Some labs encourage critical and quantitative thinking, some emphasize demonstration of principles or development of lab techniques, and some help students deepen their understanding of fundamental concepts Hake, Where possible, the lab should be coincident with the lecture or discussion. Before you begin to develop a. Here are a number of possibilities:. Exercise curiosity and creativity by designing a procedure to test a hypothesis. Developing an effective laboratory requires appropriate space and equipment and extraordinary effort from the department's most creative teachers.
Still, those who have invested in innovative introductory laboratory programs report very encouraging results: Many science departments have implemented innovative laboratory programs in their introductory courses. We encourage you to consult the organizations and publications listed in the Appendices. Education sessions at professional society meetings are another opportunity to get good ideas for labs in your discipline. Some faculty members have given up lecturing and large. A major goal of this course is to teach students how to do science: Each lab is two weeks long, with the equipment and animals available for the entire time.
All of the materials that students could plausibly need are stored on shelves for easy and immediate access. In the first hour, we discuss the lab and possible hypotheses, and look over the materials at hand. Each group then formulates an initial plan, obtains approval for their plan, and conducts the experiment. The most flexible labs utilize computer-controlled stimuli. In one lab, students are asked to determine to what features of prey a toad responds. Although they begin with live crickets and worms, they are encouraged to use a computer library of "virtual" crickets and toads.
Students are given instructions for making new prey models, or modifying existing ones, to test the toad's response to different features. The library includes variations of shape, motion, color, three-dimensionality, size, and so on, plus a variety of cricket chirps and other calls. In general, students quickly discover that virtual crickets work almost as well as real ones-better in that they provide more data since the toad never fills up!
A simple statistical program on the computers helps minimize the drudgery of data analysis, enabling the students to concentrate on experimental design and results rather than tedious computations. A number of other labs in the course make use of computer-generated and modified stimuli.
Labs using this strategy deal with mate recognition in crickets and fish, competitor recognition in fish, predator recognition in chicks and fish, imprinting in ducklings, color change in lizards, and hemispheric dominance in humans. Students in two laboratory sections of a chemistry course for nonscience majors worked in groups of three on two experiments about acids, bases, and buffers. The experiments were devised using a modified "jigsaw" technique, in which each student in a group is assigned a particular part of a lesson or unit and is responsible for helping the other members of the group learn that material.
The week prior to the laboratory, students were given lists of objectives and preparatory work that were divided into three parts. Students decided how to divide the responsibility for the preparatory and laboratory tasks, but were informed that the scores from their post-laboratory exams would be averaged, and that all members of a group would receive the same grade. Two control sections of the same laboratory were conducted in a traditional manner, with students working independently.
All four groups of students were part of the same lecture class, and there were no significant differences in age, gender balance, or previous number of chemistry classes. Although the control sections had an overall GPA higher than the cooperative learning sections 2. The authors conclude that use of cooperative learning in the laboratory has a positive effect on student achievement.
Such workshop methods have been devised for teaching physics Laws, , chemistry Lisensky et al. Although this is not feasible at many institutions, some of the ideas developed in these courses translate reasonably well to courses in which a lab is associated with a large-enrollment course Thornton, in press.
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Laboratories can be enriched by computers that make data acquisition and analysis easier and much faster, thus allowing students to think about their results and do an improved experiment. Computers can also be used as an element of the experiment to simulate a response, or vary a stimulus. Computers offer convenience, flexibility and safety in the laboratory, but they should not completely replace the student's interaction with the natural world. Laboratory teaching methods vary widely, but there is certainly no substitute for an instructor circulating among the students, answering and asking questions, pointing out subtle details or possible applications, and generally guiding students' learning.
Although students work informally in pairs or groups in many labs, some faculty have formally introduced cooperative learning into their labs see sidebar. Some instructors rely on a lab handout, not to give cookbook instructions, but to pose a carefully constructed sequence of questions to help students design experiments which illustrate important concepts Hake, One advantage of the well-designed handout is that the designer more closely controls what students do in the lab Moog and Farrell, The challenge is to design it so that students must think and be creative.
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In more unstructured labs the challenge is to prevent students from getting stranded and discouraged. Easy access to a faculty member or teaching assistant is essential in this type of lab. Once you have decided on the goals for your laboratory, and are familiar with some of the innovative ideas in your field, you are ready to ask yourself the following questions:. How have others operated their programs?
Seek out colleagues in other departments or institutions who may have implemented a laboratory program similar to the one you are considering, and learn from their experiences. How much time and energy are you willing to invest? Buying new equipment and tinkering with the lab write-ups will probably improve the labs, but much more is required to implement substantial change. Changing the way that students learn involves rethinking the way the lab is taught, writing new lab handouts, setting up a training program for teaching assistants, and perhaps designing some new experiments.
What support will you have? Solicit the interest and support of departmental colleagues and teaching assistants. Are the departmental and institutional administrations supportive of your project and willing to accept the risks? Determine how likely they are to provide the needed resources. Are you prepared to go through all of this and still get mediocre student evaluations?
All teaching assistants perform the laboratory exercises as if they were students to determine operational and analytical difficulties and to test the instructional notes and record-keeping procedures. Teachers discuss usual student questions and misconceptions and ideas for directing student learning. Teachers review procedures for circulating among student groups to ensure that each group gets attention. Groups are visited early to help them get started. Each group is visited several other times, but at least midway through the lab to discuss preliminary results and interpretations and toward the end of the lab to review outcomes and interpretations.
