Using digital technologies in the science classroom to promote conceptual understanding.

For decades some have asserted that digital technologies can promote more meaningful learning in science. Yet, these assertions have not been sufficiently documented with classroom data. This article reviews findings from a study that examined interactions between learners, instructors, and technology tools in an introductory college physics course. (The course had been designed to engage students in investigation supported by computer tools.) The study substantiated assertions, that: (a) Dynamic matching of supplied graphs of motion and time can engage students in cognitive conflict, enables discussions of alternative explanations, and promotes conceptual growth and understanding; (b) Technology tools can provide students with means to rapidly gather, reflect on, and analyze trends in data and related representations; (c) Technology tools can provide students with means to rapidly evaluate hypotheses with multiple representations. Data gathered in the study also offered evidence that multiple representations responsive to adjustments in variables controlled by students can enhance their understanding. The dynamic representations made possible by MBL tools enabled students to negotiate meanings and to examine alternative explanations throughout the course. The article also discusses ways that teachers could increase the effectiveness of the student dialogue to promote more effective conceptual learning.

A Historical Perspective

Science educators have been exploring the use of digital technologies and computer enhanced curriculum materials since the 1 960s in attempts to improve the effectiveness of science education. Lunetta and Dyrli (1970), for example, published an article in Science Education that outlined the multiple kinds of computer applications in science teaching that seemed to make the most sense at the beginning of the 1970s when secondary schools and students did not have easy access to calculators and when they were just beginning to have access to time-sharing on mainframe computers. Applications discussed in that article included: providing classroom administrative support for teachers and "individually prescribing" problems and lessons for students based on the individual student's understanding and skill; engaging students in computer programming to help them describe and understand science problems and to analyze data from investigations; and engaging students in "simulated experiments." In that article, Lunetta and Dyrli suggested that using programming tools would be "even more meaningful if the computer terminal were capable of displaying the solutions graphically." They predicted that "it is most valuable for a student to see the effects on a curve when he changes particular variables." In 1970 when the article was published, CRTs and computer-based data sensing devices would become available for use in school science classrooms only in the distant future; the input-output device in most schools was a time-shared teletypewriter terminal not easily amenable to graphic manipulation!

In the early years of computers in school science, there were only limited efforts to use computer tools with special attention to promoting meaningful, conceptual understanding of science. One such early study conducted in the late 1960s and early 1970s reported the evaluation of a series of "simulation dialogues" using computer capabilities that appeared to have special potential for enhancing students' scientific understanding (Lunetta & Blick, 1973). In those years studies usually examined students' learning using quantitative methodologies that compared pre--posttest difference scores but seldom examined the nature of students' engagement and the nature of specific activities with computer tools that might be especially linked with students' cognitive development. The examination of learning outcomes is very appropriate, but with many new ways to engage students in using computer tools today and with the development of qualitative research methodologies, it has become increasingly important to examine t he nature of student engagement that may increase the probability of enhanced conceptualization and skill.

Contemporary Goals and Needs

The National Science Education Standards (National Research Council, 1996) advocated a shift in emphasis from "focusing on student acquisition of information" to "focusing on student understanding and use of scientific knowledge, ideas, and inquiry processes" (p. 52). Proponents have argued that digital technologies should promote more meaningful learning as envisioned in the National Science Education Standards. To that end, technology tools should extend and expand explorations throughout the science curriculum. They should not simply enable the ritualistic manipulation of algorithms and rote learning; instead they should promote problem solving and the understanding of scientific concepts.

Enhanced methods of improving students' understanding of scientific concepts and their applications must be developed and utilized, in general, if we are to move beyond the low levels of scientific literacy and the less-than-scientific conceptions that have been so well documented in the last two decades of the twentieth century and to achieve the shift of emphasis advocated in the National Science Education Standards. Advocates have argued that computer technologies offer new tools that can assist teachers in promoting increased understanding in the science classroom. Digital technologies, when part of an appropriate learning environment, have an important role to play in providing experiences and information that can promote conceptual understanding for students in contemporary science classrooms.

The Need for Research to Inform Curriculum Development and Teaching Practice and the Study Reported

In spite of the arguments that have been advanced for many years about the potential positive effects of computer technology tools in science classrooms, contemporary research offers relatively little information that can be used directly by teachers and curriculum developers to improve science curricula and teaching practices. Some studies reported in the literature today continue to examine learning outcomes using quantitative research methodologies--often with multiple choice paper and pencil tests. (See, for example, Pena & Alessi, 1999.) While that kind of research provides some of the important information needed in the field, it does not usually provide the complementary qualitative, practical information that teachers and curriculum developers also need to help them understand how to improve the effectiveness of teaching practices that engage students with computer tools and other curriculum resources to result in more meaningful, conceptual learning.

This article reviews selected findings from a recent, qualitative research study that observed and identified the interactive processes employed by learners and instructors in an introductory college physics course as they attempted to solve investigation activities. The course had been especially designed to engage students in regular investigation and problem solving supported by computer tools to enable them to develop enhanced conceptual understanding of physics that has been shown to be so elusive in the contemporary physics and science education literature. The research discussed in this article gathered data about the nature of student interactions with technology tools, with each other, and with instructors that can be used by teachers and curriculum writers to change instruction in ways that increase the probability of more meaningful conceptual learning.

