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MUSE | Earth-Moon-Sun Dynamics | Course Overview and Materials | Introducing Scientific Models | Course Material 1D: Black Box | Instructional Notes

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UNIT 2: Building the EMS Model


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INSTRUCTIONAL NOTES


Intended Learning Outcomes

Students will:

  • Create models to account for phenomena.

  • Recognize data patterns.

  • Design experiments to test models.

  • Assess models for data fit and consistency.

  • Use models to make predictions.

  • Use classroom norms (basic interpersonal skills) in group work.

  • Make observations.

  • Organize data.

  • Make diagrams.

  • Understand that models are ideas that scientists use to explain patterns they see in the world. In other words, models are explanations that scientists develop for natural phenomena.

  • Understand that models are judged to be acceptable or not based on how well they explain the data, how consistent they are with other knowledge, and how well they can be used to predict.


Supplies

(*optional)

  • Poster-making materials (paper, markers, etc.)
  • *Food coloring
  • *Rubber tubing, coffee cans, etc. [components (or their equivalent) of the box]
  • "Black boxes" (one box per 3-4 students)

Note that you may choose to build your own "black box" (as we have done) or use something from your environment that possesses the essential qualities of a scientific "black box." In our case, we built a set of boxes that:

  • are sturdy. Students can manipulate them without destroying them.

  • afford students an opportunity to collect data and make predictions.

  • can be manipulated. Students can add water in any reasonable amount while testing their models about how the box works. They can also add food coloring to the water, etc.

  • contain no expensive or particularly delicate parts.

  • give rise to empirical patterns that students might not expect. In this case, the students are generally surprised to see that the volume of water that exits the box is sometimes larger than what was added and sometimes smaller than what was added. Students are thus motivated to create models that can account for this apparently anomalous pattern.

In the past, we have utilized pre-made or even hypothetical "black boxes" for this activity. For example, we have used detergent cartons that dispensed a pre-measured amount of soap whenever they were tipped, and asked students to account for the internal mechanism that gave rise to this phenomenon. We have also proposed a hypothetical box into which marbles were inserted and subsequently exited according to a set pattern. This box is described more fully in the NCISLA technical report RR 98-1: Assessment of Explanatory Models in Genetics: Insights into Students' Conceptions of Explanatory Models. It is important to note that the particular black box that you use is less important than the structure of creating, sharing, and judging models according to particular scientific criteria. The box we used in this material was sufficiently complicated for 9th graders - a simpler version might be more appropriate for younger students while still affording them an opportunity to engage the ideas about scientific modeling.

For specific instructions about building the box described in Material 1D, contact us at jcartier@facstaff.wisc.edu.


Time Frame and Sequence

The Black Box material is designed to give students an introduction to scientific modeling and argumentation. Students use their skills of data gathering, pattern recognition, and model assessment as they propose a variety of models to describe how the inner workings of the box lead to particular empirical results.

This material includes several activities that are designed to focus students' attention on key aspects of modeling, such as pattern recognition and the need for empirical and conceptual consistency. Consequently, it will require 11 class periods to complete this material. Although this may seem like a large time investment – and it is – our work with 9th graders has shown that the time is both necessary (for building foundational ideas) and well-spent. Our students generally emerge from this set of activities with a good sense of what a scientific model is and how one uses and judges such a model. Moreover, they have developed a working vocabulary to discuss these ideas and many students are readily able to apply the concepts as they gradually build the Earth-Moon-Sun (EMS) model throughout the remainder of the unit.

Day 1: Introduction

Plan to spend 15-20 minutes introducing the Black Box at the end of science class on Day 1. Bring one of the boxes up to the front of the room and tell students that you are going to demonstrate what happens when you put water into the box. As you add water, tell students what volume you are adding and what the measured volume is that emerges from the outlet tube (if any). After several additions/collections, stop and ask the students to summarize what they just saw. Go to the board and ask them to repeat the volumes that you added and what came out. Note that it is unlikely that they will be able to recall this information because few or none of them will have taken careful notes. Use this opportunity to emphasize the importance of recording data on the box. Tell them that they will have an opportunity over the next several days to work with their own boxes and to try to explain the data pattern that they see.

teacher with black box model

Day 2: Explanation of the Task & Data Collection

Today's goal is principally to help students understand the task before them: to propose a model (idea) that can explain their Black Box data. Thus, begin the day by reminding students about yesterday's demonstration and telling them that you have an idea about how the box might work. Draw this diagram (right) on the board to represent your idea.

Ask the students if they think this is a good idea. Most students will probably reject this model. Ask them to justify their decision. Students are likely to say that if the box were made as you've drawn it, then water would emerge every time you add some and that is not what they saw during the demonstration. Ask the students to propose some vocabulary to express this idea in general and then write it on the board (for example, they may say the idea needs to "explain the observations," etc.).

diagram of black box

Next, draw the following diagram on the board:

diagram of "not realistic" black box

Ask students to evaluate the model represented by this diagram. Again, ask them to justify their positions and to propose some vocabulary that generally captures the "problem" with this model. Students are likely to say that this model is "not realistic" or "doesn't make sense." Add these to the list on the board.

