This article was downloaded by:[alessandro@coltec.ufmg.br]
On: 29 November 2007
Access Details: [subscription number 787572227]
Publisher: Routledge
Informa Ltd Registered in England and Wales Registered Number: 1072954
Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
International Journal of Science
Education
Publication details, including instructions for authors and subscription information:
http://www.informaworld.com/smpp/title~content=t713737283
Students' Performance in Investigative Activity and Their
Understanding of Activity Aims
Alessandro Damásio Trani Gomes a; A. Tarciso Borges a; Rosária Justi a
a
Federal University of Minas Gerais, Brazil
First Published on: 23 October 2007
To cite this Article: Gomes, Alessandro Damásio Trani, Borges, A. Tarciso and
Justi, Rosária (2007) 'Students' Performance in Investigative Activity and Their
Understanding of Activity Aims', International Journal of Science Education, 30:1,
109 - 135
To link to this article: DOI: 10.1080/09500690701697520
URL: http://dx.doi.org/10.1080/09500690701697520
PLEASE SCROLL DOWN FOR ARTICLE
Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf
This article maybe used for research, teaching and private study purposes. Any substantial or systematic reproduction,
re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly
forbidden.
The publisher does not give any warranty express or implied or make any representation that the contents will be
complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be
independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings,
demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or
arising out of the use of this material.
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
International Journal of Science Education
Vol. 30, No. 1, 15 January 2008, pp. 109–135
RESEARCH REPORT
Students’ Performance in Investigative
Activity and Their Understanding of
Activity Aims
Alessandro Damásio Trani Gomes*, A. Tarciso Borges and
Rosária Justi
Federal University of Minas Gerais, Brazil
alessandro@coltec.ufmg.br
0Taylor
00
Mr.
000002007
AlessandroGomes
&
Francis
International
10.1080/09500690701697520
TSED_A_269633.sgm
0950-0693
Original
2007
and
Article
(print)/1464-5289
Francis
Journal of Science
(online)
Education
This study investigates the relationship between the students’ understanding of the aims of an
investigative activity and their performance when conducting it. One hundred and eighty-one year
nine students from a public middle school in Brazil took part in the study. Students working in
pairs were asked to investigate two problems using a computer-based environment. All their
attempts to collect information were recorded in a log file, which registered the history of each duo
investigation. After completing each investigation, all the participants were asked to explain in
writing what the objective of the task was. Results obtained showed that a proportion of the
students had some difficulties recalling the declared aims of the activities. However, those who
succeeded in recognising the stated aims of the tasks showed a superior performance in conducting
their investigations. This performance was graded according to both the proportion of adequate
and consistent tests carried out and the quality of the investigation which was done.
Introduction
In many countries, recent years have been characterised by a remarkable interest in
redefining the aims of secondary science curriculum, caused by both a general lack
of satisfaction with the current state of science education as well as by the rapid
spread of new technologies which has changed our everyday lives regarding many
essential aspects (AAAS, 1990; Millar, 1996). These innovations introduced in
science curricula have long been part of the debates about (i) the most important
aims of the science curriculum in compulsory education; and (ii) the teachinglearning strategies that seem to be more suitable to prepare students for citizenship
and to deal with the uncertainties of a world in a quick process of changing. In this
*Corresponding author. Federal University of Minas Gerais, Faculty of Education, Belo
Horizonte, Brazil. Email: alessandro@coltec.ufmg.br
ISSN 0950-0693 (print)/ISSN 1464-5289 (online)/08/010109–27
© 2008 Taylor & Francis
DOI: 10.1080/09500690701697520
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
110 A. D. Trani Gomes et al.
context, from comprehensive educational and curricular concerns, the debate about
school laboratories assumes a new importance.
Practical work is not a distinctive aspect of secondary education in Brazilian
schools, due to scarce resources and inadequate initial science teacher education.
However, secondary and university teachers seem to believe that science teaching
could be greatly improved if schools had well equipped laboratories contributing to
raising students’ learning and interest toward science (Borges, 2002).
As in many parts of the world, elementary and secondary education in Brazil
experienced the recent movement towards curriculum reform. One of the dominant
ideas in science education in this movement is that schools should give priority to
scientific literacy for all students, instead of delivering traditional propaedeutic
teacher-centred education aiming at preparing a minority of secondary students for
further studies at university level. Educational researchers, scientific committees and
associations have been advocating the teaching of science based on open problem
solving and inquiry.
The role of practical work in science teaching is a perennial issue in academic
forums (Millar, 1991; Minstrel and van Zee, 2000), although most scientists and
science educators endorse a view that practical work has the potential of making a
difference in terms of students’ interests and views of science. However the matter of
how effective practical work could be and what students should learn about in the
laboratory is still an open discussion. In line with these arguments, we have been
involved in disseminating the ideal of teaching science adopting problem-based
activities and short investigation, so that students have opportunities to practice
making decisions, elaborating and sharing their plans for conducting specific investigations and for learning from solving more open problems (Borges, 1997; 2002).
We are quite aware that the productive use of investigative activities as tools for
teaching and learning science depends mostly on students’ competence for developing reasoned plans to conduct their investigations and to understand what they are
expected to achieve on completing them. That is needed so they can engage in
practices of producing knowledge claims and assessing critically the findings of their
colleagues. Then providing secondary students with opportunities to practice solving
empirical ill-structured problems becomes a promising strategy for teaching and
learning science. It has the potential to help students in the process of making their
thinking about the world more scientific (Lijnse, 1995).
We see investigative activities as tools for constructing arguments about questions
of scientific interest and thus, school investigations should be concerned with the
solving of ill-structured problems that have no beforehand known answer and seek
to give opportunities to students to emulate the doings of professional scientists.
These activities are posed with the main purpose of engaging students in developing
a plan for the course of their work, and in looking for information and evidence they
need to be able to put forward solutions for the problems they face (Borges, 1997,
2002).
However, the use of investigative activities in science education as a teachinglearning strategy depends mainly on the students’ competence and skills when
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
Students’ Performance in Investigative Activity and Their Understanding 111
planning and implementing the tasks to obtain conclusions (Germann & Aram,
1996). This involves developing some understanding of the role of theories and
evidence in science (Koslowski, 1996; Kuhn, Amsel & O’Loughlin, 1988), and how
scientific knowledge is produced and evaluated, which seems to be learned in lecture
classes or by reading since it involves tacit components. In order for this to happen
we think all science students should be given opportunities to engage in practices
aiming at developing abilities and competences related to aspects of the scientific
investigation process e.g. the design and testing of hypotheses, and the analysis of
evidence. We believe that the starting point is developing an understanding of the
role of measurements and errors in practical work (Lubben & Millar, 1996; Lubben
et al., 2001), and understanding how to devise and conduct fair tests, i.e. adequate
and consistent tests to produce unconfounded data. This involves conceptual and
procedural aspects, as pointed by Chen and Klahr (1999), and is not just a matter of
following a set of rules or being told about the ‘scientific method’.
