Instructional Design for Understanding

We have formulated an instructional design philosophy from our work with students as design partners (Christie & Buckley, in progress; Christie & Horwitz, in progress). The basic tenet of our instructional design philosophy that supports our educational technology development efforts relies on a fundamental theory of learning: students learn much of what they have reasonable opportunity to learn. In the educational literature, this basic theory is appropriately named "Theory One" (Perkins, 1995).

There are four common-sensical conditions that operationalize "reasonable opportunity to learn." The first is "clear information." Students need both descriptions and examples of the learning goals, the knowledge needed, and the expected performances. The second, "thoughtful practice," states that students need opportunities to engage actively and reflectively with whatever is to be learned. Thirdly, students need "informative feedback" that helps them to understand their performances and to proceed more effectively. Fourth and finally, reasonable opportunity to learn involves "strong intrinsic or extrinsic motivation" such that activities facilitate other achievements that the learner values or is otherwise concerned with or are interesting or otherwise engaging.

We know from our current and prior research that opportunities to learn with BioLogicaÔ and its predecessor GenScopeÔ provide strong intrinsic motivation (Christie, 1999; Christie & Buckley, in progress). That is, the majority of students report that these activities are highly engaging. In student language, this is often described in terms of "fun," which could be misleading; teachers will often reply "The students think it is fun. But when will they receive instruction?" However, with very little probing on students� meaning, an interesting connection to learning and instruction emerges. These BioLogica students overwhelmingly report, as did their GenScope predecessors, that fun means "not boring," which in turn means "interesting," and moreover, very unlike their common instructional experiences in science class.

In addition to being unlike students� common instructional experiences in science class, BioLogica experiences stand in stark contrast to students� common technology experiences. As one student explained, experiences with BioLogica teach students about the purposes and possibilities for technology: "It�s good because you get to give other kids that maybe haven�t had an experience to use computers or to know that there�s different things you can do on the computers besides type or go on the Internet �cause some people just think that�s what it�s all about like goin� on the Internet and typing you know like listen to music but it�s really not what it�s all about? �Cause I have like � I know people that buy computers and doesn�t use it for anything but the Internet. But they don�t know what they�re missing. There�s like lots of other things that you can buy and use the computer for � that you can do things with it."


Three Educational Goals

In the next iteration of our BioLogicaÔ instructional module, we intend to continue its tradition of engagement as well as provide students with reasonable opportunities to achieve three related educational goals: learning to think, learning to do, and learning to learn. These are described in brief below.

Learning to think: students should develop genetics content understanding about traits, trait rules, and the genetics processes that concern inheritance of recessive traits.

Learning to do: Students should develop inquiry skills including predictions, reflections, experimentation, and exploration (i.e., aspects of the meta-curriculum).

Learning to learn: students should learn how to view learning as a consequence of understanding.

We have addressed students� desires for exploration, explanation, and evaluation at several levels. First, we have developed an overall structure for our activities catalogue, one that describes activities in terms of instructional strategies: exploration, experimentation, formalization, and transfer. Second, we intend to implement a virtual coach that aids in exploration and explanation. Third, we intend to implement an "online notebook" to aid in evaluation (for students, teachers, and researchers) by logging student work, students� developing understandings, and all relevant questions and answers. Each of these design principles is discussed below.

Four Instructional Strategies

The exploration strategy is the one closest to open-ended inquiry learning that is often synonymous with constructivist teaching and learning. Students are given a problem, the context, and appropriate tools. They are asked to explore and to follow where their own questions lead them.

The second strategy, "experimentation," is used to ask students to work with models of genetic phenomena and to construct and use inheritance experiments. The primary learning focus is on observed and expected outcomes. As such, these tasks are guided by an underlying structure, rather than an open-ended exploration. An overall goal of the experimentation strategy is to give students active experience with the scientific method. In particular, experimentation gives students opportunities for thoughtful practice at constructing inheritance laws through observation, rather than copying them from a blackboard. It moves students toward the experience of learning to learn like a scientist and learning to think like one.

