Striving to learn to do chemistry in the laboratory: epistemic conation as a fundamental driver of inquiry

Abstract

Learning to do chemistry in the laboratory involves dispositional, motivational, and volitional factors that sustain and direct inquiry. These aspects have been theorised as constituting an incentive dimension that serves as a fundamental driver of inquiry, and they are often conceptualised as grit, perseverance, motivation, and similar notions emphasising individual characteristics or personality traits in students’ striving to learn. While concepts like grit and perseverance treat learning motivation as stable individual traits, epistemic conation captures the dynamic, knowledge-specific intentions that emerge when learners actively seek, evaluate, and apply scientific understanding—shifting focus from who the students are to how they intentionally engage with epistemic practice. Based on a series of studies within the context of laboratory education in pharmaceutical analytical chemistry, which is also a part of a large, recently concluded project, the paper unfolds how epistemic conation manifests in students’ collaborative and individual practices during laboratory experiments, highlighting how it encompasses conative dispositions, motivational factors, goal orientations, and volitional strategies. Through a mixed-method approach involving 30 students in the focus groups’ data and 43 students in the laboratory discourse data, we show that the social aspects of key constructs, such as perseverance, epistemic motivation, experimental goal orientation, and active help-seeking, are crucial in student learning and competence development in the laboratory. These findings suggest that effective laboratory instruction requires assessing how perseverance and motivation emerge through group dynamics rather than evaluating students’ perseverance or motivation as a personal trait, and instructors would need to assess how these qualities emerge and function within group dynamics and peer interactions. Implications for research and practice are presented.

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1. Introduction

Rosalind Franklin's iconic accomplishment as an English chemist and a trailblazer in molecular biochemistry demonstrates how perseverance emerges through and creates supportive environments across time and space (Latawa, 2021). She resisted gender discrimination in science during a pivotal moment when higher education finally, gradually admitted women (Shah, 2018). Through her persistence in doing research that led to the discovery of the DNA structure, albeit not credited initially (Julian, 1983), Franklin not only navigated hostile institutional contexts but also cultivated a legacy that now provides an inspirational environment for underrepresented scientists and students in STEM (Latawa, 2021; Sunasee, 2023), showing how perseverance is as much an individual resource as a collective state of affairs.

Franklin's experience encapsulates a key question about what makes human beings strive to pursue knowledge and persist in their striving. Historically, this question has given rise to a range of theories about what constitutes such striving and how it can be promoted. This has recently been addressed through the idea of “grit”, which has been used as a predictor of success and integrated into educational and training programmes (Stix, 2011; Duckworth and Gross, 2014; Audley and Donaldson, 2022). However, the notion of grit has been criticised for overemphasising individual perseverance while overshadowing systemic barriers to success (Kirchgasler, 2018). An important critique (Credé, 2018) concerns the way the focus on grit tends to disregard social conditions, particularly those of marginalised, underrepresented students. In general chemistry, the deficit narrative of grit has been studied among Black and Latinx student populations, reasserting that grit is not an inherently equitable concept, as it tends to be imposed disproportionately on low-income students of colour, reinforcing the belief they must be “grittier” than their peers from privileged backgrounds (Vincent-Ruz et al., 2024). As an effect, Carter and colleagues (2024) call for: “What other models, frameworks, and understandings can we apply to challenge current applications of grit in higher education?” (p. 7, emphasis added).

In response to this call, we propose a reconceptualisation of what it means to strive to learn – a phenomenon often described as noncognitive constructs, such as grit or perseverance. Noncognitive aspects of learning have been studied extensively, primarily in educational psychology but also in chemistry education research, referring to patterns of thoughts, feelings, and behaviour that play a crucial role in learning processes but are distinct from cognitive skills or formal conceptual understanding (House, 1995; Turner and Lindsay, 2003; Abedi and O’Neil, 2005; Wilmot and Ones, 2019). In this study, we aim to substantiate how striving to learn is entangled with the social and epistemic dimensions of doing chemistry in the laboratory. To do so, we revisit the classic notion of conation, discussing it in relation to the notion of epistemic practice (Agustian, 2025a). We introduce the concept epistemic conation, defined as intentional aspects of seeking, acquiring, evaluating, and using scientific knowledge, encompassing drives, desires, and efforts that individuals and groups exert to engage with epistemic practice. We propose that epistemic conation allows for an understanding of the noncognitive dimension of learning and knowledge-making, viewing them as dynamic, situated, and non-deterministic (Agustian et al., 2025). To investigate how it unfolds in a naturalistic setting, we look into the chemistry laboratory.