Your list should now read:

  • Model must explain observations (data).

  • Model must be realistic or make sense.
Above this list, add the title: "What makes a good model?"

Tell students that they will now begin to collect data with the Black Boxes and that at the end of about two weeks they should be able to describe their ideas about why the boxes produce the data patterns that they do. In other words, they will need to have a model of how the box works on the inside. They will be able to tell if their model is a good one by asking if it can explain what they observed and if it is realistic.

Divide the class into groups of 2-4 students and assign each group a work station with a box, measuring containers (graduated cylinder and beaker), and a water source. You may want to provide some "ground rules" about working with the boxes, such as not picking them up, not attempting to take them apart, etc. Then pass out a data collection worksheet to each student and remind them to keep track of their data and their ideas throughout this task. Note that they can create their own worksheets if they prefer, or you may choose to create a worksheet format together as a class. Give the groups the remainder of the period to collect data with the boxes.

Day 3: Patterns in Data

Begin today by telling students that after they are finished with the Black Boxes in a couple of weeks, they will be learning about some astronomical phenomena and building a model of the motions of the Earth-Moon-Sun. Point out that they are already beginning to collect data that they will need to use during the astronomy part of the unit: they are observing the Moon daily (Material 1C) and have also collected a series of Sun plot points (Material 1B). Before continuing to work on the boxes today (which students will probably be eager to do!), students will look at some data related to Moon phases. They will then apply the skills they learn in this activity as they continue to experiment with the Black Boxes.

Ask students to arrange themselves in their small groups at their work stations. Hand out one set (four) of Moon cards to each group of students (note that a single sheet of Moon Phase Cards can be found in the Activities section of this material. You will need to print one sheet for each group of students and cut the sheet into four horizontal strips such that each strip contains four Moon diagrams. It is also a good idea to print these on a color printer to enhance resolution and to laminate the strips for longevity in the classroom).

Ask the students to look at the cards and organize them in some logical way. After about 10 minutes, ask for volunteers to share their organization with the class. Lead a discussion about the similarities and differences in the ways that students chose to organize their cards. If some students have noted the cyclical nature of the Moon phase patterns, ask these students to discuss the basis for this claim – what did they notice within the cards that led them to this conclusion? Did they have any prior knowledge that supports this conclusion? If students have not noted the cyclical pattern, show them one card and ask them to predict what the Moon might look like during the next few days. Ask them to justify their prediction. (At this point, most students will be making predictions based on the pattern that they see in the cards rather than on a model of how the Moon is orbiting the Earth.)

Conclude the activity by asking students about the skills or processes they used to organize their Moon data: How did they decide where to begin or what came next? Note that these same skills will be useful in organizing their Black Box data. Discuss with students the fact that scientists collect data all the time, but before they can attempt to explain the data, they need to find patterns. In other words, the patterns in the data allow scientists to ask specific questions during inquiry. Up to this point, the students have been collecting data with the Black Boxes, but many of them probably have not attempted to identify a pattern (qualitative or quantitative) in that data yet. This is the next step in their inquiry. Hand out the Pattern Recognition Homework assignment and ask students to complete it before tomorrow's class. Some groups might choose to work on the assignment during the remaining class time while others will want to collect additional data with the boxes.

Day 4: Patterns and Data Collection

Begin class today by reviewing the Pattern Recognition Homework assignment. You may choose to allow students time to reflect on their answers to the assignment in their small groups or to proceed immediately to a large class discussion of questions 1-5. The focus of discussion should be on using skills similar to those employed during the Moon phase pattern activity (Day 3) to identify both qualitative and quantitative (if possible) patterns in the Black Box data thus far. Reviewing the questions on the homework assignment should prompt students to share their ideas about these patterns. At the end of this discussion, students should recognize some general pattern in their data such as "water emerges from the box in small amounts or really large amounts in alternating instances," etc. Keep a list of the general patterns on the board as students discuss them.

Given that each box might be constructed slightly differently (in terms of the volume of the receptacles within them) and the tendency for experimental error, this classroom discussion is likely to conclude with discrepant quantitative data. For instance, one group might notice that their box releases some water after each addition of at least 200 mls, while another group might say that at least 350 mls is needed to get any water to come out of the box. Ask students why they think such discrepancies exist and list these ideas on the board as well. Students will probably suggest that experimental error occured. They might also believe that each box is structurally unique. If students express this latter concern, ask for evidence in support of the idea. Refute the idea by noting that the same general (qualitative) pattern that was seen with all of the boxes suggests there are common - and not unique - structural elements among them.

Finally, ask students to gather with their groups and discuss their answers to Questions 6 and 7. Circulate among the groups and ask the students to describe their data pattern, their plan for confirming or disconfirming the pattern, etc. Once each group has formulated a plan with specific predictions that would either confirm or disconfirm their empirical patterns, give students the remainder of the class period to collect additional data with the boxes. The goal for today is to determine an empirical pattern for each group's box. In subsequent days, the focus will shift to being able to account for that pattern by proposing a mechanistic model.

