Poster Sessions

Friday, August 26, 2016 - Poster viewing Session A (11:00 a.m.-11:45 a.m.)

Audience Poster


Investigating Undergraduates’ Use of Deep and Surface Approaches to Modeling

Steve Bennett, Tammy Long, and Amelia Gotwals

Using video recordings of undergraduates engaged in modeling tasks we analyzed student use of surface and deep approaches to modeling as the modeling tasks changed. We were able to distinguish between deep and surface approaches by modifying and expanding on the coding method used by Chin & Brown (2000). The new coding method focused on students’ explanations, generative thinking and metacognition. Examples of surface and deep approaches will be presented.


Brain research applications in STEM

Ruth Streveler

brainSTEM (brain research applications in Science, Technology, Engineering and Math) is a new research lab at Purdue University’s School of Engineering Education. Headed by Associate Professor Ruth Streveler, brainSTEM aims to connect the STEM education community with the neurosciences. Our goal is to use emerging findings in brain research to inform learning theory and educational research methods. Our current work focuses on more careful consideration of the kinds of learning of which we are not consciously aware and the kinds of knowledge that are not declarative.  As a newly launched effort, brainSTEM is actively seeking collaborators.


Recruiting and Retaining Women in STEM through Living Learning Communities

Geraldine L. Cochran, Beth Bors, and Nicole Wodzinski

Living-learning communities (LLCs) for women in science, technology, engineering and mathematics (STEM) have emerged as a promising tool for recruiting and retaining undergraduate women majoring in STEM.  Furthermore, when combined with research experience, student programs, and peer mentoring, benefits of participating in LLCs increases.  At the Douglass Project for Rutgers Women in Math, Science & Engineering we have seen some successes, including a 94% retention rate (and 90% graduation rate) for women participating in our engineering LLC.  However, the success of our other LLCs has varied.  To better understand the potential of our LLCs to recruit and retain undergraduate women in STEM, we are implementing major changes in our assessment and evaluation protocols and investigating our efforts to build community.


How Students Learn to Coordinate Mathematical and Physical Models in Physiology

Matthew E. Lira

I will present a study that illustrates how students learn to coordinate knowledge of mathematical and physical models in physiology. I will describe how undergraduate physiology students used a multi-representational learning environment to coordinate their knowledge of mathematical and physical models and how an innovative assessment instrument reveals their learning. Analysis of students’ talk and eye-movements provided contrasting cases of success- some students learned to coordinate physical quantities and others did not. Despite the contrasting nature of the cases, an analysis of students’ performance on the written assessment revealed similar patterns of growth. These finding suggests that multiple pathways to success exist for students. At the same time, the finding calls our attention to the important role that modality plays in assessment.


Comparing Model Building and Model Investigation on Student Knowledge of Gene Regulatory Networks

Joseph T. Dauer, Heather E. Bergan-Roller, Nicholas J. Galt, and Tomas Helikar

Biology instructors frequently incorporate models in their classrooms and less frequently allow students to practice modeling. We compared conceptual knowledge for students investigating pre-constructed computational models compared to students building their own computational model. Students simulated gene regulation in the lac and trp operons in a computational modeling activity. We analyzed student Pre/Post conceptual models along with mechanistic reasoning during the activity. Despite the additional cognitive effort required to build a computational model from scratch, the iterative process of building and revising their own model allowed Building students to better connect the structures within a gene regulatory network. 


Undergraduate Engineering Students’ Dimensions of Expertise during a Cognitive Apprenticeship Approach for Integrating Modeling and Simulation Practices

Hayden Fennell, Alejandra Magana, Camilo Vieira, Anindya Roy, Mike Reese, and Michael Falk

Analytical problem solving abilities that support the design process are more critical than ever, with many modern engineering workplaces now using modeling and simulation practices (coupled with computational tools) to aid the analysis and design of systems. This study investigates students’ dimensions of expertise when engaged in modeling and simulation practices guided by a cognitive apprenticeship model that combines the modeling process with disciplinary course material. Materials for this study were collected from one of six computational challenges assigned during a freshman materials science and engineering course. Qualitative analysis of the projects shows positive effects on the performance of students who followed cognitive apprenticeship guidelines and who were able to successfully activate prior knowledge during the modeling and simulation process.


