Let the Students Do the Talking - ACS Symposium Series (ACS

Chapter 5

Let the Students Do the Talking

Downloaded by UNIV OF FLORIDA on December 28, 2017 | http://pubs.acs.org Publication Date (Web): October 31, 2017 | doi: 10.1021/bk-2017-1266.ch005

Colleen Megowan-Romanowicz,*,1 Larry Dukerich,1 and Erica Posthuma-Adams2 1American

Modeling Teachers Association, 5808 13th Avenue, Sacramento, California 95820, United States 2University High School of Indiana, 2825 W 116th Street, Carmel, Indiana 46032, United States *E-mail: [email protected].

Modeling Instruction has been practiced in science and mathematics classrooms across the US and around the world for over 30 years. Concept inventory scores from over 30,000 students confirm that this approach is one of the most successful science education reforms in the last 50 years. Designed to leverage what cognitive science has uncovered about how we think, Modeling Instruction guides students to construct and apply a handful of basic conceptual models that form the content core of chemistry. Students work together in groups of three or four to accomplish this by engaging in carefully chosen laboratory activities. Written work is displayed on large student whiteboards, with problem representations and solutions collaboratively constructed and then shared and interpreted in discussion with the entire class. Managing student discourse that surrounds the preparation and sharing of whiteboarded representations requires skill on the part of the teacher. This chapter will explore what teachers can do to optimize the way students interact using whiteboarded representations and classroom discourse as tools for thinking with and thinking about the fundamental conceptual models of chemistry.

© 2017 American Chemical Society Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Introduction There is nothing more humbling for a chemistry teacher than to review the results of students’ performance on an end-of-term concept inventory. Despite our best efforts, we see indisputable evidence that students retain many of the naïve beliefs they possessed when they entered our course. A careful item analysis might uncover the misconceptions at the root of these beliefs, thus providing the reflective practitioner an opportunity to modify instruction so as to explicitly confront them next time around but for students who have just completed the course, it’s too late to undo the problem. In all probability, it will take us years of slowly improving concept inventory results before we can hit upon the right moment and the right way to confront particularly stubborn misconceptions. To do this well, we need a way to detect when these misconceptions are activated and how they interact with what we are trying to teach. The best way to know precisely when this is happening is to hear it from students’ very own lips, but this can be difficult in a typical classroom or lecture learning environment. Interactive lecture/discussions, while providing opportunities for students to ask or answer questions, can be a daunting environment for students. Only a handful of students typically have the courage to speak up. Even teachers who routinely solicit student feedback using “clicker questions,” only see the aggregate results of responses to the multiple choice questions they pose (1). They cannot know what an individual was thinking that led to the selection one answer over another. Students do not enter our classrooms as tabula rasa. New learning is overlaid upon a great deal of pre-existing ‘organized’ knowledge (2, 3). And in addition to common sense chemistry concepts, students also bring over a decade of motivations, goals and cultural expectations—about the meanings of the words as they are used in scientific contexts, about why they need to learn chemistry, about what counts as knowing chemistry, about the value of schooling and how “good students” play “the school game (3, 4).” How can we remodel our classrooms into learning environments where students are actively engaged in making sense of what they observe and are motivated to share their thinking aloud? In this chapter we will describe the Modeling Method of Instruction (5–8) an approach to teaching chemistry that redesigns the learning environment to actively engage students in making sense of observable physical phenomena in collaboration with their peers. This method provides the teacher with frequent opportunities to hear how students are thinking as they construct, refine, and apply the fundamental conceptual models that form the content core of chemistry.

A Brief History of the Modeling Instruction In 1983, Malcolm Wells, an award-winning veteran high school physics teacher began a PhD program in physics education research under the directon of David Hestenes. Hestenes and his student, Ibrahim Halloun had been refining a model-based approach to teaching freshman physics. They created a multiple 72 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


choice survey to probe student misconceptions about motion and forces (9). Wells was certain his honors physics students would perform well on it. He administered it at the end of school year and was shocked at how poorly his students did post-instruction on this seemingly simple measure of student beliefs. After interviewing his students to explore the nature of their misconceptions, Wells redesigned the mechanics portion of his physics course, organizing the content around the fundamental conceptual models identified by Hestenes (6, 10). Adding the structuring framework of models to his existing instructional design of learning cycles (11, 12), Wells developed the Modeling Cycle: 1) model development, consisting of description, formulation, ramification and validation and 2) model deployment, in which the model developed in phase 1 is applied to a variety of novel physical situations (7). He tested his new model-centered collaborative inquiry approach the following year and the results were dramatic. Students’ post-test scores on the concept inventory increased by more than a standard deviation. When his results were shared with a National Science Foundation (NSF) program officer at a conference the following summer, Wells and Hestenes were encouraged to apply for funding to develop and disseminate this new approach to physics instruction. Thus began a program of research and teacher professional development supported by 16 years of continuous NSF funding. Wells passed away in 1993 but Hestenes and his colleagues carried on the work that Wells began, expanding into other areas of physics and then applying the instructional model to teaching and learning chemistry, biology and middle school science.

The Modeling Method The Modeling Method of Instruction, or Modeling Instruction (MI) as it is now called, provides an instructional framework that mimics how scientists “do science.” Students use experimental observations and evidence to construct and refine conceptual models of physical phenomena. Then they apply their models in different situations to answer questions or make predictions, and they probe the boundaries of their models to find out where it breaks down—where a better model is needed to explain what they observe. How does someone learn to play a video game? By playing it, right? Typically one does not sit down and read the manual before firing up the computer and giving it a try. And good videogames are designed to be learned by playing the game (13). By contrast, in traditional chemistry classrooms, students learn the rules of ‘the game of chemistry,’ via lecture and problem solving. Then (perhaps) they might get to play the chemistry game—in the laboratory. MI, like a well-designed videogame, turns the traditional approach upside down. Similar to a research laboratory, the Modeling classroom is a collaborative working/learning environment. Students work together as a class (guided by the teacher with carefully chosen demonstrations) to determine the phenomenon they need to investigate. Then they break up into smaller teams—lab groups of three or four students each. They design an experimental procedure, collect and analyze data and represent their findings on whiteboards. Then they reconvene as a whole 73 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

class to share and discuss their results—illustrated on their whiteboards—and to engage in a collective sense-making conversation. At the end of this “model construction” activity, everyone in the class shares the same conceptual model of the phenomenon they investigated. In a sequence of follow-up tasks students continue to work together in this way, toggling between small group work and whole group whiteboard-mediated sense-making, to refine and apply their model in a variety of situations—driving toward model failure, which will provide them with the seeds of a better, more finely tuned model.