Day 5: Norms & Assessment

During the first part of class today, introduce the basic classroom norms to the students. Note that their task – creating a model to explain the Black Box data requires them to work together in teams. Discuss the similarities between these working teams and scientific teamwork. Then ask the students to review the basic norms on their handouts and allow them to ask questions if they have any. Note that in order to learn science in this class, they will need to develop their skills of working with others and offering/accepting constructive criticism. Their progress in developing these skills will be assessed regularly by the teacher using clip board check lists (CBCL’s). Show the students an example of a CBCL and share with them your plan for using these check lists to assess their group working skills, including any plans for regularly sharing the results of these assessments with individual students.

Initial Models

For the remainder of the class, allow the students to collect data with the boxes and work on their explanations (initial models) for the data. Pass out a "Model Discussion Worksheet" to each student. Note that the students will spend most of the period tomorrow discussing their models with other groups. In class today (and for homework if extra time is required), they will need to answer the first five questions on the worksheet. Some groups will be prepared to do this right away, as they will have a tentative model in mind. Other students will need to collect additional data or conference with their own group members for a longer time. All students should have the first five questions completed prior to class tomorrow. As the students work, circulate in the classroom and use a CBCL to note their use of the basic norms.

Day 6: Discussion of Initial Models

Begin class today by asking students to identify the criteria for judging models in science. List these on the board as students volunteer them:

  • A model must be consistent with other ideas (or realistic).
  • A model must explain all the data.

Ask students if there is any other criterion for judging whether or not a model is a good one. Remind them that they planned experiments to test their models and part of their plan included making a prediction about the outcome from a particular operation with the box. When that prediction matched their results, they were more confident about their ideas. Thus, add the following criterion to the list:

  • A model can be used to correctly predict experimental outcomes.

Ask students to spend about 10 minutes with their group members reviewing their written responses to questions 1-5 from last night's homework. After this period of time, ask the students to pair up with one member of their group. Next, assist each pair in finding another pair from a different group to complete the discussion activity. Thus, you will have several teams of four students, representing two students from each of two different research groups.

Note that you will be circulating among the groups during this activity, recording students' use of classroom norms and modeling skills on a CBCL that is customized for this purpose.

Once the students have formed their discussion teams, ask them to take turns (1) describing their model(s) and the data that support those models; and (2) describing aspects of their own models that still need work (additional conceptual or empirical refinement). While one pair is describing their model, the other pair should be listening carefully, sketching the model and/or taking written notes, and asking questions. Stress the need to ask questions in order to clarify ideas. Note that the students will be evaluated based on how well they are able to describe someone else's model, so questioning is extremely important.

After the pairs in each team have had a chance to share and discuss their models, ask each student to complete Questions 6-8 on the discussion form and to give their completed forms to a member of the opposite group. Thus, each student should have one sheet of written feedback about his/her model to assist in the homework assignment for the evening.

Hand out the Model Discussion Homework. Ask each student to use her/his notes from the day's discussion, as well as the written feedback that was received, to complete the questions on the homework assignment. You can allow students to work on this at the end of class if time allows.

Day 7: Testing Models

Review yesterday's discussion activity as a class: ask students in what ways they found the process of sharing their ideas and offering/receiving criticism about their models to be helpful. Point out that such discussions are a crucial aspect of scientific practice.

Next, list the goals for today on the board before asking students to meet with their group members:

  • Talk with group members about homework assignment.
  • Develop an experimental strategy to test your models.
  • Predict the outcome of the experiment you've chosen.
  • Discuss the experiment with the teacher before moving on.
  • Collect any necessary data.
  • Refine your model.

Once the students are in their research groups, ask them to share their responses to last night's homework and to choose what seems to be the best plan for testing their initial box models. Once they have decided on a plan, ask them to discuss it with you before moving on. In your discussions with individual groups, be sure to ask them what they predict will happen as a result of their experiment and exactly how such an outcome will help them refine their models. For example, they might be attempting to distinguish between two different mechanisms and want to either confirm one or rule out another, etc.

Allow students to work on their data collection and model refinement for the remainder of the class today. Prior to the end of class, hand out the Model Reading. Ask students to complete the reading and questions prior to class tomorrow.

Day 8: Discussion of Model Reading

Most of class time today should be spent reviewing the model reading and homework questions. Generally, we elect to have this discussion as a whole class, but small group work might also be a good way to accomplish this review. At the end of this discussion, the students should understand the following concepts:

  • "Scientific models" are ideas that scientists use to explain something about how the world works.
  • Drawings, graphs, maps, photographs, etc. are not models. However, these items are frequently used to communicate models.
  • Models are judged based on three criteria (which should be review for them):

A. explanatory power
B. predictive power
C. consistency with other ideas and prior knowledge

Presentation Guidelines

Note that the next few days are going to be spent wrapping up the Black Box activity. Each group will prepare a presentation to inform their classmates about their Black Box models and how their data support those models. Also, students will be expected to discuss elements of their models with which they are not yet satisfied and explain why. The Model Reading should help them to realize that their Black Box models are not complete – and that scientific study is never complete. New knowledge is always being generated and scientists revise old ideas regularly. Thus, for the presentations that they are planning, students will be evaluated on the basis of how well they can communicate their ideas and discuss how their models either do or do not meet the criteria established for good scientific models. Stress that they will not be evaluated based upon whether or not their models "work" or whether or not they have the "right" models.

