Flexible, creative, constructive, and collaborative: the makings of an authentic science inquiry task

ABSTRACT

To promote scientific literacy in school science, students need to learn key concepts in science, along with the nature of scientific knowledge and how it is generated. Ideally, this learning mirrors authentic scientific inquiry through student engagement in three key epistemic practices: flexibility and creativity, knowledge construction, and collaboration.

This paper draws on findings from a larger research study investigating the implementation of a guided-inquiry multimodal approach to teaching science. It reports on a case study of three Australian Year 9 science students investigating sustainable design strategies for houses, as a summative task. Through a post-hoc analysis, this paper explores how, and to what extent, the task supported the epistemic practices of authentic scientific inquiry. To address these questions, the author developed and applied an Authentic Inquiry Framework (AIF) to analyse the students’ dialogue and interactions during this task.

The findings reveal that the students applied a flexible and creative approach to addressing their inquiry questions, through their own experimental design and engagement in provisional and collaborative knowledge construction. To support their investigation, the students were also able to productively integrate disciplinary-specific tools and technologies.

KEYWORDS:

• practical work
• inquiry-based learning
• epistemic practices

Introduction

The current global goal of scientific literacy emphasises students as reflective citizens who act through high-level engagement with ideas of science and science-related issues (OECD, 2019). To support this goal, recent policy directions highlight the need for school science to integrate authentic science inquiry, creativity, and critical thinking (OECD, 2019, 2020). This implies students’ active and critical participation in investigations (Duschl & Grandy, 2013; Melville, 2015) to find solutions for real world issues.

Practical work¹ has been an essential feature of most school science classrooms for over a century, based on the widely accepted premise that scientific knowledge should be learned alongside inquiry practices (Dewey, 1910). These activities ought to reflect what scientists do to generate knowledge (Abd-El-Khalick et al., 2015). Such activities have been described as epistemic practices, ‘the socially organized and interactionally accomplished ways that members of a group propose, communicate, assess, and legitimize knowledge claims’ (Kelly & Licona, 2018, p. 140).

Though contemporary school science curricula and approaches include practical work as central to science classroom learning (e.g. Bybee, 2011), and involve specific inquiry skills, they tend to over-emphasise a single ‘scientific method’ with pre-determined procedures and predictable findings (Harlen, 2010; Schweingruber et al., 2012). School-based investigations bear little resemblance to authentic scientific inquiry (Osborne & Dillon, 2010; Schweingruber et al., 2012) and do little to help students make conceptual links (Abrahams & Millar, 2008; Hofstein & Lunetta, 2004; Minner et al., 2010). Over the past 100 years Abd-El-Khalick et al. (2015, p. 513) noted ‘pendulum swings’ in students’ experiences of inquiry. Educational researchers today are reaffirming the need for classroom curriculum reform that emulates the dynamic, generative interactions and creativity inherent in authentic scientific inquiry (Eberbach & Hmelo-Silver, 2015; Osborne & Dillon, 2010), alongside specific ‘inquiry skills’ such as generating hypotheses, conducting investigations, and interpreting data (e.g. Harlen, 2015).

Authentic inquiry approaches involve teachers guiding students’ exploration of a phenomenon through a series of open-ended questions and facilitating class discussions (Harlen & Allende, 2009). Students ask authentic questions, plan meaningful investigations, and use the results to address the questions by drawing on scientific evidence and reasoning (Kelly & Licona, 2018). Students also collect and use evidence and to test their explanations of the phenomena under study (Harlen, 2010), using evidence-based reasoning to justify claims (AAAS, 1989; Schweingruber et al., 2012). Rather than being static, knowledge is dependent on available evidence and may change as new evidence emerges (AAAS, 1989). Authentic inquiry activities open up opportunities for creativity and imagination as students grapple with the provisional and collaborative nature of knowledge (Harlen, 2010; Schweingruber et al., 2012).

Research indicates students who learn through inquiry are more actively engaged in their learning (Ainsworth et al., 2011) and have a more positive attitude toward science (Harlen & Allende, 2009; Furtak et al., 2012; Kang & Keinonen, 2018; Minner et al., 2010). Inquiry practices also form a basis for students to link scientific concepts to observed phenomena and develop a deeper understanding of content (Deboer, 2004). Experiencing epistemic practices helps students develop discipline-specific skills, knowledge, and processes (Harlen, 2010), appreciate the fluid nature of scientific investigations and the tentative nature of science (Deboer, 2004). There is growing evidence that students develop disciplinary literacies of science (Tang & Danielsson, 2018), including their ability to construct, coordinate, and communicate through representations (diSessa, 2004; Xu et al., 2021).

Typically, however, school science curricula emphasise the ‘skills’ of inquiry, such as: asking questions, generating hypotheses or possible answers, making predictions, planning, and carrying out investigations, analysing and interpreting data, constructing explanations based on evidence, and evaluating and communicating findings (Harlen, 2015). Though these activities are consistent with scientific inquiry, they do not fully capture the nuanced epistemic practices experienced by scientists. In addition to ensuring students develop the typical science inquiry skills, they also need to experience other epistemic practices to engage in authentic inquiry.

Due to an overemphasis of transmissive teaching practices, many science teachers ‘lack of first-hand experience’ of authentic inquiry (Harlen, 2015, p. 48) and across the globe, many are teaching science ‘out of field’ (Hobbs & Törner, 2019). Teachers need support to understand how inquiry works within the scientific community and its role in generating scientific knowledge, and build their own confidence with authentic inquiry practices in classroom settings. Science studies help illuminate such practices.

Understanding the nature of scientific inquiry

Wong and Hodson (2009) investigated the practices of 13 international scientists working in experimental or theoretical research – from astrophysics to molecular biology. They identified that the method of inquiry was determined by the nature of context of scientist's respective problems, with creativity playing a key role in all stages of the investigation. Rather than following a fixed scientific method, they noted flexibility in their approaches to investigations and, in some cases, no working hypothesis.