Teachers review the students' notebooks or reports and then meet to discuss difficulties and misconceptions. Discussions of grading and comments that might be made are important because these procedures can influence student performance and attitudes on subsequent exercises. The various methods by which students report their lab work have different pedagogical objectives. The formal written report teaches students how to communicate their work in journal style, but students sometimes sacrifice content for appearance.
Keeping a lab notebook, which is graded, teaches the student to keep a record while doing an experiment, but it may not develop good writing and presentation skills. Oral reports motivate students to understand their work well enough to explain it to others, but this takes time and does not give students practice in writing. Oral reports can also motivate students to keep a good notebook, especially if they can consult it during their presentation. In choosing this important aspect of the students' lab experience, consider how your students might report their work in the future.
Many benefits of carefully planned laboratory exercises are realized only if the instructional staff is well prepared to teach. Often the primary, or only, lab instruction comes from graduate or undergraduate teaching assistants or from faculty members who were not involved in designing the lab.
Time must be invested in training the teaching staff, focusing first on their mastery of the lab experiments and then on the method of instruction. It is a fine art to guide students without either simply giving the answer or seeming to be obstinately obscure. Teaching assistants who were not taught in this way can have difficulty adapting to innovative laboratory programs, and the suggestions below will you help you guide their transition. A good part of the success of a course depends on the group spirit of the whole team of instructor and teaching assistants. Many such groups meet weekly, perhaps in an informal but structured way, so that the teaching assistants can provide feedback to the instructor as well as learn about the most effective way to teach the next laboratory experiment see sidebar.
The responsibility for preparing teaching assistants is largely dependent on the setting. While many faculty members at four-year institutions are responsible for preparing their teaching assistants, this task is handled on a department-wide or campus-wide basis in programs with large numbers of graduate students. Many professional societies have publications on this topic see Appendix A.
The American Association for Higher Education is another excellent source of information. Their publication Preparing Graduate Students to Teach Lambert and Tice, provides numerous examples of teaching assistant training programs in a wide array of disciplines. Effective science teaching requires creativity, imagination, and innovation. In light of concerns about American science literacy, scientists and educators have struggled to teach this discipline more effectively. Science Teaching Reconsidered provides undergraduate science educators with a path to understanding students, accommodating their individual differences, and helping them grasp the methods--and the wonder--of science.
What impact does teaching style have? How do I plan a course curriculum? How do I make lectures, classes, and laboratories more effective? How can I tell what students are thinking? Why don't they understand? This handbook provides productive approaches to these and other questions. Written by scientists who are also educators, the handbook offers suggestions for having a greater impact in the classroom and provides resources for further research.
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A Handbook Chapter: Looking for other ways to read this? General Principles Page 9 Share Cite. The National Academies Press. Page 10 Share Cite. Enhancing Learning in Large Classes. In teaching formal genetics, I draw out a genetic cross first in general form in this example, a Drosophila eye color inheritance test: Page 11 Share Cite. Hints for More Effective Lecturing.
This is to be expected when students investigated in different ways. In the scientific community, for example, there is an expectation that relationships will be stated precisely and backed by unambiguous and reliable data. It should also be recognized that claims can be stated in the negative, thus indicating a relationship that is claimed to be inaccurate—for example, the brightness of the light source does not affect whether light reflects from an object.
Such claims help the community narrow its consideration of possible relationships. Another role of the teacher is to help students attend to issues that may affect the quality of their public presentation. For example, teachers can encourage students to draw as well as write out their ideas to communicate them more effectively. Furthermore, teachers can prompt students to evaluate their poster for its effectiveness in communicating findings. Are things clearly stated? Is there enough information for others to evaluate the claim or be convinced of its validity? Finally, a key role for the teacher is to monitor the types of claims students are generating and the nature of the evidence they are selecting.
With respect to content, the teacher determines whether and when to focus students on particular strategies for interpreting or analyzing their data or to provide additional information to support students in writing claims. It may be necessary for the teacher to help groups reorganize their data to find patterns. For example, Table shows two tables. The top table shows the data as they were originally recorded.
The order of the columns matches the order of places that students looked to check for light from the flashlight. The order of objects in the first column is simply the order students selected to observe them. The bottom table shows the same data in a similar form,. This type of reorganization and simplification of data is common for scientists, and may be necessary for students to find patterns from which to make a claim.
It does not include evaluating whether their data support the claim; that is part of the reporting phase and should be shared by the class. On the other hand, the teacher may choose to support students in making additional claims based on the data they have, particularly in instances where the group has unique data to make a claim that the teacher believes would promote desired knowledge-building for the class.
Given that this was the only group in the class making such a claim from that body of evidence, Ms. Lacey supported the group to ensure that they would include the claim in their poster so it would be introduced to the whole class. The emphasis in this case may be on ruling out the possibility of disconfirming evidence. With this approach, the teacher monitors during the investigation phase whether students are checking for multiple possibilities, and will know whether the students observe light interacting with objects in more than one way.
The following excerpt from an investigation of light by third graders shows a typical teacher—student interaction as students attempted to generate knowledge claims. As a result, different groups of students investigated with different types of materials. In the transcript, note that the students did most of the talking.
Note also that the teacher reflected an important norm of scientific activity by asking the students how they planned to represent the observations supporting their claim. We had to change it because we thought that the speed of light would be a [second-hand investigation]. So, light can reflect off a mirror. You see, when the light hits the paper, it disappears. But before it disap-.