Hand out the Presentation Rubric to each student and tell them that tomorrow's class will begin with a question/answer period about the rubric and expectations for the presentation.

Day 9: Planning the Presentation

Begin class today by allowing students to ask questions about the Presentation Rubric and clarifying the basis for evaluation of the presentations. You can use the rubric as a guideline for a general discussion of what is expected, or you may choose to write up a more simplified handout for students that could serve as a "checklist" as they prepare for the presentations in class today.

Have poster making supplies available for the students to use today. They will need large sheets of poster paper or butcher paper, markers, rulers, and any other appropriate supplies. Be sure to stress the importance of planning both their posters and their presentations during this class period – if they do not budget their time adequately, they will need to complete the preparation for homework. By the end of the class period today, students should:

  • have a completed poster that shows both their model and the data that support that model

  • have planned the presentation (who will say what and in what order!)

Days 10 & 11: Presentations

Typically, it takes 10-15 minutes for each group to present and discuss their box models, including time to set up the poster and for classmates to ask questions. Thus, depending upon how many groups you have in your class, it will take between one and three days to complete the presentations. Remind the class that you will be evaluating the presentations according to the rubric that was discussed on Day 9 and also that you will continue to evaluate the rest of the class as they practice their norms of good listening and questioning. As individual groups present their models, encourage students to ask questions, to challenge ideas, and to offer suggestions where appropriate.

Wrap-up

After each group has had a chance to share their model, plan to spend 10-15 minutes to wrap up the entire Black Box material by discussing the ways in which these activities were similar to (and different from) actual scientific practice. Ask students whether they expected to spend two weeks studying a box in science class and ask them to speculate why they did just that, etc. Generally, this discussion helps to summarize some aspects of scientific practice that are exemplified in the Black Box modeling task and to prepare students to continue to practice these skills in the upcoming EMS unit.

Conclude the Black Box material by giving the students the quiz about scientific models.


Student Ideas and Teaching Strategies

Day 1: Introduction

One advantage to our Black Box is the way in which it can be "rigged" to produce anomalous or unexpected results for this initial demonstration. You may want to take advantage of this characteristic to peak students' interest immediately. For example, with our box, you can add water prior to the class demonstration such that adding 400 mls more water will result in 1000 mls exiting the box during the demonstration. In other words, you are beginning the demonstration in the middle of the box cycle. One dynamic way of introducing the box then is to claim that "I've designed a box that makes water! Over the next several days you will test it for me. Here is a demonstration!" and then proceed to add 400 mls of water and measure the output of 1000 mls, etc.

This initial "tease" with the box also serves to point out the importance of careful data recording. After several minutes of observation, asking students to reconstruct five or six additions/outputs will probably result in much guesswork and disagreement among the students. It is a good opportunity for you to emphasize the need for them to keep careful track of their own data as they attempt to explain how the boxes work over the next several days.

Finally, the teacher is the only person who manipulates the box on Day 1, so the students are generally quite eager for their turn on Day 2!

Day 2: Explanation of the Task

The combination of students' natural curiosity and their years of experience in traditional outcome - driven science classes almost always results in their strong desire to know what is really inside the box. The objective of this material is to help students practice collecting data, recognizing patterns, and proposing "good" models to account for that data. Consequently, setting up this task is extremely important: students should not be under the impression that their task is to "discover" or "figure out" what is really inside the box. Rather, their task is to propose a model that can explain their data and is plausible or realistic. In order to make the analogy between the Black Box activity and scientific practice, it will be important to stress this distinction from the beginning and to note that scientists frequently can not "open the box" and "see" the answer to their inquiries. Scientists can only propose models (ideas) based on the data and their prior knowledge about the world.

Providing students with concrete examples of models (which are represented in Diagrams 1 and 2 in the Time Frame & Sequence section) has proven to be an effective way to initiate a dialog about what constitutes a "good" model or an acceptable explanation. After only a brief experience watching the teacher add water and measure output, students will respond that the model represented in Diagram 1 is inadequate. When asked why they feel this way, many students will reply "because it wouldn't work." Continue to challenge these students to explain their reasoning, define the terms they use (e.g.: "work"), etc. and establish a convention for judging ideas. By the end of this introductory conversation, the class should have in mind two criteria for judging their models:

  • Model must explain observations (data).
  • Model must be realistic or make sense.

Because these criteria will be applied to judge all models (including the EMS model) throughout this unit, it is important that students understand them and practice using them during this Black Box material. It is also a good idea to keep this list in a prominent place in the classroom for regular reference.