The laboratory in university chemistry education has been established as a place to learn to do chemistry (Seery, 2020), where students are engaged in various aspects of scientific, epistemic, and professional practices (Jiménez-Aleixandre and Reigosa, 2006; Chadwick et al., 2018; Carmel et al., 2019; Agustian, 2022, 2025a; Soucie et al., 2023). In pharmaceutical science education where this study was conducted, typically around onethird of the curriculum is dedicated to laboratory courses (Agustian et al., 2022b). Examining epistemic conation in the context of the laboratory, we draw on Seery's (2020) notion to encompass an elevated, goal-oriented, socially enhanced, and epistemologically informed argument of doing chemistry.

In the chemistry education research literature, the term conation has only been used sparsely, mainly in relation to affective aspects of learning (Rahayu, 2014), but it is either only mentioned briefly with no elaboration (Harsh, 2016; Harsh et al., 2017) or vaguely defined as “a tendency to behave or do something in a certain way” (Bučková and Prokša, 2021, p. 165). There is a lack of conceptual clarity in how the notion is theorised and operationalised beyond a cursory usage. A perspective article in this Journal has made an attempt to clarify and conceptualise “conation” in light of the learning processes involved in laboratory work as epistemic practice (Agustian, 2022). This empirical study seeks to further characterise this important aspect of chemistry learning. As such, the study contributes to learning theory development in chemistry education, as well as offering practical guidance by demonstrating how philosophical constructs of epistemic conation can be systematically observed and analysed in authentic laboratory contexts.

2. Epistemic conation and the conative domain of learning

2.1. Conation and its philosophical basis

The philosophical writings on conation are telling of a longstanding interest in what drives inquiry and learning. As a theoretical construct, “conation” dates to ancient Greece. Aristotle describes conation as a faculty of mind rooted in our natural impulses to want, desire, and attain (Price, 2022). The philosophical basis for conation can be traced back to his theory of ethics, and his discussion of the concepts of continence (enkrateia) and incontinence (akrasia), which relate to self-control and the lack thereof (Cope and Sandys, 1877; Price, 2022). In his philosophy of mind and theory of ethics, Aristotle posits that “[d]esire, together with intelligence and perception, is responsible for ‘action and truth’… that is linked to practical motivation” (Price, 2022, p. 233).

Several other philosophers have corroborated the idea of conation, touching on the relation between desire and thinking. In Spinoza's conatus principle, “each thing, as far as it can by its own power, strives to persevere its being” (Carriero, 2011, p. 69). In human beings, conatus can be a matter of the mind – in which case, Spinoza calls it Will – but it can also be corporeal, an asset of the mind and body together, in which case he calls it Appetite. For Kant, conation refers to an inherent drive of reason, which he describes as the “peculiar fate” of Reason being “burdened by questions which it cannot dismiss” [cited in Kleingeld (1998), p. 79]. When Reason's “inextinguishable desire” is met, this results in a feeling of satisfaction. Kant's emphasis on the conative character of reason in this way assumes an emotional element in the workings of reason itself, thus challenging his otherwise prominent distinction between reason and feelings.

Recent deliberations of scientific knowledge-making extend these views of practical motivation, appetite, and emotion in human interest or drive to also consider the socio-material conditions shaping the process of scientific work. Illustrating how training a nose for perfumiers is closely tied to the training of discriminating specific smells, Latour (2004) highlights how “the path to science requires […] a passionately interested scientist who provides his or her object of study with as many occasions to show interest and to counter his or her questioning through the use of its own categories” (p. 218). For Latour, the scientists’ interest and questioning, however, do not take shape in isolation, but in relation to specific socio-material circumstances, such as a specific selection of smells or a particular field of knowledge, the construction of a laboratory, and its position in the wider societal milieu. Indeed, interest in this view is a consequence and not a cause of the way humans and things become associated in action (Latour, 1983).

2.2. Conation in the learning sciences

Although the notion of conation has fallen in and out of educational discussions, its importance for human performance has recently been recognised within the sciences. Within neuroscience and neuropsychology, conation describes striving and the lack thereof. Hoffmann (2016), for example, posits that conation refers to the natural tendency to direct mental and physical efforts towards a goal. Conation has also been demonstrated as one of the four categories of neurological and psychological functioning, besides cognition, affect, and behaviour (Reitan and Wolfson, 2000; Hall et al., 2006; Dennis et al., 2013), although its precise location in the human brain remains debated, based on brain injury and neurological research (Heilman, 2002; Panksepp, 2004; Dennis et al., 2013). In studies on mind-body and brain-behaviour relationships, Heilman (2002) and Hoffmann (2016) describe how patients with superior frontal lobe injuries demonstrate loss of drive and initiative (abulia), voluntary movement (akinesia), and persistence, while their cognition and affect remain intact. These studies suggest that conation is a crucial human function that affects many life aspects, including learning.