Similarly, case studies of scientific invention in physics demonstrated that scientists often follow chaotic, complex, and contradictory pathways to new knowledge in their investigations (Pickering, 1995). In what Pickering (1995) characterised as a ‘mangle of practice’, scientists adapted to unexpected results through ongoing revision of hypotheses, concepts, methods, and technologies, which contrasts with the prescriptive and predictable approaches common in classroom investigations.

These studies reveal scientists have particular ways to make sense of the real world and communicate their ideas. Wong and Hodson (2009) found that scientists created various models, which they used as calculating devices or tools for thinking as they constructed theories to describe the real world and share ideas. Disciplinary-specific technologies played a role in collecting, manipulating, and presenting data, or to monitoring and controlling experiments. These technologies not only extended scientists’ capacity to ‘see and hear,’ they provided ‘new ways of seeing and hearing’ (p. 119).

Latour (1999) elaborated how scientists construct knowledge using ‘inscriptions’ (e.g. models, representations), which link ‘real world’ objects (e.g. rocks, biological specimens) to scientific knowledge. This sense-making process involves ‘cascades of ever more simplified and costlier inscriptions’ as increasingly abstract iterations of real-world phenomenon, that are easier to share, evaluate, modify, reproduce, recombine, and translate to written text (Latour, 1986, p. 21).

These studies emphasise three key epistemic practices displayed by scientists: flexibility and creativity in the process; the active, provisional and discipline-specific nature of knowledge construction; and the need to compare and share ideas.

This paper argues that inquiry learning in science classrooms should aim to emulate these three epistemic practices as far as practicable, yet also develop students’ inquiry skills and conceptual understanding. To achieve this, the paper proposes an Authentic Inquiry Framework (AIF) that specifically links these epistemic practices to inquiry skills to promote more authentic inquiry learning practices in science classrooms. The paper then reports on a case study, in which the AIF was used in the post-hoc analysis of a summative science task involving Year 9 students to identify the prevalence of authentic science inquiry practices evident in the task. Understanding how authentic inquiry works within the science, and its role in generating scientific knowledge will help teachers make practical work in classrooms more authentic.

Theoretical framework

Science classrooms differ significantly from the research setting because scientific inquiry is both a means to develop inquiry skills and procedural knowledge, as well as a means to learn concepts (Abd-El-Khalick et al., 2015). In the context of school science, ‘scientific inquiry’ describes how scientists conduct their practice as they explore the natural world, whereas ‘inquiry teaching’ uses specific pedagogical approaches to facilitate the learning of the skills, procedural knowledge, habits of mind, and subject matter for school science (Abd-El-Khalick et al., 2015; Deboer, 2004). Authentic inquiry learning in classrooms involves students engaging in epistemic practices guided by inquiry teaching approaches. This involves students constructing understandings by emulating how scientists develop claims to scientific knowledge. In developing the AIF, there was a need to examine in more detail what the three key epistemic practices involve and what each might look like in classrooms.

Flexibility and creativity in the planning and conduct of investigations

This epistemic practice involves experiences that go beyond the prescribed procedures with predictable findings of school-based investigations. A Delphi study highlighted many commonly featured practices such as posing hypotheses and making predictions, using scientific methods and critical testing, posing questions, analysing and interpretating data, and considering levels of certainty – all common to most science curricula (Osborne et al., 2003). The study also pointed to more nuanced epistemic practices, such as creativity, diversity of scientific thinking, historical development of scientific knowledge, cooperation, and collaboration. Students need guidance to transition from prescriptive ‘stepwise’ approaches to more open and flexible approaches (Eberbach & Hmelo-Silver, 2015) to generate tentative hypotheses (AAAS, 1989), undertake inquiries that are led by questions (Lederman et al., 2014), or proceed without a hypothesis (Wong & Hodson, 2009). Generating questions for investigations facilitates different research designs leading to many possible and acceptable solutions for a given problem (Lederman et al., 2014; Wong & Hodson, 2009).

As central practices in scientific inquiry (Hadzigeorgiou et al., 2012), creativity and imagination are needed in all stages of an investigation (Osborne et al., 2003; Wong & Hodson, 2009), and particularly for creating and testing hypotheses and addressing unexpected results (Pickering, 1995). Scientific inquiry is thought to involve an interplay of creativity and rationality (Kind & Kind, 2007), as a blend of logic and imagination (AAAS, 1989).

The role of the creativity alongside flexibility in science has been stressed by Feynman (1995, p. 2):

Experiment, itself, helps to produce these laws, in the sense that it gives us hints. But also needed is imagination to create from these hints the great generalizations – to guess at the wonderful, simple, but very strange patterns beneath them all, and then to experiment to check again whether we have made the right guess.

Thus, flexible and creative planning and conduct of investigations allows for provisional hypothesis development, investigations with no hypothesis, or ones that are led by questions, and may have more than one solution. It also allows for changes to the research design as activities are underway.

Provisional knowledge construction in science

This epistemic practice elaborates on specific activities involved in collecting, analysing, interpreting, and sharing findings. In science, data is collected by observation of phenomena, either through one’s own senses or aided by technology. These data are then manipulated, analysed, transformed, refined and shared as explanations are developed and related to previous knowledge (Latour, 1999; Wong & Hodson, 2009). Thus, there is a clear distinction between data and evidence (Lederman et al., 2014). In some cases, claims can be strengthened by controlling conditions (AAAS, 1989), but any subsequent claims or conclusions need to be consistent with the data. Linking data, evidence, and knowledge are central to the explanatory and predictive nature of scientific inquiry (AAAS, 1989; Lederman et al., 2014).

Constructing knowledge in science, therefore involves iterative and multimodal meaning-making processes through the interplay of observation, data, knowledge, tools, and technologies. Science has a long history of using representations to convey ideas (e.g. inscriptions, models) (Latour, 1990). Representations are considered disciplinary tools for thinking and knowing (Ainsworth et al., 2011; diSessa, 2004) and are used to mediate links between the real-world and scientific knowledge (Latour, 1986). Wong and Hodson (2009, p. 127) reported that scientists’ use of models had ‘an instrumental role as calculating devices or as tools for thinking as they move toward realist theories’.