Deep Learning in Introductory Physics: Exploratory Studies of Model-Based Reasoning

Mark Lattery

The diverse, creative, and sometimes unexpected ways students construct models, and deal with intellectual conflict, provide valuable insights into student learning and cast a new vision for STEM teaching. This project examines student thinking and reasoning with mechanical models in a university physics course. To achieve this goal, we are developing a new set of physics education research tools based on programmable air-pulsed carts. Project outcomes include a better understanding of student thinking and reasoning with mechanical models, and effective physics instruction that uses scientific models and modeling to promote a deep understanding of science content and of the nature of science (Spencer Foundation #200800161, University of Wisconsin System #106-01-7000-2, University of Wisconsin Faculty Development Board FDR 913, FDR 982). 


Group Members’ Contributions to Mechanistic Reasoning in Introductory Mechanics

May Lee

To help undergraduate non-physics majors meaningfully engage with core disciplinary concepts and practices in physics, students collaborated in small groups to solve model-based scenarios in a transformed introductory calculus-based mechanics course. Through video analysis of one of the groups, I analyzed which aspect(s) of mechanistic reasoning each member contributed towards in the group’s solution of a scenario and how those contributions changed across the different parts of the scenario (e.g., establishing the problem, designing a solution). Preliminary findings indicate members took up different aspects of mechanistic reasoning, taking turns in clusters, based on where the group was in the process of designing a solution. 


Transferring Modeling Practices from Secondary School to Undergraduate Courses

Karin Lohwasser et al.,

This poster illustrates our understanding about and examples of framing, anchoring, scaffolding, and supporting student collaboration around conceptual (2D) modeling and model-based reasoning. Through building a networked-improvement-community of secondary science teachers and educators, we could explore implementation and adaptations of model-based inquiry in secondary schools over the past 5 years. The question is, in how far can practices and strategies from secondary education be transferred to undergraduate courses in general, and to a "science for teaching" course (to be designed) that aims to attract potential teachers and students that would normally not aim for a foundational knowledge of science, such as elementary or special education teachers, education policy majors, or educators in informal settings. 


Education and Systems Comparison

M. Austin Creasy

Many models of engineering concepts are built on similar principles.  An example is impedance modeling of both circuit theory and acoustic systems.  Both impedance models provide relationships of numerous elements and the math behind both models is similar.  Different learning models have been developed, but few of these models are based on an engineering concept that engineers can use to understand education from engineering principles.  Systems theory views the relationship between the input and output of a system.  Education can be viewed in a similar fashion where the input is the instruction and the output is the gained knowledge.  This relationship may allow for unique analysis of education as systems theory is used to accommodate the current understanding of education.


Misconceptions in STEM are Misrepresentations of One Kind of Processes as Another Kind

Michelene T.H. Chi

We claim that there are two kinds of science concepts of processes that students have to learn in school, from middle school to college, referred to as emergent and sequential processes. We further claim that misconceptions occur when students misrepresent emergent kind of processes as a kind of sequential processes. Because students were never told about such categorical misrepresentation, nor were students ever taught about the nature of emergent processes, our research attempts to develop an online module that teaches students about the two kinds of processes and how to differentiate them, using contrasting everyday emergent and sequential processes. We hope that such instruction will allow students to transfer their knowledge of emergence to understand emergent science processes correctly.


Representational models of amino acid chemistry: The delivery medium as the message

Sepehr Ehsani

A question arising from the use of educational technologies in the classroom is how a given technology can genuinely augment the learning process. The aim of this project was to propose an activity using an educational technology, namely the side-by-side comparison of a physical ball-and-stick amino acid model and its software-generated three-dimensional visualization, not as a means of content delivery per se, but as an essential pedagogical element of a subsection of a theoretical/philosophical biology syllabus focused on the use and meaning of models in biochemistry. It is hoped that this exercise would show the utility of comparing alternative representations of a molecular model across different mediums and demonstrate the nature of models as "metaphors" with inherent representational limitations.


Integrating Technology in Classroom by Developing and Implementing Interactive Simulations in Chemistry

Tanya Gupta and Gregory Albing

Nature of chemistry offers significant challenges to chemistry learners. These challenges include balancing reaction equations, identifying and manipulating variables in an experiment, solving chemistry problems, developing explanations, and successfully transferring content knowledge to real-world experiences, or even solving novel problems in a closely related discipline such as environmental sciences. In order to address some of these challenges, the TCERG at South Dakota State University is developing interactive simulations in chemistry. 