MI for Chemistry Energized by 16 years of NSF support, MI in Physics enjoyed great popularity among high school physics teachers nationwide beginning in 1990. By the early 2000’s over 100 high school physics Modeling teachers (a.k.a., Modelers) who also taught chemistry had begun applying Modeling methods in their chemistry courses. A small group of expert Modelers who had been working to restructure their chemistry teaching to make it more model-centered, articulated the fundamental conceptual models of chemistry, developed a learning sequence for these models, and created activities and resources needed to support a Chemistry Modeling Workshop and chemistry classroom instruction. Modeling Chemistry (5, 8) was developed in 2004 and 2005 for use in high school chemistry courses. The Chemistry Modeling teacher community has subsequently grown to over 2000, thanks to numerous workshops offered nationwide each summer, and the curriculum resources originally developed to support workshops have evolved and expanded. Why Models and Modeling? In redesigning the learning environment, one needs to have a way of thinking about how existing knowledge is structured and accessed, how new information is assimilated into or coordinated with existing models, and how the interactions between students, their tools and artifacts affect the learning experience. Over the last 80 years, as cognitive science has risen to prominence in psychology and the learning sciences, many theories of how knowledge is structured have been advanced, but at their foundation most have features that are similar. All posit the existence of basic cognitive units or structures, although they carry a variety of names—elements, concepts, schemas, chunks, scripts, models—which can be constructed, modified, combined, and/or elaborated (3). Theories of Cognition upon Which Models and Modeling Are Founded In his seminal work, A Theory of Remembering (14), Bartlett first proposed the term schema to identify the way knowledge is structured and stored in memory. His research revealed that memories are essentially reconstructed each time we “remember” them rather than being reproduced intact as if from some “mental motion picture file.” Since Bartlett first advanced his schema theory, numerous 74 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


cognitive scientists have tried to discern the mechanisms by which schematizing takes place. In her Prototype Theory, Rosch (15) introduced a definition-based model of categories that corresponds well to the elements that interact within schemas. The prototypical member of a category is one that possesses the most attributes that are characteristic of that category. So for example, in the category, ‘bird,’ which has the features beak, feather, wings, and flight, a robin is more prototypically a bird than a penguin is. Schank and Cleary extended Bartlett’s assertion that memory is schematic with Script Theory (16) in which knowledge structures (schemas) may be conceived of as subconscious scripts that determine how ‘something’ or ‘someone’ behaves in a particular situation. For example a “going to the movies” script might include standing in line at the box office, purchasing a ticket, entering the theater, buying popcorn, finding a seat in the theater, watching the movie, and exiting after the credits. Catrambone’s (17) investigation of story analogs and Fauconnier’s studies (18) of conceptual blending reinforce the notion that in making sense of something novel, people draw on familiar situations or narratives (i.e., scripts), mapping new elements onto an existing well understood structure and then manipulating that structure to answer questions or make predictions. The Information Processing theories of cognition that became popular in the 80s and 90s (19–22) are a class of schema theories that have employed the computer as a metaphor for modeling human cognition. ACT-R Theory (Adaptive Control of Thought-Rational) (23) subdivides memory into declarative memory, production memory, and working memory. According to Anderson, declarative memory is a long-term repository of fairly stable knowledge structures (schemas), production memory is the place where these schemas are actively used and modified, and working memory is where incoming perceptions are encoded and outgoing actions are produced. The capacity of working memory (think RAM) is limited—too many bits of knowledge at once and the cognitive load becomes too great to manage. Chunking of information—connecting lots of little bits into a few big bits—is a strategy to help manage this load (24). For instance, consider the following digits: 5, 5, 8, 9, 0, 5, 9, 7, 6 and 1. As a list of 10 digits these would be difficult to memorize, but if they are chunked like this—558-905-9761—they become an easily memorized phone number. Over the last two decades, these theories of cognition have been extended and modified to become embodied theories of cognition—grounded in the sensorimotor system, with the physical conformation of the human body (e.g., bipedal, upright stance with the head at the top of the body, bilateral symmetry, upper extremities with hands that can grasp and manipulate, close-set eyes that face forward, etc.) supplying an implicit context that shapes the way we think and make sense of our world. Embodied theories of cognition are also schema theories. Fauconnier’s theory of Conceptual Blending (18) is an embodied theory of cognition that has many parallels with neural network modeling, which also views cognition as grounded in the sensorimotor system. Building upon the notion that “if cognition in science is an extension of common sense, then the structure of models in science should reflect structure of 75 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

cognition in general,” Hestenes has advanced the Modeling Theory of Cognition (25). He makes a crucial distinction between mental models (private constructions within an individual’s mind, i.e., schemas) and conceptual models (a structure encoded in symbols that activates a corresponding structure in others’ minds). Conceptual models, then, are public, shared representations and cognition (i.e., modeling), as defined by this theoretical perspective, is the construction and manipulation of conceptual models from private mental models.


Models Teaching and learning are thus concerned with designing learning environments in which students’ mental models are reshaped and reformulated to become robust, coherent, shared conceptual models. A model is a representation of structure in a physical system—a conceptual representation of a real thing (25) whose structure corresponds to the structure of what it represents. Models are composed of elements, operations, relations, and rules (26). Models of physical phenomena—the kinds of models we think with and think about in chemistry have different types of structure: geometric structure, temporal structure, object structure, systemic structure, and interaction structure. The fundamental models of chemistry are essentially particles of increasing complexity, and the interactions between particles that this complexity enables. Simple questions can be addressed with simple models. A simple featureless particle—the Democritus model—is sufficient to develop the ideas of mass, volume, density and conservation. In teaching young children about the simplest properties of matter all we need is Democritus’ model of a substance (not Bohr’s).

Why Organize a Course around a Series of Particle Models of Increasing Complexity? A careful observation of how students describe matter and its changes reveals that beginning chemistry students do not have and often never fully develop a consistent mental model of matter as discrete particles. Instead they tend to view matter, at least in part, as a continuous material with no substructure, even in the face of explicit instruction in atomic theory and its applications (27–32). Those who have robust mental models at this stage tend to be more successful in dealing with the more demanding reasoning involved in characterizing reactions and solving problems in stoichiometry. Those with weaker microscopic models, or who utilize particulate reasoning inconsistently, struggle more as the course progresses, usually resorting to algorithmic approaches rather than conceptual understanding. This difficulty persists in spite of teachers’ sincere efforts to communicate the particle nature of matter to their students. Students may use the right language in assignments, but it falls apart for many when pushed to give explanations of the observed behavior of matter. This lack of clarity often causes more difficulty in later topics that require particle-based reasoning such as balancing equations. 76 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


To address this difficulty, the opening units of MI in Chemistry are very deliberate in requiring students to connect observed macroscopic phenomena to the characteristics of microscopic particles. After recognizing that a particle model of matter can account for mass conservation and density differences in the opening unit, students try to apply their model to explain phenomena such as diffusion and the dependence of the pressure of a gas on its volume, the number of particles, and temperature. They soon realize that their static particle model is inadequate and are gradually led to develop an understanding of Kinetic-Molecular Theory. This pattern of model development and subsequent modification as new phenomena are introduced has its roots in the CHEM-Study curriculum (33) introduced in the 1960’s. In the Modeling approach students are introduced to phenomena that require them to modify their model of matter introducing features only as needed to account for their observations. For example, a model of the atom with complex internal structure is not necessary to understand why gases behave as they do. This sequence of model development (Democritus → Dalton → Avogadro → Thomson → Rutherford → Bohr) is outline below in Table 1. It has been our experience that Modeling Chemistry students find this approach to learning chemistry more engaging than plowing through the myriad of topics found in most traditional chemistry textbooks. Textbooks are, unfortunately, often organized in a way that is sensible to those who already understand chemistry (34). Organizing the content of the chemistry course around a series of models that become more complex as the need arises makes it easier for students to make sense of their observations of the macroscopic world.