This initial discussion is a good time to introduce the term "model" and begin to define it. Many students will have heard this term used in a variety of contexts prior to this class. You may want to ask them to offer definitions for the term and list these on the board. Note that in this class, the term "model" will be used exclusively to refer to an idea that can explain something. Words, diagrams, charts, and three-dimensional replicas are all ways to represent models or ideas, but they are not models themselves. Later in this material the students will read about scientific models and this distinction will be made again. The importance of introducing the ideas here and drawing such a sharp distinction between models (ideas) and representations stems from our research with 11th and 12th grade genetics students in a modeling classroom: these students were unable to apply the criteria for judging ideas in part because they couldn’t distinguish ideas from physical representations or from data. Consequently, students frequently focused on the quality of representations rather than whether or not the data were accounted for in a realistic manner when judging models. Introducing the term "model" and its use in the classroom early in the genetics course eliminated this confusion to a significant extent (see Assessment of Explanatory Models in Genetics: Insights into Students’ Conceptions of Scientific Models, RR 98-1; & Using a Modeling Approach to Explore Scientific Epistemology With High School Biology Students, RR 99-1).

Day 3: Patterns in Data

One thing to be mindful of in today's lesson is the fact that most students will be eager to work with the Black Boxes again. Consequently, it will be difficult for them to focus on an activity that seems unrelated to their objective of creating a model to account for the box data. Try to make the connection between seeking data patterns in Moon data and any other type of data (including the box data) apparent throughout the task. Note that the skills that they practice during the Moon pattern activity will be necessary in order for them to successfully propose a box model that will meet the criteria of a "good" model.

Many students will organize the cards according to the Moon phases represented in the diagrams. However, some students will organize them alphabetically (or possibly according to some other characteristic). During the class discussion, try to point out the different organizational schemes and note that these result from prioritizing different aspects of the data.

The Moon cards are labeled with names of renowned astronomers rather than letters or numbers to avoid the implication of hierarchy or linearity. Despite this, most students tend to place the four cards in a linear arrangement based on overlapping data points. For example, the last Moon on the Galileo card is in a new phase. The first Moon on the Kepler card is also in a new phase. Thus, most students will place the Kepler card in order immediately following the Galileo card.

During the discussion (after students have arranged the cards), try to help students articulate this particular strategy:

  • look for redundancy or overlap
  • use redundancies to sequence events

Either by capitalizing on the work of students who have already arranged the cards in a cycle or by asking them to predict what the Moon might look like a few days after the final diagram, you can introduce the idea of cyclical patterns to the whole class. Ask students to describe how they can use redundant data points to establish a cycle as a natural extension of their initial sequencing activity. Conclude by noting that they will apply these same skills while seeking to describe a pattern in their box data.

The purpose of this activity is to have students practice looking for cyclical data patterns with a limited and well-defined set of data. In the past, we have found that identifying a pattern in the Black Box data is a significant obstacle for some students. Frequently, these students have not been completely systematic in collecting data (i.e. they have used varying amounts of water during each addition to the system, etc.) and have encountered experimental or mechanical errors that result in "messy" data. Furthermore, the quantitative nature of the data in this case (students are recording volumes of water added to and emerging from the box) tends to overshadow more general qualitative patterns. The following data is fairly typical of students whose additions have been consistent (i.e. the same volume each time):

Water In (ml) Water Out (ml)
400 0
400 415
400 590
400 405
400 0
400 1020
400 0
400 395
400 610
400 400
400 0
400 990

Rather than treat output results of 415 mls, 405 mls, 395 mls and 400 mls as being qualitatively equivalent, the majority of students will see these as completely inconsistent results. Also, (at least early on) students usually treat each data point as an isolated event and don't attempt to lump together additions and outputs to identify a general pattern. It is quite rare for students to conclude, for example, that the addition of 1600 mls of water results in 1400 mls (roughly 400 + 600 + 400) coming out; and the addition of 800 more mls of water will cause the box to eject 1000 mls all at once. This is a cyclical pattern. You may want to prompt students to use strategies such as mathematically combining data points or attempting to establish qualitative trends in their data as they continue to work on their models. The Pattern Recognition Homework assignment encourages the use of some of these skills as well.

Day 4: Patterns in Data

Begin the class discussion today by focusing on homework questions 1-5 and attempting to identify both general qualitative patterns and more specific quantitative ones. As described above (for Day 3), students will probably encounter initial difficulties recognizing qualitative patterns. You can assist students by asking them to round their numbers to the nearest 50 mls or to mathematically combine additions and outputs, etc.

Once a qualitative pattern has been established, ask students to begin to quantify it by approximating volumes necessary to trigger the box to release small volumes of water versus large ones, etc. Due to the slightly different construction materials in each box, as well as experimental error, it is likely that students' numerical data will vary from group to group (or from day to day, for that matter). Prompt students to think about why this might be the case and try to establish that the general qualitative pattern seen for all boxes suggests some underlying structural similarities despite the variation in volumes.

Before allowing the students to continue data collection, be sure to talk briefly with them about their "research plan." Each group should have a tentative idea of the empirical pattern associated with their box and be able to discuss a specific data collection strategy that will either confirm or disconfirm this pattern. Furthermore, ask the groups to predict the outcome of their experiments given the pattern they have seen thus far. The ability to notice when an outcome matches a prediction (that is based, in this case, on an empirical pattern, but later will be based on a conceptual model) is an important method of evaluating ideas. You can begin to make this point here and again in the last few minutes of class as students evaluate their data collection in light of their initial predictions.