Digital Technologies May Enhance Understanding in the Science Classroom

Consistent with the means and ends advocated by the National Science Education Standards (National Research Council, 1996), Salomon, Perkins, and Globerson (1991) suggested that the computer should be used to promote "mindful engagement" that will act as a starting point to help the learners build knowledge. Jonassen, Carr, and Yueh (1998) argued that technologies should be used as "Mindtools" to enhance "cognitive learning." When using technologies as Mindtools, students use "computer-based cognitive tools and learning environments that have been adapted or developed to facilitate critical thinking and higher order learning." In the science classroom, for example, digital technologies can provide students with instantaneous visual information about the data being gathered in their investigation, giving them time to reflect on the investigation they have just performed. This instantaneous feedback can allow the learners to modify their hypotheses, test alternative hypotheses, and evaluate the results (Friedle r, Nachmias, & Linn, 1990). Digital technologies can provide multiple representations of information such as tables, graphs, simulations, and video animation, which may allow learners to achieve improved outcomes by providing students with a variety of representations to use in evaluating their ideas and their understanding of the information they have generated in their recent investigation. These choices may reinforce learning by enabling students to use their unique learning styles. Mokros and Tinker (1987) have suggested that digital technologies can provide a real-time link between a concrete experience and its symbolic representation, thus providing a link between the student's formal and concrete operations, to use Piagetian terminology. Mokros and Tinker are among many in the literature who have suggested that digital technologies can allow the learner to collect and analyze real data that can provide a more meaningful scientific experience for the student. However, there is a need to confirm these cl aims with carefully conducted studies in contemporary classrooms and to infer more specific recommendations for those who will use digital technologies in their teaching.

While much has been written about the potential impact and power of new technology tools in school science and about ways to use those tools to promote learning, high technology tools have been available for use in science classrooms for only a limited number of years. As noted earlier, one result is that there continues to be insufficient research designed to examine how teachers and students can use new technology tools to promote more meaningful learning and to enhance students' conceptual understanding in the science classroom. This article reports information from a study that examined university students' learning of mechanics in an introductory physics course in which students were heavily engaged in problem solving that included regular use of high technology tools. The study produced substantial documentation that supports some of the recommendations that have been proposed in the literature about optimal ways to use high technology tools to promote meaningful learning in science.

THE CLASSROOM LEARNING ENVIRONMENT AND THE RESEARCH STUDY

Dynamic Physics

In response to concerns that the standard lecture-recitation style of teaching was not providing students with a deep conceptual understanding of physics, a senior physics professor in a large university worked with colleagues and staff to design a mechanics course responsive to cognitive problems described in the physics education literature. The course that emerged was called Dynamic Physics, and it involved students in substantial amounts of investigating and problem-solving during class time using high technology tools. A classroom was renovated to contain computer/laboratory stations that would accommodate teams of three students each. Each student team was intended to be a group that would work together collaboratively to solve the inquiry-based investigations that formed the core of the new Dynamic Physics curriculum. Each student team was to engage in specially prepared authentic investigations and each had easy access to Microcomputer Based Laboratory (MBL) tools.

Figure 1 shows that inquiry-based investigations formed the core of the curriculum around which all the lectures and collaborative laboratory activities were interwoven. During each class, the student groups functioned in an activity-based, laboratory environment with computer technology tools available to each group. The investigation activities paralleled a category of learning tasks that Slavin (1996) described as "controversial tasks without single answers" (p. 59). This type of task contains a high level of "cognitive complexity" with no immediately apparent path to a solution or to one correct answer. These tasks or activities are likely to promote differences of opinion leading to increased discussion (Slavin, 1996). The investigations in Dynamic Physics were specifically designed so the groups of learners would have to work together to find solutions to the problems posed. These investigations were organized to cause the students to apply physics concepts in real world situations such as automobile ac cidents, rocket launches, bungee jumps, carnival rides, and so forth. To solve these investigations, the students had to go beyond the typical manipulation of mathematical algorithms that occurs so regularly in problem-solving in conventional physics courses; they used MBL hardware and software to collect and analyze data. In beginning their investigations, the students in each group had to come to agreement on the questions to be investigated, on predictions and hypotheses they had, and on the procedures they would use. In many class sessions, short interactive lectures provided students with a scientific basis for the investigations and activities that were to follow and introduced relevant materials and resources available in the laboratory-classroom, on the Internet, and in other accessible locations. The professor who led the short interactive lectures often posed questions to the students to engage them more personally into the ideas being examined and to stimulate them to suggest explanations and to as k questions. The lectures in Dynamic Physics were intended to occupy less than one-fourth of the class time so the majority of the students' time could be dedicated to activities associated with solving the investigations (Figure 1).

THE RESEARCH STUDY

As noted earlier, the qualitative research study observed discussed here identified interactive processes employed by the learners enrolled in Dynamic Physics as they attempted to solve the investigation activities and develop conceptual understanding of physics. A separate study (Mackin, 1998) showed that students in the course had made above average achievement gains when compared with similar students in conventionally designed introductory mechanics courses. Mackin's study based these claims on pre and posttesting of the students in the course using a variety of prepost measures including a modified form of the widely reported Force Concept Inventory (Hestenes, Wells, & Swackhammer, 1992). In contrast, the research study reported in this article examined the nature of the students' interactions with each other, with the instructors, and with the technology tools, and it examined the nature of their understanding. The role of group interactions in the construction of understanding was carefully examined a s the students worked within the technology rich classroom environment of the Dynamic Physics classes using modified case study methodology (Bower & Clapper, 1989). Continuous data collection (Niedderrer, 1997) in a natural field setting was used to identify the technology applications used by the learners and the aspects of those applications that seemed to promote the construction of conceptual understanding. Video tapes of students in a selected group were a primary data source for the development of the detailed descriptive study. Video recordings were made throughout the entire semester of the research study on learning referenced here, and there was evidence that the students were not inhibited by the presence of the video tape recorder. Subsequently transcriptions were made from the video tapes. The transcripts enabled the researcher to isolate elements of the student interactions and to observe carefully what was occurring in order to describe and explain aspects of the learning environment. These dat a were analyzed to examine the role of the interactions between the students, the students and the instructors, the students and the technology tools, and the students and the problem presentation. As patterns of interactions involving the students in the selected group evolved within the transcribed data, they were categorized and coded. The coded categories were then organized into episodes that were representative of the type of interactions observed during the semester of study. The episodes formed the principal data source for the development of the assertions that follow.