The Design of the MI Learning Environment The MI learning environment is carefully designed to support student communication and teacher listening and facilitation. For students to engage in productive scientific discourse they need to see and hear each other. A learning space where student desks are arranged in clusters or in a large circle is more conducive to student discussion than traditional rows of desks. Students must learn to value “thinking aloud” together—to appreciate that knowledge resides in their peers, not just in their teacher. Therefore, the most desirable classroom layout allows students to sit facing one another around a whiteboard on which they can all write and enables teachers to remove themselves from the center of attention and, instead, move freely among student groups, listening to how their thinking is developing as they work together to prepare their whiteboards (3, 36).

77 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Table 1. The MI Progression of Models (35) Unit

Model - features

Concepts addressed

Description

1

Simple particle Every substance (element or compound) can be represented as a simple particle (BB) with no internal structure (Democritus model)

Conservation of mass, Extensive properties: mass and volume, Intensive property: density

Any sample of matter has properties of mass (inherent property due to number and type of particle) and volume (due to number and size of particles). Mass is conserved during change because particles are only rearranged during different types of change. Density (first viewed as the slope of a graph of mass vs. volume) is due to the kind and arrangement of the basic particles; it is a property of a substance. Gases are much less dense than solids and liquids due to the separation of particles.

2

Particles in motion Particles are in constant, random, thermal motion

Temperature as measure of thermal energy, Gas pressure, Kinetic Molecular Theory

Diffusion in gases and liquids provides evidence for the motion of particles. Temperature is a measure of the thermal energy (Eth) of the particles - this is related to particle motion. Gases exert pressure due to collisions of particles with walls of container. Proportional relationships between P, V, T and n are developed empirically; no memorization of Gas Law equations. KMT is introduced to account for observed relationships.

3

Particles store and transfer energy The particles exert attractions on one another. Metaphor of energy as conserved substance-like quantity

Unitary energy concept, Energy storage and transfer rather than "forms" of energy, Conservation of energy

Rather than describe different types of energy, a unitary model of energy is introduced, with emphasis on the ways a system stores energy (accounts) and mechanisms for transfer between system and surroundings. Changes in thermal energy ΔEth are related to mass, change in temperature and type of substance. Phase energy (Eph) is related to the arrangement and attractions between particles in a given phase; attractions always lower the energy of bound particles. Continued on next page.



Table 1. (Continued). The MI Progression of Models (35) Unit

Model - features

Concepts addressed

Description

4

Compound particles The particles that make up substances can be compounded from smaller particles. (Dalton model)

Dalton model of atom, Laws of definite and multiple proportions, Avogadro’s Hypothesis

Matter is composed of pure substances or mixtures of pure substances. The molecules of pure substances have definite composition and properties whereas the composition and properties of mixtures are variable. Molecules of pure substances can be broken down into simpler particles (atoms or molecules). Introduction to Avogadro’s Hypothesis - from combining volumes of gases at the same T & P, we can determine the ratio in which molecules react.

5

Atoms/molecules have definite masses Counting/weighing particles too small to see

Avogadro’s Hypothesis and molar mass

From masses of gases at same T & P we can determine the relative mass of individual molecules. From these results it is possible to determine the molar masses of the elements; using these masses and formulas of compounds, one can determine molar masses of compounds. These tools allow one to relate “how much stuff” to “how many particles”.

6

Atoms with internal structure (Thomson model)

Thomson model of atom to account for electrical interactions, Molecular vs ionic compounds, Nomenclature

Examination of the behavior of charge leads to the Thomson model of the atom. Charge plays a role in the attractive forces that hold solids and liquids together and binds the atoms in molecules or crystal lattices. Molecular substances are composed of neutral molecules, whereas ionic substances are lattice-work structures of ions. These two kinds of substance have different structures and physical properties. Continued on next page.




Model - features

Concepts addressed

Description

7

Atoms in compounds can rearrange Chemical reactions involve rearrangement of atoms in molecules to form new molecules.

Chemical and thermal energy, Balanced equations

Chemical reactions involve rearrangement of atoms to form new molecules. A chemical reaction can be represented symbolically as a balanced equation. This rearrangement of atoms results in change in the chemical potential energy (Ech) and thermal energy (Eth) of the system as well as energy transfers between system and surroundings.

8

Application of models from units 5&7 Relate numbers of particles (molecules or formula units) to weighable amounts of these particles.

Stoichiometry I (mass-mole)

Particle diagrams help to make sense of balanced equations as symbolic representations of chemical change. Emphasis is placed on using coefficients to describe ratios of substances involved in a reaction system. The treatment of stoichiometry is based on these ratios used in conjunction with the skills learned in unit 5, rather than on a set of algorithms that divorce the problem from its reaction-system context; e.g., (grams → moles A → moles B → grams B).

9

Further applications of models from units 5&7

Stoichiometry II (gas volumes, molarity, ΔH)

The balanced equations representing chemical reactions can also relate numbers of particles (molecules or formula units) to volumes of gases, solution volumes, and the change in chemical potential energy.

10

Rutherford and Bohr models of atom

Internal structure of nucleus, Interaction of light and matter

From an examination of the radiation emitted by hot metals and atomic gases we conclude that atoms must have internal structure not explained by Thomson’s model. Students examine evidence for Rutherford and Bohr models of the atom, including the contributions made by Milliken, Moseley, and Chadwick. Continued on next page.



Model - features

Concepts addressed

Description

11

Men-in-well model of electron arrangement Bohr model extended

Electron structure, Periodicity, Ionic and covalent bonding, Covalent bonds as shared pairs of valence electrons

Examination of successive ionization energies can be used to extend the Bohr model to many-electron atoms, using it to provide a structural explanation for the organization of the Periodic Table. The men-in-well model accounts for ionic bonding; the Lewis model represents covalently bonded molecules.


Lewis model of covalent bonding

12

Atoms with uneven distribution of valence electrons

Intermolecular forces, Molecular polarity, solubility, Biological macromolecules

Model accounts for various types of intermolecular forces of attraction (London, dipole-dipole, hydrogen bonding) and molecular polarity. The type and strength of attractions account for trends in mp and bp, miscibility of liquids and solubility of solids. The role of these forces in the structure and function of important biological macromolecules is also examined.

13

Kinetic model of opposing processes Equilibrium transfer game

Collision theory and reaction rates, Equilibrium, LeChatelier’s Principle

Role of temperature, concentration and pressure in chemical kinetics. Various equilibria in processes (liquid-vapor, solute-solution, partition) and reactions are modeled by the exchange of particles between "containers". This exchange explicitly models rates of opposing processes.

14

Bronsted-Lowry model of acids and bases

Properties of acids and bases, Strong vs. weak acids

Exchange of H+ ions between species in acid-base equilibria and relative strengths of acids and bases is viewed in terms of competition by bases for H+ ions.