In the classroom, knowledge construction involves students’ direct engagement with disciplinary ways of talking, writing, representing phenomena to pose and refine theories and models, and support their claims through observations and experiments (Duschl & Grandy, 2013; Osborne, 2014). There is a growing body of research demonstrating these guided-inquiry knowledge construction activities in science classrooms (e.g. Cirkony et al., 2022; Cirkony & Kenny, 2022; Kenny & Cirkony, 2018; Tang et al., 2019). Teachers guide students to create, coordinate, evaluate, and refine multimodal representations of phenomena (e.g. diagrams, models) to make sense of concepts and solve problems (Waldrip et al., 2013), thereby engaging directly in epistemic practices of science (Ainsworth et al., 2011; Latour, 1999; Tytler et al., 2013).

The process of interpreting data and constructing explanations is far from a straightforward journey from method to results. As described by Pickering (1995), scientific practices involve an iterative and adaptive dance between scientists and how their technologies interact with the physical world. Unexpected results require scientists to adapt their approach through the revision or redesign of hypotheses, conceptual accounts, methods, and technologies. Both scientists and students need to have the knowledge and creative insights to recognise and interpret the unexpected, and the flexibility that comes from knowing that scientific knowledge is provisional and subject to change (AAAS, 1989; Osborne et al., 2003).

Thus, provisional knowledge construction involves disciplinary-specific tools and technologies to assist with data collection, analysis, and sharing through the iterative development and refinement and connection of ideas and claims through multimodal representations.

Collaborative inquiry

This epistemic practice concerns the importance of collaboration in genuine inquiry. Though there is a perception that the conduct scientific inquiry is an individual pursuit, in fact, it rarely takes place in isolation (Osborne et al., 2003; Wong & Hodson, 2010). Investigations are often carried out in groups, which can be multidisciplinary and international (Osborne et al., 2003). They involve cooperation, collaboration, and even competition among scientists – with collaborative teams the most productive (Wong & Hodson, 2010). The competitive element, sometimes viewed as motivating for scientists, also plays an important role in providing critical feedback on findings (e.g. peer review). Both collaboration and competition have an essential role in the validity and reliability of scientific knowledge generation.

For the classroom context, Eberbach and Hmelo-Silver (2015) emphasised the role collaborative investigations play to support student engagement with disciplinary knowledge, reasoning, and epistemic practices as they explore scientific questions or problems. Through guided-inquiry approaches, students make sense of their experience together as they make observations, construct models, or design and run experiments. Authentic practical investigations should enable students to develop reasoned arguments (e.g. pose questions, formulate and reach a consensus about hypothesis, describe observations, reason about cause and effect, and summarise results) (Mercer et al., 2004). Such guided approaches speak to careful design and progression of tasks, and to the crucial role of the teacher developing their expertise as facilitators to help students make the desired connections to theory (Cirkony et al., 2022; Kenny & Cirkony, 2022). These ideas are consistent with the call by Hofstein and Lunetta (2004) to re-visit group work in the school laboratory and are examples of the importance of social interaction in learning and thinking processes.

While many of these activities are common in school science inquiries, evaluating the quality of collaboration can be challenging. To assess the collaborative nature of tasks, Mercer’s (2004) typology identifies three forms of students’ dialogue around disputational, cumulative, and exploratory exchanges. In disputational dialogue, there is a predominance of disagreement and individual decision making with few attempts to share resources, offer constructive criticisms or make suggestions. In cumulative dialogue, partners build on each other’s ideas, constructing knowledge through accumulation, but offer no critique. In exploratory dialogue, partners engage critically and constructively with each other’s ideas. Statements are made for joint consideration and challenges are justified, with alternative ideas offered. Everyone actively participates and opinions are invited and considered as part of a consensus. Of the three types of exchanges, exploratory talk is most strongly correlated to improvement of subject matter knowledge and the ability to participate in problem solving tasks more effectively (Mercer et al., 1999).

Thus, collaborative inquiry involves students engaging in critical and constructive discussions, building on each other’s ideas; students may cooperate or be in competition with one another.

Flexibility and creativity in the planning and conduct of investigations, provisional knowledge construction, and collaborative inquiry in school science activities represent the authenticity of inquiry activities that support the development of students’ inquiry skills and help build their conceptual understanding.

Research aim, questions and design

This paper has identified flexibility and creativity in the planning and conduct of investigations, provisional knowledge construction, and collaborative inquiry as key epistemic practices of authentic scientific inquiry. Drawing on these three key practices, this paper proposes an Authenic Inquiry Framework (AIF) to explore the feasibility of authentic inquiry as a genuine pedagogical practice in school science classrooms. The remainder of the paper reports on a case study involving three Year 9 students undertaking a guided-inquiry investigation as final summative task in a physics unit. This paper uses the AIF to address the following research question: How, and to what extent, did the task support three key epistemic practices of authentic scientific inquiry?

Research methodology

This paper draws on data from a larger research study investigating the implementation of a guided-inquiry multimodal teaching approach for science education through a video-based ethnography (Cirkony, 2019). The participants included 27 Year 9 students aged 14–15 years old, along with their teacher, at an all-girls Catholic secondary school in Melbourne, Australia.

This article focuses on a case group of three students who were selected for an in-depth exploration of their experiences during their end of unit summative task (Yin, 2014). The three students, Jessica, McKinley, and Clara, worked together throughout the unit, were chosen because they presented the most comprehensive data set, demonstrated one of biggest learning gains during the unit, and the strongest capacity to verbalise their reasoning (Furberg et al., 2013).