It goes to the piece of paper. It disappears when it hits that piece—that object. Hang on a second. And you think the rest of the light just disappears? But if it hits the mirror it can reflect off of it. Are you going to try to prove that some way to the group? You have to show some data. How could you show that? We could get another piece of paper. How could you show on another piece of paper how the light is different with different—with the mirror and with the paper?
How could you show it? What you just said—so you could show it to the rest of the group? We can draw the top and just say that the light is coming through—put light right here. And then the light through—going out of the box. And then we can put, make like a little part of it. So, Kevin is saying, when the light hits the mirror, it looks one way.
When the light hits a piece of paper, it looks another way. How could you show how it looks those two ways on a piece of paper? And, another thing is, I sort of drew this thing. But you guys need to stick to one claim and deal with that. When you think you have evidence for that, if you want to explore something else and have some time, you could do that. This phase has two parts see Figure First, groups of students who have been investigating together present their claims and evidence, which are discussed by the class in terms of their own merits and in light of the findings presented by previous groups.
Second, the class discusses the commonalities and differences among the claims and evidence presented, noting claims that can be rejected, developing a class list of community-accepted claims, and determining claims or questions that need further investigation. In addition to providing occasions for discussing important issues related to the investigative process e.
Thus, there is an important opportunity for the teacher to support and guide students in the use of scientific terms to facilitate their communication. When students first experience this activity, the teacher plays a pivotal role in communicating and modeling expectations for audience members. This includes establishing and maintaining conversational norms. Despite the fact that children may need to challenge the ideas or work of their classmates, the teacher is key in setting the tone so that this is done with the.
The primary expectations for audience members are to determine whether there is a clearly stated claim that is related to the question under investigation, whether there is evidence backing that claim, and whether the evidence is unambiguous in supporting the claim. As each group shares its claim s and describes the relationship between these claims and their data, the teacher assumes multiple roles: This discussion of claims typically results in identifying where there is disagreement among claims or contradictory evidence related to particular claims e.
Excerpts from classroom instruction illustrate various aspects of teacher and student activity during this phase. The following transcript is from the beginning of the reporting phase in Ms. She also forewarns students that they have conflicting views, anticipating the need to prepare the students to hear things from their classmates with which they will not agree. Her decision to alert students to the presence of conflicting ideas provides an authentic purpose for paying attention to one another during the reporting phase and stimulated metacognition.
In the next excerpt, a student questions one of the claims made by the reporting group. What do you mean? So you guys are saying that you think light is a gas because light is like air? Lacey could have pointed out that a claim about light being a gas is unrelated to the focus of this particular investigation; she could have trun-. Lacey chose not to interject at all. Such questions can provide opportunities for students particularly interested in a question to pursue it outside of class, or resources might be brought into the class books or descriptions downloaded from the Internet that provide information pertinent to the question.
In the next two excerpts, Ms. In the first excerpt, she responds to confusion that she suspects arises from the way students are interpreting language in the phrasing of claims. It could go different ways. We tried it on the flashlight. Light can go [other ways] [shows with hand]. Can you draw a diagram on the board?
The girls used a context from their preinstruction assessment—a tree, a person, and the sun—to show two different possibilities regarding the path of light: They drew multiple paths from the sun and pointed to the straight lines as the representations that matched their claim. In the second excerpt, a student struggles to make sense of the claim that light reflects and goes through.
Reflect and go through—on the plastic tray. When you put it on reflect, it reflected off the plastic tray. And when you put it on go through, it went through the plastic tray. If it reflected off, then how did it go through? Well, we put it on an angle and shined it and it went on our screen. And when we put it straight, it went through. Stefan, are you having a hard time thinking that light can do two things at once? If it reflects off, why did it also go through?
In both of the above examples, as well as in the excerpt at the beginning of this chapter in which a second-grade student objected to a claim about light reflecting from wood, students are revealing that they lack a conception of light that allows it to behave in the ways indicated by other students. Brad does not have a way to think about light that would account for its ability to reflect from wood. Stefan does not have a way to think about light that would account for its ability to simultaneously reflect and pass through an object. These are reasonable issues, and we should not be surprised that the students do not readily accept claims.
Indeed, there are numerous examples of scientific papers that presented novel scientific claims and were rejected by top scientific journals because of their inconsistency with prevailing knowledge and beliefs, but later became highly regarded and even prize-winning.
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There are several ways to proceed in such circumstances. Some research has demonstrated that having students observe relationships can lead them to change their initial thinking about those relationships, 28 or at least come up with alternative ideas. Other researchers have proposed engaging students in reasoning through a series of phenomena that are closely related, 30 helping students bridge analogous circumstances.
In the case of disbelief about light reflecting from wood or other nonshiny solids, this might mean starting with observing instances of reflection that students readily accept e. The bridging could go as far as examining reflection from black felt, a material students are initially quite sure does not reflect light, but can be observed to do so if the room is dark enough.
Another approach to addressing the nonacceptance of claims that contradict everyday experience is to tell students that part of learning science means developing new conceptions of reality. Despite the challenge of accepting claims that are initially counter to everyday thinking, we have regularly observed students, even very young children, developing new ideas that are counter to their initial thinking. Does it support the claim that objects make more than one shadow? Because um [touching each of the posters with multiple shapes of shadows], all shadow, all shadow, all shadow, all shadow.