Day 5: Norms & Assessment

Most of the model building, sharing, and idea assessment that occurs in this course requires cooperation among students. In particular, students need to develop skills for participating in group work, sharing ideas and listening to ideas of others, and judging ideas based on specific scientific criteria. The classroom norms (basic, intermediate, and advanced) are descriptions of skills and behaviors that lead to productive group work within a scientific classroom community. Without a firm grasp of these skills, students are seldom capable of fully participating in group work or class discussions in this student-centered classroom. Thus, it is important to introduce these norms early in the unit, to return to them throughout the unit, and to regularly assess them using the CBCL’s or some other type of assessment.

As students demonstrate proficiency in using the basic norms, introduce the intermediate norms. Similarly, shift emphasis to the advanced norms when students appear ready to do so. It is possible that the students will not become proficient with the intermediate or advanced norms during the 9-11 weeks of this unit, but these norms should be emphasized throughout the school year.

The CBCL’s were designed by our teacher-researcher team and are still being modified to suit specific teaching styles and classroom dynamics. You will probably need to experiment with and modify the CBCL’s for ease of use in your own classroom, too. Regardless of the specific format of assessment that you use, however, we recommend sharing a summary of your evaluation with students on a regular basis, perhaps every 6 weeks or so. Using a single CBCL for each group, you can collect these worksheets in a binder and summarize individual students’ strengths and weaknesses every couple of months. This type of feedback can assist students in improving certain skills. Also, our teachers have used students’ performance on classroom norms assessments as a formal indicator of their "citizenship" grades, as our cooperating high school requires teachers to formally assess each student’s citizenship qualities.

You may want to ask students to complete self-assessments of their use of classroom norms as well. Samples of such self-assessments are provided with the materials on this website.

Initial Models

The purpose of this worksheet is to help students articulate a model to explain their data, identify specific data that support (and do not support) their models, and plan experiments that will assist them in improving their box models. Typically, student groups will be at very different stages as they begin this task. Some groups will already have a well-defined model to describe and test, while others will still be struggling to identify a specific pattern in their data. Given their work on Days 3 and 4, however, most students should have at least a general understanding of their pattern and you should encourage them to focus on attempting to explain this. While not all groups will have completed the worksheet during class today, all groups should have discussed the first 5 questions so that they are prepared to discuss their ideas with other groups tomorrow.

Day 6: Discussion of Initial Models

There are two main objectives for this discussion activity:

  • First, students have an opportunity to practice their listening and questioning skills, both of which will be essential during the EMS modeling later in the unit. You can stress the importance of these skills both implicitly (by virtue of the fact that you are using a CBCL to assess them) and explicitly (through your own interactions with students) during these discussions. One strategy that has proven successful is to ask a student to describe for you the model of another group. Initially at least, most students struggle with this seemingly simple task, believing that they have paid close attention but realizing that there are holes in their understanding as they attempt to explain or describe a new model to someone else. This is a good opportunity to stimulate further questioning and conversation among students and to point out that each student is responsible for her/his own learning: if they don't understand something, students must ask questions until they are more confident of their knowledge.

  • Second, we have found that many students enjoy the physical manipulation of the box and tend to prioritize data collection over the more conceptual work of formulating a model to explain that data. By asking students to focus on describing their models and offering specific data that support (or refute) those models, we are essentially forcing them to prioritize conceptual work rather than empirical work.

A CBCL specific for this discussion activity is included in the assessment section of this material. We recommend that you share this form with your students prior to or during the activity so that they are aware of the basis for their evaluation. Also, try to keep track of instances that are exemplars of particular skills or behaviors so that you can share these with the class tomorrow. For example, one student might ask a particularly good question about how another group's data supports their model, etc., and you can use these real examples to encourage your students to continue to practice these skills.

Day 7: Testing Models

The focus of this day is for students to design and carry out tests on their boxes that will enable them to answer specific questions about their models. Random data collection is not acceptable. Students will need to have at least an initial model from which to propose specific experiments and they will also need to be able to mentally "run" the model in order to predict a particular empirical outcome. As you check in with individual groups regarding their work plans, ask them to describe the expected outcomes from their experiment(s) and to explicitly note how such outcomes would assist them as they refine their model(s).

Several students in our classes have, independently of teacher input, chosen to use food coloring in their experiments to test the notion of whether there is a single container within the box or two such containers. For example, a student might propose that there are at least two containers to hold water within the box. Given this model, the addition of green water, for example, could result in the output of clear water. The inference is that the green water went into a separate container and didn't mix with the clear water that was already inside of the box. Other types of experiments that students might attempt include varying the volumes of water that they add, listening for specific types of noises within the box, or attempting to reproduce some aspect of their results externally with materials such as rubber tubing, coffee cans, etc. It is a good idea to have a supply of such materials on hand or to encourage students to bring their own materials to class prior to Day 7.