Data available included: participant observation, field notes, video capture, video-stimulated recall interviews of the case group following the task, and students’ artefacts (e.g. project books, report). A free-standing GoPro™ video camera with a wide-angle lens was placed with the case group during the lesson. The camera captured their conversations, gestures, their materials (e.g. project books, scientific apparatus), and movements as they performed the task, enabling their dialogue, multimodal representational resources, and interactions to be linked in context (Cirkony & Hubber, 2018). The follow-up interviews provided further insights on the students’ individual experiences.

The physics unit was designed using the context of sustainable housing and followed the prescribed national curriculum statement ‘Energy transfer through different media can be explained using wave and particle models’ (Australian Curriculum, Assessment and Reporting Authority [ACARA], 2018, ACSSU182). The full unit was designed using a representation construction approach (Tytler et al., 2013), and was sequenced over 12 modules. Initial modules focused on establishing the context of climate change and sustainable housing, with subsequent modules following a conceptual and representational focus on energy transfer through conduction, convection, and radiation, culminating in the final summative task.

This paper focuses on the final summative task, in which students applied their conceptual knowledge and skills from previous modules to investigate passive (i.e. energy efficient) design strategies used in sustainable housing. Table 1 provides a synopsis of this task with a description of the teacher’s actions and the activities completed by the students over these final three lessons in the unit.

Table 1. Summary of lessons and activities for the Sustainable Housing inquiry task.

Lesson Number	Description of Lesson	Activities
Lesson 9 (75 min)	Introducing the inquiry task and planning.	Part 1: Teacher introduced the Sustainable Housing Inquiry task and reviewed the seven inquiry questions, elaborating on possible experimental designs. Students chose groups and began planning their investigation.
Lesson 10 (60 min)	Conducting the investigation.	Part 2: Students completed the planning process, gathered their materials, set up their dataloggers, set up and conducted their respective experiments, and completed data collection. Material resources included their individual project books, scientific apparatus (e.g. lamp, model/carton houses), their personal iPhone camera, and the datalogger. Teacher circulated among the groups to help when needed.
Lesson 11 (75 min)	Constructing the report.	Part 3: Students drafted their group report together. Teacher circulated among the groups to help when needed.

The teacher provided guidance by outlining the task and the assessment rubric and provided general oversight during the next two lessons, but with limited interactions due to the summative nature of the task. The task included seven possible inquiry questions from which students chose one to investigate. The task also required the use of a datalogger, which is a digital device that automates the task of collecting temperature data and converting it to a graph. Students had experience with this device earlier in the unit. Student-groups designed their experiment based on a research question they chose and were provided with relevant materials. Students were required to collaborate during the investigation, as indicated in the assessment rubric, and to submit a single group report for their final mark in this unit.

Developing an authentic inquiry framework (AIF) to analyse inquiry tasks

The AIF comprises three key epistemic practices as a construct for authentic school science inquiry informed by the literature (e.g. AAAS, 1989; Osborne et al., 2003; Wong & Hodson, 2009). It was also informed by a validated instrument developed for teachers and researchers to identify and measure key attributes of science inquiry (Lederman et al., 2014). As a holistic framework, the AIF contains explicit indicators for each of the three epistemic practices (see Appendix).

For the post-hoc analysis, the AIF was used to analyse students’ dialogue, observable actions, and outputs during the summative inquiry task to assess if and to what extent their experiences emulate how scientists conduct investigations. It guided the qualitative interpretive analysis, drawing on the research traditions of grounded theory (Corbin & Strauss, 2014). Examples from the data were mapped to the indicators and functioned as proxies to illustrate which practices were evident, and to what extent they represented the three epistemic practices. The more indicators of epistemic practices were identified in the students’ interactions, the higher the extent of their engagement was ranked. Given that the AIF elaborates on specific inquiry skills and is not meant to be exhaustive, the analysis was descriptive in nature.

Results and discussion

This section discusses the findings in relation to the research question:

How, and to what extent, did the task support three key epistemic practices of authentic scientific inquiry? It is organised by the three epistemic practices and highlights six episodes featuring student dialogue and interactions during the investigation as experienced. Salient examples from the dataset are presented as evidence to illustrate how each of the indicators specified in the AIF were reflected and supported in this task (see Appendix). The indicators are italicised for illustrative purposes.

Flexible and creative planning and conduct of investigations

The case group demonstrated flexibility and creativity during all stages of the investigation from planning through to report writing (Wong & Hodson, 2009). For students, the key enabling aspect of the task was the initial provision of seven inquiry questions, supporting multiple possible results or solutions. The design of the investigation was led by a question rather than a hypothesis (Lederman et al., 2014). Of their own volition, the case group chose two inquiry questions. Each question prompted imaginative deliberations over their predictions, along with their gradual development of hypotheses as evident in the following examples.

Developing the hypothesis (Episode 1)

During the planning stage, the case group engaged in a discussion to develop their own hypothesis that aligned with their two inquiry questions: ‘Does foil inside a wall keep a room cool in summer? Is foil inside a wall just as effective as having foil outside a wall?’. While McKinley briefly absented herself, Jessica and Clara discussed what might happen in each scenario as depicted by the questions (Episode 1, lines 1-8). Jessica initiated the discussion, saying ‘I reckon that foil inside the room … Actually, I don’t know, to be honest. Maybe once it’s inside it’s already hot. If you know what I mean. Like maybe it’s gotten through so it’s already hot’ (line 1). To explore these ideas further, Jessica and Clara deliberated, using the carton to demonstrate their ideas.

Jessica: … [grabbing the carton], but if its inside, it means the heat is already in. [orienting the carton to demonstrate] (line 2)

Clara: I think if the foil is inside [demonstrates with carton] … OK if the foil is inside, then the lamp is like shining here [places clenched hand into carton], shining towards the house, the foil will prevent the heat … (line 3)

Jessica: I reckon, if it’s on the outside, it might just heat up … Because if it’s on the outside, it might heat up and then put it in. (line 4)

Clara suggested both scenarios might have the same outcomes, then corrected herself: ‘Oh no, it’s like, cause like if the foil is outside, it will attract the heat to the house … It would absorb the heat’ (line 5). Jessica agreed: ‘That’s what I thought [gestures to support the idea of foil attracting the heat]. Let’s write that down … We’ll say: foil on the inside would be helpful to keep it cool. But we might be able to say, but its better if it’s on the outside’ (line 6).

