Abstract
According to variation theory, it is essential to enable students to focus on the object of learning and discern its critical features, but the features that it is possible to discern often depend on the equipment used. Thus, in labs, the experimental technologies used may shape students’ experience of focal phenomena, in a human–mediating tools–world manner, by placing some aspects of reality in the foreground, others in the background, and visualizing certain aspects that would otherwise be invisible. However, this mediating role is often neglected, and instruments and devices are often seen as having little cognitive value. Hence, the role of experimental technologies in labs as tools for learning is examined here through a case study, in which three sets of students investigated the same physical relationships (Newtonian motion in an inclined plane), but using different measurement technologies. The results demonstrate that what it is possible for students to experience in a laboratory is heavily influenced by the chosen technology. Some technologies do not afford the discernment of features regarded as crucial for students to learn. Furthermore, analysis of video recordings shows that the three sets of students’ discourses differed, although they studied the “same physics”. Hence, the role of experimental technologies in students’ learning in labs should not be neglected, and their courses of action should be seen as material-discursive practice. Moreover, general conclusions about learning in labs should be drawn cautiously, specifying the conditions and technology used, and discussions about learning technologies should not be limited to the use of computers.
Similar content being viewed by others
Introduction
Almost 80 years ago, Müller (1940) argued that “there is little evidence to show that the mind of modern man is superior to that of the ancients. His tools are incomparably better” (my italics). He reminds us “that the history of physical science is largely the history of instruments and their intelligent use” (ibid.). Strikingly, for example, four centuries ago Galilei (1610/1989) revolutionized the practice of science by introducing a major perceptual technologyFootnote 1: the telescope. This amplified users’ senses and “made hitherto invisible things visible” (van Helden 1989). Importantly, Galilei (1638/1954) not only observed nature using a technology but also subsequently described, in Two New Sciences, active experimentation using technologies both in experimental setups and for measuring physical quantities.
Many new technologies have emerged and been used in scientific inquiry and learning science since the days of Galileo and his introduction of technology into the practice of science. Moreover, in recent decades there has been increasing interest in “technology-enhanced learning”: the use of new technologies to support the learning of science (e.g. Kyza et al. 2009). The learning technologies that it has been claimed can support meaningful learning in science have been categorized by Kyza et al. (2009) as: (a) scientific visualization tools, (b) databases, (c) data collection and analysis tools, (d) computer-based simulations, and (e) modelling. The use of laboratory learning activities in formal instruction in science and engineering is intimately related to technology-enhanced learning, as technologies for data collection and analysis (and other technologies) are typically used in labs (in various ways) to support student learning. Indeed, the laboratory is seen as having a “central and distinctive role” in science education (e.g. Hofstein and Lunetta 1982, 2004) and as a learning environment that “sets science apart from most … subjects” (White 1988).
Nevertheless, the emergence of affordable and more powerful personal computers has raised questions regarding the possibility of “virtual labs”—where technology is used in the form of computer-based simulations—successfully replacing “real labs”—where technology is used in the form of data collection and analysis tools. Investigations of this possibility and related issues have yielded conflicting indications. Some studies have found that simulations can provide better, or at least equal, learning outcomes (e.g. Zacharia and Olympiou 2011; Chini et al. 2012). However, others have found that simulations have detrimental results, or tend to create virtual worlds that hinder students’ development of links between theories and models to objects and events in physical reality (e.g. Lindwall and Ivarsson 2010; Jensen 2014). Moreover, it is important to recognize that a scientific theory or model is “always merely an approximation to the complete truth” (Feynman et al. 1963, italics in original), as it is inevitably based on simplifications, assumptions, and limitations, which might not be valid in a practical situation. Furthermore, complex interactions may occur in real phenomena that limit the applicability of theories and models. Hence, there are clear theoretical limitations to the ability of simulations and virtual labs to replicate “reality”, and as succinctly stated by Feynman et al. (1963), “the principle of science … is the following: The test of all knowledge is experiment. Experiment is the sole judge of scientific ‘truth’.”Footnote 2 (italics in original).
To return to the issue of students’ learning in the laboratory: the description of the work by Galilei indicates that in the laboratory one does not have direct experience (human–world) of the world, but primarily a mediated experience (human–tool–world), shaped by the use of physical and symbolic tools (e.g. Dewey 1925/1981; Ihde 1991). The physical tools (the technologies used) function as “agencies of observation” (Bohr 1958), i.e. as tools for collecting, analysing and presenting physical data. In labs, as Dewey (1925/1981) notes, “appliances of a technology [such as] the lens, pendulum, magnetic needle, [and] level [are deliberately adopted in scientific inquiry] as tools of knowing”. Although experience in labs is mediated using “appliances of technology”, the role of these experimental technologies in students’ learning in laboratories has, with few exceptions, rarely been studied or problematized in educational research. This may partly be due to learning activities in the laboratory being commonly seen as providing direct, practical and concrete experiences of the physical world (e.g., Hofstein and Lunetta 1982; Trumper 2003; Singer et al. 2006). Another reason may be the traditional belief “that … instruments and experimental devices … per se … have no cognitive value” (Lelas 1993), i.e. they are seen as merely neutral vehicles for the transportation of information. Hence, technology in the form of experimental equipment is commonly seen as something that is simply “manipulated” (e.g. Lunetta 1998; Lunetta et al. 2007). Accordingly, Hucke and Fischer (2002) explicitly describe “object-related” action (manipulating objects) as a low complexity level of cognition and “concept-related” action (manipulating ideas) as a high complexity level. That is, in traditional beliefs about science, the technological means by which nature is perceived leaves no trace in our conceptions of nature (e.g., Kroes 2003). Popper (1972), for example, restricted his epistemology to the “world of language, of conjectures, theories, and arguments”. Furthermore, if “tools and artifacts” are discussed at all they are, as described by McDonald et al. (2005), “generally referred to, rather than described, or seriously studied” and technology is often taken to be synonymous with computers.
A consequence of these views is that the role of technologies is often neglected or taken for granted, and researchers focus instead on the concepts, ideas, and structures of labs, for example, how “open” labs are (e.g. Domin 1999). Researchers may also treat real physical labs as homogenous settings when comparing them with, for example, virtual labs. The views described above may also result in educators lacking awareness of the role of experimentation in a curriculum, and failing to exploit the full advantages of experimental technologies and labs for learning. Furthermore, neglecting the role of measurement technologies in science and engineering leads to naïve realism or naïve idealism (Ihde 1991; Ihde and Selinger 2003).
Thus, investigations of technology-enhanced learning in science and engineering should include detailed consideration of the role of the experimental technology used to promote students’ learning in labs. As succinctly expressed by Marton et al. (2004): “If we are interested in how students learn … we must ask ourselves what critical features of the object of learning students can possibly discern in a particular classroom situation” (my italics). This paper examines the affordances of experimental technologies, and the restrictions they impose, as tools for learning. More specifically, it considers whether different technologies that can be used to study the same object(s) of learning have different cognitive values and afford different possibilities for learning. The analysis is based on observations drawn from a case study in which three sets of students investigated the motion of a body in an inclined plane, using different measurement technologies. Since all the students investigated the “same physics” (Newtonian motion), the learning possibilities afforded by the different technologies could be studied and compared.
Learning and awareness
A later section presents background information about the cases studied, but the general theoretical framework of the investigation is summarised here. Key elements of the framework are variation theory and theories of mediated experience and awareness, rooted in pragmatism and (post-)phenomenology. However, most of the results and conclusions are also consistent with, and could be described in terms of, the main tenets of other theories, such as “conceptual conflict” (e.g. Hewson and Hewson 1984) and “cognitive conflict” (e.g. Posner et al. 1982) theories.
An important concept in various educational philosophies and theoretical frameworks (such as pragmatism, phenomenography, phenomenology, and activity theory) is intentionality—the idea that learning, thinking and experience cannot be studied in isolation from their content. Meaning emerges as a person directs his or her awareness to an object; “There is no learning without something learned, there is no thinking without something thought, there is no experiencing without something experienced” (Bowden and Marton 1998). Awareness is the totality of our experiences, but it is differentiated in such a way that some aspects are brought to the fore, i.e. focal awareness, while other aspects are marginalised and constitute the background (Gurwitsch 1964). Hence, learning is seen as “a qualitative change in the relation between the learner and that which is learned” (Booth 2004, my italics).
Learning through variation
Dewey (1925/1981) regarded labs as important environments for learning, and experimentation as an “indispensable instrument of modern scientific knowing”. He points out that we learn through experiencing differences, so by intentionally altering and controlling conditions in experiments “subject-matters which would not otherwise have been noted” may be disclosed and discovered (my italics).