A hallmark of all Modeling classrooms is the use of student whiteboards. Typically measuring 24” x 32,” these dry erase surfaces are large enough that several students can write on them simultaneously and a single board can accommodate multiple representations of the phenomenon under investigation. A useful means for communication and collective sense-making, these work surfaces are integral to the culture of the Modeling classroom and the conversations that take place around preparing and sharing them provide the teacher valuable insights into student thinking. Whiteboards serve as the central tool for student communication as they present and defend their ideas (3). The regular use of whiteboards increases participation and persistence, and leads to more discussion when compared to paper and pencil or computer screen (37). Most Modelers arrange their classrooms so that there is an area in which students can easily convene as a group with their whiteboards for “board meetings.” If there is enough space, students gather with their chairs in a circle and prop their whiteboards against their knees so that everyone can see everyone else’s whiteboard and they can comfortably carry on an extended conversation. The goal is to encourage student-to-student conversation, so every effort is made to have students face one another, rather than facing the teacher or the front of the classroom. When classroom space is at a premium, however, teachers have developed novel ways to accommodate board meetings, including hooks on the walls so that students are surrounded by their whiteboards. Effort is made to have students’ talk and to make the whiteboarded representations of their thinking the center of everyone’s attention, rather than the teacher and the teacher’s utterances.

Whiteboard-Mediated Discourse Student discourse is a vital element in the MI classroom. It is where students exteriorize their thought processes, compare them with one another, subject them to reasoned analysis, justify (or discard) them, and, ideally, identify the limits or boundary conditions of the model they are constructing. Gee and Green (38) identify multiple discourses, which they calls ‘big D’ and ‘little d’ discourses. ‘Big D’ discourses belong to language communities, (i.e., chemists, Republicans, gang members, etc.) and include non-language cultural references that are specific to particular identities and/or activities, while ‘little d’ discourse is simply language in everyday use. We are all members of many different discourse communities. These discourses can influence each other and give rise to new “hybrid” discourses. It is this formation of a new hybrid discourse that can prove challenging to chemistry students and their teachers. The two types of discourse that routinely occur in Modeling classrooms address this challenge: small group work and board meetings. The “think-aloud” conversations that small groups of three or four students have as they prepare their whiteboard are a hybrid discourse. This hybrid moves toward the ‘Big D’ discourse of the chemistry community as the academic year progresses. The goal of small group work is for students to share a mutual understanding of the phenomenon they are attempting to represent, and their whiteboarded 82 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


representations—diagrams, graphs, symbols—are tools with which they think together—tools for helping student groups build this shared understanding. The Board Meeting, in which the entire class discusses all groups’ findings and works toward a consensus model of the phenomenon under study, is an opportunity for the teacher, as a representative of the Chemistry Discourse Community, to help students learn how to use discipline-specific language, i.e., to develop skill in ‘Big D’ Discourse. Rather than introducing vocabulary before concepts, students construct and define concepts using everyday language. Only after they have arrived at an operational definition does the teacher supply the term they have defined. Board Meetings are guided rather than led by the teacher, which requires the establishment of a classroom culture that embraces uncertainty and values the expression of partially formed ideas in pursuit of advancing collective understanding. Students must feel comfortable and safe sharing their thinking.

Classroom Culture Creating a climate that fosters this sort of productive discourse takes time and effort. A decade or more of immersion in the conventional culture of schooling leads most students to believe that to succeed they need to be quiet, listen to the teacher, take notes, follow directions, finish in the allotted time, and, above all, get the right answer. The teacher sets the agenda and calls the shots, and the ultimate goal for students who wish to do well is to get the teacher to give them points (39). In a Modeling classroom a new classroom culture is negotiated, and to do this both teacher and students must be clear about their goal: useful, collaboratively constructed, shared conceptual models. Teacher questioning is best when it is open-ended. We must learn to wait through those inevitable awkward silences rather than rushing to call on someone or simply supplying the answer ourselves so that the discussion can move ahead. And rather than passing judgment on whether a student’s utterance is right or wrong, we need to accept every answer, for it is a potential window on student thinking—a snapshot of a student’s conceptual model as it is being built (or dismantled and rebuilt). Both teachers and students must learn to press for answers to be justified. In short, the typical hierarchy of the classroom must evolve to a more horizontally integrated learning community where students feel that they can look with confidence to their peers to help them learn. Redesigning the learning environment in this fashion is effortful for both teacher and student but with time a rich discourse community emerges. It is the skill with which the teacher learns to manage this discourse that determines the quality of the conceptual models that students construct, and it is the acquisition and practice of these discourse management skills that is the chief activity of the Modeling Workshops teachers attend to learn the practice of MI.



Sense-Making in the Making So what does Modeling discourse look like “in the wild?” What follows is a brief excerpt from a 45-minute Board Meeting discussion in a high school chemistry classroom that illustrates the interaction between teacher, students, and whiteboarded representations. At the start of a new unit in which students learn to represent chemical change at the particle level, the instructor has asked his students to prepare whiteboards of a laboratory activity they just completed. They were asked to represent a copper(II) chloride solution before and after iron nails were added. His goal was to see how students understood the role water molecules played in the solution. After the students spent a few minutes preparing their whiteboards and coming to an agreement in their small groups about how they would describe what took place in solution, they gathered in a circle displaying their whiteboards to their classmates. After spending a few moments to review each other’s whiteboards, the instructor asked them if they had any questions they wanted to ask others about the representations they saw on others’ whiteboards.

Figure 1. A student whiteboard showing a particle diagram of a copper (II) chloride solution before and after iron nails were added. (Photo Courtesy of Carlos Montero) 84 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