An adequate and consistent test is one that takes the set of variables describing a
problem-situation and manipulates them in a way similar to that defined by Chen
and Klahr (1999) as a control of variables strategy. The test is adequate if the
variable whose effect one wishes to know will be taken as an independent one. The
test is consistent if only this independent variable changes in two or more repetitions
of the experimental trial, whilst all the other variables are kept unaltered.
The definition of adequate and consistent testing, as outlined above, is appropriate for discussions of the match of students’ understandings to their performance in
conducting practical tasks and the role of experiments in science education. Other
forms of producing evidence are important and ordinary in many areas of science. If
one masters this strategy for controlling variables, he/she should be able to plan
experimental tests in which the focus variable (by which we mean the one whose
effect one wishes to determine) is changed, while the others are kept constant. Moreover, one should be able to refuse inconsistent tests in which this situation has not
occurred. It is only by using an efficient strategy of control and a systematic
combination of the many variables involved in the solution of the practical problem
that one can get reliable and uncontroversial data. Without this, the experiments do
not produce data and evidence which will support the statements and conclusions
for the problem being solved.
This issue has been discussed in developmental psychology by researchers of the
Piagetian school, and there are reasons to doubt that children’s scientific thinking
develops as easily and naturally as it was previously believed (Fisher and Silvern,
1985). We are not concerned with the control of variables skill per se. Our concern is
that this is not a subject taught explicitly at any point of elementary and secondary
school in Brazil, and it seems to be essential if we want children to be able to understand how to deal with practical tasks and why they do it that way, i.e., if we want
students to learn the role of experiments in science and how scientific knowledge is
produced and validated.
Besides dominating sound control of variables strategies, the student should
understand what the task he/she has to solve is about; that is, the purpose of the
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
112 A. D. Trani Gomes et al.
investigative activity which he/she is conducting (Schauble, Klopfer and Raghaven,
1991). This study was designed to investigate whether there is any relationship
between the recognition of the aims of investigative activities that are proposed to
year 9 students and their competence to perform adequate and consistent experimental tests. The activities were proposed as open problems and developed in a
computer simulation environment.
The Relationship Between the Identification of the Aim and
Experimentation Strategies
The investigative activity is a complex task that calls for a combination of a chain of
skills and processes. The main goal of this kind of activity is to produce knowledge
through setting up hypotheses by means of experiments.
According to Gott and Duggan (1995), the processes and skills related to investigative activities may be seen as results of the interaction of two different kinds of
knowledge: conceptual and procedural. Conceptual knowledge consists of understanding the scientific ideas, which are based in facts, laws and principles relevant
to the situation. Procedural knowledge refers to necessary knowledge to make
science, that is, the procedures and strategies needed to obtain the solution of a
problem.
By recognising the importance of the interaction between conceptual and
procedural knowledge, Klahr and Dunbar (1988) proposed an integrated model
of the cognitive processes involved in the solving of practical problems. They
identified two different but related areas: the hypothesis and the experimental
spaces. According to these authors, in any process of discovery—no matter
whether it is scientific or the solving of a daily problem—the first step is searching for a hypothesis. Regularly, the hypotheses consist of propositions stating any
kind of causal relationships between the problem variables. Thus, that first
hypothesis leads the process of discovery while it is not replaced by any other.
The initial hypothesis is generally based on the solver’s previous knowledge about
that specific domain. This hypothesis may be set up by means of exploratory
experiments, if either the knowledge is not enough or there is lack of fundamental
information.
The hypotheses are evaluated, changed and even reformulated by means of
experimentation. If the aim is to create information to formulate new hypotheses,
then the experiments should be configured to generate reliable, interesting, and
revealing information. If the aim of the experimental search is to test hypotheses, the
tests realised should allow the researcher to distinguish between plausible hypotheses and their opponents.
Klahr and Dunbar argued that the model they developed, named SDDS (Scientific
Discovery as Dual Search), can be applied in any situation in which hypothesis
formulation and data gathering occur. Their model characterises scientific research
as a series of complex and cyclic processes based on hypothesis formulation, experimentation and the evaluation of evidence.
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
Students’ Performance in Investigative Activity and Their Understanding 113
Studies that investigate the performance of individuals while conducting
investigations (Schauble & Glaser, 1990; Schauble, 1996; Chen & Klahr, 1999;
Gomes, 2005) agree that various factors influence the strategies adopted to complete
a practical task. Some studies suggest that students’ understanding of the aims of
the tasks may affect their choice of the strategy for controlling variables. Schauble,
Klopfler and Raghavan (1991) encouraged 16 students from 5th and 6th grade to
work in two different ways, which they defined as the ‘engineering model’ and
‘science model’ of investigation. Table 1 introduces the characteristics of each
model.
Two different activities were developed in order to carry out this research. These
were named ‘canal task’ and ‘spring task’. For the canal activity a scale model of a
navigation canal, in which the depth of the canal could be changed, was built. Boats
were represented by geometric forms in which the mass, shape, and size could be
varied. For the spring task, a spring was tied to a support. Bodies with different
masses and volumes and a transparent container in which the bodies were
suspended by the spring, but submerged in water, were used. Both tasks included
about the same amount of possible variation, and both were designed to include a
mix of some effects that were consistent with children’s prior beliefs and others that
were inconsistent. In this way, prior beliefs and the evidence must both be considered in figuring out how the systems work.
In order to encourage each group of children to adopt one way of working, the
researchers used explanatory texts to persuade each child to work following only one
model. Half of the children were randomly assigned to an engineering problem
context and the other half to a science context. All the children worked with both
activities.
The canal task is not usual in school science classes, so the authors expected that
students would try to describe the characteristics of a boat that would cross the canal
in a higher speed. In this way, the structure of the activity was consistent with the
Table 1.
Goal
Strategies
Procedure
Features of children’s engineering and science models of experimentation, according to
Schauble, Klopfer & Raghavan (1991)
Engineering Model
Science Model
Make a desired or interesting outcome
occur or reoccur
Understand relations among causes and
effects
Compare highly contrastive instances
Establish the effect of each potentially
important variable
Emphasis on making exclusion, or non
causal, inferences and inferences of
indeterminacy
Seeks to test of all combination, if feasible
Inferences
Emphasis on making inclusion, or
causal, inferences
Search
Focuses on variables believed to cause
the outcome
When the desired outcome (or
acceptable approximation) is achieved
Stop rule
When systematic test of each manipulable
variable is completed
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
114 A. D. Trani Gomes et al.
aim of trying to produce an expected result (a faster boat). On the other hand, the
spring task is a traditional school activity, and familiar to the children. This task does
not include results that can be readily interpreted as more or less desirable. Therefore, it was expected that the investigation would be more careful, determining
which variables would influence on the stretch of the spring.
In spite of the differences between the activities, it could be perceived that when
students were working with the science approach, they were more engaged in
determining what factors were or were not influencial on each activity. In other
words, children worked in a more systematic way, establishing the effect of each
variable on the system. When students worked with the engineering approach, they
preferred to choose contrastive combinations and concentrated themselves on
factors they believed to be causal. At the same time, they ignored the factors thought
of as non-causal, even in a mistaken way, in order to optimise the results.