Here, Amanda correctly pointed out that the data did not conclusively support one claim over the other, drawing attention to the ambiguity of the results. This provided a reason to investigate further, so the teacher suggested that the class do so the next day. After examining the data from the second day, all of which showed more than one shadow, Amanda provided a different evaluation of the evidence:. We need to find out if the documentation supports that a shape can make one shadow or more than one shadow. Does this evidence support the claim … [points to the two posted claims]. Because one object can make more shadows, see?
Because look at all these shadows on the papers.
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Of note is that Ms. Amanda needed the time of several cycles of investigation to become convinced of a different idea from the one she initially held. Thus, the cycling process of investigation within the same context is an important aspect of promoting desired development of scientific knowledge and ways of knowing. Our focus thus far has been on the development of understanding through first-hand investigation. However, inquiry-based science instruction can also profitably include learning from text-based resources as suggested by the National Science Education Standards.
The question is how to engage students in such activity in a way that keeps them actively engaged intellectually relative to scientific ways of knowing and permits a skeptical stance that is common to a scientific mindset. To achieve this goal, we developed a novel type of text for inquiry-based instruction, whose use is called a second-hand investigation. These texts are modeled after the notebook of a scientist and so are referred to as notebook texts.
Of note are the various ways that the teacher, Ms. The students identified those claims on which there was consensus and those that were still under consideration, but for which there was insufficient evidence. In addition, there were numerous instances in which Ms. Transferring their knowledge about other units of measure, they inquired about the system from which this unit. That almost all the objects did and maybe if we used a light meter, we might have found out that every single object did a little. In this exchange, we see how Ms. This led to a discussion of two issues: Lesley used a light meter to collect her data, while the children had no means of measurement; they simply described their visual observations as precisely as possible.
Sutton introduced the possibility that additional investigation might have yielded a different finding, to which Megan responded that the class had not investigated with all the materials yet. In this instance, Lesley is reporting the data for what happens when a flashlight shines on a piece of black felt.
Teacher and Teaching Effects on Students’ Attitudes and Behaviors
She reports that no transmitted light was recorded by her light meter. The majority of students, however, reported having seen transmitted light. Here the class considers why there might be these different findings:. When we stuck the lamp like, not like directly next to the black but a little bit up close to the black, it came out a maroon color on the other side.
So we were getting some transmitted. We thought we had some transmitted light, too.
What might cause a difference in results from what you did and from what she did? She might have either had a weaker flashlight or a thicker piece of felt or something. What about the light meter? How would the light meter make it harder to detect transmitted light? This exchange is significant to the extent that the students demonstrate an appreciation for the role variables play in the design of an investigation. With this understanding, they are now situated to.
One final observation about the successful use of text in inquiry-based instruction is the importance of students assuming a skeptical stance rather than simply deferring to the text. The following three excerpts are illustrative. The second excerpt begins when one student, Katherine, expresses concern that Lesley has not provided sufficient information about the kinds of materials with which she investigated.
This leads a second student, Megan, to observe that the objects with which Lesley investigated are quite similar i. In a related criticism, Kit observes that Lesley needed to consider not only the color of the object she was investigating, but also the material of which it was made:.
Okay, so you would get a—if you had a light meter to measure like she did and you were measuring all the black objects on this list, do you think you still would get different readings? Thus, inquiry in any topic area requires multiple cycles of investigation. Discussion of how to design curriculum units with cycles of investigation and the interplay between first- and second-hand experiences is beyond the scope of this chapter. The important point is that students need to have multiple opportunities to learn concepts i. The purpose of this section is to discuss how teachers might think about the development of knowledge across cycles of investigation.
The classroom community determines the fate of any knowledge claim generated by a group. Within and across each cycle, knowledge claims are generated, tested, refuted, tweaked, embraced, discarded, and ignored. Figure illustrates this process. In this case, the class worked with five.
Following the reporting phase, two of these claims were abandoned: Three claims survived this first cycle of inquiry: The two remaining claims survived, but were revised in ways that suggested they might be related. Cycle 3 began with the class considering three extant claims.
During the reporting phase, the two claims that appeared to be related became combined and synthesized into one claim. This is a significant development from a scientific perspective given the value placed on simplicity and parsimony of claims about the physical world.
The final claim, while still in the running, was not accepted by the class, but neither was it rejected. This progression of events with the community knowledge claims resulting from each cycle is like threads that when woven together create the fabric of scientific knowledge and reasoning on the topic of study.
Some threads will dangle, never fully attended to; some will be abandoned; while others will be central to understanding the topic of study and may need to be blended together to create a strong weave. The fate of each thread is determined by classroom community judgments about which claims have the most evidence, account for the greatest range of data, and are simple and concise; that is, the standards for acceptance are values adhered to by scientists in the production of scientific knowledge.
Furthermore, whereas dangling threads in a fabric are problematic, they are important to the process of learning science because the reasons for rejecting or abandoning claims form part of the understanding of scientific ways of knowing. Imagine now that the students have been through several cycles of investigation.
What is to prevent these cycles from being experienced as a set. How are the students to develop, elaborate, and refine conceptual frameworks from repeated inquiry experiences? Conclusions from How People Learn tell us that the formulation of a conceptual framework is a hallmark of developing deep understanding, and that a focus on the development of deep understanding is one of the principles distinguishing school reform efforts that result in increases in student achievement from those that do not. The development of organized knowledge is key to the formulation of conceptual frameworks. Developing organized knowledge is enabled by well-designed curriculum materials, but requires specific guidance by teachers as well.
