Taylor Martin - US grants

This CAREER award investigates whether challenge-based curricula are widely adoptable in engineering education and whether the innovation and efficiency gain in challenge-based educational experiences transfer to innovative performance in professional engineering settings. The PI will conduct a five-year program of developmental and comparative research organized into two concurrent, interrelated strands. Strand 1 involves classroom research and addresses how undergraduate-level engineering faculty innovate in their teaching to adopt Challenge-Based Instruction. Strand 2 investigates the transfer of innovation from engineering learning environments to the world of engineering work. The two main research questions are: Does Challenge Based Instruction increase retention rates of underrepresented students so that they stay in the pipeline toward an engineering career and successful employment in professional engineering? This work leverages the NSF's investment in the VaNTH ERC (Vanderbilt, Northwestern, University of Texas, and Harvard/MIT Engineering Research Center). In addition, this work will benefit society more broadly by producing engineers who are ready for the challenges of their field in the future, and who can innovate in the global economy.

This award is co-funded by the Directorate for Engineering, and the Directorate for Education and Human Resources.

The University of Texas at Austin's Cockrell School of Engineering is partnering with the successful UTeach Natural Sciences program and the Austin Independent School District to develop and deliver UTeachEngineering, an innovative, design- and challenge-based curriculum for preparing secondary teachers of engineering. To meet the growing need for engineering teachers in Texas, and to serve as a model in engineering education across the nation, UTeachEngineering has the following four professional development pathways to teacher preparedness, two for in-service teachers and two for pre-service teachers:

1. UTeach Master of Arts in Science and Engineering Education (MASEE);
2. Engineering Summer Institutes for Teachers (ESIT);
3. Engineering Certification Track for Physics Majors; and
4. Teacher Preparation Track for Engineering Majors.

Key elements of the four UTeachEngineering pathways are four new engineering courses focusing on engineering content and pedagogy: Fundamentals in Engineering and Design, Knowing and Learning in Engineering, Engineering Energy Systems, and Design of Machines and Systems. Leveraging these four courses in combination with existing UTeach pedagogical courses, UTeachEngineering anticipates reaching 650 teachers (80 pre-service and 570 in-service) over the first five years. In the future, it is expected that UTeachEngineering will be sustained as a vital program at the University of Texas at Austin.

UTeachEngineering is firmly rooted in current research in the field of engineering education and affords a much-needed opportunity to study the teaching and learning of engineering. During the development phase, UTeachEngineering is examining impacts on three specific groups: (1) Engineers involved in the creation of curricular materials and course design, (2) participants (current and future teachers) enrolled in the engineering courses, and (3) the students of these teachers. While the focused goal of UTeachEngineering is to train a cadre of secondary teachers, the project's vision is that all students are "engineering enabled," acquiring the design and interaction skills that would enable them to be successful in an engineering career should they choose one, while enhancing their lives and participation as global citizens even if they do not become engineers.

This engineering education research project will develop the software and high school curricula to teach programming using mobile devices that will allow students to walk around and interact with each other as they develop programs for virtual robotics competitions. The project is driven by the hypothesis that a mobile, collaborative programming platform will improve learning outcomes in designing, generating, and evaluating algorithmic knowledge. The hypothesis has been subdivided into four measurable outcomes focusing on learning, transfer of programming knowledge, engagement, and procedures. Both learning and system development are addressed in the proposed research.

The broader significance and importance of this project, if successful, are to fundamentally change how students learn computer literacy. Rather than sitting at a computer to develop programs, students actively move around and interact in the task of preparing for robot competitions. This work may engage a more diverse group of students in learning the basics of programming, a key skill for the 21st century workforce.

Mathematical literacy is a critical need in our increasingly technological society. Fractions have been identified as a key area of understanding, both for success in Algebra and for access to higher-level mathematics. The project uses learning analytics and educational data mining methods to examine how elementary students learn in Refraction, an online game designed to teach fractions using the splitting model. The project uses the data from a pre- and posttest of fraction understanding and log data from 3000 third-grade students' gameplay to examine the following questions:
1) Is splitting an effective way to learn fractions?
2) How do students learn by splitting?
3) Are there common pathways students follow as they learn by splitting?
4) Are there optimal pathways for diverse learners?
Splitting is a well-known theory of fraction learning and has significant expert buy in. However, few of the research questions above can be advanced past the field's present level of understanding with either current qualitative or quantitative methods. By using data mining methods such as cluster analysis, association rule mining, and predictive analysis, the project provides numerous insights about student learning through splitting, including: classification of learning profiles exhibited in unstructured learning environments, common mistakes and sense-making patterns, the value or cost of exploration in learning, and the best path through learning for different students (such as those who score low on a pre-test).
The project staff shares the methods and results through traditional and novel outlets for maximum impact on the field and on policy. In addition to conferences and journal publications, the principal investigator is working in several contexts in which this work is an exemplar of new ways the field can develop understanding of learning. In addition, many of these contexts have connections to efforts such as the Chief State School Officers' Shared Learning Collaborative, leading to a high probability that the findings and products can quickly impact large numbers of schools across the country.