More often, students working in the engineering problem context used a trial
and error strategy and students in the science problem context carried out a more
careful and systematic inquiry. This result shows that the strategies of variable
control used by the students are closely related to the ways they perceived the aim
of the task. It also showed us that we should be concerned much more with having
the students understanding not only the content, but also the aims of the activities
they do.
However, the difficulty in understanding the aims of the practical activities is not
only an issue for students. Sá and Borges (2001) argued that there is a ‘decline’ in
understanding the goals originally attributed to a determined activity by its authors.
They concluded that both students and teachers, mainly the less experienced
teachers, are not able to understand clearly the aims of the activities when they read
the guidebook.
Borges (1997, 2002) points out that only rarely do teachers develop systematic
plans for the practical work they assign to students, followed by explanations and the
discussion of their aims. Quite too often, teachers and students work with unclear
and implicit pedagogic aims. Students can learn well and rapidly how to survive the
lab tasks, by following the ritual of executing the instructions provided, preparing
the equipment and measuring the quantities they are ask to obtain, and being able to
represent the numbers they get in tables and graphs, even when they show poor
understanding of why they did all those activities and measurements in those
particular ways, and of what those numbers and graphs mean. Therefore, they fail to
learn the ideas intended by the teacher or the curriculum designer from practical
work activities.
Hart et al. (2000) established differences between the purposes and the aims of a
practical activity. According to them, the first are the pedagogic purposes established by the teacher, that is, the reason why such activity is being conducted, the
way it is organised, and what educational results the activity may produce to the
students’ learning process. The aim of the activity refers to the narrative presented
to the students on the activity sheets, i.e., the aims are ordinarily specific content
topics (for instance, to obtain the relationship between tension and current for a
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
Students’ Performance in Investigative Activity and Their Understanding 115
conducting wire). This is a source of difficulties, according to the authors, because
at most times students only recognise the immediate aims of the activity, and they
neither create or establish connections between the activities done and the learning
caused by them.
As we have already seen, some studies (Hart et al., 2000; Sá & Borges, 1999,
2001) point out that students find it difficult to understand correctly the aims of
the proposed activities, which can impede their performance and their potential to
learn from them. In this sense, if we compare the so-called understood aims with
those previously stated we can evaluate the degree of ‘fading’ of the understanding
of task aims and try to become conscious of how the grasping of the aims of the
activities influences both the performance and the strategies of students during
their performance.
Research questions
This is one part of a broader research project in which we tried to develop a methodology which characterised the students’ thought along investigative tasks. We
also sought to find the relationships among students’ understandings of the activities’ aims and of the concepts involved, and their performance when conducting
them.
The research questions that have guided this work are:
(a) Do Brazilian middle school students think of adequate and consistent tests in
planning and conducting practical investigations?
(b) Is there any relationship between the way students understand the aim of a
practical problem and their performance in planning and conducting an
investigation to solve it?
These are aspects which deserve investigation because the structure of the Brazilian
system of compulsory education is distinct from the English-speaking and European
world in many respects. The most important difference is that practical work is not
usual in science education. In Brazil, primary education used to start at age of 7 and
lasted for 11 years. The first eight years comprise the ‘fundamental school’, and the
last three years form the ‘middle school’. As a result of educational reform of neoliberal inspiration during the 1990s, there are different systems of progression in
school in many states. In middle school it is usual that to be promoted on to next
year, students are required a minimum attainment, normally a mark of 60% of the
points in each subject matter. Those who failed are kept, studying the same subjects
again, in the following school year. Over time, increasing drop out and the phenomenon of school repetition has distorted the correspondence between age and schoolyear. In this way, it is common to find students one or two, and even more, years
behind the school-year of children of the same age.
Middle school is distinct from fundamental school mainly because it marks the
first contact of students with teachers specialised in science disciplinary fields.
Science is taught as separate subjects—physics, chemistry and biology—to all
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
116 A. D. Trani Gomes et al.
students. Science teachers from the initial years of Fundamental school normally
have backgrounds in biology and related areas, while physics, chemistry and biology
teachers are expected to have a university degree in their area, which entitles them to
get a licence to teach these subjects.
A recent change added another year to elementary school so that, from 2005,
children start attending fundamental school at age 6. We will be referring the
previous status quo because all the students who took part in this study had 11 years
of basic education.
Methodology
We split the data collection into two different sessions, in which different instruments were used. In the first session, students simulated two investigations in a
computer environment and all their choices were registered in log file. After doing
the two activities, the aims the students attributed to the activities were probed. Only
data relative to the second investigation are analysed in this work.
Subjects
181 middle school students with ages from 14.9 to 18.3 years from a large urban
area in the south-east of Brazil took part in the research. Their age distribution is
shown in Figure 1. Participants were distributed into seven classes taught by four
physics teachers. One of the authors of this work taught two classes, but had no
direct participation in data gathering. The school is attached to a public university
and has an experimental character, and this was one of the reasons for choosing to
conduct the study there. Another reason was that all year 9 students in this school
have regular physics and other science laboratory classes on alternate weeks. This is
their first contact with practical work, as it is usually done in upper secondary
education. In this way, the prior learning experiences of science practical work of
the students participating in this study are not much different from more typical
Brazilian schools.
Figure 1. Histogram of students’ age distribution
Instruments
As mentioned above, the empirical material of the research was gathered using two
investigations and one open question. In order to better organise this paper, these
research instruments will be described separately, according to the sequence in
which they were introduced to the students.
Investigative Activities
The two investigative activities were carried out using computer simulations. The
preference for simulations instead of the didactic experiments ordinarily used was
motivated by the need to allow students enough time to conclude their experiments
Mean:
StDev:
N
&
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
Students’ Performance in Investigative Activity and Their Understanding 117
16.0
0.7
181
$
"
#
Figure 1.
$
%
&
Histogram of students’ age distribution
and to facilitate the collection of interesting data for the research. The use of a
traditional hands-on activity would demand excessive time for students to set up
the experiments, collect and analyse data, draw conclusions and write up their
reports.
Moreover, our interest neither depended on how students would collect the data
nor on their correct readings of apparatus, data organisation and manipulation of the
equipments. We only wanted to know:
(i) whether students were able to spontaneously think of adequate and consistent
tests when planning how to conduct and execute simple investigations; and
(ii) what is the relationship, if any, between their understanding of the aims of the
activities and such thoughts.
The simulations were specially developed for this research and they included many
characteristics of an investigative activity. Using a friendly interface, developed
under Windows platform (the main screens of each one are presented in Figures 2
and 3, respectively), the simulations showed themselves to be easily understood and
useful for the students. All the students participating in the study attended weekly
computer classes. Thus, the use of the computer to run the simulations was not a
source of difficulties to them.
Two simulations were developed approaching two distinct topics (thermal equilibrium and movement on an inclined plane), but with the same structure of variables
and procedures. The aim of each investigation was to establish which variables, in a
group of independent ones, had influence on a determined dependent variable. In
Figure 3.
2. Main screen of the simulation of the ramp
thermal
problem
equilibrium problem
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
118 A. D. Trani Gomes et al.
Figure 2.