Both recorded their ideas into their respective project books. Jessica wrote that the: '… foil outside might attract heat rather than repel it? Foil on the inside could possibly create a barrier? Just as effective’ having the foil outside is effective, but is it effective on the inside?’ (line 7). McKinley returned to the group and they worked out other planning details. Near the end of the lesson, Jessica stated: ‘ … we decided together that outside would be more effective because it’s on the outside, it’s pushing the outside light away’ (line 8). By the end of the planning stage, they had selected their two inquiry questions, decided that the ‘position of the foil’ inside and outside the carton would be the variable, identified the materials, and determined their roles and responsibilities.

The group initially translated their ideas into a tentative hypothesis (AAAS, 1989) as indicated by their use of hedging language (i.e. might, could, but) and their drafting of sentences as questions (line 7). Though not yet formalised, Jessica’s explanation at the end of this stage illustrated a more decisive and scientific statement (line 8), as a tentative hypothesis.

The group organised their experiment as consecutive trials starting with the control (i.e. no foil carton/house), then foil on the outside and foil on the inside cartons/houses. After the initial control trial, they proceeded to the foil-outside trial. Despite tentative beginnings, the students’ hypothesis was a constant point of reference as they tested and refined it throughout the experiment and in the final report, as illustrated in Episodes 2-6.

Testing and refining the hypothesis during Trial 1 (Episode 2)

During the first foil-outside trial, the students were watching the decreasing temperature graph on the datalogger and deliberating on possible explanations (Episode 2, lines 1-6):

Jessica: The idea is that the foil pushes it [gestures]. (line 1)

Clara: It reflects the … . (line 2)

Jessica: We don’t really want it to get hot. (line 3)

Clara: Yeah. (line 4)

McKinley: Because it’s supposed to be cool. (line 5)

Jessica explained their ideas to the onlooking teacher-helper: ‘Well our theory is that the foil on the outside will protect the inside from the heat. It’s doing what we wanted’ (line 6). In this first trial, Jessica revised their initial tentative hypothesis as she explained these initial results to the teacher-helper.

Making predictions (Episode 3)

The graph continued to indicate decreasing temperature, prompting the following commentary:

Jessica: It’s going down! (line 1).

McKinley: Yep. Cause I think the heat’s bouncing off the reflective … (line 2).

Jessica: [Simultaneously gestures how heat bounces off surface]. (line 3).

McKinley: … surface. (line 4).

Jessica: That’s what we wanted. (line 5).

Clara: That’s what we predicted. (line 6).

During her explanation, Jessica used hand gestures indicating the bouncing of rays. Clara summarised the results: ‘So the result of the foil being outside the carton box thingy was what we predicted, like, it would reflect the heat’ (line 7). Clara connected their results to earlier predictions (lines 6, 7). The students transitioned to the foil-inside trial, maintaining the same setup with the heat sources outside the house. They noticed the temperature increasing:

Jessica: Look how good it’s going [pointing to the datalogger]. This is what you want for winter. (line 8).

McKinley: But this is for summer. (line 9).

Clara: We thought that they’d both be the same. (line 10).

Jessica: No, we didn’t, we thought this would be worse. (line 11).

McKinley: No, we thought this would be hotter. (line 12).

Clara: Let’s see our hypothesis [learning over to get McKinley’s project book]. (line 13).

The students continued to test their hypothesis, but could not reach consensus due to confusion over initial predictions (lines 8-13).

Testing and refining the hypothesis during Trial 2 (Episode 4)

The second series of trials enabled the case group to compare with their previous results. As they began the second trial for the control house, all three students closely watched the datalogger and remarked that the temperature remained constant, similar to their first trial. Reflecting on their results so far, Jessica commented: ‘It’s going right like our hypothesis says’ (line 1). Clara confirmed: ‘it’s what we predicted’ (line 2). These comments indicated that students were continuing to test their hypothesis and make predictions.

For their final report, students demonstrated a consensus on their hypothesis:

We predict that foil on the outside of a house will keep a house cooler than having it on the inside. This is because having foil on the outside will reflect heat off the house, and foil on the inside will trap the heat inside, acting as an insulator.

Episodes 1–4 illustrate students’ iterative hypothesis development as part of the ‘(often messy and confusing) context of discovery and the (inferentially tight) context of justification’ (Kelly & Licona, 2018, pp. 146–147). The next section discusses flexibility demonstrated in the conduct of their investigation.

Developing and adapting the experimental design

After initial difficulties clarifying the hypothesis, students proceeded with their first series of trials, making an impromptu decision to do two sets of trials instead of three due to time constraints. While this response demonstrated flexibility in adapting their research design, the time restriction also necessitated collaboration amongst group members to complete the task on schedule, much like genuine scientific inquiry (Latour, 1999). Similarly, there was an awareness of time constraints between trials, illustrated through McKinley’s comment at the end of the first foil-outside trial: ‘We’re almost done and we gotta really quickly put the next one in.’

During the first trial, the case group established their procedure, and gained confidence operating the datalogger, recording the data from the datalogger to their data table and participating in ongoing discussions around the results and approaches. During the second control trial, students commented that the temperature remained constant and this result was consistent with their first trial, demonstrating their knowledge about control trials (AAAS, 1989).

Adapting to changing conditions (Episode 5)

As the second control trial was underway, McKinley shared her ideas about the temperature differences being more important than the actual temperature itself, pointing to the already increased temperature:

You know what I think the thing will be about … It will be about how much its risen, instead of like what temperature it gets to. Because I’m noticing that this is a lot higher [pointing to the datalogger] than the first time we did it. I think that just because it’s already like heated up [pointing to the carton] (line 1).

