Wednesday, August 6, 2008
Exhibit Hall CD, Midwest Airlines Center
Background/Question/Methods The tools and methods for species identification have remained unchanged for hundreds of years. Despite the increasing roles and capabilities of technology in society, a seemingly obvious opportunity has been overlooked: image databases. Instead of searching text-based, jargon-laden “databases” that are spread across countless pieces of paper, species identification can utilize databases and computer interfaces. Image-driven field guides are more intuitive and, thus, would better facilitate student learning. Assessment techniques for species identification have remained similarly stagnant. Historically, field courses in biology have involved the instructor pointing to an organism and the student recording the correct scientific name. Student learning has, therefore, focused on memorizing a set of specific organisms, not the process of species identification. Field identification that is process-driven (instead of outcome-driven) would allow instructors to more readily identify roadblocks to student learning. To this end, we have populated a plant image database for the Carroll College Greene Field Station. We are designing image-driven software for plant identification, including an integrated assessment tool. Combining an electronic database, an image-driven computer interface, and an integrated assessment tool shifts the learning outcome from rote memorization to problem solving.
Results/Conclusions The development of the plant image database has been challenging. Originally, we hypothesized that diagnostic traits of different plant species could be grouped under a broad umbrella of terms that could be consistently applied across all species, from trees to sedges. If true, this would simplify the design of the computer software. Yet, the utility of images of different parts of the plant varies with plant architecture. For example, the stems of a tree and a grass are fundamentally different with respect to how useful they are in the identification process and how easily their features can be photographed. Therefore, the image database and software require flexibility in how they are designed and linked. Because it is contingent on the final structure of the database and software, the assessment tool is still in the design phase. The assessment tool will include the ability to track the “paths” that students take in the identification process, a summation of the number of selections made by a student, and time stamps associated with those selections. These indicators will reflect the duration and efficiency of how each student navigates the identification process.
Results/Conclusions The development of the plant image database has been challenging. Originally, we hypothesized that diagnostic traits of different plant species could be grouped under a broad umbrella of terms that could be consistently applied across all species, from trees to sedges. If true, this would simplify the design of the computer software. Yet, the utility of images of different parts of the plant varies with plant architecture. For example, the stems of a tree and a grass are fundamentally different with respect to how useful they are in the identification process and how easily their features can be photographed. Therefore, the image database and software require flexibility in how they are designed and linked. Because it is contingent on the final structure of the database and software, the assessment tool is still in the design phase. The assessment tool will include the ability to track the “paths” that students take in the identification process, a summation of the number of selections made by a student, and time stamps associated with those selections. These indicators will reflect the duration and efficiency of how each student navigates the identification process.