ASSERTIONS DEVELOPED FROM STUDENTS' USE OF TECHNOLOGY TOOLS

The research on learning during the Dynamic Physics investigations provided concrete observations of students working toward negotiated meanings of motion concepts and increased conceptual understanding through the use of digital technologies. Analyses of these observations supported assertions substantiated for some assertions made earlier about the importance and use of technology tools in facilitating student learning and to promoting understanding of physics concepts. Although many assertions could be developed about the use of technology tools to facilitate learning, this article will discuss two assertions that appeared to be particularly important and supported by data gathered in the research.

Assertion 1. Dynamic matching of supplied graphs of motion and time can engage students in cognitive conflict, enables discussions of alternative explanations, and promotes conceptual growth and understanding.

As shown in the annotated transcript that follows the students were trying to match the graphs that were provided in a rotary motion investigation. The first part of this investigation was intended to provide experience for the students in the use of a high technology rotary motion sensor and let the students gain first hand knowledge of the motion of a spinning disk. The students were to make observations and inferences about rotary motion in terms of angular position, angular velocity, and angular acceleration. In this part of the investigation activity, the students were provided with a series of graphs of rotary motion that represented angular position, angular velocity, and angular acceleration versus time. The students were then asked to use the rotary motion sensor to try to match the graphs that were provided. To match the graphs, the students had to rotate the disc shaped handle on the top of the rotational motion sensor either clockwise or counter-clockwise while attempting to match the distance, s peed, or acceleration graphs.

While the students tried to match a particular graph, the computer displayed the results of their motion on the screen, and they were able to compare the graph of their motion with the graph that had been provided. In the annotated transcript segment that follows, a student group tried to match an acceleration versus time graph that was provided to them in the investigation activity. The students were to try to match the graphs they were making to the graphs provided with the activity.

S3  What am I doing? Bottom one     S3 is asking which graph on
                                    the screen display he is to
                                    match for the group.
S1  Bottom one (line 269)
S2  What are you doing?             S2 cannot find the curve or
                                    line that S3 is generating
                                    (in real time).
S3  The bottom one? (line 273)
S2  Where are you? (line 275)       S2 still cannot find the curve
                                    that S3 is generating.
S1  You, let me do this             [taking the probe away from
                                    S3]
S3  What? You do. All you do is     S3 tries to explain to S1 what
    go back and forth.              he thinks needs to be done to
                                    match the graph that has been
                                    provided.
S1  Nope. Go                        S1 disagrees and tells S2 to
                                    start the computer data
                                    recording.
S3  What the heck are you doing?    S3 is watching S1 make real time
    (line 283)                      graphs on the screen and seeking
                                    clarification. (Asking why?)
S2  You like average out whatever   S2 shares what he thinks need
    you did there. (line 285)       to be done and offers an
                                    explanation.
S3  You don't make these [pointing  S3 tries to explain that he
    at the graph S1 is making on    thinks the thinner lines on
    the screen] lines with this     the graph are there only for
    thin line. You don't go up to   reference and do not display
    here. (line 288)                the motion.
S2  Fast.                           S2 tells S1 to turn the probe
                                    quickly to get to the next
                                    point, that is what will make
                                    the thin lines that S3 is
                                    referring to.
S3  Jump to here. Jump to here.     S3 is agreeing with S2 in that
                                    the probe needs to be turned
                                    rapidly so that the line just
                                    jumps from place to place.
S1  Well, just trying to imitate    S1 explains that he is trying
    the graphs (Lines 267-294)      to match the curves that had
                                    been provided in the
                                    investigation guide.

As seen in line 285 (1) where S2 says "you like average out whatever you did there," though inexactly expressed, students were critically examining the motions they had made and how those motions could be modified to produce the acceleration graph that was to be matched. The learners must critically examine their previous conceptions of what type of motion would produce the desired graphical representation and then modify these conceptions to meet the current information. The cognitive conflict caused by the need to modify the students' prior conceptions also caused some frustration as can be observed in line 277 where student S1 takes the equipment from the hands of student S3, and also in line 283 where S3 says "What the heck are you doing?" The immediate feedback provided by the real-time graphical representation of motion using the probe and attempting to match a graph provided by the investigation guide or instructor may be a powerful tool in promoting conceptual change and understanding of physics moti on concepts.

The video tapes and other evidence suggests that the students were communicating comfortably with one another, and the video taping did not appear to inhibit their interactions in any way. Note that although the students seemed to understand the meaning of what was said, the conversation between the students was very imprecise and incomplete. This was seen in line 285 where S2 answers the "Why?" question of S3 even though it was not explicitly stated. The students often spoke in a kind of verbal shorthand supplemented with nonverbal motions, such as pointing at the video screen and at the apparatus used in the investigation. The transcript also suggests, however, that the students were not exchanging information and their personal explanations especially well at this point. If an important goal of having the students work in small groups is to have them efficiently share and develop conceptual understanding, then they will need to ask specific questions and to share ideas and hypotheses, and so forth, for th e questions they are considering. The cognitive conflict caused by the investigation did help the students examine and try out revised hypotheses and take the probe from other members of the group as observed in line 277. However, analysis of the transcript suggested that for greater effectiveness in attaining the conceptual understanding and the collaboration that has been sought, in the future the instructor would find it useful to invest more time helping the students improve their verbal communication skills and the effectiveness of their overall collaboration. That might include asking the students to take the time to explain their questions and hypotheses about a particular part of the investigation to one another and explain the physics concepts that caused them to have the questions and hypotheses.