Main screen of the simulation of the thermal equilibrium problem
communicating this to the students, the aims of the activities were presented in a
colloquial and easily understandable way. The aims which were told to the students
and other characteristics of the developed investigative activities are presented in
Table 2.
Among the independent variables there were three causal ones (those that
influence the dependent variable) and one non-causal (the one that has no effect on
a dependent variable). Two versions of each problem differing only in the number of
independent variables (2-var or 4-var) were prepared. This was done attributing
some constant values to two out of the four variables, so that students could not
modify them in the 2-variable versions of each problem (see Table 2).
Throughout the data gathering process, when selecting values for the independent
factors, students were asked to write down the reasons for their choices in a dialogue
box provided in the screen. Then, after clicking the ‘run’ button, the result was
presented in the screen, followed by a brief animation. Next, students were asked to
make a remark about the obtained result. When they had done this, they had the
choice to go on investigating, i.e., they could either choose another set of parameters
and carry out another experiment or leave the programme. When leaving the
simulation, all the data, justification and comments were saved in a log-file to be
analysed. Through this data, which represent the investigation history, one can know
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
Students’ Performance in Investigative Activity and Their Understanding 119
Figure 3.
Main screen of the simulation of the ramp problem
whether the students carried out good tests, how many tests they did, and what they
were thinking during their investigation, revealing features of the strategies they used
to conduct each task.
Table 2.
Characteristics of the investigative activities
No.
variables
Theme
Aim
Thermal
equilibrium
To determine which
factors influence the
final temperature of
the system
2
To determine which
factors influence on
the time it takes for
the spheres to slide
down the slope
2
Ramp
4
4
Factors/Variables
Kind of variables
Surface area of the block
Temperature of the liquid
Surface area of the block
Temperature of the liquid
Kind of liquid
Mass of the block
Distance
Mass of the block
Inclination
Friction
Discrete (20 levels)
Integral
Discrete (20 levels)
Integral
Nominal (3 levels)
Discrete (10 levels)
Integral
Discrete (20 levels)
Integral
Discrete (20 levels)
Nominal (3 levels)
Discrete (6 levels)
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
120 A. D. Trani Gomes et al.
The investigative tasks used in this study were designed to promote an opportunity to observe the research strategies that students used in a context in which their
previous knowledge and the ‘sense of mechanism’ are very strong (diSessa, 1993).
Besides this, they also make it possible to analyse how these strategies evolved or
were modified over the course of the activities. As in other studies (Klahr, Fay &
Dunbar, 1993; Penner & Klahr, 1996) we planned tasks which required the students
to coordinate their searching both in the experimental space and in the hypotheses
space, as well as the evaluation of the evidence produced during the experiment.
This represents all the three major processes involved in the SDDS model proposed
by Kahr and Dunbar (1988).
Test on the Aim of the Achieved Tasks
The last phase of the data gathering process consisted of individually answering a
question about the aims of the investigative activities. The question asked after the
conduction of the second investigation was: ‘You have just conducted an activity
through a computer simulation. In your own words, write down what was the aim of
the computer simulation you have done.’
Procedures
The gathering of the empirical material occurred during the class time by one of the
authors of the study. The 181 students who participated in the study were separated
into seven classrooms. However, the analysis focuses on the 78 pairs who did all the
activities and tests.
Students were given 100 minutes to conduct each investigation. The topic order of
the investigative activity varied from class to class. Thus, some of the students had
activities related to the ‘thermal problem’ on the first day and related to the ‘ramp problem’ on the following day. The other part conducted the activities in the reverse order.
The test was answered individually, but students did the investigations working in
pairs. The assignment of groups to both problems and their versions of two- and
four-variables, was at random, depending on the place in which they had chosen to
sit. The option for having students working in pairs was an attempt to simulate the
usual approach in science laboratory classes, where students rarely work by
themselves. Moreover, there are some clues (Roth, 1995; Meyer & Carlisle, 1996;
Kaselman & Kuhn, 2002) indicating that the performance of students when working
in duos is frequently of superior quality than when they work individually.
Data analysis
The Aims of the Activities Given to the Students
In order to answer our research questions, we first coded students’ answers to the
question about the aim of the task they had completed. A coding framework was
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
Students’ Performance in Investigative Activity and Their Understanding 121
developed based on both categories identified in previous studies (Driver, Leach,
Millar & Scott, 1996; Welzel et al, 1998) and a preliminary reading of the whole set
of data to check the suitability of those categories. The categories of the answers
related to the aim of the activity are presented in Table 3.
The aims of the activities done through computer simulation were explicitly told
to the students. To the ‘thermal problem’, it was to determine which of a set of
variables influenced the equilibrium temperature of the system. To the ‘ramp problem’, the aim was to find out which of the involved variables affected the time a
sphere takes to get down the slope.
A0—The correct aim
We considered as correct the answer that had at least one of the aims referred above.
Most of the answers that were classified in this way referred correctly to the aim of
the former conducted activity. Very few students referred to both activities.
Examples:
‘To check what are the physical quantities that interfere with the time spent by the
sphere to get down the slope.’
‘The aim was to identify the factors that influence on the final temperature of the
system.’
A1—To confirm hypotheses or theories
To define the aim of experimental activities as well as confirming any hypothesis or
theory is ordinary enough among students. Many times, the teacher himself contributes to a distorted view of the experimental activities when he proposes, by instance,
‘Check that voltage is proportional to electric current in an ohmic conductor’. To
some students, both practical tasks have only one goal, which is to check something
we either already know or expect to happen.
Examples:
‘To support and to explain the hypotheses or opinions we had on the subject.’
Table 3.
Categories of the given answers on the aim of the accomplished task
Code
Category
A0
A1
A2
A3
A4
A5
A6
Correct aim
To confirm hypothesis or theories
To obtain a given result
To evaluate the student’s knowledge
To conduct virtual experiments
To acquire knowledge from the practice
Other different aims
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
122 A. D. Trani Gomes et al.
The aim was to prove and understand the ideas I have raised in the previous
questionnaire.’
‘To prove whether my thoughts about the influence of mass and distance were
correct.’
A2—To obtain a given result
To many students, the two experimental tasks have the only one goal: ‘to work well’,
i.e., to obtain determined results which are normally already expected.
Examples:
‘To get the final temperature of the blocks.’
‘To verify the temperature averages, the lowest and highest ones.’
A3—To evaluate students’ knowledge
Some students answered that the conducted activity aimed at evaluating their
knowledge; that is, what they really knew about the theoretical domain in question.
In this case, the great majority of them referred to conceptual knowledge, but not
procedural knowledge.
Examples:
‘[It is] to test our knowledge in a practical way.’
‘The aim of any experiment is to check what we really know about the topic.’
A4—To conduct virtual experiments
The investigative activities were conducted through computer simulations instead of
from manipulation of equipments. That was why some students characterised the
aim by emphasising the aspect of that different way of conducting an experimental
activity.
Examples:
‘To conduct experiments we have really done, but this time virtually.’
‘The goal was to make us foresee, specify the temperature, and, through the experimental procedure, make us write our remarks and conclusions. It is doing all these
things using the computer.’