Her comment demonstrated an understanding of experimental errors, as well as an ability to adapt to changing conditions.

For the second foil-inside trial, Jessica inserted the temperature probe and positioned the house in front of the lamp. McKinley pointed out that the house was not the same distance away from the light so they re-adjusted its position. The students simultaneously yelled out ‘Go!’ as they turned on the lamp and started the datalogger (line 2). They noticed that the temperature immediately began going down. As the datalogger continued to measure the temperature, Jessica interjected: ‘look how much it’s going down!’ (line 3). With 10 min left, the case group began the last foil-inside trial. The initial results were not what they expected.

Jessica: What’s it doing? It’s not going up [looking at the data logger]. Um this hasn’t got foil – oh yeah it does. (line 4).

McKinley: Yes, it does. Is there foil along here [pointing]? Its fallen down? (line 5).

Jessica: Why is it going down? (line 6).

Jessica: It’s not doing the same thing. Now, it’s going up [looking at the datalogger]. (line 7).

Clara: It shows that how insulation … no – that’s conduction … (line 8).

Jessica: Because its [the foil] fallen down a little bit. Maybe because its fallen down a little bit, it’s not doing it. (line 9).

With only a few minutes left in the lesson, the temperature began increasing.

Jessica: OK, wait. Why does it go down first [looking at the datalogger]? (line 10).

Clara: [Announced into the microphone] Our results are really strange. It has gone down first and then gone back up again [focusing the camera on the datalogger]. (line 11).

Students were anticipating the temperature would increase as it did during the first trial, but they were presented with unexpected results. They began troubleshooting potential issues with the trial, confirming that this carton had foil on the inside (lines 4-5, 9) and generating possible explanations (line 8), thereby demonstrating attempts to adapt to the changing conditions (Pickering, 1995). Episode 5 demonstrated the need for ongoing adjustments to their method as the investigation was underway.

Students demonstrated an awareness of these adaptations, as illustrated in their reflections on their experimental method in their final report:

The method was effective, however further consideration should have been put into how much time was allowed, and what could be completed in that time. Originally the method planned for three trials, but the group could only do two in the time to complete that prac. Also, more time should have been left for the cardboard boxes to cool, so accurate and consistent results could have been collected. Both the cooling time and extra trials could have contributed to better and more reliable results.

The students elaborated on their decision about experimental design during the interview. They re-iterated their initial preference for conducting three trials. Clara commented: ‘We thought that having three would be a good average. We would get good really results out of it’. They also suggested having more time between trials to allow the probe to cool down. They echoed McKinley’s prior comments about allowing the houses to cool down between trials (Episode 5, line 1). Their reflections were indicative of their understanding of variable control and reliability in scientific investigations, consistent with authentic inquiry practices (AAAS, 1989).

As a more flexible approach to planning an inquiry, the task was led by questions. These five episodes demonstrate how students engaged in the generation of a tentative hypothesis that was tested and refined throughout the experiment, constantly compared with predictions, and formalised in the written report. Students also demonstrated flexibility during the investigation through last minute changes to number of trials and troubleshooting unexpected results, adapting research design to meet changing conditions. The interplay of creativity with flexibility afforded by this task was demonstrated through students’ insights to recognise and interpret unexpected results and apply rational practices (e.g. variable control, experimental errors, reliability) to complete the investigation in a systemic manner (AAAS, 1989; Kind & Kind, 2007; Osborne et al., 2003). The descriptive analysis illustrated that students experienced all six indicators within this key epistemic practice, indicative of their strong engagement.

Provisional knowledge construction

Building on the iterative hypothesis generation, the case group continued to engage in knowledge construction through increasingly refined explanations about the concepts, assisted by disciplinary-specific tools and technologies.

Construction knowledge with tools

The case group’s initial and refined explanations were demonstrated through their refined multimodal representations. They used these disciplinary-specific tools to aid their thinking across all stages of the task (Ainsworth, 2011; Wong & Hodson, 2009). For example, during the planning stage (Episode 1), students drew on two key representational resources, cartons and gestures, to help them link the ‘real-world’ with the abstract idea of energy transfer. Jessica and Clara explored their initial ideas, drawing on their previous knowledge as they manipulated the carton and made gestures to demonstrate the placement of the light and explain how foil attracts the heat (lines, 2-3, 6). Their gestures played an important role in communicating each other’s ideas about the effects of light and heat, notably students’ spatial reasoning about the movement of heat (Goldin-Meadow, 2014). Engaging in these kinds of visualisations is also is considered central in science curriculum and teaching, and is thought to foster creativity and imagination (Hadzigeorgiou et al., 2012). Though their ideas about foil ‘pushing the outside light’ away (line 8) were inconsistent with a scientific perspective, these set the foundations for their hypothesis and experimental design and demonstrated their engagement in provisional knowledge construction.

During the experiment, students integrated a third representational resource by generating their own data tables as there was none provided (Figure 1).

Figure 1. Data tables generated by Jessica (above) and McKinley (below).

Students represented the data generated from the datalogger as part of a process to develop, revise, and refine explanations in their own way. They referred to one of the tables to summarise the results.

Reflecting on the results (Episode 6)

Jessica and Clara summarised the results, reading off Jessica’s data table in her project book (Episode 6, lines 1-11).

Clara: So the results show us that the control was consistent – mostly consistent. And it didn’t go up by … (line 1).

Jessica: It went up. (line 2).

Clara: It went up by point 5. Like a half a degree [showing results table in the project book]. (line 3).

Jessica: And the foil on the outside … [focusing the camera on that part of the table] (line 4).

Clara: It went down. The foil on the outside went down because maybe they don’t attract to the heat and it reflects the heat. (line 5).

Jessica: So the foil inside … interesting results … It began to go down at the start. (line 6).