In spite of the communication problems that have been noted and that should be addressed in future work of this kind, matching graphic representation of motion in "real-time" was a very powerful learning experience for the students. Students in conventional physics classes are often provided with lectures, discussions and/or demonstrations of various types of motion in physics and physical science classes. Much less often have the students been provided with the opportunity to experience the motion for themselves or attempt to recreate a specific type of motion. As the students in this episode struggled to find the appropriate movements that would match specific graphic representations (e.g., lines 283--294), they were forced to reflect on the shape and the nature of the graph that represented the current state of motion and on how the motion could be modified to match specific motions and the graphic representations of those motions that were sought. The data gathered in this study provides some evidence th at supports the predictions made earlier that new technology tools can provide experiences for students that can enhance their understandings of concepts of displacement, velocity, acceleration, and of some of the relationships between force and motion.

The brief transcript excerpt shown in this section was selected from an activity in which students were matching graphic representations of angular motion. The hardware and software with which students can examine more elementary linear motion is relatively inexpensive and easy to acquire. There is evidence that it should have an important place in introducing students to kinematics concepts (motion).

Assertion 2a. Technology tools can provide students with means to rapidly gather, reflect on, and analyze trends in data and related representations.

The technology tools allowed the students in Dynamic Physics to make rapid repetitions of the data collection that they decided were necessary to find solutions to the investigations. With the aid of the technology tools, the students were able to change one variable of their agreed upon collection strategy while holding the other variables constant. That is, they could make multiple runs during relatively short time while adjusting one variable and observing the effects of the change on dependent variable. The annotated transcript excerpt from the research study supports this assertion. The transcript was generated during a Tractor Pull Investigation in which the students were trying to find the optimum angle to use in a tractor pull competition like those still common in rural country fairs.

The Tractor Pull Investigation is based upon the country fair tractor, ox, or pony pull, where a heavy load is placed on a sled which in turn is pulled by the tractor, oxen, or ponies using a rope or chain attached to the front of the sled. The pull is won by the tractor, ox, or pony that pulls the sled farthest in the shortest time. In the following annotated transcript excerpt the students in the group had been discussing the ways they could collect data that would lead to a solution to the investigation. The question to which the students were trying to find a solution was whether there is an optimum angle at which to apply a force to the load. Prior to the transcript excerpt, they had decided that they should try to pull the sled with a constant force at a series of angles that they would vary in different trials.

S1  Multiples of 10, or what? Should we  SI is asking about the measure
    do 10s, 5s, or what?                 of the angles they used to
                                         collect data.
S3  What did we do? Did we do 30?        S3 asked if they had collected
                                         data at 30 degrees.
S1  We did 30. Do 15,30,45? (line 1376)  SI confirms that they already
                                         collect at 30 degrees and ask
                                         if they should they now
                                         collect data at 15, 30, and 45
                                         degrees
S3  Was any of the data different? Was
    say the data different Let's say
    the 30. [pointing the screen]
    (lines 1378-1379)
S2  Let's look for it, we have the       S2 says that they can look for
    graphs. (line 1381)                  the data at 30 degrees because
                                         they have saved the graphs of
                                         the data on computer.
S1  And we can just change.              S1 infers that they could just
                                         change the angle and redo the
                                         graphs with the new data.
S2  Right I'm going to save this one.    S2 agrees and saves the
    How [inaud] am I going to save it    current data to a disk and not
    as [he does this to a disk] OK. Now  the hard drive. He then asks
    let's do 40? Are we going to do it   what will be the series of
    10's, 5's?                           angles they will collect data
                                         for.
S1  15 and 45. (line 1389)               Measure of the angles at which
                                         he will apply the force to the
                                         cart.
S2  So we gotta do 15 next Right?
S1  Yeah. 15.
S2  OK. Let's get to it then.
    (line 1395) (Lines 1372-1395)

The technology allowed the students to make repetitions of the data collection that they decided was necessary to find a solution to the investigation rapidly. (In this case, they were using a constant force while varying the angle at which they were applying the force.) With the technology, the students in the group changed one variable (the angle of the applied force) while keeping the applied force and other independent variables constant. They then measured the effects of the angle of application of the force on dependent variables such as speed and acceleration. The students were able to make and test a number of different ideas about the data they needed to collect and how they should collect it to answer the questions they were investigating. In this case, the students decided to collect data using angles that differed by 5 degrees and ranged from 0 to 45 degrees. These decisions were worked out during the interactions that occurred in the vicinity of lines 1376-13 89. If one method of collection did not seem to be effective in pursuing the investigation, they quickly changed their strategy and collected data using a new or modified method. The technology played a key role in allowing the students to modify their hypotheses about the investigation and then to change their data collection techniques and analysis quickly and easily to evaluate their new hypotheses. For example, in the tractor pull investigation, the students could keep the force constant and measure the acceleration at various angles of application of that force, or they could keep the velocity or acceleration constant and vary the angle of force application and observe what magnitude of the force was needed to do that. The students discussed the alternatives, but these discussions are not shown in the transcript excerpts included here. The technology provided the students in the group with the time and means to critically reflect on and evaluate their data collection techniques and corresponding data. In line 1378 student S3 asked "Was any of the data different?" and student S2 answered "Let's look for it; we have the graphs." (line 1381) The MBL tools and the ways they were used by the students (under the guidance of the instructor and the investigation guide) enabled the students to reflect upon and to evaluate the data critically and quickly (see for example lines 1378-1381). As a result, they were able to engage in cognitive processing such as critical reflection, reflective abstraction, or co-construction of understanding, that appeared to promote the deeper conceptual understanding that was documented through pre and posttesting in the Mackin (1998) study.