A5—To acquire knowledge from the practice
The statements classified here referred to the learning students had got when
conducted the activity. Some students answered in a vague and short way, others
were specific enough about the conceptual knowledge.
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
Students’ Performance in Investigative Activity and Their Understanding 123
Examples:
‘I always think that the major goal of any experiment is to provide knowledge.’
‘It is to learn more about slopes and temperature’ [sic].
A6—Other different aims
Various answers could not be classified in any other category. Some were related to
immediate aspects perceived by the students. They referred to particular views of
some aspect of the conducted activity.
Examples:
‘It is to help Alessandro in his research project.’
‘To understand in a practical way what happens to the materials.’
We categorised the answers of the 181 students that provided some answer to the
aims of the first or the second activity, according this system. However, only 78 duos
did the two investigations. Table 4 shows the number of answers and the total
percentage for each one of those categories.
The first aspect that deserves to be highlighted is the relative difficulty the
students had in recollecting the aims of the tasks as they were proposed. Only nearly
60% of them correctly identified the declared aims of the activities even after
conducting two investigations with nearly the same aims and structure of variables.
Students’ difficulties in understanding the goals had already been realised
throughout the activity developments. They were instructed to read carefully the
initial screen of the computer simulation. It displayed a description of the activity
containing all the necessary pieces of information to conduct it, including the
intended aim. But, even with the presentation screen and the explanations orally
given by the teacher, while preparing and executing the investigations, many of the
groups of students still asked what they had to do.
Although most of the participants had limited previous experience in practical
classes, the confirmatory view of the school science laboratory as assigning to
experiments and observations the aim of proving or verifying some determined
Table 4.
Category
A0
A1
A2
A3
A4
A5
A6
Total
Number and percentage of answers by category.
Number of answers
Percentage
108
23
11
6
5
4
24
181
59.7
12.7
6.1
3.3
2.8
2.2
13.3
100.0
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
124 A. D. Trani Gomes et al.
hypothesis, or a theory, is well spread out among the students. In fact, 12.7% of
the total number of students (or 31.5% of students who incorrectly answered the
questions about the aims of the activity) had their answers categorised as A1. This
can indicate a strong belief that school practical activities, and even the scientific
ones, are conducted to check hypotheses and theories. This distorted view of the
experimentation process can be attributed, perhaps, to the excessive unquestionable nature of science practical activities normally conducted in schools. Additionally, it can be attributed to the characterisation of both the scientific knowledge and
the scientific activities by the media and books, including science textbooks (Carey
et al., 1989; Solomon, Duveen & Scott, 1994; Tsai, 1999). Practical work developed in schools usually aims only at illustrating phenomena studied in theory or at
demonstrating ‘the truth’ of scientific laws. Therefore, students get the view that
the experiment always ‘will work’ according to the theory, and the results which
will be obtained are always predictable (Category A2).
One of the reasons pointed out by literature to explain the low level of students’
learning from practical tasks is indeed their frequent unawareness of the purposes of
the activities they do. Tamir (1991), as well as many other researchers (for instance,
Sá & Borges, 2001; Hart et al, 2000), states that the aim perceived by the students
when conducting a practical activity is different from the aim intended by the
teacher, and that students may not understand the relationship between a task
purpose and the procedures adopted during the class.
The analysis of the files containing the history of the investigations conducted by
each pair of students showed that, in several moments, various pairs attributed aims
different from those conceived for the activities. Many of the identified aims were
completely different and far away from the aims originally intended.
Before doing each experiment, as we have already presented in the methodology,
students had to assign values to some variables. They were also supposed to predict
the result. In many historic files, it was possible to identify answers such as:
‘To check the maximum time it takes the sphere to get down the slope.’
‘To obtain the higher temperature possible.’
The occurrence of these answers is consistent with the results obtained by Schauble,
Klopfer and Raghavan (1991) that many times students did not consider the
proposed aims of the task and considered other more practical ones, which is a characteristic of the ‘engineering model’ of investigation. However, the fact of considering more practical aims itself does not prevent students from conducting the task
satisfactorily, since some care is taken and they plan adequate and consistent tests to
identify the variables that really influence the independent variable.
The Relationship Between the Identification of the Aims and the Performance in the
investigations
To explore any possible relationship between the way students understood the aims
of the tasks and their performance in conducting the investigations, all the duos were
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
Students’ Performance in Investigative Activity and Their Understanding 125
Table 5.
Number and percentage of answers by category
Code/Category
Description
BC
OC
Duo in which both students correctly identified the aims of the activities.
Duo in which one student correctly identified the aims of the activity
whereas the other incorrectly done it.
Duo in which both students incorrectly identified the aims of the activity.
BI
divided into three groups according to the way each student of the pair recollected
the objectives of the activities done, as shown in Table 5.
The second investigation history was analysed for the 78 pairs of students who
completed both investigations and answered the other instruments developed for the
entire research. Table 6 shows the number of duos coded according the categories of
in Table 5 and to the type of their second task. From now on we are considering
only data from the second task done by each duo.
From the second investigation history we obtained the number of experiments
conducted by each pair of students (following the classification presented in Table 5).
Then, we identified, how many of the tests conducted could be considered as consistent and adequate to the task aim. An investigation score, defined as the proportion
of good experiments in the second investigation, was calculated for each pair of
students. A higher score indicates that most of the duo’s experimental tests done are
consistent and relevant to solving the problem. On the contrary, a low score means
that students do not run good experiments to test their hypotheses, because they do
not understand what they are expected to do, or else, because they cannot distinguish
uncontroversial and relevant tests to solve the problem. One test consists of assigning
values to the independent variables and then running the simulation to obtain the
corresponding value of the dependent variable.
The total number of tests done is not a good indicator of students’ performance in
conducting practical investigations, because on some occasions students may not
engage in searching for an empirical solution for the problem (Gomes, 2005). Some
of them think they already know the answer and that only a few tests are needed to
solve the problem. This might be related to good conceptual understanding of the
matter or to deeply entrenched alternative conceptions about it.
Table 6.
Number of duos according to the activities and their categorisation
Number of duos per category
Activity
Thermal equilibrium
Ramp
Total
Number of variables
BC
OC
BI
2
4
2
4
8
9
5
11
33
5
10
7
4
26
7
4
4
4
19
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
126 A. D. Trani Gomes et al.
Table 7.
Total number of experiments done and mean investigation score (InvS)
Total number of tests done and mean investigation score
BC
Activity
Thermal equilibrium
Ramp
OC
BI
Number of
variables
n
InvS
n
InvS
n
InvS
2
4
2
4
26
71
21
60
0.61
0.77
0.55
0.68
18
67
27
21
0.55
0.67
0.62
0.67
26
16
14
31
0.56
0.20
0.59
0.46
On the other hand, there are groups that get confused and decide to do an excessive number of tests. Some of these tests are good ones, but students seem unable to
draw conclusions about the contribution of their actions to solving the problem.
Table 7 shows the total number of tests done according to group of understanding of
task aim, as well as the mean investigation score. Table 8 presents descriptive statistics of duos’ investigation performance.