Using Augmented Reality for Unmanned Aerial Vehicle Airworthiness

Brian Kozak and Tim Ropp

Unmanned aerial vehicles, or drones, have become prevalent within the national airspace systems of countries around the world.  As the use of drones continues to rise for civilian and commercial business use, more drones occupy the active airspace.  As a result, responsibility for ensuring drone operational safety and airworthiness is now falling to the civilian operator.  This presents a problem, as many civilian operators may have little to no knowledge of basic airworthiness inspections and maintenance practices common in commercial air transportation. One solution is to incorporate easy to understand, graphics based y assistive augmented reality to assist with the inspection of UAVs. Augmented reality uses computer generated image overlays onto real objects.  This composite image allows for rapid identification of key features of the vehicle and possible problem area.  Augmented reality also provides access to engineering and technical drawings to help technicians identify problems and find solutions quickly.


Target Inquiry: Using Particulate Models to Promote Conceptual Understanding in Chemistry

Deborah Herrington and Ellen Yezierski

As part of the Target Inquiry program high school and college instructors have develop a number of inquiry-based activities that incorporate particulate level models aimed at helping students develop deep conceptual understanding of key chemistry concepts. This poster will highlight a number of different particulate level models and provide data supporting the use of these models in helping students develop conceptual understanding. 


Student Selection of Relevant Elements During Computational Modeling Increases Conceptual Understanding in an Integrated STEM Biology Unit

Anita Schuchardt, Kathy Malone and Christian Schunn

Students' naive conceptions about scientific phenomena often occur because their conceptual models lack an essential element. An integrated STEM unit in population growth and evolution was designed to allow students to select specific elements to include in computational models of scientific phenomena; and compare the outcomes of their models with outcomes of scientifically accurate models. Compared to students who received traditional instruction, students who experienced computational modeling showed greater pre/post gains on a multiple choice assessment (Population Growth: MCM = 19%, MT = -6%, F (1, 22) = 14, p = .001; Evolution: MCM =  20%, MT = 4%, F (1, 36) = 8.4 , p =  .006), suggesting that selecting elements included in computational models can aid conceptual understanding.

Friday, August 26, 2016 - Poster viewing Session B (12:30 p.m.-1:15 p.m.)

Location Poster


National Science Foundation Funding Opportunities that Support Projects Focused on Modeling in Undergraduate STEM Education

Dawn Rickey

Several programs in the National Science Foundation's Directorate for Education and Human Resources (NSF's EHR) provide opportunities to obtain support for projects focused on modeling and model-based reasoning in undergraduate STEM education. This poster will describe the Improving Undergraduate STEM Education (IUSE: EHR), EHR Core Research (ECR), Faculty Early Career Development (CAREER), and Research Experiences for Undergraduates (REU: EHR) programs and the range of projects they support. In addition, an NSF program director will be available to assist with selecting the NSF program that is the best fit for your project, as well as tips for developing meritorious proposals.


Role of Students’ Self-Reflection in a Course-Based Undergraduate Research Experience

Chandrani Mishra, Kristy L. Daniel, and Kari L. Clase

Reflection is a process that links one’s thoughts to their actions. In an academic context, it acts as a bridge to connect students’ experiences and their learning. Lack of study on role of such self-reflections in a science laboratory course led to this study. Using a qualitative research design, we collected data from students enrolled in a course-based undergraduate research experience through semi-structured interviews and from their responses to weekly online reflection prompts. Students were found to engage in various levels of reflection and further analysis revealed several benefits of self-reflection such as development of self-awareness, thinking ability and communication skills. Findings from our study also provides a rationale for educators to engage students in reflections in their class.


Using Student-generated Concept Models to Explore Content Knowledge and System Thinking

Jennifer Momsen, Sara Wyse, and Bethany Munson

We developed a model-based pedagogy to help undergraduates conceptualize and reason about complex biological systems, specifically ecosystems. Using classroom-based assessments in general biology and general ecology, we ask (1) what structures and behaviors do students spontaneously identify as relevant to a given ecosystem, (2) how do students organize those system components, and (3) how do students reason about the movement of carbon in an ecosystem following perturbation. Students were able to identify and organize system components to convey the function of carbon cycling. However, student exhibited linear, single-causality reasoning about a system perturbation. These preliminary results reinforce much of the research on system thinking in K12 learning environments and underscore a need to more explicitly teach system thinking.