Some of that guidance needs to involve pressing students to work from the perspective of the norms for knowledge building in the scientific community. For example, scientists assume that there are regularities in how the world works. If the sky appears gray with no evidence of clouds or the sun, a scientist, who has seen the sun in the sky every other day, will assume that it is still there and infer that something must be blocking it. This perspective dictates different questions than one that does not assume such regularity. Another area of guidance comes from pressing students to focus on the relationships among the claims they are making.
Sorting out these relationships may result in multiple claims being revised into a single claim, as shown in Figure Alternatively, revisions may need to be more extensive to fit the expectation of scientists that relationships within a topic area fit together; that is, they are coherent with one another. It is not coherent to claim that light does and does not reflect from a mirror.
Similarly, it is not coherent to say that light transmits through glass but not through a glass object i. Of course, the coherent view is that light is transmitted through glass, but in the case of a mirror, it is transmitted through the glass part but reflects from the backing that is placed on the glass to make it a mirror. To develop these kinds of perspectives, students must learn concepts in combination, with attention to the relationships among them.
In this section we trace the development of student understanding about light over four cycles of investigation in Ms. This instruction took place over. We present concept maps constructed from classroom discourse during the instruction. Transcript excerpts accompany the maps to illustrate the nature of the conversation among the students and teacher.
During Cycle 1, students focused on the differences among objects, assuming that light interacted with each object in only one way. During reporting, they made statements such as: It just stopped at the object. Figure suggests that students thought light could interact with matter in one of three ways. This particular group did not recognize the significance of its findings, focusing instead on the one way it should categorize objects from which it had observed multiple interactions.
In the following excerpt, the teacher encourages the group to think of its results as a new claim. Yes, because some were really see-through and reflection together, but we had to decide which one to put it in. With the introduction of the idea that light can interact with matter in more than one way, the students embarked upon a second cycle of investigation with the same materials, with the intent of determining which if any objects exhibited the behavior claimed by Kevin and Derek.
From this second round of investigation, all groups determined that multiple behaviors can occur with some objects, but there was uncertainty about whether these interactions occur with some types of materials and not others see Figure In addition, some students expressed puzzlement about how light could interact with a material in more than one way. In response to this question, one group introduced the idea that there was a quantitative relationship among the multiple behaviors observed when light interacted with an object:.
If you said that light can reflect, transmit, and absorb, absorb means to block. How can it be blocked … and still go through? During the third cycle of investigation, in which the students and the teacher interactively read a Lesley Park notebook text about light using reciprocal teaching strategies, 44 the students encountered more evidence that light can interact with matter in multiple ways see Figure This led to conversation concerning how general a claim might be made about the behavior of light:.
I think we need to clarify something, because you said one thing, Corey, and Miles said something else. It just says light can be reflected, absorbed, and transmitted by the same object. So you say not all can. Do we have any data in our reading that tells us that not all things absorb, reflect, and transmit?
We have evidence that all objects reflect and absorb [referring to a table in the notebook text]. However, students did not yet add those ideas to their class claims chart. In the fourth cycle of investigation, students returned to a first-hand investigation and were now quite comfortable with the idea that light can simultaneously interact with matter in multiple ways. In addition, despite not having tools to compare the brightness of the light, they qualitatively compared the amount of light behaving in particular ways. This is represented in the map in Figure Do all students have the understanding represented in Figure ?
1. Introduction
The excerpt below suggests that this is unlikely. In this excerpt, a student reveals that he and his partner did not think light would reflect from an object even after the class had established in the previous cycle that light always reflects:. Yeah, we learned that this blue felt can do three—reflect, transmit, and absorb—at one, at this one object. It reflected a little, and transmitted some and it absorbed some.
That it was only going to transmit and absorb. But we know that for such a claim—that light reflects off all materials—many experiences may be needed for that knowledge to be robust. Relationships such as this for which we have no direct experience or that are counterintuitive we see reflected light from objects, not the objects themselves take time and attention, as well as recursive tacking to knowledge-building processes and the conceptual framework that is emerging from those processes. Conceptual frameworks that represent the physical world in ways we have not experienced e.
At the core of teacher decision making featured in this chapter is the need to mediate the learning of individual students. To do this in a way that leads to targeted scientific knowledge and ways of knowing, teachers must be confident about their knowledge of the learning goals. At the same time, having accurate subject matter knowledge is not sufficient for effective teaching. When students claim that light is a gas, it is not sufficient for the teacher to know that light is energy, not a state of matter. The teacher also needs to know what observations of light might convince students that it is not a gas, which in turn is informed by knowing how students think of gases, what their experiences of gas and light have likely been, and what it is possible to observe within a classroom context.
This knowledge is part of specialized knowledge for teaching called pedagogical content knowledge because it is derived from content knowledge that is specifically employed to facilitate learning. It is the knowledge that teachers have about how to make particular subject matter comprehensible to particular students. Pedagogical content knowledge includes knowledge of the concepts that students find most difficult, as well as ways to support their understanding of those concepts. For example, it is difficult for students to understand that the color of objects is the color of light reflected from them because we are not aware of the reflection.
Having students use a white screen to examine the color of light reflected from colored objects can reveal this phenomenon in a way that is convincing to them. Pedagogical content knowledge also includes knowledge of curriculum materials that are particularly effective for teaching particular topics. A still valuable resource for the study of light in the elementary grades is the Optics kit mentioned earlier that is part of Elementary Science Study curriculum materials developed in the s.
Finally, pedagogical content knowledge includes ways to assess student knowledge. Arrows should be drawn from the sun to the tree to the person, but it is not uncommon for students to draw arrows from the sun to the person and the person to the tree. Use of this item at the beginning of a unit of study can provide a teacher with a wealth of information on current.
