3NM course, which we were also teaching for the first time and revising significantly as we did, was conducted in a standard flipped model wherein students viewed video lectures and took diagnostic quizzes prior to attending class, and where class time itself focused on discussion and problem solving. In contrast, based on our significant prior experiences teaching the Y1PS course, and upon its organization as a mixed-mode lecture/problems solving course, we did not take a standard flipped approach to it. Instead, in the Y1PS course, students watched videos during the class periods themselves; such a structure was facilitated by the classroom having one computer per student, to accommodate the programming portion of the class. We refer to this “watch in class” model as a hybrid-flipped classroom, and have found this approach to work significantly better in terms of student engagement and learning than the standard flipped model did for us. With that experience, we modified the Y3NM class to the hybrid-flipped model in subsequent offerings. We recognize that the hybrid-flipped model is resource intensive because it requires far more classroom technology than traditional lecture, and also that our positive results are in part due to the computer-intensive nature of both courses in which we have implemented this model. We report here about our experiences, both positive and negative, with flipped and hybrid-flipped approaches, and provide guidance for instructors considering such changes themselves.

Elementary teacher preparation programs are generally tightly packed, with limited room for additional coursework. As states adopt Next Generation Science Standards (NGSS ), the need for teacher education programs to provide meaningful exposure to engineering is growing, and a multitude of approaches can be taken to meet this need. While the topics of engineering and engineering design are typically incorporated into science teaching methods courses, this research presents an alternative approach where we expose elementary pre-service teachers to engineering prior to or concurrently with enrollment in their methods courses. Specifically, we describe here our efforts in building a 12-contact-hour non-credit short course – based upon NGSS-aligned learning outcomes – that was delivered to 10 students in fall 2016. We provide details on our approaches, including examples used in the course, and explain our decision to offer this as a short course instead of a full-semester course. We also report on results from the pre- and post-surveys which we used to assess student learning in the course, and to understand what aspects of the course needed improvement.

Summary: This research examined the impacts that a half-semester mechatronics project in a first-year undergraduate course had on students’ motivational orientation.