Modeling, Revision and Reflection: A Window into Students' Learning of Biology

Elena Bray Speth

Guided by the Structure-Behavior-Function theoretical framework (Goel et al., 1996), we have articulated an instructional design for introductory biology that intentionally distributes student learning activities in class and out of class. Before each class, students learn about structural components of biological systems, and often construct pre-class conceptual models that elicit their current understanding of mechanisms and relationships among structures. Students’ pre-class models become the basis for group discussions, classroom activities and further, post-class revisions, as students acquire new knowledge and develop new understanding.
Students' revision of their conceptual models, accompanied by their written reflections over what they changed and why, offers a window into students’ knowledge restructuring as they learn about complex biological systems and processes. 


Research into Practice: Evidence-informed, Best Practice Visualization for a Deep Understanding of Science

Roy Tasker

A deep understanding of science requires visual mental models of imperceptible worlds’ molecules, force fields, cellular processes. We need to help students to construct these models by applying findings from cognitive science research on the factors determining how the brain perceives, processes, stores, and retrieves audiovisual information. Educators can then develop learning designs that prime the perception filter, avoid overloading the working memory, and enable elaborate linking of new ideas to prior learning.


Writing In-Code Comments to Self-Explain in Computational Science and Engineering Education

Camilo Vieira and Alejandra Magana

This poster presents two case studies that explore the use of in-code comments as self-explanations in Computational science and engineering (CSE) education. The first study corresponds to the glass box approach, a programming course for materials science and engineering students that focuses on introducing programming concepts while solving disciplinary problems. The second case study corresponds to a black box approach, the introduction of python-based computational tools within a thermodynamics course to represent disciplinary phenomena.


Investigating Students Understanding Early Atom Model via Model-Based Inquiry

Tugba Yuksel and Lynn Bryan

This research is part of a larger study that examines students’ cognitive structures and model-based reasoning of fundamental quantum mechanics concepts as they engage in a model-based inquiry instruction. Within the scope of this research, we investigated how undergraduate freshmen level students’ cognitive understanding about atomic structure evolves when they were encouraged to use physical representations of early atomic models. With physical models and computer-based simulations, students had a chance to imitate Thomson and Rutherford experiments. We analyzed students’ model development progress by using case study approach. The findings indicated that students who received model-based learning module (MBLM) showed a drastic change in their existed model of atom toward scientific model in compare to traditional group and be able to use their reasoning to explain phenomena.


Use and Coordination of Representations in Two-Body Pulley Model

Daryl McPadden, Vashti Sawtelle, and Eric Brewe

Models are constructed and communicated through a variety of representations, especially in physics where representations (such as graphs, equations, pictures, etc.) are the foundational tools that students use to understand and explain models of physical phenomena.  Consequently, representation use is a primary focus in the Modeling Instruction introductory physics courses, with explicit class time spent on introducing, practicing, coordinating, and applying multiple representations.  For this project, we studied how students from the Modeling Instruction courses were using and coordinating representations in a think-aloud problem solving session about a two-body pulley system to create a consistent and coherent model.  


Retro-Modeling Activities (RMAs) for Teaching Biological Concepts

​Abdi M. Warfa

Typical instructional approaches for teaching biological concepts in undergraduate courses often start with an overview of a concept followed by schematic models that diagram and narrate the overall process (e.g., a model that depicts RNA polymerase assembly and binding to DNA). Here, we describe a data-centric pedagogical approach that reverses elements of the traditional instruction and what the students do, an approach we're call retro-modeling. In the new approach, students use retro-modeling activities (RMAs) that, after providing an overview of concept, task students to interpret and make sense of relevant original experimental data (e.g., DNA footprint experiment results) and generate their own schematic models of the concept studied. This poster will describe designer features of RMAs and present example RMA module.


Investigating Student Explanations for Acid-Base Reactions

Sonia M Underwood, Hovig Kouyoumdjian, and Melanie M Cooper

The ability to use a structure to predict and explain chemical and physical properties is at the heart of chemistry. Predicting the reactivity of a compound is an example of such a property. In this study we focus on acid-base reactions in particular to better understand how second-semester college general chemistry students’ use a given chemical reaction to provide causal mechanistic reasoning for why such a reaction would occur. Examining student explanations for what is happening on the molecular level during a reaction as well as why the reaction occurs revealed that the question prompt plays an essential role. That is, scaffolding the students explanations through an assessment task administered on the computer program beSocratic elicited more sophisticated explanations from students when using their models of an acid-base reaction to explain why the reaction occurs. The results from this study will be presented.