When and how to employ particular strategies in the service of supporting such knowledge building is a different issue, but the topic-specific knowledge for teaching that is identified as pedagogical content knowledge is a necessary element if students are to achieve the standards we have set. Science instruction provides a rich context for applying what we know about how people learn. A successful teacher in this context is aware that he or she is supporting students in activating prior knowledge and in building upon and continuing to organize this knowledge so it can be used flexibly to make sense of and appreciate the world around them.
To do this well, the teacher must be knowledgeable about the nature of science, including both the products—the powerful ideas of science—and the values, beliefs, and practices of the scientific community that guide the generation and evaluation of these powerful ideas. Furthermore, teachers must be knowledgeable about children and the processes of engaging them in knowledge building, reflecting upon their thinking and learning new ways of thinking. We have proposed and illustrated a heuristic for conceptualizing instruction relative to the opportunities and challenges of different aspects of inquiry-based instruction, which we have found useful in supporting teachers in effective decision making and evaluation of instruction.
We have argued that the development of scientific knowledge and reasoning can be supported through both first- and second-hand investigations. Furthermore, we have proposed that the teacher draws upon a broad repertoire of practices for the purposes of establishing and maintaining the classroom as a learning community, and assessing, supporting, and extending the knowledge building of each member of that community. All of these elements are necessary for effective teaching in the twenty-first century, when our standards for learning are not just about the application of scientific knowledge, but also its evaluation and generation.
These materials, originally developed in the s, can be purchased from Delta Education: Whereas some view conceptual change as referring to a change from existing ideas to new ones, we suggest that new ideas are often developed in parallel with existing ones. The new ideas are rooted in different values and beliefs—those of the scientific community rather than those guiding our daily lives. Our decision to focus on instruction in which investigation is central reflects the national standard that calls for science instruction to be inquiry based.
Barnes, ; Bybee et al. All of the instruction featured in this chapter was conducted by teachers who were a part of GIsML Community of Practice, a multiyear professional development effort aimed at identifying effective practice for inquiry-based science teaching. Approximately 45 percent of the students in this district pass the state standardized tests, and 52 percent are economically disadvantaged. This class is in a school in a relatively small district about 3, students near a major industrial plant in a town with a state university.
Approximately 38 percent of the students in this district pass the state standardized tests, and 63 percent are economically disadvantaged. While we are featuring contexts in which there is a single question, teachers could choose to have a context in which children are investigating different questions related to the same phenomenon.
However, it is important to recognize the substantially greater cognitive and procedural demands this approach places on the teacher, so it is not something we recommend if a teacher is inexperienced in conducting inquiry-based instruction. Although it can be motivating and conceptually beneficial for students to be placed in the role of generating questions for investigation, the teacher needs to be mindful of the consequences of taking time to investigate questions that may be trivial or peripheral to the unity of study. The teacher may judge the time to be useful as students can still learn a great deal about investigation, but.
This person monitors the time the group is taking for the investigation to support the students in examining how efficiently they are working and deciding whether it is necessary to adjust the tempo of their activity to finish in the allotted time. It is very reasonable for the teacher to discuss these issues with the whole class during the preparing-to-investigate phase and to invite the class to specify procedures. Addressing these matters with the whole class gives the teacher opportunities to model thinking for the benefit of all.
Thus it is important for the teacher to give students an opportunity to make these types of decisions on their own during some investigations. The students inadvertently interpreted the idea of categorizing to mean that light would behave in only one way with each object. This led many students to stop observing an object as soon as they had identified one way light behaved with it.
In both cases, the fact that we can see the object tells us that light is reflected. However, students had not yet established that relationship, so we refer here only to the direct evidence of light. This class is in a moderately sized district about 16, students in a town with a major university. Approximately 70 percent of the students in this district pass the state standardized tests, and 16 percent are economically disadvantaged. We observed a group of children in a fourth-grade class working very hard to determine if black felt reflects light.
They piled their materials in the bathroom in the classroom, taped around the door to block out any light, and studied the black felt. They were quite proud to report their evidence that it did indeed reflect light. From communication to curriculum. Elementary School Journal , 88 3 , Guided discovery in a community of learners. Integrating cognitive theory and classroom practice pp. Science and technology education for the elementary years: Frameworks for curriculum and instruction. National Center for Improving Science Education. Calculating the variation across individual teacher effect estimates using Ordinary Least Squares regression would bias our variance estimates upward because it would conflate true variation with estimation error, particularly in instances where only a handful of students are attached to each teachers.
The coefficients on these variables are our main parameters of interest and can be interpreted as the change in standard deviation units for each outcome associated with exposure to teaching practice one standard deviation above the mean. One concern when relating observation scores to student survey outcomes is that they may capture the same behaviors. For example, teachers may receive credit on the Classroom Organization domain when their students demonstrate orderly behavior.
In this case, we would have the same observed behaviors on both the left and right side of our equation relating instructional quality to student outcomes, which would inflate our teaching effect estimates. While the direction of bias is not as clear here — as either lesser- or higher-quality teachers could be sorted to harder to educate classrooms — this possibility also could lead to incorrect estimates.
To the extent that instructional quality varies across years, using out-of-year observation scores creates a lower-bound estimate of the true relationship between instructional quality and student outcomes. We consider this an important tradeoff to minimize potential bias. An additional concern for identification is the endogeneity of observed classroom quality.