Day 8: Discussion of Model Reading

Although the definition of "scientific model" was introduced during Day 2, it is our experience that many students will continue to have difficulty distinguishing between conceptual models (ideas) and physical representations or replicas. This distinction might be seen as arbitrary (and to some extent it is), but it is an important point to make early and regularly when teaching science curricula that require students to create, use, and revise scientific models. Our work with both 9th graders studying EMS and 12th graders studying genetics has shown that without a grasp of this distinction, students tend to judge models based upon the quality of their representations or the social status of their creators rather than upon their ability to account for and predict data and their consistency with prior knowledge. Thus, it is important to stress this distinction again when reviewing the Model Reading. The following discussion illustrates one approach to reviewing this topic with students and also provides examples of typical students' responses to the discussion questions that follow the reading. The transcript is reconstructed from researcher field notes in a 9th grade EMS classroom:

Teacher: I'd like to have three volunteers to write your answers to question 2 on the board. Joe, Nathan, Noah.

Students go to the board and write their answers to question 2. Joe writes: "A scientific model, to me, is an explanation about something." Nathan writes "A scientific model is a model that demonstrates patterns that can be noticed through data taken by someone/body." Noah writes "Drawings, graphs, and 3-D structures."

Teacher: OK, so we have "A scientific model is an explanation about something." "A scientific model demonstrates patterns that can be noticed through data." And we have a scientific model is "drawings, graphs, and 3-D structures." Which one is right?

Ben: All of them.

Elizabeth: My idea is pretty much the same as Joe's.

Teacher: OK What about Nathan's answer up here? What do people think about that? Can you notice a pattern without really having a clue about why that pattern is occurring? Just think about working with the boxes. Did you see a pattern before you knew what was happening to cause that pattern?

Tamara: Yeah. I guess that's true. The pattern comes first.

Teacher: So you sort of need a pattern to help you create a model, but you don't need a model to recognize a pattern. Now, what about Noah's idea? Noah wrote that a model is stuff like drawings and graphs. Based on our reading here, is that an appropriate definition?

Elizabeth: No.

Mark: But there isn't really a wrong answer.

Teacher: In this case, there is a direction I want you to go in. This reading says that stuff like diagrams and graphs are things that people use to communicate their models to each other. But those things are not the same as the idea of what's going on. That – the idea – is the model.

The teacher writes on the board: A scientific model is an idea that explains something (a phenomenon or a data pattern).

Students have already been practicing and reviewing the criteria for judging models. These were introduced on Day 2 and reviewed on Day 6. Still, it is a good idea to review them again today and to ask for student volunteers to write them in a prominent place in the classroom, if they have not already done so.

Presentation Guidelines

When you introduce the presentation task to students today, be sure to stress that they will be evaluated based on their ability to talk about their model and describe the ways in which it does or doesn't account for their data and is consistent with prior knowledge. They will not be evaluated based upon whether or not their model is "right." Remind them about the Model Reading – this reading makes clear that "right" and "wrong" are not useful concepts to apply to scientific ideas. Rather, they should be thinking about "acceptable" and "unacceptable" based upon the model assessment criteria they just reviewed. Moreover, for this presentation, many students will have models that still can't account for all their data. Emphasize that this is not a problem! If there are aspects of data that are still unaccounted for, the students simply need to point these out and demonstrate why their models are unable to account for them.

Day 9: Planning the Presentation

The Presentation Rubric is fairly detailed and complicated in terms of format, particularly for students who have not seen a rubric before now. You might choose to share only the "4" column from the rubric or to write up a simplified worksheet that can be used as a "checklist" as students prepare for their presentations. Obviously, your choice will depend on both your teaching style and your students' abilities.

Regardless of the format you choose for sharing the criteria for assessment, be sure to review with students the essential elements of a good presentation:

  • Technique: clear speech, well organized, participation from all members, clear poster, etc.

  • Content: introduction of the problem, description of model, presentation of data, description of experiments and modeling process, etc.

  • Argumentation: model supported by data, limitations of model discussed, consistency between model and prior knowledge discussed, ability to field questions from classmates, etc.

During the discussion about the presentation and rubric, it is often helpful to give specific examples for the students. Note that they might want to discuss some models that they had early on and identify the data that caused dissatisfaction with these models. This would be an example of giving procedural details and paying attention to data/model match. You might also suggest that one way to argue for how consistent a model is with prior knowledge is to use an analogy to a phenomenon that most students will know about. Generally, our students do this quite readily, with no prompting from the teacher, but they don't recognize the use of analogy as related to consistency. For example, students may make analogies to how a toilet flush float mechanism works or how a siphon is used to clean out a fish tank.

It is important that students use their time wisely during this planning day, particularly since most 9th graders (and all middle school students) don't drive cars yet. It is usually difficult for a group of 3-4 students to get together to work on a presentation after school hours. Thus, the groups will need to start planning their presentations and posters immediately during the class period. One or two group members might have to take elements of the poster home to complete or write up notes on the presentation for homework. But all work that must be done as a group should be completed in the remaining class time.

Days 10 & 11: Presentations

Remember that the focus of the presentations is for each group to clearly communicate their idea and for the class to evaluate it based on the model assessment criteria. You should spend a couple of minutes reviewing these criteria before beginning the presentations.