This recognition is further elaborated in variation theory, developed by Marton and colleagues (e.g. Marton and Booth 1997; Bowden and Marton 1998; Marton and Tsui 2004; Pang and Marton 2005; Marton and Pang 2006, 2008; Marton 2015). This theory provides an explanatory framework for describing the conditions required for learning. It holds that for learning to be brought about, the critical aspects of the object of learning must be discerned and enter the learner’s focal awareness. Central concepts in variation theory are discernment, simultaneity and variation. Learning is seen as the process of developing certain capabilities and values that enable the learner to handle novel situations effectively. Powerful ways of acting are seen to emerge from powerful ways of seeing. Thus, aspects that can be discerned by the observer determine how something is seen, or why it is seen in a particular way. People discern certain aspects of their environment by experiencing variation. When one aspect of a phenomenon or an event changes, while one or more aspects remain the same, the one that changes is the one that will be discerned. One of the main themes of variation theory is that the pattern of variation inherent in the learning situation, the space of learning, fundamentally influences any learning within it.
Experiencing variation amounts to experiencing different instances of the object of learning simultaneously. This simultaneity can be either diachronic (experiencing, at the same time, aspects of something that we have encountered at different points in time) or synchronic (experiencing different co-existing aspects of the same thing at the same time).
As mentioned earlier, Marton et al. (2004) related learning to what it is possible for students to experience in a particular classroom situation, stating that in a learning situation “the critical aspects that it is possible [for a student] to discern … make up the enacted object of learning” (Marton 2015, my italics). Another important distinction in a learning situation is the difference between the intended object of learning (the critical aspects of a given object of learning that a student should discern) and the lived object of learning (the critical aspects that can be discerned and that the student actually discerns, i.e. what the student learns in the end).
Variation theory has been used as a theoretical framework in several empirical studies of learning in science and engineering (e.g. Carlsson 2002; Linder et al. 2006; Runesson 2006; Carstensen and Bernhard 2009; Fraser and Linder 2009; Ingerman et al. 2009a, b; Bernhard 2010; Ling and Marton 2012). Reviewing these contributions is beyond the scope of this paper, but they all highlight the importance of variation for enabling students to discern the critical aspects of the object of learning. They also confirm another observation by Dewey (1925/1981), that experiments (and hence labs) are educational tools that an instructor or course designer can use to create variations in such a way that phenomena can be discerned.
Mediated experience in the laboratory
Since students’ experiences in science and engineering laboratories are mediated, human–technology–world, through experimental technologies (which may dictate what enters their focal awareness and what remains in the background), there is an obvious need to consider how the world can be experienced through these technologies. As mentioned earlier, Dewey (1925/1981) saw technologies as “tools of knowing” in experiments. This is because mediation amplifies some aspects of the world, enabling the human investigator to perceive some things more clearly or to perceive things that are imperceptible without the mediating technology. An improved telescope, for example, enabled Galilei (1610/1989) to see stars of the 7th to 12th magnitudes, and to discover Jupiter’s moons. Thus, a mediating technology will transform experience in some way. An equally important, but often neglected, aspect of this transformation of experience by a mediating technology is that it not only provides amplification but also inevitably causes some reduction. For example, a telescope dramatically reduces the visual field. Hence it took Galileo a week to determine the correct number of Jupiter’s moons because some were outside the visual field of his telescope during some of his observations (Drake 1978). The telescope and the microscope are examples of the magnification-reduction structure that can be found in all technological mediations (e.g. Ihde 1979, 1990; Verbeek 2005; Kiran 2015).
When Galileo used his telescope, he saw a smaller portion of the sky in one glance than he saw using the “naked eye”, but he could still perceive only wavelengths that are visible to the human eye. In modern astronomy the range of wavelengths that can be used to obtain information has been vastly extended. However, it is important to realise that although the use of new wavelengths enables the observer to see previously “invisible” features of studied objects, other features will either remain or become invisible, depending on the wavelength range of the instrument used (cf. Ihde 2007, 2010). Similarly, in materials science, there are differences in what can be “seen” using electron, X-ray, or neutron diffraction instruments (e.g. Bacon 1975; Bowen and Hall 1975), while in medicine different radiological methods allow different observations (cf. Berg Friis 2015). These are examples of the revealing-concealing structure found in technological mediation (e.g. Ihde 1979, 1990; Kiran 2015).
Mediation through a technology has also an enabling-constraining structure, and an involving-alienating structure (e.g. Kiran 2015). In summary, the concept of mediation encapsulates an important but often overlooked aspect of measuring technologies used in techno-science—they are not neutral transmitters of information. Instead, as pointed out by Bohr (1958), it is impossible “in physical experience … to distinguish between phenomena themselves and their conscious perception” through measuring instruments. Moreover, he argues, “tools of observation play [a role] in defining … physical concepts”.
All dimensions of mediation contribute to what enters our focal awareness and what is pushed into the background, i.e. mediating technologies strongly influence figure-background relationships in human experience. Based on the theories of mediated experience presented above, I propose a simplified model (Fig. 1) of the reductions of horizons involved in human inquiry when using experimental technologies. Amplifications & reductions, revelations & concealments, and enablements & constraints are involved in each step of the subject matter—object of study—tools for measuring and collecting data—tools for processing and (re-)presenting data chain. The first step, the selection of the subject matter for investigation, sets choices and restrictions regarding the concepts that matter (sic!), i.e. the concepts that will and will not be applicable or relevant. The cases discussed in this paper concern the domain of mechanics within the subject physics. Hence, within such an investigation, not only non-physical concepts but also physical concepts linked to domains outside mechanics (at least in our current understanding of the domain), such as colour, are beyond the sphere of the inquiry. As discussed in more detail below, further amplification, revelation and enablement are afforded—and reduction, concealment and constriction imposed—by the choices of setups used as an object of study and the technologies used as tools for measuring and collecting data. Finally, the tools used for processing and (re-)presenting the experimental data afford and impose further amplification–reduction, revelation–concealment and enablement–constriction, for example, through signal processing.
Illustration of the selective horizons of experimental technologies and humans in relation to humans’ life-world: a. A general model of succesive selective horizons. b. Illustration that different tools for measurement and collection of data can have different horizons
In addition, the limitations of human perception (indicated by the dashed ellipses in Fig. 1) must be considered. A student will not necessarily perceive what an experimental technology enables him or her to perceive. What is actually perceived (and hence provides material from which it is possible for him or her to learn) depends on the instructional features of the situation, such as scaffolding from the teacher and instructions. Hence, as indicated by the arrows on the dashed ellipse, human foci can be shifted through educational interventions. Indeed, human agency is involved in all steps in the chain, and that the categorizations in the model shown in Fig. 1 are solely for analytical purposes. In a real case, there are no sharp “boundaries” between them. Accordingly, Harré (2003) highlights this by describing it as “apparatus – world complexes”.
Furthermore, as indicated by the dashed ellipses in Fig. 1a, students experience physical phenomena not only through experimental technologies. Other tools such as mathematics, drawings and verbal language are used, and senses such as touching, hearing, smelling, and tasting may be involved, in addition to seeing. Indeed, there are strong indications in the literature that learning is enhanced by the use of multiple modalities (e.g. Kozma 2003; Ainsworth 2008; Jornet and Roth 2015). However, in this paper the specific analytical focus is on the role of experimental technologies.
Aims, setting and methodology
As already mentioned, Marton et al. (2004) related learning to what it is possible for students to experience in a particular classroom situation, but the possible experiences that are enabled by mediating technologies in labs, and the constraints imposed by these technologies, are generally neglected or trivialised. The aim of this paper is to elucidate “the way in which something can be experienced” (Marton et al. 2004, italics in original) when mediated by technologies (agencies of observation) in science and engineering education laboratories. The main research question (RQ1) addressed is: “Do experimental technologies in the form of different instrumentations differ in the possible learning they enable when the same object of learning is studied”. To rephrase this in terms of the terminology used in variation theory: “Do different technologies lead to different possible enacted objects of learning?”. A secondary research question (RQ2) is: “Are the observed differences in students’ courses of action associated with the instrumentation used?”. Again, in variation theory terminology, this becomes: “Are there differences in students’ lived objects of learning?”.
We have addressed these research questions in a case study, focusing on the learning affordances (and constraints) of three experimental setups in labs for studying the motion of a body (cart or glider) under uniform (gravitational) acceleration in an inclined plane. In these labs, students studied the same physical relationships, but used three different measurement technologies: photogates, a tape timer, and probeware (MBL).Footnote 3 These measurement technologies are commonly used in schools and the “same physics” was studied. Thus the instrumentation varied but not the intended object of learning. Hence, the use of three technologies should enable differences (if any) in enacted and lived objects of learning afforded by the technologies to be detected (cf. Gibson 1979). Further, we expected that the results could be understood and generalized outside the domain of physics.
This was not an intervention study, and the labs discussed in this paper were regular labs in physics courses and they were studied in naturalistic settings, i.e. ordinary scheduled labs. In all cases, the labs were instructed by well-qualified teachers with good reputation; none of the teachers had less than 20 years of teaching experience. The photogates and tape timer labs were parts of a physics course in a foundation year programme for students who had not specialised in science (or engineering) in upper secondary school. Thus, the level of the course corresponded to a similar course in the Swedish upper secondary school, although the students were slightly older. The probeware lab was part of a science course in a teacher education programme. As in the foundation year programme, the students participating in this programme were not required to have studied physics in upper secondary school. Hence, all the students were at approximately the same scholarly level with regard to the physics content. In all three cases, the tasks in the labs were typically performed by groups of 2–3 students.