The third strategy is the "formalization" strategy, which is concerned with helping students shape their developing understanding and knowledge into rules, models, and theories. Specifically, students are asked to construct rules that describe the behavior of the genetics models, in terms of dominant and recessive trait expressions: horns, legs, wings, plates, scales, tails, and fire-breathing. This strategy is introduced off-the-computer, where students work in small groups of two or three to document their initial ideas about rules, models, and theories and provide evidence that supports their ideas. Though they have access to the computer for their Journals as well as additional exploration and experimentation, the major work of this strategy involves off-the-computer activity: articulating understanding in small groups, presenting ideas to other groups, and providing supporting evidence.

The fourth strategy is "transfer," where students are asked to apply laws they have formulated to answer predictive questions that are isomorphic to questions they have previously investigated through exploration strategies. In addition, students have an opportunity to apply their developing understandings about the biology of trait inheritance, the scientific method, and learning for understanding.


Sample enhancements

The Virtual Coach

Students are introduced to general goals of the curriculum by a virtual "coach." The coach explains that students will be exploring genetics in order to gain an understanding about trait inheritance. Specifically, that students will: 1) explore genetics and the inheritance of traits, 2) be able to manipulate or change the genetics information of organisms to learn about the effects on trait expression in the population, and 3) develop an understanding about how to figure the inheritance of traits, such as sickle cell. The coach explains that students will be doing a simulation, so that they are not really changing the genetic information, but modeling this for learning purposes. Finally, coach explains related terms, like inquiry, theory, rules, experiment, and evidence in addition to simulation and modeling.

Once the Coach sets clear goals, students are introduced to dragons and asked to explore the rules of this fictitious species by manipulating organism and chromosome models. The first few minutes of the exploration are used as a software primer, driven by student exploration. Context-sensitive help pops up as students move cursor over each selection or choice and relevant tools, buttons, or menu items are highlighted. The primer starts automatically once students sign in, and can be disabled by student at any time that they wish to begin exploring.


The Online Notebook

The online notebook provides repeated opportunities for students to make their learning explicit and to receive feedback. For example, in cases where students are reporting on scientific facts or rules in response to particular prompts, students will receive feedback directly from the activity. Opportunities for students to demonstrate applied thinking or reasoning and receive feedback will be presented via a more open-ended prompting. This in-situ interviewing is accomplished by the interactivity, as it presents the student with key questions at different points in the guided problem-solving process. The interactivity will ask reflective questions and encourage students to explain their incremental reasoning and developing understanding by writing it out on the screen. This will give students an opportunity to hear themselves articulate their own problem-solving and as such, support aspects of the meta-curriculum, such as reflection. Furthermore, these texts will be saved into the online notebook for review at any time. Upon saving, students can selectively print pages from their notebook.



Christie, M. (1999). "We understood it more 'cause we were doin' it ourself:" Students self-described connections between participation and learning. Paper presented at the annual meeting of the American Educational Research Association, Montreal, Canada, April 20, 1999.

Christie, M. & Buckley, B. (in progress). Students as design partners.

Christie, M. & Horwitz, P. (in progress). Instructional design for understanding.

Horwitz, P. & Christie, M. (2000). Computer-based manipulatives for teaching scientific reasoning: An example. M.J. Jacobson & R.B. Kozma, (Eds.), Learning the sciences of the Twenty-first century: Theory, research, and the design of advanced technology learning environments. Hillsdale, NJ: Lawrence Erlbaum & Associates.

Horwitz, P. & Christie, M. (1999). Hypermodels: Embedding curriculum and assessment in computer-based manipulatives. Journal of Education, 181(2), 1-23.

Perkins, D. (1995). Smart schools: Better thinking and learning for every child. NY: Free Press.


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