The instructor is to be complimented for engaging students in investigations such as the Tractor Pull that enabled them to examine the effects of multiple variables on an authentic problem and that engaged them simultaneously in thinking about and developing important physics concepts. However, as previously mentioned, the videotape transcript also suggests that the learning experience might have been more effective had the instructor assisted the students in improving verbal communication skills. Transcripts from the study suggest that students would have engaged in more effective dialogue about physics concepts (metacognitive behavior) if they had been more consistent and complete in expressing their understanding of the physics concepts. Silence can be important at times, but in dialogue like that in the vicinity of line 1389, it is important for students to share ideas more precisely and to question the understanding of their teammates. In this particular case, there appears to have been evidence that ea ch student did understand what the other students meant in spite of the cryptic verbalization. However, there were other times in this research study when students probably misunderstood each other. The misunderstandings probably would have been identified more quickly (by the students in the group as well as by the teacher) had the students been expressing themselves with greater technical precision. If the teacher were to ask the students, for example, to always specify units when discussing numeric variables, that process could help members of each group, as well as the teacher, identify alternative ideas, for example, when a student is discussing acceleration but using units of velocity. (This problem/teaching opportunity was observed in several of the videotape transcripts prepared in the study.) Although limited verbalization may have indicated a degree of intersubjectivity on the part of the students in this group, it probably would have been beneficial for students who are to collaborate in a group to share ideas precisely and question each other's understanding. Here the instructor has an important role to play in promoting more precise, technical dialogue among the students that is likely to improve their ability to question one another, learn from one another, and develop scientific understanding more efficiently.

Assertion 2b. Technology tools can provide students with means to rapidly evaluate hypotheses with multiple representations.

To examine this assertion we will return to the rotary motion investigation discussed in the first videotape transcript. Based on prior knowledge of motion and time graphs, each student had his/her own hypothesis about how to match the position, velocity, and acceleration versus time graphs that had been provided in the investigation. In the excerpt from the following videotape transcript the students were taking turns operating the rotary motion probe in an attempt to match the position versus time graph that had been provided.

S2  Yeah, when they're getting from      [motioning with his hand
    here, to here, it's going fast. Try  in a turning motion
    positive, try negative, positive.    first clockwise then
    Positive stop, let it go. Negative   counter clockwise]
    stop, let it go.                     He is trying to show
                                         how to turn the probe
                                         and match the velocity
                                         vs. time graph.
S1  This position. (line 304)            The position of the line
                                         on the graph
S2  Yeah, That's not angle, beta's       S2 elaborates what he
    angle. Like every Time you turn      meant as they look at
    it if your going up you're           the graph.
    going up another degree here.
    180 right here. You just, you
    just you see what.
S3  Move it. Move it. It's just like     S3 tells S1 rotate the
    going back and forth.                motion probe handle
    (line 310)                           clockwise and counter-
                                         clockwise.
S1  Try it again.
S3  Trying to match the top.             S3 tells S1 they are
                                         trying to match the
                                         top graph (position
                                         versus time)
S2  All right, now stop. (line 316)
S3  Smaller, We're going radians         [refering to the scale
                                         on the graph they are
                                         producing] and meaning
                                         turning it through
                                         a smaller angle

S2  No, but what I'm saying,
    this'll go to
S3  I know.                              Although not expilcitly slated,
                                         they share an understanding of
                                         how the probe seems to operate.
S2  That's all it is. Up, down, up.      S2 confirms that the graph is
    That's all it is. Now, what          produced by the clockwise and
    do we have to do? (line 326)         counter-clockwise rotations of
                                         the probe's handle.

In this transcript segment all three students in the group appeared to have and to share ideas about what motion would match the graph provided in the investigation. It should also be noted that so far, none of the students had been successful in their attempts to match the velocity versus time graph provided. The students were able to make and test a number of different hypotheses about the type of motion that was needed to produce the required graphical representation. The technology tools played a key role in enabling the students to modify their explanations and hypotheses underlying the outcomes of their investigation while they were conducting it. They were able to make and evaluate new hypotheses quickly and easily by viewing and then reflecting on the meaning of the instantaneously generated graphical representations. This dynamic adjustment of hypotheses and understanding during the investigation was made possible as a result of the instantaneous graphic representations on the computer screen of the rotary motion displayed as displacement, velocity, and acceleration versus time with a common time axis of the moving object. The data gathered in the study of the Dynamic Physics learning environment offered direct evidence to support the assertion that appropriate multiple representations that are responsive to instantaneous adjustments in variables controlled by student investigators can enhance the students' understanding.

The ability to examine multiple representations had been suggested as a way to promote learning for understanding by Lehtinen and Repo (1996). They had written: "our hypothesis is that it is the use of multiple representations and the continuous movement between different representational formats that encourage students toward reflective abstraction, especially when they are learning abstract concepts" (p. 110). Reflective abstraction is a process of knowledge construction described by Piaget as a means of developing deeper and more abstract levels of knowledge. Lehtinen and Repo (1996) suggested that reflective abstraction "refers to a process in which the student tries to construct abstract structures and operations by reflecting on his or her own activities" (p. 108). The learners in this study struggled to find the appropriate movements that would match the graphic representation they were trying to match. In this case, they did not clearly verbalize their ideas about the underlying conceptual relationshi ps between the variables and their representations in the particular transcripts excerpted here.