One student, Sam, looking at one group’s whiteboard (Figure 1), pointed out: Sam: On the bottom right picture, there’s a green circle with 3 water molecules and 2 open green circles that aren’t on any of the other pictures and that confused me because I don’t know what’s going on. Instructor: Josh (who is holding whiteboard), how do you respond? Josh: (pointing to whiteboard) To explain that I’d say that we meant to surround those, like you see these right here, with water, but the problem is that we were still deciding what’s going on over there and I guess we got a little bit distracted on the whole drawing here and … But this was supposed to be like that and... this is just incorrect. Instructor: OK, so, what is supposed to be happening? We’ve already been discussing how solutions form and how water keeps the ions apart [pause] and Will said that – look - they’re split. How do we know that the ions are split? Why aren’t the copper and chloride ions together? [Calls on Liam] Liam: Because when we tested the conductivity, it still conducted electricity, so that means the ions would have to be separated, because if it were together it would be neutral. Instructor: So you’re saying that this solution right here [holds up beaker with copper chloride solution] would be able to conduct electricity? Shall we test that once again? [The instructor puts electrodes into solution and the bulb lights. Students agree that the solution does conduct electricity] OK, so it does conduct... so Liam is right. There are several points worth noting in the above transcript: The instructor asked the students about the questionable representation, rather than critiquing and correcting it himself. After Josh responded, the instructor asked students for the evidence to support the representation showing that the ions were split up in solution: “How do we know…?” Only after Liam cited evidence did the instructor perform a confirming demonstration. The discussion then turned to the orientation of the water molecules surrounding the Cu2+ and Cl1- ions. Instructor: How do we know that the waters have that orientation towards the ions? Bill: [Begins to answer, but misunderstands point of question, asserting that the oxygen has to be connected to the hydrogen for the water to be water. His partner corrects him.] Linda: No, because the chloride is negative and the hydrogen is positive, and negative and positive attract…because two positives can’t be attracted to each other. Instructor: And how do we know that hydrogen is positive? Linda: Because in water, oxygen is negative so hydrogen must be positive. Instructor: How do we know that? Linda: Because we also tested it. Instructor: How did we test it? Jared: We separated it with, um… 85 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Linda: Oh, I know why. Instructor: Let Jared express it. Jared: We separated it with that over there [pointing to Hoffmann apparatus], and then when you um… Instructor: What separated the water? Jared: Um, electricity. Instructor: OK, when you did that how did you figure out what part of water is positive and what is negative? [Jared hesitates, others raise their hands. The teacher offers Jared some encouragement.] You’re doing well. You said, “We separated water with electricity,” so how do you know the oxygen is negative and the hydrogen is positive? Jared: Oh, because of what it was attracted to, like well the hydrogen will attract the negative. Instructor: Negative what? You’re almost there. [Calls on student next to Jared] Isabella, can you help him finish? How do we know that in water the oxygen is negative and the hydrogen is positive? Isabella: I can’t exactly remember… Instructor: What do you think? How did we split water up? [Isabella essentially repeats what Jared said earlier] You’re missing an important thing. Jared, still don’t remember? Maybe Stephan will remind us. Stephan: We know that hydrogen was positive because it formed on the negative side where we were sending the negative electricity to. Instructor: Oh, so the hydrogen gas formed where? [Students affirm it was the negative side.] On the negative electrode. [Here the instructor used this as an opportunity to “coach” students in Big D Discourse by rephrasing their utterances using the correct term (negative electrode).] This excerpt from a much longer discussion reveals how the instructor works to move students from answer-making towards sense-making. Rather than accepting the first student’s remark about the orientation of the water molecules around the ions—in effect rewarding him for providing “the right answer” and bypassing the rest of the students’ thinking about what was taking place, he continued to probe the class, asking students to provide evidence to support the claims they were making. He did not stop after Jared pointed to the electrolysis apparatus used to separate the water. Instead he asked for specifics about a demonstration that had been performed earlier, helping the students apply what they had learned from the earlier demonstration to this new situation. Developing Discourse Habits and Skills Productive classroom discourse does not just happen on its own. Instructors must set the tone in developing an environment where everyone feels comfortable contributing. Establishing discussion norms and expectations from the start of the semester allows cooperative construction of a supportive learning environment. Since the aim is to build a collaborative learning community, the establishment of norms should be done in conversation with students to help foster their buy-in. The most important norm is for students to listen to each other and respectfully 86 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


respond to one another (40). The teacher must constantly model this for students by critiquing ideas, not individuals. Some students may need encouragement to speak up so that everyone can hear them. All should understand that everyone is expected to participate. Once these expectations are agreed upon, they must be regularly reinforced and revisited until they become thoroughly engrained in the classroom culture (40). The entire classroom community shares the burden of asking questions and pressing their peers to justify their assertions. This should not be perceived as the exclusive purview of the teacher. Some coaching and encouragement will be necessary to help students build this habit, but eventually they will learn to step up, especially when the teacher refuses to do it for them. Two types of questions are generally used in such a discussion: the clarification question and the extension question.

Clarification Questions How do you know …? Where did you get…? How did you know …? What does ____ tell you? What does ____ mean? Where on your (graph, diagram, chart, etc.) does ______?

Extension Questions What if we changed ____? How is this problem different from ____? How is this problem similar to ____? How does ____ compare to ____? Is there another way to do this problem? Can you show us? Teachers do not typically need a question “cheat sheet”—they know that these are good questions, but to help students build their questioning skills, many teachers will provide students a question list like the one above (or co-construct one in a class discussion). When students are first learning how to drive the discussion, a little support is welcome. They won’t need it for long. Initially students will feel exposed in this collaborative sense-making environment. Teachers need to help students learn to trust the process and allow themselves to be vulnerable. Students need to recognize the value of learning from one another, even when they struggle to do so, as an essential part of becoming critical thinkers. They need to realize that the processes of conducting an investigation, making sense of their observations and defending their thinking are as important as knowledge they acquire in doing so. To this end, instructors should acknowledge students who take chances, step up, and participate in class. Be generous with praise and positive feedback to all students who take part in 87 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


the process, not just to those who provide correct answers. It is important to show students they have a measure of control over their own learning. Students become more resilient learners when they realize they are capable of constructing knowledge and are not merely passive receivers of knowledge the expert transmits to them (41). For many teachers, the shift away from direct instruction is challenging. It is effortful to develop trust in students’ ability to learn with increasing independence (40). The teacher’s role in this interactive environment is different, but not diminished. They must carefully prepare for each discussion, identifying the elements, operations, relations, and rules of the model they want students to grasp and connect, and actively engaging with students throughout the lesson. Prior to any episode of classroom discourse, the teacher needs to make explicit its purpose, try to anticipate how the discussion will unfold, be aware of potential misconceptions so they can guide the discussion, and be ready to ask follow up questions to attain the desired outcome (40). Ultimately sense-making is a team effort. Everyone in the classroom is a player on this team—even the teacher, who is both player and coach. The playing field is chemistry, the rules of the game are the norms of practice and engagement that you establish with your students. The tools are laboratory equipment, whiteboards, representations, language, and most importantly—models and modeling—the foundation of the scientific enterprise. Modeling Tasks Since Modeling classrooms are “lecture-free,” student construction, refinement, and application of conceptual models is mediated by a sequence of tasks. Initial model construction is generally done in the context of a laboratory activity. Tasks that students are given in Modeling Chemistry afford some measure of both arousal and control—two primary characteristics of intrinsic motivation (42) that function to keep students engaged in the learning process. Activities are embedded in familiar contexts, and the Modeling Cycle that frames the learning process guides students to continually express, test and revise their model as they construct it. They ‘play the game of chemistry’ as they learn its rules. In the Modeling Cycle from which the above excerpt of discourse was taken (35) the students exit the whole-group discussion with a conceptual understanding of chemical reactions involving electron exchange, precipitation, and solvation of ions. The knowledge constructed here lays the foundation for further study into balancing equations, types of reactions, and predicting products. From the masses of iron lost by the nails and copper that formed, students determine the moles of iron consumed and copper formed in the reaction. They can use bingo chips representing the atoms to help them visualize the process, and then translate what they are seeing into the symbolic representation of a balanced chemical equation. Next they use bingo chips or model kits to represent different reactions at the atomic level, reinforcing the idea that every atom of the reactants will be found in the products—nothing appears or disappears. This representation also helps them see the physical distinction between subscripts and coefficients in writing balanced 88 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


equations. The use worksheets to practice translating between particle diagrams, chemical equations, and verbal descriptions. Subsequently, students observe sample reactions of different types and write balanced equations for each reaction. Discussion of this lab uncovers the energy changes that were observed and puts them in terms of changes in chemical potential and thermal energy. The difficulty students often have with this concept is that they cannot directly measure chemical potential energy (Ech). Instead, a change in Ech must be inferred by a change in the temperature of the system. Students find the notion that a process that increases the temperature of the system as one that results in a lower energy arrangement of particles as counterintuitive. Only when they are forced to consider that an increase in one energy “account” must be accompanied by a decrease in another “account” (in a closed system) do they really grasp the notion of energy conservation (43). Ultimately, students’ conceptual model of matter is a fully featured Thomson model of the atom with mobile electrons which can be exchanged between atoms, allowing for the development of balanced equations to represent reactions. Their energy concept is refined to include chemical and thermal energy storage and transfer. The progressive sophistication of the model sets the stage for stoichiometry based on ratios rather than a set of algorithms that divorce the problem from its reaction-system context.