To address RQ1, the aspects of accelerated motion in an inclined plane that could be experienced through the measurement technologies and experimental equipment provided in the labs, and the dimensions of variations they afforded, were identified and circumscribed by phenomenological inquiry (Ihde 1986). This method can be seen as a “blend of empirical and philosophical research methods” (Rosenberger and Verbeek 2015; see also Verbeek 2015). The results of this inquiry, presented below, analyse the enacted object of learning, i.e. “the researcher’s description of whether, to what extent and in what forms, the necessary conditions of a particular object of learning appear[ed] in a certain setting” (Marton et al. 2004), by means of the experimental technologies and agencies of observation used in the labs.
To address RQ2, the course of action of each group of students in the labs was recorded by a (separate) video camera. Three, two and eight groups were recorded in the photogates, tape timer and probeware labs, respectively, resulting in about 15 h of video. This was subsequently used to detect typical patterns of interaction and to find evidence supporting, or refuting, hypotheses regarding the generality of these patterns (Jordan and Henderson 1995). I particularly focused on how the students interacted with and through the technology, what they did, what they made relevant, and how they oriented themselves towards the object of learning (cf. Verbeek 2015). All the video recordings were repeatedly viewed in this initial analysis, then episodes containing particularly interesting and comparable activities related to the research questions were identified and transcribed to allow detailed examination of patterns of interaction. In the transcriptions, standard conventions used in conversation analysis were used (ten Have 2007) and the transcripts presented here have been translated from Swedish into English. Finally, the patterns of interaction observed in the labs involving the use of the three different technologies were compared and contrasted to find similarities and “differences that make a difference” (Bateson 1972; cf. Lindwall and Ivarsson 2010). This analysis examined “the lived object of learning, i.e. the way that students [saw, understood, and made sense of] the object of learning” (Marton et al. 2004). This paper focuses mainly on the affordances and constraints of the experimental technologies used in the labs for the possibilities of discernment and learning (RQ1). Findings concerning the students’ lived object of learning (RQ2) are here only briefly described in the Results section. Some illustrative excerpts from transcripts regarding these findings are presented. Although important, the roles of instructions and the instructor in relation to the enacted and lived objects of learning were ignored in this study, and the sole analytic focus was on the role of the three technologies.
Motion in an inclined plane
The primary issues investigated were the learning affordances and constraints of the technologies used in the three experimental setups. I present here some background information related to motion in an inclined plane to complement the general background presented earlier. In addition to his optical studies (also mentioned above), Galilei was one of the first scholars to observe motion in an inclined plane and systematically investigate what we currently call “uniformly accelerated motion” (e.g. Galilei 1638/1954; Drake 1978; Garfinkel 2002; Ford 2003). Studying motion in an inclined plane has several advantages over studying the motion of a freely falling body, because the acceleration is lower (\(a = g \cdot \sin \theta ,\) where θ is the angle of inclination of the plane, g is the acceleration due to gravity and a is the acceleration under which the body moves), so the motion is slower and easier to observe. Given the difficulties of measuring time accurately during Galilei’s period of history, this was essential. He studied the motion of objects on various slopes and argued that free-fall was a limiting case, where θ = 90°.
The students in all three labs considered here faced the problem of making the same conceptual distinctions as Galilei, although they used more advanced measurement technologies. One of Galilei’s major accomplishments was to differentiate clearly (in modern terms) between “average speed”, “instantaneous speed” and “increments of speed”. However, Galileo failed to make sense of accelerated motion for many years, since his initial hypothesis was that “increments of speed” are related to distance travelled rather than time, i.e., he initially believed that a = Δv/Δx, where v is velocity (using our current notation) and x is position. However, he eventually concluded that (in modern notation) a = Δv/Δt, where t is time, reflecting the fact that acceleration is constant for a body either in an inclined plane or in free fall (Galilei 1638/1954; Drake 1978; Ford 2003).
It is illuminating to consider the patterns of variation that may be discernible in different motions in an inclined plane, as illustrated in Table 1 and Fig. 2, and consider these in the light of variation theory. If only accelerated motion downwards on an incline is studied (or if this is the only motion that can be studied), position, velocity and acceleration all have the same direction and hence the same sign. Clearly, this limits the possibilities to discern and discriminate between these concepts, required by variation theory.
a Motion of a cart in an inclined plane. b Theoretical graphs of position, velocity and acceleration against time for motion in an inclined plane. The positive direction is down the incline
Laws (1997) contends that a good understanding of kinematics (laws of motion) is essential for the understanding of dynamics (laws of force and motion). However, various researchers (e.g. Trowbridge and McDermott 1980, 1981; Bowden et al. 1992; McDermott 1997) have shown that many students have problems understanding basic concepts of kinematics. Many students have difficulty distinguishing between velocity and change of velocity. They commonly believe that acceleration is always in the direction of motion, and that zero velocity implies that the acceleration must be zero. As will be shown in the next section, the different measurement technologies used in the labs have different affordances for addressing these difficulties in learning kinematics.
Results
Here, I first present the results of a comparative analysis of the affordances for learning related to the experimental technologies used to study motion in an inclined plane, in two parts. The units of analysis in the first part are the technologies and their properties relevant to RQ1: “Do experimental technologies in the form of different instrumentations differ in the possible learning they enable when the same object of learning is studied”. The second part briefly presents results of an analysis of students’ activities in the labs. Thus, the unit of analysis is students’ interactions with the technologies used, and the findings are pertinent to RQ2: “Are the observed differences in students’ courses of action associated with the instrumentation used?”.
Analysis of affordances of the three experimental technologies (RQ1)
This part analyses the affordances of each of the three technologies used, then compares and contrasts these affordances.
Photogates
Figure 3 shows the experimental setup with a glider on an air track. The track is inclined and the air track system provides low friction between the track and the moving object to be observed (glider). A ‘flag’ is attached to the glider, which blocks the light beam from the two photogates, designated A and B, whenever the glider passes them. If ∆s is the width of the flag and tA and tB are the times during which the light beams from gates A and B are blocked, respectively, the instantaneous speedsFootnote 4 can be calculated as |vA| =∆s/tA and |vB| = ∆s/tB. In addition to measuring tA and tB, the instruments used in this lab can measure the time that the glider takes to move between gates A and B, tAB.
Glider on an inclined air track. The glider’s speed is measured using two photogates. a A photograph of the lab, and b a schematic diagram of the setup
The velocity and the acceleration as the glider slides down the incline, or up the incline (after an initial push), can be studied, and this enables the fallacy that acceleration must act in the direction of motion to be addressed. (In the lab session studied here, however, the students were not asked to observe uphill motion.) However, since only speed—and not the sign of the velocity—is directly determined using the experimental equipment, there is a risk that students make a sign mistake when asked to perform this task. In addition, the equipment cannot provide data showing that a glider given an initial upward push will have zero velocity, but non-zero acceleration, at its turning point. Furthermore, the electronic circuits used in this lab did not permit a complete cycle of motion (upwards then downwards) by the glider to be recorded in a continuous sequence, because a maximum of four time measurements can be stored. It would be possible to measure only the speed when the glider passes gates A and B in both directions and find that |vA1|≈|vA2| and |vB1|≈|vB2|, where vA1 and vB1 are the velocities of motion upwards, and vA2 and vB2 are the velocities of motion downwards. However, due to the limitations of the electronics, neither tA1B1 nor tA2B2 could, in this case, be measured in the same measurement sequence, and it was not possible to determine a1 (up) and a2 (down) in a single experiment. It should be noted that it is not difficult to determine “acceleration” over distance, ∆v/∆x. Thus, the “wrong” definition of acceleration is afforded, but not the “right” one!
Theoretically, more data could be obtained from plotting position-time, velocity-time and acceleration-time graphs by keeping one photo-gate at the same position and moving the other. However, this is a difficult and awkward procedure.
Tape timer
A tape timer makes dots on a paper tape at equal time intervals (in this case set by the frequency of the local mains electricity supply, 0.01 s, from 2 to 50 Hz). In this setup, the paper tape is attached to a cart that moves down an inclined plane, pulling the tape through the tape timer. By measuring positions of the dots on the tape, it is easy to obtain the data required to plot a position-time graph. Velocity-time and acceleration-time graphs can then be generated by numerical derivation (v = ∆x/∆t and a = ∆v/∆t) (Fig. 4).
Motion of a cart down an inclined plane, recording position as a function of time using a tape timer. a A photograph of the lab, and b a schematic diagram of the setup
Since the tape is pulled by the cart through the tape timer, only downward motion can be studied. Upward motion would buckle the tape. Thus, it is not possible to address the direction of acceleration by using this technology to record the cart’s motion after pushing it up the inclined plane, or the magnitude of the acceleration at the turning point.