As each group presents, you will probably have to encourage students to ask questions, at least initially. Generally, students ask questions of clarification regarding particular mechanisms involved in models. For example, a group whose model involves a threshold mechanism controlled by a spring might be asked to explain how the mechanism resets itself after each release of water. Some of the questions that students asked about the subset of sample posters that are available on this website include:

  • How does the trap door open?
  • How does water go up the tube?
  • Where is the pressure coming from to force water out of that exit tube?
  • How fast does the water come out?
  • How much water goes into each container when it splits?
  • What holds up the first container?
  • How can a spring have enough momentum to reset a lead ball?

Students rarely focus on quantitative data during these presentations. You will probably have to take the lead in this regard by asking groups to describe, using their quantitative data, how their model results in the particular data they collected. That is, you might ask, "Can you take us through a cycle of data using this model?" Rather than focusing on quantitative data, most students are concentrating on understanding the mechanisms of the models presented and asking qualitative questions about those.

Wrap-up

We have found that a nice way to begin this summary discussion is to ask the students whether they expected to spend so much time studying a box in science class. The vast majority of students readily reply that they didn't expect this. The follow-up question, "why do you think we spent all this time doing this work?" generally meets with ready responses as well. Following are some examples of students' responses to this and follow-up questions:

Elizabeth: To learn all about how you think up models and think up, um . . . ways to solve a problem.

Teacher: So do you think that you were doing science while you were working on this box?

Elizabeth: Yeah.

Teacher: Why do you think that, specifically?

Elizabeth: Well, there’s a very broad definition for science. You could be doing science by like, cutting the lawn and seeing how long it is before it grows back. But, yeah. Because we had a problem and we came up with ways to solve it while observing and taking information.


Noah: I’m not really sure. Probably so they could help us learn about organizing data so that we can do that later in the year. Um and trying to find, what’s the word? Patterns. So, like if we do stuff later in the year we can. That we’re, we’ve done it before.


Peter: Well one, it kind of got us on the right foot, you know working with groups. And talking to people. And also it helped with now what we’re doing, the astrology kind of thing, the looking at patterns and all that, so it got us ready for that too.

Teacher: Do you think that you’ve been doing science?

Peter: Yeah! Pretty much, well, yeah. The thinking, figuring things out, why they work, how they work. Making models. Explaining why and how they work to others. Yeah, it’s pretty much science.


Susan: Like probably not to just sit there all the time. To actually do something. And probably understand it more than talking. Cause hands-on you can understand it more than any lecturing can do. And you can actually touch it and try to feel how it comes out and stuff and you can learn better that way.

Teacher: Do you think that you were doing science while you were working on these boxes?

Susan: Yeah, cause you have to measure stuff and see how much you get back and you gotta do data and with every experiment you have data. And then you want to solve the problem. And that’s like what we were doing.

Teacher: Can you say more about what you mean by solve the problem?

Susan: Like, find what’s in the box and probably experiment and try to find out what was in it and stuff like that.

Teacher: Are you trying to find out what was in the box?

Susan: Yeah, I really want to know what’s in the box.

Teacher: OK

Susan: Like, after two weeks, you want to know what’s in it.

Teacher: OK But do you think that was the objective?

Susan: No, I think it was to have practice collecting data and trying to figure out what was in it. Like, using your mind more and just trying to do stuff like that.

Teacher: Using your mind more?

Susan: Yeah, like trying to use your imagination to see what was in it and how people can draw different things. And one thing will be totally different from the other.

At the end of this discussion, it is desirable to be able to draw specific parallels between the Black Box modeling activity and "real" scientific practice. Some points you may want to emphasize include –

  • Scientists seek to explain what causes patterns that they've noticed in the natural world. Students explained the patterns in their boxes.

  • Scientists' ideas change as they collect more data, test ideas, and share their ideas with others. Students' ideas changed as they collected more data and shared their ideas with others.

  • Scientists judge their ideas based on how well they can explain the observations and whether or not they are consistent with other ideas. Students assessed their models based on these same criteria.

  • Scientists rely on others to help improve their ideas. Students worked in teams to pool their ideas and relied on the criticism of their peers to improve their models.

  • Scientists use representations to communicate their ideas. Students created posters to represent their box models and to share them with their classmates.

One issue that is likely to arise in the final discussion is "closure." Students will probably want to know when you are going to show them what's really inside that box! We've dealt with this issue in different ways, depending upon the age and maturity level of our students. In one approach, the teacher has told the students that they would finally get to see the inside of the box and then produced a box that contained nothing but a poster representation of one of the student group's models. This teacher went on to explain that in science, the researchers themselves decide what is really in there by providing evidence in the form of data and consistent arguments that their ideas are plausible and powerful. They never get to "see the real answer." Another strategy for addressing this issue is simply to ask the students, "Now why do you think I'm not going to show you what's really in the box?" Older students have offered many answers to this question, including:

  • Because you're mean!
  • To be consistent.
  • Because you never really know in science.
  • To drive us crazy.

Thus, your final discussion should also serve to justify, to some extent, your rationale for not sharing the box mechanism with the students. They should come to appreciate that the purpose of the activity was fulfilled – that is, they created plausible models for the box data. Opening up the box is both unnecessary to the accomplishment of this goal and inconsistent with how science is actually done.

 

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