Clara: For our second trial [pointing at the part of the table], it began to go down for a while, like maybe 2 seconds And then it went back up. So that’s interesting. (line 7).

Jessica: But it’s still gone up. (line 8).

Clara: Yeah, by a degree [still pointing at the data table]. (line 9).

Jessica: That’s our results so we’ll be able to write that up because it does support … (line 10).

Clara: Our hypothesis and what we predicted. So that is a very good result. (line 11).

Jessica’s data table corroborated their summary of results, demonstrating how they were generating claims based on the data as part of their collaborative sense-making (Latour, 1986, 1999). McKinley’s data table included the temperatures, calculations of the temperature differences, and text-based explanations, reflecting her earlier deliberations about temperature differences during the second series of trials:

You know what I think the thing will be about … It will be about how much it’s risen, instead of like what temperature it gets to. Because I’m noticing that this is a lot higher [pointing to the datalogger] than the first time we did it. I think that just because it’s already like heated up’ [pointing to the carton].

These ideas were reflected in their report, demonstrating how their conclusion was based on their claims:

In conclusion, having foil on the inside of a box isn’t effective at keeping a house cool in the summer, because in only three minutes the temperature inside a box with foil on the inside rose on average by 1.45°C. In the cardboard box with foil on the outside, the temperature decreased by 1.6°C on average, proving that a house with foil on the outside will be kept cool in summer.

The post-task interviews provided additional insights into the tentative and iterative nature of their ideas. Clara commented: ‘It was kinda opposite to what we originally thought’. McKinley and Jessica elaborated:

McKinley: It was kinda what I expected to happen. I wasn’t exactly sure how the foil on the inside was going to work. I wasn’t sure if it was going to work like an insulator on the inside because I thought it might conduct more heat.

Jessica: That’s the one we were more sure about. Like I thought that foil on the inside would heat up, but I wasn’t so sure about foil on the outside.

This reversal was indicative of the flexible and provisional nature of scientific knowledge (AAAS, 1989; Kelly & Licona, 2018; Osborne et al., 2003), and reflective of the creativity and imagination required in science (Feynman, 1995; Kind & Kind, 2007). Students’ knowledge construction was also supported by the datalogger.

Constructing knowledge with digital technology

During the trials, the immediacy of the datalogger graph accelerated students’ knowledge construction experiences through ongoing generation and revision of explanations based on the real-time results of the graph. For example, during the first foil-outside trial, students noticed the temperature decreased and began relating the data to their prediction/prior knowledge (Episode 3, lines 2-6). They progressed from general statements: ‘the foil on the outside will protect the inside from the heat’ (Episode 2, line 6), to one that included a causal mechanism: ‘the result of the foil being outside the carton box … would reflect the heat’ (Episode 3, line 7) – demonstrating how they based their claim on the data.

Similarly, after troubleshooting and resolving issues related to the unexpected temperature decrease in the foil-inside trial, (Episode 5, lines 4-5, 10), the students began to generate scientific explanations, but confused insulation with conduction (Episode 5, line 8). At the end of both trials, Jessica and Clara revised their explanations, and substantiated their claims with data that is a practice reflective of the explanatory and predictive nature of scientific inquiry (AAAS, 1989; Lederman et al., 2014), as they explained their results with a plausible mechanism (Episode 5, line 5). Clara noted the temperatures went down during the foil-outside trial and attributed this result to the foil reflecting the heat instead of attracting it (line 5), indicatinga progression in scientific understanding since the planning stage. The ongoing revisions were finalised in their report, where their written hypothesis included a simplistic though more scientifically refined explanation.

As a disciplinary-specific technology delivering both anticipated and unexpected results (Pickering, 1995; Wong & Hodson, 2009), the datalogger played a central role in prompting these refinements (AAAS, 1989; Latour, 1999; Wong & Hodson, 2009). The datalogger provided more time for meaningful interactions with the data than the mechanical processes of translating temperature readings into graphical forms afforded (Rogers & Wild, 1996). Students’ actions and reflections revealed the visual output enabled them to identify trends and changes in context and compare results across trials; in addition, they noted the datalogger provided accurate results.

The datalogger also supported the direct translation of ‘real world’ phenomena (i.e. temperature change) to abstract ideas (i.e. energy transfer), as the students constructed meaningful interpretations (Ainsworth, 2006; Rogers, 2008). This direct link addresses one of the main criticisms around traditional practical investigations: students’ inability to connect their observations with scientific ideas or representations (Abrahams & Millar, 2008). During the interview, the students commented that the datalogger ‘shows you as its going’, with ‘the line [of the graph] either dropping or going up' and provided ‘accurate’ data. They also noted it freed them from having to constantly collect the data and wait for the results.

In summary, knowledge construction was distributed across the physical (i.e. scientific apparatus), the representational (i.e. graphs, data tables), and communication (i.e. dialogue, gesture) spaces, over very short time periods, illustrative of scientific modelling practices (Rogers, 2008). The descriptive analysis illustrated that the students experienced all five indicators within this key epistemic practice, suggestive of their strong engagement.

Collaborative inquiry

The case group’s dialogue was characterised by emerging and sometimes contradictory ideas over the duration of the three-day task, eventually reaching a consensus for their report.

Collaborating and cooperating

Initially, when the case group addressed their two inquiry questions during planning stage (Episode 1), they engaged in exploratory dialogue using language such as: ‘I reckon’, ‘I don’t know’, ‘maybe’, ‘I think’ (lines 1-4). They also participated in cumulative dialogue by sharing ideas and building on each other’s claims (line 6), which was maintained during the experimental phase as the case group continued to explain and refine their ideas. For example, during the first foil-outside trial, they discussed and elaborated on each other’s explanations for decreasing temperature (Episode 2, lines 1-5; Episode 3, lines 1-3), supported these ideas with accompanying gestures (Episode 2, line 1 and Episode 3 lines 2-3), and reached consensus with their predictions (Episode 3 lines 5-7).