Probeware (MBL)
The third case also involves observation of an object moving up, down, or both up and down, an inclined plane. However, its position is measured (in real time) by a motion sensor attached via an interface to a computer, which can display (on a screen in real time) the cart’s position, velocity, and acceleration as functions of time. Velocity and acceleration are obtained by numerical derivation by the computer.
In contrast to the previous cases, the graphs (Fig. 5b) are presented simultaneously with the actual motion. However, the most important feature in the context of this paper is that a wider range of phenomena can be studied experimentally. In this case, it is as convenient to study motion up the incline as motion down the incline. Furthermore, unlike the measurement technologies used in the other cases, the probeware technology allows measurement of the acceleration at the cart’s highest point following an initial push upwards, thereby permitting the fallacy that acceleration is zero at this point to be addressed.
Measurement of a cart’s motion in an inclined plane measured by means of a motion sensor connected to a computer via an interface. The positive direction is down the incline, away from the motion sensor
Comparing affordances
A summary of the affordances of the technologies for studying motion in an inclined plane (Table 2) clearly shows that what is possible to discern depends on the technology used.
Table 2 summarises the affordances of the measurement technologies used for studying motion in an inclined plane applied in the three cases, and confirms that what is possible to perceive or discern depends on the technology used. All three measurement technologies afford the determination of velocity, but its sign cannot be determined with the photogates setup and must be inferred by the experimenter, while the tape-timer setup allows observation of motion only in one direction. Changes of velocity (one of Galilei’s important discernments) can be studied using all three setups. However, there are some subtle differences. The photogates’ affordance for determining acceleration (∆v/∆t) is problematic because students first perceive and manipulate the distance between the photogates. In contrast, with the probeware technology, the acceleration is predefined by the measurement system. Studying ∆v/∆x is possible, but the settings in the software for doing this are not as straightforward as those used to obtain a = ∆v/∆t. We see here that some conceptual distinctions have already been made for the user, or in the words of Wartofsky (1979) “artifacts (tools and languages) [are] objectifications of human needs and intentions … already invested with cognitive and affective content”. Consequently, most users will never reflect on this (and other) pre-defined choices made in the measurement system.
The analysis of affordances presented above and summarised in Table 2 describes what is possible to observe with each technology. An integrated part of most measurement technologies is a system for recording data, then presenting or transmitting the recorded data. Table 3 summarises and presents the primary outputs of data obtained using each of the technologies, together with a summary of the mode of measurement and a description of how graphs displaying the acquired data are (or can be) generated. It seems reasonable to assume that what students actually perceive in a learning situation depends, to some extent, on how the experimental data are represented by the technology used. The primary output in a photo-gate experiment is a set of three numbers corresponding to the times tA, tB and tAB. Measurements are only acquired when the flag of the glider blocks the light beam of one of the photogates, so they are taken at specific points in time. Graphing is not facilitated by this technology. In a tape-timer experiment, the primary outputs are dots printed on the paper tape as it is pulled through the tape-timer mechanism. The students must measure the positions of the dots on the tape and produce graphs manually. The measurement is practically (although not strictly) continuous. Finally, in a probeware experiment the primary outputs depend on the settings of the software controlling the data acquisition system. In a motion-in-an-inclined-plane lab, graphs of position, velocity and acceleration as functions of time are selected. As in a tape-timer experiment, the measurement can be regarded as practically continuous,Footnote 5 but the experimental data are collected automatically by the measurement technology and the resulting graphs are presented by the software in real time. Nevertheless, students must discern relevant parts of the graphs. Although the probeware experiment is typically set to present graphs (in this case), other outputs, such as tables of numerical values of position as a function of time, can be selected.
Other affordances than those summarised in Table 2 are available from the different experimental setups. For example, probeware allows measurements to be quickly repeated and experimental conditions to be varied quite easily. Like many other experimental technologies, it also allows observation of phenomena that cannot be directly observed by the naked eye through use of its software for processing and representing experimental data. It is also important to realise that the comparisons in Tables 2 and 3 are incomplete; there are other measurement technologies than those presented in the tables. Before the availability of inexpensive computers and sensors a tape timer afforded a possibility to graph motion in an inclined plane and photogates affords measurements of speed (and frequency) with higher accuracy than possible with a stop-watch. Hence, by tradition, these measurement technologies can be found in many school labs.
Moreover, there are differences in affordance of not only the measurement technologies (the agencies of observation), but also the experimental equipment (in a narrow sense). These are independent of the affordances of the measurement technologies described above, and are summarised in Table 4. The experimental equipment used in all three cases affords the slope of the inclined plane and the mass of the cart or glider to be changed. However, only the equipment used in the probeware lab allows the friction that affects the movement of the observed body to be changed (from a low-friction default). It should be stressed that this cart is not exclusive to the probeware measurement technology, and can be used with the other measurement technologies described in this paper. Moreover, the cart used in the tape timer lab or the glider on an air track can be used with probeware as the technology for measurement. Furthermore, it can be noted that the (almost) frictionless air track is an adaptation of experimental equipment to idealized physical theories, i.e. changing the world to fit theories and models!
Students’ courses of action in the labs (RQ2)
My main analytic focus in this study is on what the technologies used allow students to discern, and the discernments they do not readily permit. Therefore, only short excerpts of transcripts from records of (pseudonymised) students’ courses of action in these labs are presented here. Students’ courses of action in labs are framed by encounters with the instructions, the technology, the teacher, and other students (Bernhard 2010). Thus, factors other than the technology, such as courses of action, verbal and non-verbal communication, have influenced the discourses observed in the labs (as will be discussed in more detail in a forthcoming paper). With this caveat in mind, I briefly describe here my analysis of students’ courses of action in the labs, with a particular focus on the role of the technologies.
An analysis of the complete video recordings of students participating in the photo-gate and tape-timer labs clearly shows that the students did not focus on the intended object of learning, uniformly accelerated motion. In the photo-gate lab, the discourse of all groups consistently centred on reading and noting the numbers (the times tA, tB and tAB) from the electronics box, as illustrated in Excerpt 1. Concepts such as velocity, change of velocity and acceleration were barely mentioned.
In contrast, students performing the tape timer lab consistently focused on making sense of the dots on the tape, as shown in Excerpt 2. Although they had been instructed by the teacher to translate these dots into a position-time table to enable graphing, both of the groups studied only used the tape as an indicator of the time the cart took to move one fixed distance. Thus, they only determined the average velocity and not the instantaneous velocity. Also in this case, motion concepts featured very rarely in the students’ discourse.
It is clear that the student did not perceive the “world” through a human–technology–world mediated experience, but numbers and dots produced by the measurement technology. This is a human–technology (–world) experience (cf. Ihde 1979, 1991). The students lacked the conceptual resources required to interpret the numbers and dots, and their discourse in these cases resembles the “conceptually blindfolded” discourse described by Bergqvist and Säljö (1994). The students struggled to make sense of the measurements and to produce graphs.
In contrast, in the probeware lab students’ discourses consistently centred on making sense of motion concepts such as position, velocity and acceleration.
Experimental results were presented in graphical form in real time, readily enabling simultaneous discernment. While in the tape timer and photo-gate labs students were supposed to produce position-time, velocity-time and acceleration-time graphs themselves, these graphs were produced by the computer–interface–motion sensor complex in the probeware lab. Nevertheless, in the probeware lab, students did not simply “copy and paste” the graphs into their reports without thinking. Instead, in contrast to the tape timer and photo-gate labs there students struggled to make sense of the measurements and to produce graphs, they struggled to make sense of the graphs and the physics involved. Indeed, connecting graphs and other representations to the science involved is not a simple task but is found to be a difficult task for most students (e.g. McDermott et al. 1987; Beichner 1994; Airey and Linder 2009; Planinic et al. 2013; Hill and Sharma 2015) and even for professionals outside their own domain of expertise (e.g. Roth and Bowen 2001).
It is instructive to examine not only the affordances provided by the measurement technologies used, but also the discernible patterns of variation afforded by the ability to observe motion up, down and at turning points, as is also indicated in Table 2. If accelerated motion only down an incline is studied (or it is the only motion that can be studied), position, velocity and acceleration will all have the same direction and hence the same sign. Clearly, according to variation theory this will restrict the possibilities to discern and distinguish these concepts.
In all three labs, conceptual distinctions and discernment of critical aspects were essential for the successful completion of tasks. However, in the tape timer and photo-gate labs, students required this ability in advance, i.e. they needed to know in advance what they were supposed to learn in the lab. In contrast, in the probeware lab, the students were not required to have this ability but were led to make conceptual distinctions and discern critical aspects by the affordances of the technology and a task-structure that had been developed based on variation theory. The lab resulted in a more powerful conceptual understanding (Bernhard 2010), which enabled students to successfully complete tasks. Thus, although the students were intended to “study the same physics”, the physics they performed, as constituted in their courses of action, and the mediated relations formed, strongly differed.