Perhaps they were not consciously considering the conceptual connections. Nevertheless, the visual representations of their data made possible by the instantaneous data measurement and computer processing enabled the students to reflect critically on the shape and meaning of the velocity-time graph that resulted from different kinds of motion and to explain and hypothesize how the motion they were producing could be modified to match the desired representation provided by the investigation guide and software. The progress in student understanding promoted by the instantaneous graphic display of motion was observed in comments made by S2 when the group was engaged in trying to match an acceleration versus time graph. Starting in lines 299-302, the student states that the probe must be rotated "positive, stop, let it go; negative, stop, let it go. After some discussion both on task and off, S2 then said: "It's something like that. Have to bring it back up really fast. Oh, that was it, I almost had it" Again aft er some on and off task discussion, S2 said: "Ah, you just kinda, have to accelerate really fast." In this example the student went from simply stating that the probe handle had to be rotated positive and negative, to stating that it needed to accelerate rapidly. The student (S2) reflected on his activity made in rotating the motion probe handle "positive and negative" and constructed abstractions about the nature of rotational acceleration and motion more generally. These active learning experiences appeared to enhance conceptual understanding.

DISCUSSION OF FINDINGS

Throughout the investigations that formed the core of the Dynamic Physics course, the technology tools created an environment in which the students were able to engage in discussions that made their private thoughts overt so their ideas could become more accessible for personal reflection, comparison with the data, and examination and discussion by other members of the group. In the data that supported Assertion 1, the discussions among students resulted in some cognitive conflict that caused them to disagree about the motions that would best match a particular graph provided in the Investigation Guide. Similar student interactions have been described in the literature as "Ideational Confrontation" (Champagne, Gunstone, & Klopfer, 1985), an instructional strategy that can promote conceptual change and deeper understanding. In Assertion 2a, data from the learning study indicates that the technology enabled the students to have sufficient time and information to reflect on the data they were collecting. The te chnology freed the students from the time consuming tasks of collecting and operating on relatively small numbers of data points. The technology did this work for them, and, in a relatively short time, they were able to examine a large number of sizeable data sets that enabled them to reflect on and analyze the "goodness of fit" of the data with their expectations. Having and taking the time to reflect on and examine data can be an important part of developing deeper conceptual understanding associated with that data. Assertion 2b went beyond Assertion 2a in articulating that the students used multiple representation capabilities of the technology in evaluating successive hypotheses. The use of multiple representations can enhance student understanding (Blumenfeld, Soloway, Marx, Krajcik, Guzdial, & Palincsar, 1991) and engage the students in a process of reflective abstraction (Lehtinen & Repo, 1996) that promotes development of deeper and more abstract levels of knowledge. Facilitated by the technology tool s, the students engaged in discussions that were directly related to understanding the conceptual basis of the investigation activity and associated physics concepts. The dynamic representation of data made possible by the MBL tools facilitated students' discussions and enabled them to negotiate the meanings of the data and to discuss and examine alternative explanations and hypotheses at many points in this study. The comments on the rotational motion investigation referenced earlier in the article provide one example. The students were able to create and use a "construction zone for shared knowledge or meaning" (Roth, 1995). Enabling the development of such a construction zone for understanding may well be a very important element in using digital technologies to promote more meaningful learning in the physical science classroom.

SPECIFIC APPLICATIONS OF TECHNOLOGY TOOLS IN THE SCIENCE CLASSROOM

The research outlined briefly in this article provided data that enhanced and supported some assertions that have been made in earlier publications discussing the use of technology tools in science classrooms to support meaningful, conceptual learning. Today there is need to give special attention to ways these technologies can be used as tools to enable students to engage in higher order thinking and analyzing.

Digital technology tools can allow the learner to spend more time thinking about an investigation and its interpretation and less time performing repetitive measurements and calculations (Friedler, Nachmias, & Linn, 1990; Jonassen, 1995; Mokros & Tinker, 1987). Thus digital technologies tools can provide mechanisms through which learners can have more time for critical thinking and analysis of their investigations. However, it is very important to note that the availability of good tools is not enough. Students do not normally take the time, even if it is available, to reflect upon data and think critically about conceptual connections and explanation. In this class, the investigation reports required of each group had to include explanations of the physics concepts and the data sets gathered in the students' investigations that supported the findings they reported. Careful attention by the instructional staff to the inclusion of data based argumentation increased the attention given by students to the data sets they collected. The instructor's expectations were also an essential element in the time invested by students in the other investigations in the Dynamic Physics course studied in this research. Simply making good MBL hardware and software available to students in the science laboratory is not sufficient. If students are to use the time released by the speed and power of the new technology tools to reflect and engage in the dialogue that are elements of metacognitive processing that can lead to conceptual growth, the instructor needs to attend to these issues and establish a system of grading and assessment that rewards this kind of student activity and effort. Similarly, the investigation guide needs to provide guidance and suggestions that are consistent with the student reflection and processing that has been observed in this study and discussed briefly here.

Measurement Probes

Spreadsheets and multiple representations of collected data are useful for interpretations and analysis, whereas technologies allow for the rapid collection and storage of the initial data. Measurement probes of various kinds may be used to collect data over extended periods of time. Supporting software can present the data graphically, freeing up the learner to concentrate on analyzing what is happening and on other higher order operations (Mokros & Tinker, 1987). Motion sensing and force probes were used in the Dynamic Physics course in this manner to collect data over time. The collected information was then graphed, enabling the learner to observe trends in the force and motion data as a function of time. In this way the digital technology can provide a link between the concrete experience of measuring force and motion and the symbolic representation of that data. The technology enabled the learner to have an authentic scientific experience and to eliminate the tedious work of collecting the data and pro ducing the graph.