The Role of Representations MI relies heavily on the use of multiple representations of physical phenomena. Johnstone describes macroscopic, sub-microscopic (particulate), and symbolic representations as the “conceptual tripod” of chemistry—three ways of representing structure (44, 45). A phenomenon can be described macroscopically (e.g., salt dissolves in water), microscopically (e.g., sodium and chloride ions, attracted by polar water molecules, break free from their crystal lattice), or symbolically (e.g., NaCl(s) + H2O → Na+(aq) + Cl-(aq) + H2O). The first two encode information spatially. The last is a conceptual (or propositional) representation (3, 46). Each carries the same basic information, but encoding the information spatially (in the case of the macroscopic or particle diagrams) or propositionally (in the case of the chemical equation) calls attention to different elements of structure. We learned to think with spatial representations long before we had conceptual systems such as language or symbol systems. Infants routinely encode three-dimensional shapes of objects before they are a year old (47, 48). We learn language (a conceptual representation), by mapping spatial representations onto words. The reverse of this spatial to conceptual mapping process (e.g., from words to ideas or objects) comes later. When a student maps information from a word problem onto a symbol set, their representation is confined to the problem’s conceptual structure. They abstract the information that they can symbolize from the problem. They may also (privately) map it to a spatial representation…or not. This requires additional effort (3, 46). 89 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

As Johnstone points out, teachers move smoothly and effortlessly among these three modes of representation, often without realizing how difficult it is for students to translate from one to the next. Stepping from a macro to sub-micro requires deliberate practice. Requiring students to routinely illustrate their thinking with multiple representations hones their ability to move more fluidly within “Johnstone’s Triangle”.


Whiteboarding Whiteboarded representations have the benefit of being shareable and transportable. Certain representational practices, such as particle diagrams and chemical formulas, are well enough developed that the representations they produce are adaptable and reusable to represent a variety of relationships. Creating and interpreting such images are skills that are learned, and in the learning process, students must develop an awareness of how the choices they make in constructing a representation can be interpreted. There is a risk that the task of producing pre-determined representations can become so proceduralized that the representation becomes an end in itself and it is not employed as a reasoning tool (49). Whiteboards are created by small groups, typically three or four students. Members of each small group construct a shared understanding of a phenomenon—either observed or described—in conversation with one another as they prepare their whiteboard (as opposed to negotiating what to write down about a problem they have each solved separately beforehand). In such an activity, both the whiteboard marker and the eraser tend to pass from one member of the group to the next often during the co-construction of multiple representations as students try out various ways of illustrating their thinking. Group members typically contribute to the discourse as co-equals—no one person controls the conversation. The discussion is less apt to be about whose version of an idea should appear on the whiteboard and more apt to center on what it means to write or draw one thing as opposed to another. There is plenty of erasing and rewriting as students jot down diagrams and equations to help them communicate their thinking to their teammates or visualize the various elements in a model, how they relate to one another and how they can be manipulated. This helps them manage cognitive load (24). Offloading their ideas in the form of written representations frees up working memory to handle more information. When using whiteboards in this way, as a medium for communicating and exploring partially formed knowledge structures, some students seem to prefer to express their thinking as spatial representations while others prefer symbolic representations. Those who prefer symbolic representation are more often talking to themselves than to other members of the group. This is confirmed by the fact that although they may speak as they write, the talk is seldom directed to anyone in particular, and it is seldom answered by the speaker’s group-mates (3).



Managing Collaborative Sense-Making Central characteristics of a Modeling classroom are inquiry, observation, collaboration, communication, and reasoning. From structuring the physical layout of the classroom to cultivating a culture of trust and support, the instructor must become the conscious and conscientious architect of the learning environment. Establishing norms for classroom interactions early in the semester and sticking with them will ultimately produce a lively, engaging learning environment. While the classroom discourse that takes place in small groups around a whiteboard is largely under the management and control of students, the teacher is the architect of Board Meeting discourse (3, 39). In a typical classroom setting students expect the teacher to lead whole group discussions. A good Board Meeting is one where the teacher prompts the discussion to begin, but then pulls back so that the students assume control. This is a behavior that must be learned both by teacher and by students. This presents a special challenge for teachers who want students to step up and take the lead in helping one another make sense of complex ideas. It is critical that, from the beginning of the year, the teacher set explicit expectations regarding students’ roles and participation in Board Meeting discourse. As quickly as possible, the teacher must withdraw the supportive questioning he or she typically supplies to keep a discussion moving forward, and leave the way open for students to step in and supply one another with the necessary scaffolding. To do this, students must know how to ask good questions. They learn to do this by imitation. If the teacher asks them the same kinds of questions over and over as he or she wanders the classroom observing whiteboard preparation, eventually students begin to question one another in the same way. The questions that teachers ‘seed’ as they circulate in the classroom and drop in and out of small group whiteboard preparation conversations, are echoed by students as they help one another work through problems during Board Meetings (36). Some students who are new to MI are reluctant to participate. The teacher can draw them into the conversation by asking them whether they agree with some reasoning or interpretation expressed by a classmate or by prompting them to restate what a classmate said in their own words. If they are unable to do so, the class can be challenged to re-state the explanation in a way that this student understands. Turning the burden of explaining over to students casts them in the role of peer tutor. The goals of Board Meeting participants shift from “getting the right answer” to “making sure their classmates understand the phenomenon.” When the students embrace this goal, they will persist in discussing a complex problem long after the answer has been revealed because they understand that the real problem they are expected to solve is how to make the basic underlying structure of the phenomenon—the model—clear to all participants. One way to gauge the quality of Board Meeting discourse is to pay attention to how many student contributions are directed to the teacher. If a student addresses the teacher, other students do not actively engage in formulating a reply to their classmate’s remark. On the other hand, if a student’s remarks are directed toward the class, then every other student is a potential respondent. In fact, if some 91 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