Discussion
Validity and limitations of the results
The analysis of the cases presented here clearly confirms that the “critical features of the object of learning students can possibly discern” (Marton et al. 2004, my italics) depend on the experimental technology used, which thus answers the first research question. What it is possible for students to experience in a science or engineering laboratory is heavily influenced by the affordances of the chosen instrumentation. The analyses presented here of students’ courses of action when studying motions of an object in an inclined plane show that affordances for discernment, variation and (hence) learning depend on the technology used. The experimental technologies analysed in this study do not include all commercially available technologies, and others may not be subject to the identified limitations. For example, systems that allow up to four photogates to be used simultaneously are available. Moreover, the technologies analysed in the three cases only afford the study of one-dimensional motion: other measurement technologies afford the study of three-dimensional motion (Ronen 1995). However, this does not contradict my conclusion that experience in labs is mediated through technologies, and hence the possibilities for discernment and variation depend on the technology used. Indeed, the existence of other technologies supports my claim that affordances for learning are technology-dependent. As the unit of analysis in the investigation related to RQ1 is the affordances of the technologies used, the results of the analysis will hold regardless of contextual features such as, for example, instructions and educational setting.
The analysis and conclusions presented in this paper are based on three cases of students studying accelerated motion of a body in an inclined plane. The paper gives examples of the variations in possibilities for learning afforded by the technology used. Many other examples of experimental technologies used in science or engineering settings that provide different affordances could have been chosen, and I maintain that in many other (if not all) cases, what is possible to discern depends on the technology used. For example, Haglund et al. (2015) have demonstrated how new cheap infrared cameras make it possible to carry out experiments in thermodynamics that gave immediate results, and Bernhard (1999) has demonstrated how probeware technologies enable the visualisation of oscillation modes in coupled harmonic oscillators. In both of these examples, the technologies make it possible to visualize in experiments phenomena and concepts that are impossible or difficult to discern with a “naked eye” (although some heat phenomena in thermodynamics experiments can, of course, be discerned somewhat rudimentarily with a “naked hand”).
The use of probeware in mechanics labs and in interactive lecture demonstrations promotes good learning results, as measured by conceptual tests (e.g. Thornton and Sokoloff 1998; Trumper 2003; Sokoloff et al. 2007; Thornton 2008; Bernhard 2010; Sharma et al. 2010), but it is important to understand that the use of computers and other “high-tech” equipment is not a requirement for student learning. Indeed, simple materials are used in the Physics by Inquiry curriculum developed by McDermott and co-workers at the University of Washington (e.g. Shaffer and McDermott 1992; Wosilait et al. 1998). Furthermore, the use of probeware in mechanics labs does not necessarily lead to good learning results, and an instructional approach based on variation theory is also essential (Bernhard 2003, 2011). Similar conclusions concerning the use of probeware in electric circuit labs and electro-chemistry labs have been reported by Carstensen (2013) and Ling and Marton (2012), respectively. Crucial aspects of the technology used are not its simplicity or complexity, but the opportunities (and constraints) for discernment and (hence) learning it affords. It is the responsibility of the teacher to use these possibilities in a constructive way by carefully designing tasks that help students to discern the critical aspects of the object of learning. Thus, belief in technological determinism is fallacious.
Technological determinism is related also to the second research question, regarding the relationship (if any) between the technology used and the actions of the students in the labs, i.e. their discourse. As mentioned above, the labs considered in this study were investigated in situ, i.e. in a naturalistic setting. It is possible that the students’ discourses in the tape-timer and photo-gate labs would have differed if they had been instructed in a manner based on variation theory. I believe so, but with qualifications. As is summarized in Table 2 there would still have been specific limitations in the opportunities to introduce variations and the simultaneous experience that variation theory considers to be essential for learning would have been difficult to arrange. Nevertheless, results presented by Bernhard (2003, 2011), Carstensen (2013) and Ling and Marton (2012) show that students’ discourses and lived objects of learning depend on the affordances of technology used, and on how well these possibilities are utilized by the teachers in their design of lab tasks and when writing lab instructions. Hence, while I contend that the answer to RQ1 is independent of the educational setting, since it is related to the inherent possibilities of the technologies, the answer to RQ2 may depend on the educational setting. Students’ discourses may be slightly different in settings other than those investigated in this study. It is, however, still the case that the primary output of experimental data and the type of measurement (see Table 3) seem to influence students’ discourse. More extensive observations of interactions in further settings are required before any conclusions can be drawn concerning the extent that a specific technology and other factors (for example, student background, nature of instructions given and task structure) affect the students’ discourse.
Finally, the dependence of my conclusions on the theoretical framework should be considered. Although I have performed this study, and interpreted the results, within the framework of variation theory and philosophies of technology based on pragmatism and (post-)phenomenology, I maintain that most of my results and conclusions are valid also when regarded in terms of other frameworks. For example, my results show that some technologies do not permit the design of an experimental predict–observe–explain (POE) sequence (e.g. White and Gunstone 1992; Coştu et al. 2012) for the acceleration at the turning point (where the velocity is zero) when the travel of a cart changes from going up an inclined plane to going down. Similarly, some technologies do not permit the arrangement of “cognitive conflict” (e.g. Posner et al. 1982; Gorsky and Finegold 1994) or “conceptual conflict” (e.g. Hewson and Hewson 1984) in the same situation. Indeed, the lab to investigate motion in an inclined plane using probeware was designed using POE theory, according to Thornton (2008), while Haglund et al. (2015) have based thermodynamics learning tasks on POE-sequences. Furthermore, as experimental technologies as mediating technologies strongly influence figure-background relationships, i.e. through their amplification–reduction, revelation–concealment and enablement–constriction structures, their roles could be considered within a cognitive load framework (e.g. Chandler and Sweller 1991). For example, in this framework, it would suitable to analyse an actual technology’s contribution to extraneous cognitive load suggested to be detrimental for learning (Chandler and Sweller 1991) or its enablement of germane cognitive load suggested to be useful for learning (Muller et al. 2008). It is beyond the scope of this paper to go further in detail how my results could be interpreted in other frameworks.
Implications
In the context of quantum physics, Bohr (1958) argued that “the word phenomenon [should] exclusively … refer to the observations obtained under specified circumstances, including an account of the whole experimental arrangement [and] it is … impossible to distinguish sharply between the phenomena themselves and their conscious perception”. In a similar vein, textbooks used in the education of engineers and scientists, for example Microscopy of Materials (Bowen and Hall 1975), stress that an experimenter must ask “what can this instrument be used for, and which … is best for [a] particular problem?” Furthermore, Bowen and Hall stress that an experimenter must thoroughly understand the instrument and its “advantages and limitations”. The results of this study show that Bohr’s and Bowen and Hall’s arguments are also valid for students’ learning in labs in educational settings. For example, they demonstrate that it is difficult for students to “distinguish sharply between the phenomena themselves and their conscious perception”. Indeed, the etymology of the word phenomenon comes from Greek ϕαινόμενον “thing that appears”. However, as pointed out by Garfinkel (2002), you may “losing the phenomenon” due to the way an experiment is set up and the equipment used. This implies that researchers in science and engineering education, teachers, and educators of teachers must consider “which instrument is best for a particular object of learning”, while teachers need to “thoroughly understand [the] advantages and limitations” of different labs and experimental technologies for student learning. Hence, we should avoid discussing learning in labs in general terms and draw general conclusions very cautiously.
Accordingly, this study shows that there is no general answer to the common questions: “Do labs really assist students’ learning?” and “Are they worth the cost?”. Their value for a specific object of learning depends on the suitability of the technologies used for that object of learning and on the design of the tasks in the lab. Neither can “real” labs be compared in general terms to “virtual” labs. For example, Olympiou et al. (2012) contend that virtual labs, but not real labs, allow quick repetition of experiments, relatively easy variation of experimental conditions and observation of phenomena that cannot be directly observed by the naked eye. However, this paper shows that some experimental technologies, such as probeware, also have these affordances. This confirms the main conclusion of this paper: that different experimental technologies have very different affordances, and one should draw general conclusions regarding the affordances of experimental technology very carefully. Moreover, this is also true for virtual technologies, as shown by, for example, comparisons of virtual experiments with and without touch sensory (haptic) feedback (Zacharia 2015). Thus, it is impossible to compare “real” and “virtual” experimentation as undifferentiated categories.