In contrast, their first foil-inside trial (Episode 3) stimulated some disputational exchanges over summer versus winter effects (line 8-9), which prompted further reflections as they continued with the trial. During the second series of trials (Episode 4), the case group reflected on their results, and built on their ideas through cumulative dialogue (lines 1-2), and shared ideas about temperature differences through exploratory dialogue (Episode 5, line 1). The group demonstrated their cooperation as they announced the start of the foil-outside trial (Episode 5, line 2), and when they noted the temperature decrease (line 3).

For the final foil-inside trial, the initial decrease in temperature prompted both cumulatory (lines 4-6) and exploratory dialogue (lines 7-10) to make sense of the unexpected results. These collaborative exchanges were also indicative of the creativity essential in science classrooms (Hadzigeorgiou et al., 2012).

Throughout the investigation, the students engaged in both cumulative and exploratory dialogue, building on each other’s ideas as they shared and refined their ideas through this joint task (Mercer et al., 2004). Students were able to formulate and reach a consensus about hypothesis, describe observations, reason about cause and effect, and summarise results, practices consistent with authentic scientific investigations in the classroom (Mercer et al., 2004).

The case group also demonstrated a high degree of cooperation and productivity throughout their investigation, despite occasional disagreements (Wong & Hodson, 2010). This was evident during their setting up of experiments, coordination of tasks during the trials, adjustments to two trials, and generation and interpretation of data tables.

In a departure from traditional methods of writing individual laboratory reports, the students worked together on a single GoogleDoc. Applying prior experience with this technology, the students collaboratively constructed a written report. They sat together, each working from her own laptop as they accessed the same GoogleDoc but focused on different parts of their report. Their dialogue centred around clarifying and critiquing each of these sections, and the construction of the data table. Their efforts culminated in a written explanatory statement that included scientifically valid terminology and command of the science report genre.

During the post-task interview, students commented on how GoogleDocs enabled them to work together on the same document at the same time or at different times and places. They mentioned the features they used to edit each other’s work for spelling and grammar, identify each other’s contribution through the different font colour, and make comments in the chat window that group members could read later, demonstrating their proficiency with this technology.

Beginning with the planning stage, the students’ explanatory accounts were honed throughout the investigation as they engaged in a process of representational re-description and refinement that resulted in a concise representation of their findings, as reflected in their report (Latour, 1999). During this process, they experienced the provisional and collaborative nature of scientific knowledge, having to revise their ideas based on new data and base their claims on evidence (AAAS, 1989; Harlen, 2010; Osborne et al., 2003).

The descriptive analysis illustrated that the students experienced all three indicators within this key epistemic practice, though they were not explicitly in competition with others.

Finally, while the epistemic practices of authentic inquiry (as per the AIF) were presented as three separate instances, there was much overlap where students were engaging in multiple epistemic practices. The datalogger and GoogleDocs were two disciplinary-specific technologies that presented affordances consistent with all three practices.

Conclusion

The three-lesson summative task promoted strong student engagement in all three epistemic practices of scientific inquiry featured in the Authentic Inquiry Framework (AIF). Across all stages, students were able to apply their creativity in flexible ways to solve problems, and their gradual but generative application of conceptual knowledge led to a report approximating scientific views. Students were also able to constructively integrate disciplinary-specific tools and technologies to build knowledge throughout their investigation. Both digital technologies (i.e. Datalogger, GoogleDocs) presented affordances consistent with all three key epistemic practices of the AIF. Students’ experiences of epistemic practices are consistent with inquiry approaches that involve the construction, communication, evaluation, and legitimisation of a scientific explanations that answer scientific questions (Kelly & Licona, 2018). These findings establish the inquiry task as a feasible and authentic inquiry learning experience and that authentic guided-inquiry tasks can be generatively scaffolded within the constraints of classroom schedules and used effectively for summative tasks.

The AIF was a useful analytical tool to determine whether students engaged in practices as described through the indicators, and to what extent these represented each of the three epistemic practices. The potential for the AIF for planning guided inquiry tasks, and evaluating them through quantitative methods can be further explored in future research.

Ethics statement

This project was granted ethics approval by the Deakin University Human Research Ethics Committee (DUHREC), project number 2013-230.

Acknowledgements

The author wishes to acknowledge the Deakin Faculty of Arts and Education and their generous support through a post-graduate publication award. The author also wishes to acknowledge Dr. Anne Hume from the University of Waikato, for the preparation of this manuscript, and Professor Russell Tytler from Deakin University for support in earlier drafts. The author acknowledges the following thesis from which this manuscript is inspired: Cirkony (2019). Students learning science: Representation construction in a digital environment, Deakin University. https://dro.deakin.edu.au/view/DU:30129406.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported by Australian Research Council: [grant no LP130100233].

Notes

1 In this article, the author uses the terms ‘practical work’ synonymously with ‘laboratory activities’ as terms that refer to students working with real objects and materials to observe and understand the natural world, often as part of experiments and investigations (Abrahams & Reiss, 2012; Hofstein & Lunetta, 2004).

Appendix: Authentic Inquiry Framework (AIF).

Table

Epistemic Practices of Authentic Science	Indicators of each Practice
Flexible and creative planning and conduct of investigations	Undertake inquiry that have multiple possible results or solutions Undertake inquiries without formal hypotheses (e.g. using questions to lead inquiries) Generate own hypothesis, including provisional or tentative hypotheses Test and refine hypotheses Make predictions Adapt research design to meet changing conditions
Provisional knowledge construction	Use disciplinary-specific technologies to assist with data collection, analysis, and sharing Use disciplinary-specific tools to link the ‘real world’ with abstract ideas, through increasingly refined multimodal representations Develop, revise, and refine explanations Relate data with previous knowledge Base claims/conclusions on the data
Collaborative inquiry	Experience cooperation, collaboration, and/or competition Build on each other ideas through cumulative dialogue Engage in critical and constructive discussions of ideas through exploratory dialogue