student does not respond, the conversation will stall and an uncomfortable silence will ensue. For students to take and hold the floor in a Board Meeting, then, the teacher must refrain from rescuing them when periodic silences occur. Unless the discussion is at an end (which means that members of the group are satisfied that everyone understands the solution to the problem and the underlying conceptual model upon which it is based) the teacher should wait, just as the students are waiting, for some student to get it moving again. If they have genuinely reached an impasse, students will resort to directing their question to the teacher. At this point, the teacher must enter the discourse. To keep from taking control of the conversation, the teacher can reply with a question that redirects students to some element of the conceptual model or some aspect of the problem that students can use to reengage with the problem. Or, if the teacher thinks, based on the content of the conversation, that one of the students in the group has an important idea that has been overlooked, he or she might suggest that the group revisit this person’s idea. It is important in this case to give students as little prompting as possible to help them restart the discussion and then to once again withdraw to the role of observer leaving the students to control the conversation. At the close of a Board Meeting some student (or group) should summarize what was learned. The teacher must re-enter the conversation at this point, and will usually prompt this closing summary by asking a student to recap the discussion. If the teacher feels there is still some confusion about key ideas, or if the scope of the conversation is so large that elements may be overlooked, he or she may guide the group to construct a summary by asking a series of questions that help them identify the key elements and how they connect. In addition to Board Meetings, some Modeling teachers have small groups take turns coming to the front of the class to present their results and conclusions. The structure of discourse is different in this case. Initially the group talks about what is written on their board—sometimes they take turns describing the different representations and other times one member of the group is delegated to do a full description. The group’s presentation is typically followed by a pause—an interval, sometimes quite extended, during which everyone waits for ‘someone’ to ask a question or make a comment. Students are typically better at waiting than teachers, and the questioning that ensues, while appropriately Socratic, is usually controlled by the teacher. Unfortunately, such questioning typically becomes a two way exchange between teacher and student-group, and often the rest of the class is observed to tune out, or to spend their time writing furiously, capturing what is written on the whiteboard before the questioning is finished and the presenters sit down. Students seem to treat this presentation format as a performance rather than any opportunity to discuss and make sense of their results. Their goal is to have the “right answers” and to escape to their seats as soon as possible. Their peers are usually complicit—not challenging their reasoning or asking them difficult questions. In fact they rarely ask any questions, preferring to leave that task to the teacher. There may be many factors that account for the way this format plays out—years of conditioning as a result of oral presentations in other classrooms, 92 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

the physical layout of the classroom (rows of desks facing forward so that students aren’t looking at each other), the ever present desire for points and good grades—but whatever the reason, effective use of this format requires great effort on the part of the teacher in establishing and enforcing norms for participation similar to those for a Board Meeting.


Zooming: Keeping an Eye on the Model It is easy for students to lose sight of the model during routine classroom activity (3, 39, 49). The initial paradigm lab ultimately yields a chemical equation, and sometimes students mistake this for the model rather than as a symbolic representation that encodes conceptual information about the structure of the model. It is as if students are ‘zoomed in’ on the details of the model (as if viewing the phenomenon through the high power lens of a microscope) and they can no longer see the overarching big picture. Students who can ‘zoom out’ and see the spatial-temporal and interaction structure of a phenomenon as well as its propositional structure demonstrate a more coherent understanding of the conceptual model and are more readily able to apply it to new contexts (39, 49). Students who start by constructing spatial representations are typically better at zooming in and out that those who begin with symbolic representations (3). For this reason, we encourage the use of particle diagrams and graphs before the use of equations in solving quantitative problems. Teachers can help students who get stuck on the mathematical and symbol manipulation details of a problem to zoom out by asking questions that redirect them to the physical situation being modeled.

Conclusion Implications for Instruction It is said there are three stages to becoming a teacher (50): concern for self (a.k.a. survival), concern for task (a.k.a. becoming efficient), and concern for students (becoming an educator). Good teaching is not the same as polished lecturing and slick PowerPoints. Good teaching only happens when good learning results. Imagine how much easier it is to know that learning is happening when you can hear your students construct their understanding. Here are some strategies that are known to be effective in Modeling classrooms (3, 39):

Choose Good Tasks Create tasks for small group work that are open-ended. Focus on probing the process rather than requiring the use of a certain algorithm or producing a particular answer. Whiteboard problems that require students to apply the model in a new way. Often this means that everyone in the class may be whiteboarding the same 93 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

problem. Use Board Meetings rather than formal whiteboard presentations to share these exercises with the whole group. They are a more effective use of time.


Set Board Meeting Expectations Set the explicit expectation that your students will lead the discussion in board meetings. Hand over the floor to them so that they can exchange ideas freely. Encourage them to talk to their classmates—not to you. Follow their lead. Prompt rather than grill. Probe the boundary between spatial and propositional representations and make sure students can move easily back and forth across it. If students are zoomed in on the computation process, help them zoom out by redirecting their gaze to the problem context and physical phenomenon. And if an important question is on the table, such as how a model applies, do not let them off the hook by answering it yourself. Make them find the answer themselves, even if you have to come back to it later. Make them arrive at the answer themselves—answers they can justify—not just answers they have guessed right. Make sure they are convinced and can convince each other that they are reasoning correctly about a situation. Then take the vital step of checking with other students to see if they are convinced. And do not take ‘yes’ for an answer. Make them articulate what they understand in their own words, and listen to see if there are any important elements missing from what they say. Make sure they can zoom in and zoom out without their model falling apart.

Watch and Listen Attend to students as individuals as well as in groups. Learn to watch and listen to what students who are listening to the presentations of others say and do. Are they engaged? Are they perplexed? What are they taking from what is being said? This is difficult to do unless we let someone else have the floor. When we are on deck, we do not have the attention to spare to focus on individual responses to the discourse. We need to practice not taking charge of the conversation. Listen for the kinds of things that students think are important enough to question. Is the student zoomed in or zoomed out? Change their focal plane and see what happens. Listen for potential gaps in their model that are betrayed by the questions they ask. Take time to get to know the students—what they value, what they think—what the telltale signs are that reveal when they are bluffing or guessing.

Build Good Discourse Management Habits Break the habit of soliciting or listening for particular words, phrases or answers—particularly when these answer are just a two or three words long. Sometimes when teachers hear one or more students answer their question with the ‘magic word’ (or words) they are listening for, they take it as a signal that they can move on because the students “get it.” Remember to check on what it is that 94 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

the student “gets” and consider checking with the rest of the class to see if they are following this student’s line of reasoning. MI—in chemistry or any other subject—is a powerful teaching practice. If you ask its creator, David Hestenes, he will tell you that it works because it is based on the way we think—“we think in models, we think with models, we think about models”—and the organizing principles of models and modeling are how scientists do science. If you ask Modelers what makes MI powerful, they will tell you that it enables them to hear their students think. That’s not just a power—it’s a Superpower!


References 1.

2.

3. 4. 5.

6. 7. 8.

9. 10. 11. 12. 13. 14. 15.