We aimed at investigating the possible relationship between the way students
recollected the aim of a practical problem and the students’ performance in conducting an investigation to solve it. The problem investigated had no effect on the average performance. In order to look whether duos’ investigation performance was
affected by their correct recalling of the task aim a non-parametric Kruskal–Wallis
test was initially conducted. Differently to ANOVA, Kruskal–Wallis statistic does
not require a normal distribution of the dependent variable for each value of the
independents.
Kruskal–Wallis resulted in H = 8.937 (df = 2, n = 78), which is statistically significant at the level p=0.011. Levene’s test showed that variances were not homogeneous
(see Table 9). An independent sample ANOVA showed significant differences among
group investigation-score means (F (2,75) = 5.308, p=0.007), as shown in Table 10.
A Games–Howell post-hoc test was chosen as it is appropriate for multiple comparisons of means of groups of different sizes and with unequal variances (Table 11).
Table 8.
Descriptive statistics—investigation score
95% Confidence Interval for
Mean
BI
OC
BC
Total
n
Mean
Std. Dev.
Std. Error
Lower Bound
Upper Bound
Min
Max
19
26
33
78
.465
.635
.662
.605
.274
.216
.176
.229
.063
.043
.031
.026
.333
.547
.599
.553
.598
.722
.724
.656
.000
.000
.290
.000
.750
.880
.860
.880
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
Students’ Performance in Investigative Activity and Their Understanding 127
Table 9.
Levene Statistics—investigation score
Levene Statistic
2.398
df1
df2
Sig.
2
75
.098
Two interesting aspects can be distinguished in Table 11. First, there is no
significant difference between mean investigation-scores of groups OC and BC. A
possible explanation is that if at least one member of the duo understands the aim of
the task (OC), this student can conduct or lead the execution of the activity. So, the
duo would not show significant differences compared with the duos where both
students understood the aim of the activity BC. On the other hand, the performances in conducting the investigation of the duos characterised as BI are, in
general, worse than those of BC and OC duos. In fact, Table 11 indicates statistically significant differences only between groups BI and BC. This means that the BI
duos, in which neither student has understood the aims of the activities when asked
individually, had their performance compromised. Eta-squared amounts to 0.124,
which means that understanding of task objectives explains 12.4% of the variance in
all duos investigation-scores (Table 12).
In order to clarify what makes a difference in students’ performances, the duos
were selected according to the number of variables in their second task, and an
independent sample ANOVA conducted on each case. Tables 13 and 14 show the
mean investigation-score differences between the groups carrying out investigations
in tasks with two and four variables, respectively. Table 13 indicates that there is no
significant difference in performance of duos investigating a problem limited to two
independent variables (F(2.33)=0.265, p=.769). Post-hoc comparisons display the
same pattern and are not include here, for the sake of brevity.
For those duos investigating problems with four independent variables, the
situation changes drastically. Table 14 indicates a differentiation in groups’ performances. Group means show significant differences, as indicated by ANOVA
(F(2.39) = 7.126, p=.002). This indicates that those duos that were able to recall
the declared aims of the tasks after doing them showed better performance in more
complex investigative tasks. Post-hoc comparisons (Table 15) show significant
differences between BI and OC group means, at the level p=0.01, and between BI
and BC group means (p=0.002). Tukey HSD test was used as pos-hoc test
because in the case of sub-groups working with four variables the variances across
groups are equal. In all the cases, Welch and Brown–Forsythe tests were also
conducted and corroborated the results presented. Eta-squared in this case
Table 10.
ANOVA for investigation score, including all duos
Understanding of second task objective
Error
Total
Sum of Squares
df
Mean Square
F
Sig.
0.499
3.527
4.026
2
75
77
.250
.047
5.308
.007
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
128 A. D. Trani Gomes et al.
Table 11.
Post hoc test—multiple comparisons, including all duos, for the investigation score
95% Confidence
Interval
(I) Understanding of task objective
(J) Understanding of task objective
Games–Howell
BI
JOC
BC
Mean
Difference
(I–J)
Std.
Error
Sig.
Lower
Bound
Upper
Bound
−.169
−.196*
.169
−.027
.196*
.027
.076
.070
.076
.052
.070
.052
.081
.025
.081
.865
.025
.865
−.356
−.370
−.017
−.154
.022
−.100
.017
−.022
.356
.100
.370
.154
OC
BC
BI
BC
BI
OC
* The mean difference is significant at the .05 level.
increased to 0.268, which appears to be very high, taking into account the host of
other variables at individual and social levels beyond control in educacional and
social research.
To obtain more evidence about the influence of the understanding of the aim on
conducting an investigation, we analysed the files containing the history of investigation of each pair of students. They were coded according to the categories of quality
of investigation presented in Table 16. This categorisation is concerned overall with
the management of the experimental domain, and with the use of adequate and
consistent experimental tests. Therefore, the data obtained from this categorisation
inform us about the general quality of the activity achieved by the duos.
Table 17 shows the distribution of the number and percentage of student duos
according to the categories presented in Table 16, as well as with the identification
of the aims of the activities.
The data presented in Table 17 indicate that the quality of the investigation
conducted by the students improves with the understanding of the aims of the
activities. Once more, the quality of the investigation conducted by BC and OC
duos are similar and relatively better than those from BI duos.
Assuming the SDDS model (Klahr & Dunbar, 1988), we can also provide a
plausible explanation for the results obtained. The investigations with two variables
present limited experimental and hypothesis spaces, and thus they seem easier to all
students. On the other hand, in activities with four variables both the experimental
and the hypothesis spaces are larger and thus more demanding.
Table 12.
Investigation score *
Understanding of task objective
Measure of association including all duos
R
R Squared
Eta
Eta Squared
.320
.102
.352
.124
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
Students’ Performance in Investigative Activity and Their Understanding 129
Table 13.
ANOVA for investigation score, for problems with two independent variables
Understanding of second
task objective
Error
Total
Sum of Squares
df
Mean Square
F
Sig.
.020
2
.010
.265
.769
1.273
1.293
33
35
.039
The answer to our research question is positive. Students from the first year of
Brazilian middle school (which is their ninth year of basic schooling) planned and
developed experimental tests relevant to the solving of the task they were conducting,
while adopting appropriate variable control strategy. The overall mean investigation
score was .605, ranging from .465, in the case of duos with no understanding of task
aim, to .662 in the case of group BC where both members of the duo got the task
objective as intended (see Table 8). In the simpler task with two independent variables, those figures rose to 0.540 for BI and 0.593 for the group OC performing
better than BC group. In the activity with four independent variables, the mean score
of BI duos was 0.363, while BC duos reached a mean score of 0.708. We have no
data concerning students’ performance in doing investigation before middle school,
but most of them have never had practical work of this kind before. The more likely
is that they had only observed their science teacher doing demonstrations.
Data for this study were collected at the beginning of the last term of the school
year and the involvement of students in conducting more open tasks in the physics
lab during the first and second term have afforded them invaluable learning opportunities. About 60% of the participants were able to recall the aims of the tasks as
they were declared. The findings that about 13% of them identified objectives to the
tasks as category A1 (to confirm hypotheses or theories), and as category A6 (other
different aims) were expected.