In other words, specific teaching practices are not randomly assigned to teachers. Our preferred analytic approach attempted to account for potential sources of bias by conditioning estimates of the relationship between one dimension of teaching practice and student outcomes on the three other dimensions. Then, we generated a correlation matrix of these teacher effect estimates.
Despite attempts to increase the precision of these estimates through empirical Bayes estimation, estimates of individual teacher effects are measured with error that will attenuate these correlations Spearman, To address this concern, we focus our discussion on relative rankings in correlations between teacher effect estimates rather than their absolute magnitudes. Specifically, we examine how correlations between teacher effects on two closely related outcomes e.
In light of research highlighted above, we did not expect the correlation between teacher effects on the two math tests to be 1 or, for that matter, close to 1.
We begin by presenting results of the magnitude of teacher effects in Table 4. In other words, relative to an average teacher, teachers at the 84 th percentile of the distribution of effectiveness move the medium student up to roughly the 57 th percentile of math achievement. This suggests that our use of school fixed effects with a more limited number of teachers observed within a given school does not appear to overly restrict our identifying variation.
In Online Appendix A , where we present the magnitude of teacher effects from alternative model specifications, we show that results are robust to models that exclude school fixed effects or replace school fixed effects with observable school characteristics. Given that we do not have multiple years of data to separate out class effects for this measure, we interpret this estimate as the upward bound of true teacher effects on Happiness in Class.
Rescaling this estimate by the ratio of teacher effects with and without class effects for Self-Efficacy in Math 0. Cells contain estimates from separate multi-level regression models. We present unconditional estimates in Table 5 Panel A, where the relationship between one dimension of teaching practice and student outcomes is estimated without controlling for the other three dimensions. Thus, cells contain estimates from separate regression models.
In Panel B, we present conditional estimates, where all four dimensions of teaching quality are included in the same regression model. Here, columns contain estimates from separate regression models. We present all estimates as standardized effect sizes, which allows us to make comparisons across models and outcome measures. Unconditional and conditional estimates generally are quite similar. Therefore, we focus our discussion on our preferred conditional estimates. In Panel A, cells contain estimates from separate regression models.
In Panel B, columns contain estimates from separate regression models, where estimates are conditioned on other teaching practices. All models control for student and class characteristics, school fixed effects, and district-by-grade-by-year fixed effects, and include and teacher random effects.
Models predicting all outcomes except for Happiness in Class also include class random effects. At the same time, this is one instance where our estimate is sensitive to whether or not other teaching characteristics are included in the model. For two other dimensions of teaching quality, Emotional Support and Ambitious Mathematics Instruction , estimates are signed the way we would expect and with similar magnitudes, though they are not statistically significant.
Given the consistency of estimates across the two math tests and our restricted sample size, it is possible that non-significant results are due to limited statistical power. We interpret these results as indication that, still, very little is known about how specific classroom teaching practices are related to student achievement in math.
In Online Appendix B , we show that results are robust to a variety of different specifications, including 1 adjusting observation scores for characteristics of students in the classroom, 2 controlling for teacher background characteristics i. This suggests that our approach likely accounts for many potential sources of bias in our teaching effect estimates.
In Table 6 , we present correlations between teacher effects on each of our student outcomes. The fact that teacher effects are measured with error makes it difficult to estimate the precise magnitude of these correlations. Instead, we describe relative differences in correlations, focusing on the extent to which teacher effects within outcome type — i.
We illustrate these differences in Figure 1 , where Panel A presents scatter plots of these relationships between teacher effects within outcome type and Panel B does the same across outcome type. Recognizing that not all of our survey outcomes are meant to capture the same underlying construct, we also describe relative differences in correlations between teacher effects on these different measures.
In Online Appendix C , we find that an extremely conservative adjustment that scales correlations by the inverse of the square root of the product of the reliabilities leads to a similar overall pattern of results. Scatter plots of teacher effects across outcomes. Solid lines represent the best-fit regression line. Standard errors in parentheses. See Table 4 for sample sizes used to calculate teacher effect estimates. The sample for each correlation is the minimum number of teachers between the two measures.
Examining the correlations of teacher effect estimates reveals that individual teachers vary considerably in their ability to impact different student outcomes. As hypothesized, we find the strongest correlations between teacher effects within outcome type. Similar to Corcoran, Jennings, and Beveridge , we estimate a correlation of 0. We also observe a strong correlation of 0.
Comparatively, the correlations between teacher effects across outcome type are much weaker. Examining the scatter plots in Figure 1 , we observe much more dispersion around the best-fit line in Panel B than in Panel A. Given limited precision of this relationship, we cannot reject the null hypothesis of no relationship or rule out weak, positive or negative correlations among these measures. For example, teachers might make students happy in class in unconstructive ways that do not also benefit their self-efficacy or behavior.
At the same time, these correlations between teacher effects on Happiness in Class and the other two survey measures have large confidence intervals, likely due to imprecision in our estimate of teacher effects on Happiness in Class. Thus, we are not able to distinguish either correlation from the correlation between teacher effects on Behavior in Class and effects on Self-Efficacy in Math.
The teacher effectiveness literature has profoundly shaped education policy over the last decade and has served as the catalyst for sweeping reforms around teacher recruitment, evaluation, development, and retention. Our study extends an emerging body of research examining the effect of teachers on student outcomes beyond test scores. These findings suggest that teachers can and do help develop attitudes and behaviors among their students that are important for success in life. By interpreting teacher effects alongside teaching effects, we also provide strong face and construct validity for our teacher effect estimates.