Furthermore, the view that experimental technologies have little cognitive value is tacitly expressed or implied in at least three recent reviews and books (Psillos and Niedderer 2002; Singer et al. 2006; Lunetta et al. 2007). This can be understood in the light of the review presented earlier in this paper and the view that experimental data are simply “observables” (Hempel 1952). However, the technologies used in laboratories, and their suitability for the application, have not always been neglected. For example, considerable interest in teaching instruments for laboratories and demonstrations arose around 1800, but after WWII they “were just considered cumbersome, obsolete and useless artefacts” (Brenni 2010). This paper highlights the need to revive the interest in equipment as “teaching instruments”, but in a modern way informed by, for example, variation theory. This paper and my earlier research (for example, Bernhard 2008) indicate that the technology used in successful labs brings important concepts and relationships into students’ focal aware-ness by enabling constructive patterns of variation to be observed and discerned. In such cases, the technology is a “cognitive tool”. Furthermore, Tala (2009) has studied scientific practice in nanophysics as an example of techno-science, and argues that science education should be based on a “scientifically sound and authentic content [of science as] the necessary starting point”, taking into account (among other things) the use of technologies. In a similar vein, several decades ago Gooding (1990) stressed that “theories of learning and representation should be compatible with our knowledge of how scientists gain and use information about reality”. However, if we are not aware of the role of tools and technologies, and do not “thoroughly understand technologies and their advantages and limitations”, we will simply, as Cuban (2001) notes “[use] the new technology to maintain existing practices” or do not question existing practices.
Conclusions
The reason we are on a higher imaginative level [in modern science] is not because we have a finer imagination, but because we have better instruments. In science, the most important thing that has happened in the last 40 years is the advance in instrumental design…a fresh instrument serves the same purpose as foreign travel; it shows things in unusual combinations. The gain is more than a mere addition; it is a transformation (Whitehead 1963, my italics).
In previous sections I have demonstrated that “appliances of a technology [can be used as] tools of knowing” (Dewey 1925/1981), i.e. they have “cognitive value”. However, as demonstrated by my examples of three experimental setups that initially seem to provide opportunities to see the same physical relationships, different technologies can have very different cognitive values.
Several conclusions can be drawn from this study. First, the use of labs and experiments in education cannot be discussed in general terms. Instead, science education researchers, teachers and teacher educators need to specifically consider “which instrument is best for a particular object of learning”. Thus, teachers and educational developers need to “thoroughly understand experimental technologies and their advantages and limitations” for students learning a specific content or specific concepts when planning teaching and design learning environments (cf. Bowen and Hall 1975). If new and old technologies are used in clever ways, we may achieve not only gains, but also transformations in students’ learning.
Furthermore, my study shows that there is no general answer to the common questions regarding whether labs really assist students’ learning and whether they are worth the cost. Further, real labs cannot be compared in general terms to virtual labs. We should discuss learning in labs in general terms and draw general conclusions very cautiously.
Roth and Jornet (2014) have recently argued, with special reference to Dewey and Vygotsky (and others), that the term “experience” is undertheorized in science education research literature. Indeed, the use of tools (i.e. artifacts/technologies) plays an important role in both Vygotsky’s and Dewey’s theories of experience and action (e.g. Vygotsky 1978; Dewey 1938/1986; Cole 1996; Hickman 1990; Miettinen 2001; Cole and Derry 2005). My conclusion is that the role and use of technologies in science education is also undertheorized. I argue here that the role of technologies extends far beyond the use of computers, and their roles in the laboratory remain a neglected aspect of research in science education that warrants richer, more detailed investigation, and better theorizing.
Barad (2003), a theoretical physicist by training, argues that we must let matter matter. “To think of discourse as mere spoken or written words forming descriptive statements is to enact the mistake of representationalist thinking. Discourse is not what is said; it is that which constrains and enables what can be said” (ibid., my italics). Paraphrasing Barad, I contend that “discourse is not what is said; it is that which constrains and enables what can be said, done and discerned”. I have demonstrated in this paper that the technology used in labs constrains and enables what can be discerned and ultimately learned.
Notes
In this paper, the term “technology” is most often used to describe equipment, instruments and devices used for performing observations and making measurements in science and engineering. In addition to terms such as “instrument”, “, equipment”, “, apparatus” and “devices”, the terms “artefact”/“artifact” and “agencies of observation” can be found in the literature.
The quote from Feynman et al. (1963) should not, of course, be interpreted as a claim that establishing scientific “truth” from an experiment is simple and straightforward. There are numerous complications, as detailed in, for example, an insightful discussion regarding the philosophy of experimentation presented by Hacking (1983). However, the quote highlights the empirical essence of the natural sciences.
This type of system is often called “microcomputer-based laboratory” (MBL). However, the abbreviation “MBL” is commonly associated with an “interactive engagement” pedagogical approach and I have chosen to use the term “probeware” to stress that I am analysing the affordances of a technology.
Actually average speed throughout the few milliseconds during which the light beam is blocked, but for practical purposes it can be seen as instantaneous speed.
In reality, the data are discrete data points acquired with a certain sampling frequency. This discrete sampling has important consequences in some cases, for example in signal processing and control theory, but can be neglected here.
References
Ainsworth, S. (2008). The educational value of multiple-representations when learning complex scientific concepts. In J. K. Gilbert, M. Reiner, & A. Nakama (Eds.), Visualization: Theory and practice in science education (pp. 191–208). New York: Springer.
Airey, J., & Linder, C. (2009). A disciplinary discourse perspective on university science learning: Achieving fluency in a critical constellation of modes. Journal of Research in Science Teaching, 46(1), 27–49.
Bacon, G. E. (1975). Neutron diffraction (3rd ed.). Oxford: Clarendon.
Barad, K. (2003). Posthumanist performativity: Toward an understanding of how matter comes to matter. Signs: Journal of Women in Culture and Society, 28(3), 801–831. https://doi.org/10.1086/345321.
Bateson, G. (1972). Steps to an ecology of mind. New York: Ballantine Books.
Beichner, R. J. (1994). Testing student interpretation of kinematics graphs. American Journal of Physics, 62(8), 750–762. https://doi.org/10.1119/1.17449.
Berg Friis, J. K. (2015). Towards a hermeneutics of unveiling. In R. Rosenberger & P.-P. Verbeek (Eds.), Postphenomenological investigations: Essays on human–technology relations (pp. 215–225). Lanham: Lexington books.
Bergqvist, K., & Säljö, R. (1994). Conceptually blindfolded in the optics laboratory: Dilemmas of inductive learning. European Journal of Educational Psychology, 9, 149–158.
Bernhard, J. (1999). Hands-on experiments in advanced mechanics courses. In G. Born, H. Harreis, H. Litschke & N. Treitz (Eds.), Hands on-Experiments in Physics Education (pp. 175–177). Duisburg: Didaktik der Physik, University of Duisburg.
Bernhard, J. (2003). Physics learning and microcomputer based laboratory (MBL): Learning effects of using MBL as a technological and as a cognitive tool. In D. Psillos, K. P. V. Tselfes, E. Hatzikraniotis, G. Fassoulopoulos & M. Kallery (Eds.), Science education research in the knowledge based society (pp. 313–321). Dordrecht: Kluwer.
Bernhard, J. (2008). Humans, intentionality, experience and tools for learning: Some contributions from post-cognitive theories to the use of technology in physics education. AIP Conference Proceedings, 951, 45–48.
Bernhard, J. (2010). Insightful learning in the laboratory: Some experiences from 10 years of designing and using conceptual labs. European Journal of Engineering Education, 35(3), 271–287
Bernhard, J. (2011). Learning in the laboratory through technology and variation: A microanalysis of instructions and engineering studentsʼ practical achievement. Paper presented at SEFI annual conference, Lisbon, September 27–30 2011
Bohr, N. (1958). Atomic physics and human knowledge. New York: Wiley.
Booth, S. (2004). Engineering education and the pedagogy of awareness. In C. Baillie (Ed.), Effective learning and teaching in engineering. New York: RouthledgeFalmer.
Bowden, J., Dall’Alba, G., Martin, E., Masters, G., Laurillard, D., Marton, F., et al. (1992). Displacement, velocity, and frames of reference: Phenomenographic studies of students’ understanding and some implications for teaching and assessment. American Journal of Physics, 60, 262–268.
Bowden, J., & Marton, F. (1998). The university of learning: Beyond quality and competence in higher education. London: Kogan Page.
Bowen, D. K., & Hall, C. R. (1975). Microscopy of materials. London: The Macmillan Press.
Brenni, P. (2010). The evolution of teaching instruments and their use between 1800 and 1930. Science & Education, 21(2), 191–226. https://doi.org/10.1007/s11191-010-9326-z.
Carlsson, B. (2002). Ecological understanding 2: Transformation—A key to ecological understanding. International Journal of Science Education, 24(7), 701–715. https://doi.org/10.1080/09500690110098877.
Carstensen, A.-K. (2013). Connect: Modelling learning to facilitate linking models and the real world through lab-work in electric circuit courses for engineering students (Doctoral dissertation). Linköping studies in science and technology, dissertation no. 1529. Linköping: Linköping University.
Carstensen, A.-K., & Bernhard, J. (2009). Student learning in an electric circuit theory course: Critical aspects and task design. European Journal of Engineering Education, 34(4), 389–404. https://doi.org/10.1080/03043790902990315.
Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and Instruction, 8(4), 293–332.
Chini, J. J., Madsen, A., Gire, E., Rebello, N. S., & Puntambekar, S. (2012). Exploration of factors that affect the comparative effectiveness of physical and virtual manipulatives in an undergraduate laboratory. Physical Review Special Topics—Physics Education Research, 8(1), 010113. https://doi.org/10.1103/PhysRevSTPER.8.010113.