These figures suggest that BC duos showed more engagement in the more
complex task. In fact, Gomes (2005) shows that BC duos solved most of the simpler
problems by relying on their knowledge of the concepts underlying the task, whereas
the BI group did a lot more exploration, even in simpler tasks. This might be the
reason why students’ performance in investigating the simpler independent problems did not show any significant difference among the groups. However, in the
Table 14.
ANOVA for investigation score, in the case of four independent variables for
investigations
Sum of Squares
Understanding of second
task objective
Error
Total
df
Mean Square
F
Sig.
.717
2
.358
7.126
.002
1.961
2.677
39
41
.050
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
130 A. D. Trani Gomes et al.
Table 15.
Multiple comparisons, for investigations with four independent variables
95% Confidence
Interval
(I) Understanding of task objective
(J) Understanding of task objective
Tukey HSD
BI
OC
BC
OC
BC
BI
BC
BI
OC
Mean
Difference
(I–J)
Std.
Error
Sig.
Lower
Bound
Upper
Bound
−.308*
−.346*
.308*
−.038
.346*
.038
.099
.094
.099
.078
.094
.078
.010
.002
.010
.878
.002
.878
−.550
−.574
.065
−.228
.117
−.152
−.065
−.117
.550
.152
.574
.228
* The mean difference is significant at the .05 level
activities with four variables, the attempt to coordinate the investigation in both
experimental and hypothesis spaces is more complex and demanding. The investigation score of BI duos decreases as the number of variables involved increases, in
contrast to the other groups.
Concerning the second research question, our findings suggest that students’
performance in planning and conducting investigations to solve a practical problem
is correlated to their ability to recall the aims of the activities they have done. The
correlation is stronger in the case of more complex problems, that is, involving more
variables. The overall quality of investigations conducted by the participants of this
study was very good considering their prior experience with this type of task.
However, when having even an inadequate understanding of the declared aims of
the task, it is more likely that students can show satisfactory performance in
conducting simpler investigations, due to the small number of experiments they
need to do to collect information for advancing to a conclusion.
Final considerations
Science educators (AAAS, 1990; Millar, 1991; Minstrell and van Zee, 2000) argue
that science education should involve not only the development of comprehensive
Table 16.
Categories used to classify the investigations conducted by the students
Category
Description
Very good
Acceptable
When the duo conducted at least one adequate and consistent test for all variables.
When the duo conducted at least one adequate and consistent test for at least 50%
of the variables.
When the duo conducted the majority of the experimental tests in an inconsistent
way and investigated the influence of few variables.
Inadequate
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
Students’ Performance in Investigative Activity and Their Understanding 131
Table 17.
Distribution of the number and percentage of students’ duos according to the quality
of the investigation and the identification of the aims of the activities
Number and percentage of students’ duos in each categories for
the identification of the aims
BC
OC
BI
Quality of the investigation
n
%
n
%
n
%
Very good
Acceptable
Inadequate
Total
20
9
4
33
61
27
12
100
16
8
2
26
62
31
7
100
8
5
6
19
42
26
32
10
0
and abstract knowledge, but also of practical and contextual knowledge. In this way,
students would not only construct an effective scientific culture, but also develop
the capacity for interpreting, analysing and understanding their observations and
experimental results. One of the ways to get such teaching is the creation of an environment which allows
promoting the development of an investigative, critic and creative attitude toward
novelty, attempting to build an understanding for new situations and phenomena which
we deal with at any moment (Borges, Borges & Vaz, 2001, p.2).
If we believe that investigative activities can contribute both to more and better
science and to knowledge about science learning, we should be aware of the fact that
students face many difficulties of both a conceptual and procedural nature. One of
the sources of these difficulties is exactly the scarce attention teachers give to
communicating what they intend students to learn with practical activities and the
consequent failure of students to figure out the aims of the experimental activities
they conduct and what things they are supposed to learn by doing them. This
contributes to students performing more poorly than they could. Thus, it is important to study how the different ways students understand the declared aims of the
tasks they are doing affects their actions and thinking in the science laboratory. This
could support the production of pedagogic strategies to improve their learning from
practical work.
Our results show that some of the students of the observed group had significant difficulties in understanding the stated aims of the activities they did, thus
having their performance affected. Students who understand well the intended
aims of the activities tend to conduct better investigations, as measured through
both the scope of searching in the experimental space and the use of adequate
and consistent tests.
The data also make it clear that getting the aims of laboratory work wrong can
exert great influence on students’ performance. This effect is milder in simpler
activities and increases with task complexity. Therefore, when planning practical
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
132 A. D. Trani Gomes et al.
activities, science teachers should consider that students can attribute other aims
rather than those intended and declared. Teachers should also be aware that this
could result in students’ performance being poorer than they are capable of.
Thus, teachers should do their best to make sure that students have a clear
understanding of the purposes of each activity, the reasons for doing it, and for
using consistent experimental strategies to obtain the kind of data appropriate for
solving the task.
From the results of this study, we defend the importance of teachers favouring the
conduction of pre-lab discussion to make explicit what will be done during the activity and how, as well as the intended practical and pedagogical aims. In the same way,
at the end of the practical activity, teachers should promote the discussion of the
obtained results having in mind the initial proposed aims. The engagement of
students in both these tasks can contribute to helping them to get a more suitable
understanding of the experimentation process and to establish connections between
the activities’ aims, the actions, the adopted procedures, and the results.
In this way, the actions, attitudes and procedures of students during the tasks
should be discussed and analysed, so that they can develop a conceptual understanding of experimentation and their fundamental procedures. Students should
have the opportunity to realise that:
(i) the methods and the procedures used depend on both the nature of the problem
and the proposed aims; and
(ii) the obtained results as well as what has been learnt also depend directly on the
process of investigation conducted.
As we previously discussed, students face difficulties in correctly identifying what
they are supposed to learn from the tasks they conduct in the school laboratory.
This difficulty seems to be related not only to an inadequate understanding of
the role of the laboratory and experimental activities in education, but in science
itself. The empiricist idea that laboratory activities aim at proving theories or
laws is disseminated among students and stressed by science teachers and textbooks.
In conclusion, the present study points to questions that have neither been
satisfactorily answered nor comprehensibly investigated, but that directly influence
the whole planning and execution of investigative activities processes. Among these
questions, we can emphasise:
●
●
●
How do students understand the experimentation process?
How do their views about science and experimentation influence their performance during experimental activities?
How do students understand the production of knowledge from the experimental
activities?
These questions relate themselves directly to individuals’ views of science, scientific
knowledge, and experimentation. According to Ryder, Leach and Driver (1997), the
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
Students’ Performance in Investigative Activity and Their Understanding 133
formal discussion in classroom about subjects related to the nature of science is not
usual. Thus, students rarely come to integrate—orally or in writing—what they
understand as science or scientific knowledge. So, the comprehensions that students
have about the nature of science and the production of scientific knowledge are
mostly tacit, formed from the general media and from particular experiences inside
and outside school.
We believe that from the understanding of both what the students think and what
they do during the activities in laboratory, and what factors influence them throughout the conduction of practical activities, we will be able to contribute in order to
elucidate and better define the importance and the role of practical work in the
science curriculum.