Other types of probes, including temperature probes, radiation detectors, light intensity probes, pulse monitors, and so forth, allow the learner to collect and graph data in real time and to directly observe changes instantaneously in the graphs. In other words, "real-time" graphing capabilities associated with digital technologies allow the learner to analyze how changes in an independent variable such as applied force, for example, will in turn influence a dependent variable such as acceleration or velocity (Mokros & Tinker, 1987; Blumenfeld, Soloway, Marx, Krajcik, Guzdial, & Palincsar, 1991). Learners can make and test hypotheses receiving almost instant feedback from visuals and graphics about the validity of their ideas. This feedback provides learners with opportunities to reflect upon the thinking that led to the development of a particular hypothesis. In this way students can quickly modify and refine their ideas, hopefully leading to enhanced conceptual understanding (Nickerson, 1995).

Spreadsheets, Graphics, and Associated Representations

Spreadsheets were developed to perform repetitive calculations to replace paper and book accounting records. New data and calculated variables can build upon the information gathered and stored in the spreadsheet earlier. The opportunity to manipulate and to graph information to be examined by students can be a highly beneficial use of spreadsheets in learning. This can allow the learner to engage in "what-if" scenarios. Student can test their predictions using the spreadsheet, and evaluate results quickly and easily (Blumenfeld et. al. 1991; Kuech 1999, Sounderpandian, 1989). One example of such spreadsheet use was visible in the Rotary Motion Investigation used in the Dynamic Physics Course and reflected in part earlier in this article. In that investigation students collected position versus time data from a video clip for a car (seat) on a ferris wheel and entered this data directly into an Excel spreadsheet. The students then manipulated the data to observe the graphic representations of "What if' quest ions such as: "What if the plot were velocity versus time?"; "Acceleration versus time?"; or "What if we used radians instead of degrees?." Spreadsheets were used by the students in many of the investigations they conducted in the Dynamic Physics course. For example, spreadsheets were also used in investigations (from video clips) of: Bungee jumps, rocket lift-offs, and amusement park rides, to cite only three examples. The "what if' ability of spreadsheets provides a means through which the spreadsheet technology can serve as a cognitive tool in learning. The student can evaluate the interrelatedness of the information provided and reflect upon how changes in initial data can cause changes in the outcomes (Mokros & Tinker, 1987). Learners improve their understanding of the calculations involved in the use of spreadsheets, because they are actively involved in identifying how the information is interrelated (Jonassen, 1995). Using spreadsheets can also enable students to simulate ideal functional relationship s and data and then to compare the ideal data with real data gathered in the laboratory.

In addition to demonstrating the interrelatedness of the original and derived data collected by the students, spreadsheet software allows the learner to display data in multiple forms that may make interpretation more visible. Spreadsheet applications allow for the movement between representations, such as from tables to charts and to graphs of various kinds. Multiple representations can enable the learner to make more critical interpretations about the trends in the data being analyzed and about relationships between variables as in the Rotary Motion Investigation discussed earlier in this article. Increased understanding of the meaning of graphical representations is also an important opportunity. A learner's ability to interpret a graph and to use a graph to explain phenomena is an integral part of the ability to communicate and to defend a scientific argument, or to develop an understanding of topics in science (Kuech, 1999, Mokros and Tinker, 1987, McDermott, et al., 1987), important goals outlined in t he 5: National Science Education Standards (NSES, p. 176).

Mokros and Tinker (1987) predicted that digital technologies and data collection probes were of benefit to students because: "It is very likely the combination of these four factors (multimodal reinforcement, real-time linking of concrete and abstract, meaningful context, and elimination of drudgery) that contributes to the power of learning through MBL." Through the use of multimodal reinforcement, students can pair motion with its graphical representation, or "see" and "hear" the pitch of a sound wave (Mokros & Tinker, 1987). Concepts can be represented in multiple, simultaneous modalities such as sound, graphs, color pictures, or video (Blumenfeld, et al., 1991). MBL can provide a link between the concrete and the abstract through the use of probes that instantly show a graph of changing position (motion), temperature, force, etc. (Mokros & Tinker, 1987; Blumenfeld, et al., 1991). This same link provides a meaningful context for the gathering and analyzing of real data.

IMPLICATIONS FOR SCIENCE TEACHING, CURRICULUM AND FURTHER RESEARCH

Research on the Dynamic Physics course provided substantial evidence that digital technologies can promote conceptual understanding when used as tools for learners who are investigating authentic, scientific problems in a learning environment that encourages articulation and discussion of questions, ideas, hypotheses, explanations, and reflective abstraction. Technology tools need to be available for data collection and analysis that allow for the real-time collection and display of data. Technology tools are needed that allow students to collect and/or analyze multiple sets of data rapidly and accurately test numerous alternate perceptions and hypotheses. Then these tools need then to be coupled with investigation / laboratory guides that require students to synthesize and express what they have learned about the relationships that were investigated. The rapid collection and analysis of data with the aid of technology tools allow the students time to critically reflect on the accuracy and appropriateness of the data. The ability to rapidly test alternate hypotheses allows the students to make explicit their private thoughts and opinions for open evaluation and possible modification. Using probes and dynamic matching of supplied graphs of motion in "real-time" were very powerful learning experiences for the students in our study. The students openly expressed the difficulty they experienced in trying to match the graphs that were supplied with the activity. This difficulty led to conflicting ideas and some frustration about how to accomplish their task. The cognitive conflict caused by the difficulty in matching the graphs and the open discussion of opposing ideas are important cognitive processes that are desirable in collaborative group learning. Slavin (1996) reported that from the developmental perspective "the opportunity for students to discuss, to argue, to present and hear one another's viewpoints is the critical element of cooperative learning with respect to student achievement" (p. 49). Technology tools allow students to make rapid repetitions of data collection that they may decide are necessary to find solutions to investigations. With the aid of technology tools, the students can change one parameter of the agreed upon collection methodology and then make multiple trials with different measures of that variable. Lehtinen and Repo (1996) suggested that when working with technology the "tools should help students externalize their idiosyncratic and informal hypotheses, and to compare these hypotheses with scientific concepts" (p. 110). Investigating and problem solving in collaborative groups with the support of technology tools within learning environments that encourage conceptual dialogue and reflection as observed in this study of Dynamic Physics can be especially facilitative in promoting increased understanding in science. Data from the research on Dynamic Physics suggests that teachers may promote more effective science learning if the students in small groups are encouraged to express themselves carefully and to question one another, elaborating their ideas thoroughly and explicitly. For example, when describing a variable, the students should always use units, when commenting on the numeric value of the variable. Continued research is needed to determine how technology tools can specifically promote learning for deep conceptual understanding and the kinds of investigation guides and curricula that support such meaningful learning.