Ding, L.; Reay, N. W.; Lee, A.; Bao, L. Are we asking the right questions? Validating clicker question sequences by student interviews. Am. J. Phys. 2009, 77 (7), 643–650. diSessa, A. A. Knowledge in pieces. In Constructivism in the computer age; Forman, G., Pufall, P., Eds.; Lawrence Erlbaum Associates, Inc: Hillsdale, NJ, 1988; pp 49−70. Megowan, C. Framing discourse for optimal learning in science and mathematics. Doctoral dissertation, Arizona State University, 2007. Brown, J. S.; Collins, A.; Duguid, P. Situated Cognition and the Culture of Learning. Educational Researcher 1989, 18 (1), 32–42. Dukerich, L. Applying Modeling Instruction to High School Chemistry To Improve Students’ Conceptual Understanding. J. Chem. Educ. 2015, 92 (8), 1315–1319. Hestenes, D. Toward a modeling theory of physics instruction. Am. J. Phys. 1987, 55 (5), 440–454. Wells, M.; Hestenes, D.; Swackhamer, G. A modeling method for high school physics instruction. Am. J. Phys. 1995, 63 (7), 606–619. Posthuma-Adams, E. How the chemistry modeling curriculum engages students in seven science practices outlined by the college board. J. Chem. Educ. 2014, 91 (9), 1284–1290. Halloun, I. A.; Hestenes, D. The initial knowledge state of college physics students. Am. J. Phys. 1985, 53 (11), 1043–1055. Halloun, I. A.; Hestenes, D. Modeling instruction in mechanics. Am. J. Phys. 1987, 55 (5), 455–462. Atkin, J. M.; Karplus, R. Discovery or invention? The Science Teacher 1962, 29 (5), 45–51. Lawson, A. E.; Karplus, R. The learning cycle. In A Love of Discovery; Springer: Netherlands, 2002; pp 51−76. Gee, J. P. What video games have to teach us about learning and literacy. Computers in Entertainment (CIE) 2003, 1 (1), 20–20. Bartlett, F. C. A Theory of Remembering. In Remembering; 1932. Rosch, E. Cognitive Reference Points. Cognitive Psychology 1975, 7 (532547). 95 Kloepper and Crawford; Liberal Arts Strategies for the Chemistry Classroom ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


16. Schank, R. C.; Cleary, C. Engines for Education; Lawrence Earlbaum Associates: Hillsdale, NJ, 1995. 17. Catrambone, R. The effects of surface and structural feature matches on the access of story analogs. J. Exp. Psychol.: Learn., Mem., Cognition 2002, 28 (2), 318. 18. Fauconnier, G.; Turner, M. The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities; Basic Books: New York, 2002. 19. Atkinson, R.; Shiffrin, R. Human memory: A proposed system and its control processes. In The psychology of learning and motivation: Advances in research and theory; Spence, K.; Spence, J., Eds.; Academic Press: New York, 1968; Vol. 2. 20. Bransford, J. Human cognition: Learning, understanding, and remembering; Wadsworth: Belmont, CA, 1979. 21. Craik, F.; Lockhart, R. Levels of processing: A framework for memory research. Journal of Verbal Thinking and Verbal Behavior 1972, 11, 671–684. 22. Parallel distributed processing: Explorations in the microstructure of cognition. Rumelhart, D., McClelland, J., Eds.; MIT Press: Cambridge, MA, 1986. 23. Anderson, J. R. ACT: A simple theory of complex cognition. Am. Psychol. 1996, 51, 355–365. 24. Paas, F.; Renkl, A.; Sweller, J. Cognitive load theory and instructional design: Recent developments. Educ. Psychol. 2003, 38 (1), 1–4. 25. Hestenes, D. Notes for a modeling theory. In Proceedings of the 2006 GIREP conference: Modeling in physics and physics education, 2006; Vol. 31, p 27. 26. Lesh, R.; Doerr, H. M. Beyond Constructivism. Mathematical Thinking and Learning 2003, 5 (2&3), 211–233. 27. Novick, S.; Nussbaum, J. Sci. Educ. 1978, 62, 273–282. 28. Novick, S.; Nussbaum, J. Sci. Educ. 1981, 65, 187–196. 29. Ben-Zvi, R.; Eylon, B.; Silberstein, J. J. Chem. Educ. 1988, 63, 84–66. 30. Nakhleh, M. J. Chem. Educ. 1992, 69 (3), 191–196. 31. Gabel, D. Improving teaching and learning through chemistry education research: A look to the future. J. Chem. Educ. 1999, 76 (4), 548. 32. Kind, V. Beyond appearances: Students’ misconceptions about basic chemical ideas. School of Education, Durham University, Durham, U.K.; 2004; Retrieved Sep, 25, 2009. 33. Chemistry: An experimental science; Pimentel, G. C., Ed.; Freeman: San Francisco, 1968. 34. Herron, J. D. The Chemistry Classroom: Formulas for Successful Teaching; American Chemical Society: Washington, DC, 1996. 35. “Progression of models in chemistry. 2016. Retrieved from https:// www.eweblife.com/prm/AMTA/membership-page/view?record=23. 36. Desbien, D. M. Modeling discourse management compared to other classroom management styles in university physics. Doctoral dissertation, Arizona State University, 2002.



37. Liljedahl, P. Building Thinking Classrooms: Conditions for ProblemSolving. In Posing and Solving Mathematical Problems; Springer International Publishing, 2016; pp 361−386. 38. Gee, J. P.; Green, J. L. Discourse Analysis, Learning and Social Practice. In Review of Research in Education; American Education Research Association: Washington DC, 1998; Vol. 23, pp 119−169. 39. Megowan-Romanowicz, C. Helping students construct robust conceptual models. In Models and Modeling; Springer: Netherlands, 2011; pp 99−120. 40. Michaels, S.; O’Connor, C. Talk science primer; TERC: Cambridge, MA, 2012. Available online at http://inquiryproject. terc. edu/shared/pd/ TalkScience_Primer. pdf. 41. Fouad, N. A.; Hackett, G.; Smith, P. L.; Kantamneni, N.; Fitzpatrick, M.; Haag, S.; Spencer, D. Barriers and supports for continuing in mathematics and science: Gender and educational level differences. Journal of Vocational Behavior 2010, 77 (3), 361–373. 42. Middleton, J.; Toluk, Z. First Steps in the Development of an Adaptive Theory of Motivation. Educ. Psychol. 1999, 34 (2), 99–112. 43. Kind, V. Beyond Appearances: Students’ misconceptions about basic chemical ideas, 2nd ed.; A Report Prepared for the Royal Society of Chemistry, 2004. 44. Johnstone, A. H. Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning 1991, 7 (2), 75–83. 45. Johnstone, A. H. Teaching of chemistry-logical or psychological? Chemistry Education Research and Practice 2000, 1 (1), 9–15. 46. Jackendoff, R. The Architecture of the Linguistic Spatial Interface. In Language and Space; Bloom, P., Peterson, M. A., Nadel, L., Garrett, M. F., Eds.; MIT Press: Cambridge, MA, 1996; pp 1−30. 47. Marr, D. Vision: A computational investigation into the human representation and processing of visual information; Freeman: San Francisco, 1982. 48. Rose, S. A. Infants’ Transfer Response between Two-Dimensional and Three Dimensional Stimuli. Child Development 1977, 48 (3), 1086–1091. 49. Megowan-Romanowicz, C. Whiteboarding: A tool for moving classroom discourse from answer-making to sense-making. The Physics Teacher 2016, 54 (2), 83–86. 50. Fuller, F. F.; Brown, O. H. Becoming a teacher; National Society for the Study of Education: Chicago:, 1975; pp 25−52.


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