Cole, M. (1996). Cultural psychology: A once and future discipline. Cambridge, MA: Harvard University Press.
Cole, M., & Derry, J. (2005). We have met technology and it is us. In R. J. Sternberg & D. D. Preiss (Eds.), Intelligence and technology: The impact of tools on the nature and development of human abilities (pp. 209–227). Mahwah: Lawrence Erlbaum.
Coştu, B., Ayas, A., & Niaz, M. (2012). Investigating the effectiveness of a POE-based teaching activity on students’ understanding of condensation. Instructional Science, 40(1), 47–67. https://doi.org/10.1007/s11251-011-9169-2.
Cuban, L. (2001). Oversold & underused: Computers in the classroom. Cambridge, MA: Harvard University Press.
Dewey, J. (1925/1981). Experience and nature. In J. A. Boydston (Ed.), John Dewey: The later works (Vol. 1). Carbondale: Southern Illinois University Press.
Dewey, J. (1938/1986). Logic: The theory of inquiry. In J. A. Boydston (Ed.), John Dewey: The later works (Vol. 12). Carbondale: Southern Illinios University Press.
Domin, D. (1999). A review of laboratory instruction styles. Journal of Chemical Education, 76, 543–547.
Drake, S. (1978). Galileo at work: His scientific biography. Chicago: The University of Chicago Press.
Feynman, R., Leighton, R., & Sands, M. (1963). The Feynman lectures on physics, Volume I: Mainly mechanics, radiation, and heat. Reading, MA: Addison-Weslay.
Ford, M. J. (2003). Representing and meaning in history and in classrooms: Developing symbols and conceptual organizations of free-fall motion. Science & Education, 12(1), 1–25. https://doi.org/10.1023/A:1022643003120.
Fraser, D., & Linder, C. (2009). Teaching in higher education through the use of variation: Examples from distillation, physics and process dynamics. European Journal of Engineering Education, 34(4), 365–377. https://doi.org/10.1080/03043790902989507.
Galilei, G. (1610/1989). Sidereus nuncius or the sidereal messenger (A. van Helden, Trans.). Chicago: The University of Chicago Press.
Galilei, G. (1638/1954). Dialogues concerning two new sciences (H. Crew, & A. de Salvio, Trans.). New York: Dover.
Garfinkel, H. (2002). An ethnomethodological study of the work of Galileo’s inclined plane demonstration of the real motion of free failing bodies. In A. Warfield-Rawls (Ed.), Etnomethodology’s program: Working out Durkheim’s aphorism (pp. 263–285). Lanham: Rowman & Littlefield.
Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin Company.
Gooding, D. (1990). Experiment and the making of meaning: Human agency in scientific observation and experiment. Dordrecht: Kluwer.
Gorsky, P., & Finegold, M. (1994). The role of anomaly and of cognitive dissonance in restructuring students’ concepts of force. Instructional Science, 22(2), 75–90. https://doi.org/10.1007/BF00892158.
Gurwitsch, A. (1964). The field of consciousness. Pittsburgh: Duquesne University Press.
Hacking, I. (1983). Representing and intervening: Introductory topics in the philosophy of natural science. Cambridge: Cambridge University Press.
Haglund, J., Jeppsson, F., & Schönborn, K. J. (2015). Taking on the heat—A narrative account of how infrared cameras invite instant inquiry. Research in Science Education, 46(5), 685–713. https://doi.org/10.1007/s11165-015-9476-8.
Harré, R. (2003). The materiality of instruments in a metaphysics for experiments. In H. Radder (Ed.), The Philosophy of Scientific Experimentation (pp. 19–38). Pittsburgh: University of Pittsburgh Press.
Hempel, C. G. (1952). Fundamentals of concept formation in empirical science. Chicago: The University of Chicago Press.
Hewson, P. W., & Hewson, M. G. A. B. (1984). The role of conceptual conflict in conceptual change and the design of science instruction. Instructional Science, 13(1), 1–13. https://doi.org/10.1007/BF00051837.
Hickman, L. A. (1990). John Dewey’s pragmatic technology. Bloomington: Indiana University Press.
Hill, M., & Sharma, M. D. (2015). Students’ representational fluency at university: A cross-sectional measure of how multiple representations are used by physics students using the representational fluency survey. EURASIA Journal of Mathematics, Science and Technology Education, 11(6), 1633–1655. https://doi.org/10.12973/eurasia.2015.1427a.
Hofstein, A., & Lunetta, V. N. (1982). The role of the laboratory in science teaching: Neglected aspects of research. Review of Educational Research, 52(2), 201–217.
Hofstein, A., & Lunetta, V. N. (2004). The laboratory in science education: Foundations for the twenty-first century. Science Education, 88(1), 28–54.
Hucke, L., & Fischer, H. (2002). The link of theory and practice in traditional and in computer-based university laboratory experiments. In D. Psillos & H. Niedderer (Eds.), Teaching and learning in the science laboratory (pp. 205–218). Dordrecht: Kluwer.
Ihde, D. (1979). Technics and praxis. Dordrecht: D. Reidel.
Ihde, D. (1986). Experimental phenomenology: An introduction. Albany: State University of New York Press.
Ihde, D. (1990). Technology and the lifeworld: From garden to earth. Bloomington: Indiana University Press.
Ihde, D. (1991). Instrumental realism: The interface between philosophy of science and philosophy of technology. Bloomington: Indiana University Press.
Ihde, D. (2007). Imaging technologies: a second scientific revolution. In Proceedings of the twenty-first world congress of philosophy (Vol. 13, pp. 125–136).
Ihde, D. (2010). Stretching the In-between: Embodiment and Beyond. Foundations of Science. https://doi.org/10.1007/s10699-010-9187-6.
Ihde, D., & Selinger, E. (Eds.). (2003). Chasing technoscience: Matrix for materiality. Indiana Series in the Philosophy of Technology. Bloomington: Indiana University Press.
Ingerman, Å., Berge, M., & Booth, S. (2009a). Physics group work in a phenomenographic perspective: Learning dynamics as the experience of variation and relevance. European Journal of Engineering Education, 34(4), 347–356.
Ingerman, Å., Linder, C., & Marshall, D. (2009b). The learners’ experience of variation: Following students’ threads of learning physics in computer simulation sessions. Instructional Science, 37(3), 273–292. https://doi.org/10.1007/s11251-007-9044-3.
Jensen, E. (2014). Does teaching students how to explicitly model the causal structure of systems improve their understanding of these systems? European Journal of Engineering Education, 39(4), 391–411. https://doi.org/10.1080/03043797.2014.881320.
Jordan, B., & Henderson, A. (1995). Interaction analysis: Foundations and practice. The Journal of the Learning Sciences, 4(1), 39–103.
Jornet, A., & Roth, W.-M. (2015). The joint work of connecting multiple (re)presentations in science classrooms. Science Education, 99(2), 378–403. https://doi.org/10.1002/sce.21150.
Kiran, A. H. (2015). Four dimensions of technological mediation. In R. Rosenberger & P.-P. Verbeek (Eds.), Postphenomenological investigations: Essays on human—technology relations (pp. 123–140). Lanham: Lexington books.
Kozma, R. (2003). The material features of multiple representations and their cognitive and social affordances for science understanding. Learning and Instruction, 13(2), 205–226.
Kroes, P. (2003). Physics, experiments, and the concept of nature. In H. Radder (Ed.), The philosophy of scientific experimentation (pp. 68–86). Pittsburgh: University of Pittsburgh Press.
Kyza, E. A., Erduran, S., & Tiberghien, A. (2009). Technology-enhanced learning in science. In N. Balacheff, S. Ludvigsen, T. de Jong, A. W. Lazonder, & S. Barnes (Eds.), Technology-enhanced learning: Principles and products (pp. 121–134). Dordrecht: Springer.
Laws, P. (1997). A new order for mechanics. In J. Wilson (Ed.), Proceedings conference on introductory physics course (pp. 125–136). New York: Wiley.
Lelas, S. (1993). Science as technology. The British Journal for the Philosophy of Science, 44(3), 423–442.
Linder, C., Fraser, D., & Pang, M. F. (2006). Using a variation approach to enhance physics learning in a college classroom. The Physics Teacher, 44(9), 589–592. https://doi.org/10.1119/1.2396777.
Lindwall, O., & Ivarsson, J. (2010). Differences that make a difference: Contrasting the local enactment of two technologies in a kinematics lab. In S. Ludvigsen, A. Lund, I. Rasmussen, & R. Säljö (Eds.), Learning across sites: New tools, infrastructures and practices (pp. 364–380). Amsterdam: Elsevier.
Ling, L. M., & Marton, F. (2012). Towards a science of the art of teaching: Using variation theory as a guiding principle of pedagogical design. International Journal for Lesson and Learning Studies, 1(1), 7–22.
Lunetta, V. N. (1998). The school science laboratory: Historical perspectives and contexts for contemporary teaching. In B. J. Fraser & K. G. Tobin (Eds.), International handbook of science education (Vol. 1, pp. 249–262). Dordrecht: Kluwer.