Through continued study, more precise recommendations can be made that can guide teaching and the preparation of state of the art science curricula. In preparing the National Science Education Standards, the National Research Council, envisioned a shift in science education from acquisition of facts and information to understanding and the use of scientific processes. As new curricula are developed to suggest the shift toward more conceptually focused, authentic learning, the use of digital technologies as tools to promote student understanding needs to be incorporated carefully into curriculum design and teaching practices. Digital technologies should be designed within the curriculum as mindtools that promote critical thinking and as research tools that enable improved data collection and manipulation to promote conceptual understanding.

Consistent with the National Science Standards and relevant research, teachers must help students learn how to use digital technologies as valuable tools that can assist them in exploring scientific problems and in understanding. As teachers shift emphasis from a focus on learning science facts to promoting greater student conceptual and systemic understanding, careful use of digital technologies should play an important role in enabling more meaningful learning in school science.

Figure 1.

Distribution of class time across the semester

Quiz                       7%
'Lecture'-Discussion      18%
Investigation Activities  52%
Misc.                     11%
HmwkQuestionsDiscn        12%

Note: Table made from pie chart

Acknowledgement

The authors are indebted to Professor Paul Sokol, Department of Physics, The Pennsylvania State University for his excellent support and collaboration throughout the research study reported here.

Note

(1.) Line numbers referenced throughout the transcripts in this article identify lines in the transcript compiled throughout the semester and included in Kuech (1999).

References

Blumenfeld, P.C., Soloway, E., Marx, R.W., Krajcik, J.S., Guzdial, M., & Palincsar (1991). Motivating project based learning. Educational Psychologist, 27, 369-398.

Champagne, A.B., Gunstone, R.F., & Klopfer, L.E. (1985). Instructional consequences of students' knowledge about physical phenomena. In L.H.T. West & A.L. Pines (Eds.), Cognitive structure and conceptual change (pp. 61-68). New York: Academic Press.

Friedler, Y., Nachmias, R., & Linn, M. (1990). Learning scientific reasoning skills in microcomputer-based laboratories. Journal of Research in Science Teaching, 27(2), 173-191.

Hestenes, D., Wells, M., & Swackhammer, G. (1992). Force concept inventory. The Physics Teacher, 30, 141-158.

Jonassen, D. (1995). Computers as cognitive tools: Learning with technology, not from technology. Journal of Computing in Higher Education, 6(20), 40-73.

Jonassen, D.H., Carr, C., & Yueh, H. (1998). Computers as mindtools for engaging learners in critical thinking. Tech Trends, 43(2), 24-32.

Kuech, R.E. (1999). Student collaboration, investigation problems, and technology use in an innovative university mechanics class. Unpublished doctoral dissertation, Pennsylvania State University, University Park.

Lehtinen, E., & Repo, S. (1996). Activity, social interaction, and reflective abstraction: Learning advanced mathematical concepts in a computer environment. International perspectives on the design of technology-supported learning environments (pp. 105-128). Mahwah, NJ: Lawrence Erlbaum.

Lunetta, V.N., & Dyrli, O.E. (1970). Computers in the science curriculum. Science Education, 54, 147-154.

Lunetta, V.N., & Blick, D.J. (1973, Winter). Evaluation of a series of computer-based dialogs in introductory physics. Journal of the Association for Educational Data Systems, 33-42.

Mackin, J.E. (1998). Investigation of selected outcomes of the dynamic physics learning environment. Unpublished doctoral dissertation, Pennsylvania State University.

Mokros, J., & Tinker, R. (1987). The impact of microcomputer-based labs on children's ability to interpret graphs. Journal of Research in Science Teaching, 24(4), 369-383.

National Research Council (1986). National Science Education Standards. Washington DC: National Academy Press.

Nickerson, R. (1995). Can technology help teach for understanding. In D. Perkins, J. Schwartz, M. West, & M. Wiske (Eds.), Software goes to school (pp. 7-22). New York: Oxford University Press.

Niedderer, H. (1997). Learning process studies in physics: A review of concepts and results. Paper presented at the 1997 AERA Annual Meeting, Chicago.

Pena C.M., & Alessi, S.M. (1999). Promoting a qualitative understanding of physics. Journal of Computers in Mathematics and Science Teaching, 18(4), 439-457.

Roth, W.M. (1995). Authentic school science: Knowing and learning in open-inquiry science laboratories. Dordrecht, The Netherlands: Kluwer Academic Publishers.

Salomon, G., Perkins, D.N., & Globerson, T. (1991). Partners in cognition: Extending human intelligence with intelligent technologies. Educational Researcher, 29(3), 2-9.

Slavin, R.E. (1996). Research on cooperative learning and achievement: What we know, what we need to know. Contemporary Educational Psychology, 21, 43-69.

Sounderpandian, J. (1989). Decision analysis using spreadsheets. Collegiate Microcomputer, 7(2), 157-163.