We find that correlations between teacher effects on student outcomes that aim to capture different underlying constructs e. These findings can inform policy in several key ways. Most existing measures, including those used in this study, were developed for research purposes rather than large-scale testing with repeated administrations. Open questions remain about whether reference bias substantially distorts comparisons across schools.
Similar to previous studies, we include school fixed effects in all of our models, which helps reduce this and other potential sources of bias. However, as a result, our estimates are restricted to within-school comparisons of teachers and cannot be applied to inform the type of across-school comparisons that districts typically seek to make. This line of research shows promise but still is in its early phases. Further, although our modeling strategy aims to reduce bias due to non-random sorting of students to teachers, additional evidence is needed to assess the validity of this approach.
Our findings suggest that specific domains captured on classroom observation instruments i. One benefit of this approach is that districts commonly collect related measures as part of teacher evaluation systems Center on Great Teachers and Leaders, , and such measures are not restricted to teachers who work in tested grades and subjects.
However, in order to leverage these measures for instructional improvement, we add an important caveat: A single overall evaluation score lends itself to a systematized process for making binary decisions such as whether to grant teachers tenure, but such decisions would be better informed by recognizing and considering the full complexity of classroom practice.
Creating a teacher workforce skilled in most or all areas of teaching practice is, in our view, the ultimate goal. However, this goal likely will require substantial changes to teacher preparation programs and curriculum materials, as well as new policies around teacher recruitment, evaluation, and development. In middle and high schools, content-area specialization or departmentalization often is used to ensure that students have access to teachers with skills in distinct content areas.
Similar approaches may be taken to expose students to a collection of teachers who together can develop a range of academic skills, attitudes and behaviors. Viewing teachers as complements to each other may help maximize outcomes within existing resource constraints.
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Finally, we consider the implications of our findings for the teaching profession more broadly. However, if there indeed is a negative causal relationship, it raises questions about the relative benefits of fostering orderly classroom environments for learning versus supporting student engagement by promoting positive experiences with schooling. Our own experience as educators and researchers suggests this need not be a fixed tradeoff.
As our study draws on a small sample of students who had current and prior-year scores for Happiness in Class , we also encourage new studies with greater statistical power that may be able to uncover additional complexities e. We find that both math-specific and general teaching practices predict a range of student outcomes. Striking the right balance between general and content-specific teaching practices is not a trivial task, but it likely is a necessary one. The research reported here was supported in part by the Institute of Education Sciences, U. The opinions expressed are those of the authors and do not represent views of the Institute or the U.
Additional support came from the William T. Estimates drawn from all available data. Loadings of roughly 0. This range is due to the fact that some survey items were not available in the first year of the study. Further, in analyses relating domains of teaching practice to student outcomes, we further restricted our sample to teachers who themselves were part of the study for more than one year, which allowed us to use out-of-year observation scores that were not confounded with the specific set of students in the classroom.
Descriptive statistics and formal comparisons of other samples show similar patterns as those presented in Table 1. In the second and third years, each of the two factors has an eigenvalue above one, a conventionally used threshold for selecting factors Kline, Even though the second factor consists of three items that also have loadings on the first factor between 0. In the first year of the study, the eigenvalue on this second factor is less strong 0. In these instances, we created final scores by averaging across all available information.
By asking students to provide explanations of their thinking and to solve non-routine problems such as identifying patterns, the low-stakes test also was similar to the high-stakes tests in two districts; in the other two districts, items often asked students to execute basic procedures. Teachers were allowed to choose the dates for capture in advance and directed to select typical lessons and exclude days on which students were taking a test. Factor analyses of data used in this study showed that items from this dimension formed a single construct with items from Emotional Support Blazar et al.
Given theoretical overlap between Classroom Instructional Support and dimensions from the MQI instrument, we excluded these items from our work and focused only on Classroom Emotional Support. First, including prior low-stakes test scores would reduce our full sample by more than 2, students. Further, an additional students were missing fall test scores given that they were not present in class on the day it was administered. Second, prior-year scores on the high- and low-stakes test are correlated at 0.
Third, sorting of students to teachers is most likely to occur based on student performance on the high-stakes assessments since it was readily observable to schools; achievement on the low-stakes test was not. Ultimately, we prefer the random effects specification for three reasons. First, it allows us to separate out teacher effects from class effects by including a random effect for both in our model. Second, this approach allows us to control for a variety of variables that are dropped from the model when teacher fixed effects also are included.
Given that all teachers in our sample remained in the same school from one year to the next, school fixed effects are collinear with teacher fixed effects. In instances where teachers had data for only one year, class characteristics and district-by-grade-by-year fixed effects also are collinear with teacher fixed effects.
As expected, the variance of the teacher fixed effects is larger than the variance of teacher random effects, differing by the shrinkage factor. When we instead calculate teacher random effects without shrinkage by averaging student residuals to the teacher level i. While achievement outcomes have roughly the same reference group across administrations, the surveys do not. That said, moderate year-to-year correlations of 0.
Comparatively, year-to-year correlations for the high- and low-stakes tests are 0. Thus, scores vary across time. In both sets of analyses, we found no evidence for a non-linear relationship. Given our small sample size and limited statistical power, though, we suggest that this may be a focus of future research. Estimates of the relationship between the other three domains of teaching practice and low-stakes math test scores were of smaller magnitude and not statistically significant. Differences between the two studies likely emerge from the fact that we drew on a larger sample with an additional district and year of data, as well as slight modifications to our identification strategy.
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