Lunetta, V. N., Hofstein, A., & Clough, M. P. (2007). Learning and teaching in the school science laboratory. In S. Abell & N. Lederman (Eds.), Handbook of research on science education (pp. 393–441). Mahwah: Lawrence Erlbaum.
Marton, F. (2015). Necessary conditions of learning. New York: Routledge.
Marton, F., & Booth, S. (1997). Learning and awareness. Mahwah: Lawrence Erlbaum.
Marton, F., & Pang, M.-F. (2006). On some necessary conditions of learning. Journal of the Learning Sciences, 15(2), 193–220.
Marton, F., & Pang, M.-F. (2008). The idea of phenomenography and the pedagogy of conceptual change. In S. Vosniadou (Ed.), International handbook of research on conceptual change (pp. 533–559). New York: Routledge.
Marton, F., Runesson, U., & Tsui, A. B. M. (2004). The space of learning. In F. Marton & A. B. M. Tsui (Eds.), Classroom discourse and the space of learning (pp. 3–40). Mahwah: Lawrence Erlbaum.
Marton, F., & Tsui, A. B. M. (Eds.). (2004). Classroom discourse and the space of learning. Mahwaw: Lawrence Erlbaum.
McDermott, L. C. (1997). Students’ conceptions and problem solving in mechanics. In A. Tiberghien, E. L. Jossem, & J. Borojas (Eds.), Connecting research in physics education with teacher education. Vandoeuvre-les-Nancy: ICPE.
McDermott, L. C., Rosenquist, M. L., & van Zee, E. H. (1987). Student difficulties in connecting graphs and physics: Examples from kinematics. American Journal of Physics, 55(6), 503–513.
McDonald, G., Le, H., Higgins, J., & Podmore, V. (2005). Artifacts, tools, and classrooms. Mind, Culture & Activity, 12(2), 113–127.
Miettinen, R. (2001). Artifact mediation in Dewey and in cultural-historical activity theory. Mind, Culture & Activity, 8(4), 297–308.
Müller, R. H. (1940). American apparatus, instruments, and instrumentation. Industrial and Engineering Chemistry, Analytical Edition, 12(10), 571–630.
Muller, D. A., Sharma, M. D., & Reimann, P. (2008). Raising cognitive load with linear multimedia to promote conceptual change. Science Education, 92(2), 278–296. https://doi.org/10.1002/sce.20244.
Olympiou, G., Zacharias, Z., & deJong, T. (2012). Making the invisible visible: enhancing students’ conceptual understanding by introducing representations of abstract objects in a simulation. Instructional Science, 41(3), 575–596. https://doi.org/10.1007/s11251-012-9245-2.
Pang, M.-F., & Marton, F. (2005). Learning theory as teaching resource: Enhancing students’ understanding of economic concepts. Instructional Science, 33(2), 159–191. https://doi.org/10.1007/s11251-005-2811-0.
Planinic, M., Ivanjek, L., Susac, A., & Milin-Sipus, Z. (2013). Comparison of university students’ understanding of graphs in different contexts. Physical Review Special Topics—Physics Education Research, 9(2), 020103.
Popper, K. R. (1972). The logic of scientific discovery. London: Hutchinson.
Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientist conception: Towards a theory of conceptual change. Science Education, 66(2), 211–227.
Psillos, D., & Niedderer, H. (Eds.). (2002). Teaching and learning in the science laboratory. Dordrecht: Kluwer.
Ronen, M. (1995). A 3-D motion tracing system in high school physics teaching. Journal of Computer Assisted Learning, 11(3), 141–156. https://doi.org/10.1111/j.1365-2729.1995.tb00129.x.
Rosenberger, R., & Verbeek, P.-P. (2015). A field guide to postphenomenology. In R. Rosenberger & P.-P. Verbeek (Eds.), Postphenomenological investigations: Essays on human—technology relations (pp. 9–41). Lanham: Lexington books.
Roth, W.-M., & Bowen, M. G. (2001). Professionals read graphs: A semiotic analysis. Journal for Research in Mathematics Education, 32, 159–194.
Roth, W.-M., & Jornet, A. (2014). Toward a theory of experience. Science Education, 98(1), 106–126. https://doi.org/10.1002/sce.21085.
Runesson, U. (2006). What is it possible to learn? On variation as a necessary condition for learning. Scandinavian Journal of Educational Research, 50(4), 397–410.
Shaffer, P. S., & McDermott, L. C. (1992). Research as a guide for curriculum development: An example from introductory electricity. Part II: Design of instructional strategies. American Journal of Physics, 60(11), 1003–1013.
Sharma, M. D., Johnston, I. D., Johnston, H., Varvell, K., Robertson, G., Hopkins, A., et al. (2010). Use of interactive lecture demonstrations: A ten year study. Physical Review Special Topics—Physics Education Research, 6(2), 020119.
Singer, S. R., Hilton, M. L., & Schweingruber, H. A. (Eds.). (2006). National research council, committee on high school laboratories: Role and vision, America’s lab report: Investigations in high school science. Washington, DC: The National Academies Press.
Sokoloff, D. R., Laws, P. W., & Thornton, R. K. (2007). RealTime physics: Active learning labs transforming the introductory laboratory. European Journal of Physics, 28(3), S83–S94. https://doi.org/10.1088/0143-0807/28/3/S08.
Tala, S. (2009). Unified view of science and technology for education: Technoscience and technoscience education. Science & Education, 18(3), 275–298. https://doi.org/10.1007/s11191-008-9145-7.
ten Have, P. (2007). Doing conversation analysis: A practical guide (2nd ed.). Los Angeles: SAGE.
Thornton, R. K. (2008). Effective learning environments for computer supported instruction in the physics classroom and laboratory. In M. Vicentini & E. Sassi (Eds.), Connecting research in physics education with teacher education. Vandoeuvre-les-Nancy: International Commission on Physics Education (ICPE).
Thornton, R. K., & Sokoloff, D. R. (1998). Assessing student learning of Newton’s laws: The force and motion conceptual evaluation and the evaluation of active learning laboratory and lecture curricula. American Journal of Physics, 66(4), 338–352.
Trowbridge, D. E., & McDermott, L. C. (1980). Investigation of student understanding of the concept of velocity in one dimension. American Journal of Physics, 48(12), 1020–1028.
Trowbridge, D. E., & McDermott, L. C. (1981). Investigation of student understanding of the concept of acceleration in one dimension. American Journal of Physics, 49(3), 242–253.
Trumper, R. (2003). The physics laboratory: Historical overview and future perspectives. Science & Education, 12(7), 645–670. https://doi.org/10.1023/A:1025692409001.
van Helden, A. (1989). Sidereus Nuncius or the Sidereal messenger by Galileo Galilei: Translated with introduction, conclusion, and notes by Albert van Helden. Chicago: The University of Chicago Press.
Verbeek, P.-P. (2005). What things do: Philosophical reflections on technology, agency, and design (R. P. Crease, Trans.). University Park: The Pennsylvania State University Press.
Verbeek, P.-P. (2015). Toward a theory of technological mediation. In J. K. Berg-O-Friis & R. P. Crease (Eds.), Technoscience and postphenomenology: The Manhattan papers (pp. 189–204). Lanham, MD: Lexington Books.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge: Harvard University Press.
Wartofsky, M. W. (1979). Perception, representation, and the forms of action: Towards an historical epistemology. In R. S. Cohen & M. W. Wartofsky (Eds.), Models: Representation and the scientific understanding (pp. 188–210). Dordrecht: D. Reidel Publishing Company.
White, R. T. (1988). Learning science. Oxford: Basil Blackwell.
White, R. T., & Gunstone, R. (1992). Probing understanding. London: The Falmer Press.
Whitehead, A. N. (1963). Science and the modern world. New York: New American Library.
Wosilait, K., Heron, P. R. L., Shaffer, P. S., & McDermott, L. C. (1998). Development and assessment of a research-based tutorial on light and shadow. American Journal of Physics, 66(10), 906–913.
Zacharia, Z. C. (2015). Examining whether touch sensory feedback is necessary for science learning through experimentation: A literature review of two different lines of research across K-16. Educational Research Review, 16, 116–137. https://doi.org/10.1016/j.edurev.2015.10.001.
Zacharia, Z. C., & Olympiou, G. (2011). Physical versus virtual manipulative experimentation in physics learning. Learning and Instruction, 21(3), 317–331. https://doi.org/10.1016/j.learninstruc.2010.03.001.
Acknowledgements
I am grateful to Anna-Karin Carstensen for valuable discussions. This research was in part funded by the Swedish Research Council (Vetenskapsrådet) through Grant VR 721-2011-5570, which is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Bernhard, J. What matters for students’ learning in the laboratory? Do not neglect the role of experimental equipment!. Instr Sci 46, 819–846 (2018). https://doi.org/10.1007/s11251-018-9469-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11251-018-9469-x