New Strategies for Learning Biology
The way that students communicate, gather information and even the way they go about learning differs markedly today from how their predecessors were able to function. Digital communication, the Internet, access to networked databases, computer games, and other types of interactive environments have shaped the cognitive abilities of students since before they were in middle school. Yet, for the most part, college instruction still follows the more traditional methods of a hundred years ago, failing to address the ways students gather and use information now and will in the future, and failing to take advantage of how technology can enhance the learning process. We are proposing to a very different strategy and set of tools for teaching and learning, one more in keeping with how information will be obtained and used in the future.
A decade after his seminal work, Frames of Mind: The Theory of Multiple Intelligences (1983), Howard Gardner wrote a guide for the practical application of his research on the various ways that people learn, Multiple Intelligences: The Theory in Practice (1993). In this later work, Gardner argues for an individualized educational experience for all students for two overarching reasons: the different ways in which we learn and the increasing amount that we need to learn. He also insists on the need for intelligence-neutral assessment strategies.
Carey Phillips and Peter Schilling are approaching these issues by building a framework that accommodates and orchestrates many types of learning resources as well as creating sophisticated learning tools. The underlying technology for the framework is an on-line adaptive environment that we call "Learning-to-Learn courseware." Our first iteration of this courseware is designed for students studying introductory biology and we intend to make this an interdisciplinary experience by integrating introductory chemistry into the courseware. The adaptive-learning environment "learns" how each student interacts with content and negotiates the self-assessment exercises. It then provides personalized instruction and feedback based on each student's particular learning and memory acquisition strengths.
We are coupling this adaptive courseware with hundreds of traditional text and visual content modules as well as with, importantly, multi-tracked 3-dimensional animations and on-line virtual reality worlds. Today, the teaching of science is more about understanding process than memorizing descriptive facts. We are now expecting students to understand the dynamic interactions between objects through space and time, leaving the more traditional educational methods at a distinct pedagogical disadvantage. Technologies from a variety of related disciplines have served up enormous amounts of information, impinging upon the very processes we wish to understand. We, as both learners and teachers, must shift our intellectual and pedagogical strategies, creating tools and methods for integrating these large amounts of information from different disciplines into useful and flexible models that can be shared and used to develop appropriate cognitive frameworks for solving tomorrow's problems. We have developed high-end, multi-tracked 3-dimensional animations to help students visualize the processes that form the core of almost every concept taught in science. For example, we created an animation for the NIH demonstrating how 3-dimensional shape is important in the interactions between signaling molecules in the platelet derived growth factor pathway. Unlike traditional teaching materials, animations demonstrate dynamic temporal and spatial events. The multi-track technology allows students to explore interactively and visually areas within the animation in ways impossible to do in the more traditional methods. Similarly, the on-line virtual reality worlds offer several pedagogical advantages. Students can manipulate objects within a virtual cell, for example, to solve a problem. They can enter the virtual worlds with other students to collaboratively solve problems. The virtual worlds can be designed as a repository to collect and display student research. For example, science students from a number of local schools could collect environmental data and collaborate on the analyses, as is currently being done with Dr. Peter Lea with high school students looking at water quality in a local watershed. In these ways, we can accommodate students with visual, verbal, kinesthetic, and/or collaborative learning preferences in constructivist, problem-solving learning environments.
We have divided the content for the adaptive courseware into modules that follow the concepts and ideas covered in most biology courses. Each content module provides learning materials in a series of presentation styles to address each mode of learning and integrate these modes with face-to-face instruction. When a student enters the adaptive courseware for the first time, he or she "plays" a series of on-line games, developed by Drs. Paul Whitney and Scott Paine, educational cognitive psychologists who have been working with Bowdoin faculty, which then generates a profile of the modes of memory acquisition that a student uses. The adaptive learning framework uses this information to determine the type of content to display most prominently to the student. In addition, throughout the student's use of the Learning-to-Learn courseware, the adaptive framework continuously re-assesses the student's dominant learning style(s) and adjusts the student's profile.
The formative, self-assessment exercises provide immediate feedback to the student concerning their progress toward understanding the concept. Each type of content module has assessment exercises paired with it so that students evaluate their learning using the same mode that they used to gain their understanding of the material. For instance, if a predominantly visual/spatial learner shows a preference for animations and graphics to work through a concept, the adaptive framework would give that student visual-based formative assessment exercises. As a student interacts with the online course material, a database logs the content module(s) used, and the order in which the student used them to determine which course materials most effectively help students correctly complete the formative assessment exercises. The database generates a learning profile report that is up-dated after every interaction. The program then "learns" how the student learns and modifies the presentation of course content accordingly. The cumulative effect of every student using content in the adaptive framework also informs the valuation and characterization of each content module.
An important aspect of this courseware is to challenge the student to begin incorporating other types of learning styles into HER learning repertoire. As students progress in the course, they encounter material appropriate for their particular learning style on each topic. However, realizing that learning modes are task-specific and that the "real world" is not organized in this fashion, summative assessments at the end of each module challenge students to explore a variety of learning modes. Therefore, we encourage students to learn the material based on their individual learning strengths and, once comfortable with the concepts, push them to translate this understanding across presentation types and learning styles. This approach helps each student better understand his or her learning strengths and weaknesses as well as facilitates the development of the student's strategies across all learning styles.
The courseware also allows faculty to evaluate the content within the modules, on a student-by-student or learning style basis. The adaptive courseware provides, on demand, a number of reports concerning aspects of the content and student performance. For example, faculty can view an evaluation of the value of each piece of content, broken down by its relative usefulness to a particular learning style at any time. This capability provides extremely valuable feedback regarding the design and usefulness of various media resources, allowing them to continually adapt their teaching to the students' learning.
The Learning-to-Learn courseware is created to permit faculty and students to share files in a distributed network environment as well as collaboratively filter them for quality and effectiveness. We have designed the courseware to grow organically as faculty and students at other institutions begin to use it. Faculty can add their own content by placing it on any Internet-accessible server and, then, simply listing that content on the parent program, currently residing at Bowdoin College. This content is then seamlessly added to the content list within a topic module. The database will filter content that proves most useful to students with a given learning preference and present the student with the most effective content.
-Carey R. Phillips, Department of Biology, Bowdoin College