This Cyberlearning: Transforming Education Exploration Project is designed to advance understanding of how to personalize feedback and advice to learners as they engage in exploratory and creative activities in constructionist technology-enhanced learning environments. During such activities, learners often engage in programming (using, e.g., Scratch, Alice) with the goal of creating a model or an animation of their own choosing. Assessment of learner capabilities and conceptions would allow automated personalization of advice to learners, facilitate self-reflection, and help teachers or mentors to know the range of capabilities and understanding across a classroom. This project brings together a PI who is expert at promoting learning in the context of constructionist learning activities and another who is expert at educational data mining to identify indicators of young learners' (middle schoolers) conceptions of computational concepts and programming capabilities. The project uses a data analytic approach; data mining methods are used to mine the thousands of operations learners carry out to find patterns that might indicate understanding and capability, qualitative methods are used to analyze what learners were intending and thinking as they were carrying out those operations, patterns are identified in the observational data, and the two streams of data are matched to identify the ways conceptions and capabilities show themselves while learners are programming. The intellectual activity focuses both on the combination of data mining and ethnographic methods for such purposes and on the specifics of those indicators.

Automating assessment is difficult in a project-based learning environment where learners are creating products of their own choosing. Because the activity is quite unconstrained, collecting and analyzing the data necessary for providing help and feedback to the learner is quite difficult. This project uses a combination data analytic and ethnographic approach to find indicators of the conceptions and capabilities of middle schoolers as they are using Scratch to create models and animations of their choosing. The results of this project will make contributions in several areas: (i) advancing methods for automating assessment for learners using the Scratch programming language, (ii) advancing methods for data collection and analysis for personalizing feedback in a relatively open-ended programming environment (iii) broadening understanding of how to assess computational thinking in the context of open-ended programming assignments, and (iv) advancing methodology for automatically assessing capabilities and understanding when learners are engaged in open-ended kinds of assignments.

In 2012, the "Engage to Excel" report from the President's Council of Advisors on Science and Technology (PCAST) declared a national need for an additional one million college graduates in STEM fields over the next decade. The 40% retention rate among STEM majors nationally presents a major hurdle to meeting this need. The goal of this project is to increase the number and quality of STEM graduates by adapting and combining three proven approaches. The first, aimed at students who intend to pursue STEM majors at the outset of their college careers, will prepare students for success in STEM coursework by creating community among participants; developing necessary reading, note-taking, and time-management skills; reinforcing fundamental concepts in mathematics; and helping students understand their individual approaches to effective learning. The second approach will expand the use of effective, evidence-based teaching methods across STEM disciplines. The third approach will engage students in research in a course early in their college careers. The project's components will form a comprehensive model for increasing the quality and quantity of STEM graduates that can be replicated and sustained across many institutions.

The STEM Center at Sam Houston State University will integrate and improve upon tested interventions for retaining students in STEM. Using methods and materials developed by the Charles A. Dana Center at the University of Texas at Austin, the project team will create an immersive summer experience for entering STEM majors, "Frameworks and Foundations," to focus on frameworks of field-specific skills for collegiate success and foundations of STEM content in mathematics, chemistry, and engineering technology. The STEM Center will broaden the use of Inquiry-Based Learning (IBL) practices and Process-Oriented Guided Inquiry Learning (POGIL) methods across STEM disciplines and degree programs. To specifically engage students in scientific practice, the project will embed a course-based undergraduate research experience (CURE) in the STEM curriculum. This new research-focused course, which students will take as soon as they complete their first-year STEM coursework, will quickly prepare them to conduct meaningful scientific research on an original topic developed by each student along with an identified faculty mentor. This course will particularly target transfer students and those from groups traditionally underrepresented in the sciences. Although the project will utilize proven practices, its innovation mainly lies in its comprehensive approach. The project evaluation will add to the body of knowledge about the effectiveness of the interventions and, especially, their integration in this model. Elements of the evaluation will include measures of retention, course completion, and graduation; comparison of students taking IBL and POGIL courses with respect to gains in critical thinking skills, argumentation skills, academic performance, etc.; comparison of students' performance across disciplines to identify transferability of skills; comparison of learning outcomes of students who complete the "Frameworks" and "Foundations" courses with learning outcomes of students who do not; and surveys of students in the modified learning environments to examine perceived differences in teaching and learning strategies.