The hidden causal factor: Teacher-student interactions about models

Samia Khan

In this study, instructional strategies for model-based inquiry in a first year undergraduate chemistry class were analyzed with data collected from 3 semesters of classroom observations, a student survey, and in-depth problem-solving sessions with the instructor and students. Examination of teacher–student interactions revealed a pattern in which students generated, evaluated, and modified hypotheses about their models of chemistry throughout the entire course. The focus of this particular analysis is on the undergraduate students’ proposal of various “hidden causal factors” as they engaged in model-based reasoning about intermolecular forces. The specific instructional strategies that contributed to students’ proposal of different hidden causal factors are discussed using a central teaching episode on boiling. Broader implications of the role of hidden causal factors in model-based inquiry about unobservable phenomena in science are also suggested.


Models as an Assessment in Engineering Design

Matthew Lammi and Daniel Bates

In this paper we discuss a pedagogical strategy for assessing engineering design through modeling. In an engineering design course, the pre-service teachers purposefully created modeling artifacts to learn engineering design. The focus on modeling artifacts provided a setting for formative as well as summative assessments. Instructor prompts became a powerful tool for teaching engineering design as well. The models produced demonstrated the pre-service teachers’ design thinking and evidence of decisions made throughout the process. Modeling not only served as a vehicle for representation, but also as an aid in assessment and documentation of students’ cognitive processes. The results suggest that a focus on modeling in an engineering design challenge can be beneficial to both student and instructor.


Dragoon: A Tutoring System for Mathematical Modeling

Kurt VanLehn

Mathematical modeling is one of 8 fundamental practices in both the Common Core State Standards for Mathematics and the Next Gen Science Standards. However, it is difficult for students to learn how to construct models. Dragoon is an intelligent tutoring system for mathematical model construction. In the context of appropriate instruction, Dragoon can dramatically speed up learning. High school biology students using Dragoon learned significantly more about the human digestive energy balance system than students who used conventional materials. College students using Dragoon mastered system dynamics model construction in less than 8 hours. Dragoon is currently used in an ASU sustainability course and may soon be used in a US Navy electronics course.


Math Machines: Developing and Assessing Representational Competences

Frederick J Thomas and Robert Chaney

Representational competences are an essential component of STEM mastery, particularly the ability to coordinate algebraic, graphical and physical models in dealing with real-world tasks. In science classes, much of this coordination has traditionally been inductive: collecting laboratory data, graphing the data and seeking algebraic functions to model it. For many engineering and technological tasks, however, the process is likely to be more deductive: using established algebraic or graphical relationships to design, create and test a new physical object or process. Math Machines provide a powerful, cost-effective technique for engaging students in designing algebraic functions which control dynamic, physical events. Math Machines are also an important tool for immediate self- and instructor-assessment. Supported in part by NSF grants DUE-0202202 and DUE-1003381.


Applying modeling practices to learning cycle activities as an instructional design framework

Ellen Yezierski

Dissecting the crux concepts of one’s discipline and developing instructional activities that help students invent or discover such concepts is a challenging practice. For chemistry, incorporating models seems obvious; however, appropriately incorporating modeling and model-based reasoning principles adds an interesting yet difficult dimension to robust instructional design. The Next Generation Science Standards (NGSS), although designed for K-12, emphasize modeling and has promise for designing instructional activities created for first year college students. This poster describes how applying the modeling framework from NGSS on existing instructional materials in general chemistry has potential to guide improvements to such activities. 


Utilizing an Approach to Model-Based Inquiry to Teach about Complex Climate Models in Online and In-person Learning Environments

Morgan B. Yarker and Michel D.S. Mesquita

Models and modeling play a major role in the scientific process, particularly in the climate sciences. Societal impacts of climate change reach across a broad spectrum of regions and cultures, and require action from communities of non-scientists (including policy and decision makers) in order to mitigate and adapt to these impacts. However, scientific claims made by climate scientists are based on research using complex computer models, which are generally not understood by non-scientists. Therefore, there is a need for educational strategies that reach a wide range of learners to teach about science models and science modeling practices effectively. This poster will highlight an approach to model-based inquiry and its application in both in-person and online learning environments.


Developing an NGSS-Aligned, LP-based Assessment of Students’ Understanding of Matter

Aaron Rogat

We sought to develop a new computer-based science assessment aligned with the Next Generation Science Standards (NGSS) and a learning progression (LP) in order to determine if we could build better measures of student science learning. We selected a core disciplinary idea (Matter) and a central practice (developing and using models) as the target constructs for our assessment prototype that addresses the multidimensional features of science learning. The research question that we are addressing is: Can we effectively assess student thinking about the structure and behavior of matter through computer-based modeling tasks informed by an LP?