CIRCL perspectives offer a window into the different worlds of various stakeholders in the cyberlearning community — what drives their work, what they need to be successful, and what they think the community should be doing. Share your perspective.

Alejandra Magana is a Professor of Computer and Information Technology and Affiliated Faculty of Engineering Education at Purdue University.

Can you tell us a bit about yourself–including your work?

My work aims to characterize, promote, and scaffold complex forms of thinking and doing in science and engineering domains, particularly in the context of higher education. To do so, I use design-based research approaches to co-create learning experiences that promote disciplinary engagement mediated by practitioners’ tools and cyberlearning innovations. Within this context I focus on four main strands:

• Modeling and simulation practices for supporting discovery and innovation. Engineering workplaces now use modeling and simulation practices coupled with computational tools to aid in the analysis and design of systems. To introduce these practices into the undergraduate classroom, faculty often use experts’ tools for learning purposes. The goal of this research strand investigates how we can effectively integrate modeling and simulation practices in undergraduate curriculum through the affordances of practitioners’ computational tools. Within these contexts we want to develop learning experiences that can effectively support model-based reasoning, and while doing so, we investigate how students develop adaptive expertise in modeling and simulation. We have identified opportunities and challenges for integrating these practices at the undergraduate level, along with scaffolding and pedagogies that can support students reasoning as they engage in engineering problem solving. We have identified that when exposed to scaffolded modeling and simulation practices early and often, students were able to increase their disciplinary knowledge as well as their self-efficacy beliefs.
   
  Purdue recently launched the Integrative Data Science Initiative to advance data science-enabled research and education. To contribute to this initiative, my research program has been expanded to promote, facilitate, and scaffold the effective integration of data science and computation at the undergraduate and graduate levels with the aim of strengthening the workforce.

 

• Touch sensory feedback for promoting conceptual learning and experimentation skills. We’ve been trying to understand inconsistent empirical findings around the use of physical and virtual manipulatives for science learning. While theories of learning such as embodied cognition provide strong arguments for their effectiveness, the empirical findings have identified inconsistent supporting evidence. Specifically, the way to successfully integrate haptic feedback to traditional simulations for learning is still unclear. To address this issue, we investigate under what conditions visual and tactile feedback support students’ conceptual learning while experimenting with force-related concepts. Our preliminary findings have identified that a sequenced approach for providing visual and tactile feedback may result in higher learning gains than presenting both simultaneously.

 

• CAD simulation for promoting informed engineering design decision making. In collaboration with Concord Consortium, we have been investigating the affordances of Energy 3D, an educational CAD simulation tool, for promoting the acquisition of scientific knowledge and engineering design skills. We have identified that students often use their self-generated heuristics to engage in design practices as they design energy-efficient buildings, but do not use their science knowledge to inform their design decision-making. We are using argumentation as a scaffolding framework to help students make explicit their connections between their science knowledge and their design processes.

 

• Faculty professional development. Teaching and learning go hand-in-hand; therefore, an important component of my research program is the role of faculty in facilitating learning experiences that can result in meaningful learning. In this space we study faculty intentions for integrating disciplinary practices afforded by computational tools, their development of computational pedagogical content knowledge, their role in designing or implementing laboratories in science and engineering, and their engagement in the scholarship of teaching and learning.

What is unique about your work?

I would say that convergence is what characterizes my collaborative work. You need strong partnerships between science and engineering faculty members who are eager to change their teaching practice, or colleagues who are doing interdisciplinary work. For example, my graduate students and postdocs take a critical role in the design-based research process and most are experts in specific science and engineering disciplines such as computer scientists, designers, biotechnologists, physicists, materials scientists, mechanical engineers, agricultural and biological engineers, to name some of their expertise. We all bring our own knowledge and skill sets to learn from each other. We really depend on each other’s backgrounds, skillsets, and expertise. It often takes a great deal of discussion and iteration over ideas and drafts, but we have learned to collaborate across boundaries and understand each other’s perspectives.

What should the cyberlearning community be doing?

I would like to see more interdisciplinary work between the cyberlearning community and the discipline-based education research community. There is so much to learn from each other and I think there are a lot of opportunities for synergistic work. One of my efforts at Purdue University has been to bring the cyberlearning community together through the Cyberlearning Consortium at Purdue where we raise the level of awareness of the cyberlearning work currently being done at Purdue and facilitate mentoring and collaboration opportunities.

What are you struggling with now?

As the field progresses so do our methods for investigation. I think learning analytics tools and practices can be adopted to perform more efficient data collection, data scoring, data analysis, and insights within our findings. However, I believe there is still a steep learning curve for their adoption. Expertise exchanges along with workshops are critical for propagating these tools and practices.

What kind of help or support would you like from the cyberlearning community?

We need more cyberlearning work propagating from the K–12 level into higher education level. Some of the cyberlearning innovations, as well as principles for designing cyberlearning innovations at the K–12 level can be adopted and adapted for higher education. For example, educational software created specifically for pedagogical purposes (e.g., Energy3D) can be used as entry points to then transition into more advanced practitioners tools (e.g., REVIT, AutoCAD). Similarly, situated learning pedagogies can be adopted in the undergraduate classrooms (e.g., citizen science, community mapping) to make the content more culturally and socially relevant. For this, more work needs to be done in collaboration between the cyberlearning and the learning sciences communities with the discipline-based education research community.