Acknowledgement
The second and third authors would like to acknowledge CNPq for their personal
research grants.
References
AAAS. (1990). Science for all Americans—Project 2061. New York: Oxford University Press.
Borges, A. T. (1997). O papel do laboratório no ensino de Ciências [The role of experiments in
science teaching]. Paper presented at the I Brazilian National Conference on Research in
Science Education. Águas de Lindóia, São Paulo.
Borges, A. T. (2002). Novos rumos para o laboratório escolar de Ciências [New Directions to
school science laboratory]. Caderno Catarinense de Ensino de Física, 19, 291–313.
Carey, S., Evans, R., Honda, M., Jay, E., & Unger, C. (1989). ‘An experiment is when you try it
and see if it works’: a study of grade 7 students’ understanding of the construction of scientific
knowledge. International Journal of Science Education, 11, 514–529.
Chen, Z., & Klahr, D. (1999). All Other Things Being Equal: Acquisition and transfer of the
control of variables strategy. Child Development, 70, 1098–1120.
DiSessa, A. A. (1993). Toward an Epistemology of Physics. Cognition and Instruction, 10,
105–225.
Driver, R., Leach, J., Millar, R., & Scott, P. (1996). Young people’s images of science. Buckingham:
Open University Press.
Fischer K. & Silvem L. (1985). Stages and individual differences in cognitive development. Annual
Review of Psychology, 36, p. 613–648.
Germann, P. J. and Aram, R. J. (1996) Student performances on the science processes of
recording data, analysing data, drawing conclusions and providing evidence. Journal of
Research in Science Teaching, 33(7), 773–798.
Gomes, A.D.T. (2005). Reconhecimento e Uso de Testes Experimentais no Laboratório Escolar
[Recognition and Use of Experimental Tests in school Laboratory]. Unpublished Master
Dissertation. Belo Horizonte, Brasil: Faculty of Education, Federal University of Minas
Gerais.
Gott, R., & Duggan, S. (1995). Investigative Work in the Science Curriculum. Buckingham: Open
University Press.
Hart, C., Mulhall, P., Berry, A., Loughran, J., & Gunstone, R. (2000). What is the Purpose of this
Experiment? Or Can Students Learn Something from Doing Experiments. Journal of Research
in Science Teaching, 37, 655–675.
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
134 A. D. Trani Gomes et al.
Kaselman, A., & Kuhn, D. (2002). Facilitating Self-Directed Experimentation in the Computer
Environment. Paper presented at the Computer Support for Collaborative Learning. Boulder,
Colorado.
Klahr, D., & Dunbar, K. (1988). Dual Space Search During Scientific Reasoning. Cognitive
Science, 12, 1–48.
Klahr, D., Fay, A. L., & Dunbar, K. (1993). Heuristics for Scientific Experimentation: A Developmental Study. Cognition and Instruction, 25, 111–146.
Koslowski, B. (1996). Theory and evidence: The development of scientific reasoning. Cambridge,
MA:MIT Press.
Kuhn, D., Amsel, E., & O’Loughlin, M. (1988). The development of scientific thinking skills.
Orlando, FL: Academic Press.
Lijnsee, P. L. (1995). Developmental Research as a way to an empirically based Didactical
Structure of Science. Science Education, 79, 189–199.
Lubben, F. & Millar, R. (1996). Children’s ideas about the reliability of experimental data.
International Journal of Science Education, 18(8), 955–968.
Lubben, F., Buffler, A., Allie, S., & Campbell, B. (2001). Point and set reasoning in practical
science measurement by entering university freshmen. Science Education, 85, 311–327.
Meyer, K., & Carlisle, R. (1996). Children as experimenters. International Journal of Science
Education, 18, 231–248.
Millar, R. (1991). A means to an end: the role of processes in science education. In: B.E. Woolnough (Ed.). Practical Science. The role and reality of practical work in school science. (pp. 43–52)
Philadelphia: Open University Press.
Millar, R. (1996). Towards a Science Curriculum for Public Understanding. School Science
Review, 77, 7–18.
Minstrell, J., & van Zee, E. (Eds.) (2000). Inquiring into Inquiry. Learning and Teaching in Science.
Washington, DC: American Association for Advancement of Science.
Penner, D. E., & Klahr, D. (1996). The Interaction of Domain-Specific Knowledge and DomainGeneral Discovery Strategies: A Study with Sinking Objects. Child Development, 67, 2709–2727.
Roth, W. M. (1995). Authentic School Science. Dordrecht: Kluwer.
Ryder, J., Leach, J., & Driver, R. (1999). Undergraduate Science Students’ Images of Science?.
Journal of Research in Science Teaching, 36, 201–219.
Sá, E. F., & Borges, O. N. (1999). Identificação dos objetivos de atividades práticas por alunos
do Ensino Fundamental [Students’ identification of the aims of experimental activities].
Paper presented at the II Brazilian National Conference on Research in Science Education,
Valinhos, SP.
Sá, E. F., & Borges, O. N. (2002). Como alunos e professores entendem o propósito de uma
atividade de laboratório [How students and teachers understand the purposes of laboratory
activities]. Paper presented at the III Brazilian National Conference on Research in Science
Education. Atibaia, SP.
Schauble, L. (1996). The development of scientific reasoning in knowledge-rich contexts. Developmental Psychology, 32(1), 102–119.
Schauble, L., & Glaser, R. (1990). Scientific Thinking in Children and Adults In: Kuhn, D.
(Ed.) Developmental Perspectives on Teaching and Learning Thinking Skills. (pp. 9–27) Basel:
Karger.
Schauble, L., Klopfer, L .E., & Raghavan, K. (1991). Students’ Transition from an Engineering
Model to a Science Model of Experimentation. Journal of Research in Science Teaching, 28,
859–882.
Solomon, J., Duveen, J., & Scott, L. (1994). Pupils’ images of scientific epistemology.?
International Journal of Science Education, 16, 361–373.
Tamir, P. (1991). Practical work in school science: an analysis of current practice In: B.E. Woolnough (Ed.). Practical Science. The role and reality of practical work in school science. (pp. 13–20)
Philadelphia: Open University Press.
Downloaded By: [alessandro@coltec.ufmg.br] At: 11:30 29 November 2007
Students’ Performance in Investigative Activity and Their Understanding 135
Tsai, C. (1999). ‘Laboratory Exercises Help Me Memorize the Scientific Truths’: A Study of
Eighth Graders’ Scientific Epistemological Views and Learning in Laboratory Activities.
Science Education, 83, 654–674.
Welzel, M., Haller, K., Bandiera, M., Hammelev, D., Koumaras, P., Niedderer, H., Paulsen, A.,
Robinault, K., & von Aufschnaiter, S. (1998). Ziele, die Lehrende mit dem Experimentieren
in der naturwissenschaftlichen Ausbildung verbinden—Ergebisse einer europaeischen
Umfrage [The objectives teachers assign to practical work in science education—results from
a European study; in German]. Zeitschrift für Didaktik der Naturwissenschaften, 4, 29–44.