Conation, however, has received sparse reference in both neuroscience (Reitan and Wolfson, 2000, 2004; Panksepp, 2004) and learning sciences (Corno, 1993; Boekaerts, 2016; Goldin, 2019). In the learning sciences, a reason could be that most researchers typically settle with “motivation” as a key construct. Renowned scholars like Ryan and Deci (2020) and Pintrich (2004) have theorised motivation extensively. Their self-determination theory provides a comprehensive framework, positing that supporting students’ autonomy, competence, and relatedness enhances motivation. In chemistry education research, motivation has also been studied empirically, for instance, within context-based (Vaino et al., 2012) and game-based (Xu et al., 2025) learning. However, a growing number of scholars argue that motivation only accounts for one side of the translational process from goal setting to performance (Corno, 1993; Economides, 2009; Husman and Corno, 2010; Goldin, 2019). The other side of the process is called “volition”, referring to purposive striving, by which students implement goals during learning, sustain motivation, and strategically regulate cognition and affect (Ammoneit et al., 2024). Indeed, motivation leads to commitment, but volition denotes follow-through (Husman and Corno, 2010). McCann and Turner (2004) and Hershberger (1989) demonstrate that volitional strategies are critical self-regulatory processes underlying goal-directed behaviour. In laboratory learning contexts, volition is particularly important because students constantly make decisions guided by their goals. Furthermore, learning goals in laboratories, whether teacher-formulated or student-perceived, must manifest in actual performance, involving mental and physical functioning.

Building on this foundation, we can conceptualise the conative aspects of learning as constitutive of motivation and volition. It is considered a prerequisite to sustain learning over a longterm commitment (Snow and Jackson III, 1997; Novak, 2004; Husman and Corno, 2010; Illeris, 2018), also in chemistry (Wang and Lewis, 2022; Jaison et al., 2025). Regarding other learning domains, Reeves et al. (2021) assert that although individuals may have the cognitive capacity, affective values, and physical skills to perform tasks, they may lack the drive to strive and perform at the highest standards. Within the conative domain, motivation and volition form a continuum of commitment, from needs and wishes to wants to intentions to actions. For learners to transform intention to become an action, it must be immediately relevant in the present situation, under control in the learner-situation interaction, and protected against competing intentions and action tendencies in the ongoing performance (Corno, 1993; Snow and Jackson III, 1997; Clark, 2012). In laboratory education research, these notions are ripe for further development, and the related line of research in self-regulated learning in higher education (Pintrich, 2004; Dresel et al., 2015) may well inform this context.

2.3. Epistemic conation and striving to learn in the laboratory

Traditionally, discussions on conation and noncognitive processes, such as grit and perseverance, mainly concern individuals. However, construing scientific knowledge through experimental work occurs in socially shaped laboratory contexts requiring negotiations, contestation, and argumentation through meaningful social interactions (Latour, 1983; Knorr-Cetina, 1999; Sandi-Urena et al., 2012; Jobér, 2017). Osborne (2015) maintains that these activities are central to developing ideas in science, where students are supposed to be “engaged in argument about their data, contrasting their data with their theoretical predictions, and identifying flaws in both their own and others’ ideas” (p. 17). Consequently, students’ drive to achieve good experimental results by engaging in scientific practices is influenced by sociocultural norms of doing chemistry and social interaction with peers and instructors. The extent to which chemistry students are intrinsically driven certainly depends on the effectiveness of laboratory instruction. Recent findings highlight the importance of curricular and instructional scaffolding, clear purpose of laboratory work, and appropriate assessment practices (Seery et al. 2019; Jørgensen et al., 2023; Kaya and Kaya, 2024; Seery et al., 2024a).

This view of striving to learn as social has recently been emphasised in the science education literature. Jaber and Hammer (2016), for example, have introduced the notion of “epistemic motivation” to represent “drives connected to epistemic experience and objectives in the doing of science” (p. 161). They highlight social relatedness and contextual factors that foster or hamper epistemic motivation, such as authority and social risk within the epistemic spaces, that “might signal the need to bolster claims with stronger evidence” (p. 215). Recently, Ammoneit and colleagues (2024) mobilised the concept of “volition” in modeling competence development, to describe “how people, once committed to a course can convert their intentions and goals into action” (p. 446). They refer to psychosocial prerequisites in deliberating noncognitive constructs such as volition. We aim to substantiate that epistemic motivation and volition are closely related and can be seen as part of an overarching process we call “epistemic conation”, which involves dispositional, motivational, and volitional aspects of engaging in epistemic practice.

Epistemic conation and its different aspects should be seen as situational and contextual. While students may exhibit commendable perseverance in an epistemic-oriented task, such as evaluating experimental data for its validity, they may do less so in others, such as upholding accuracy in reading meniscus scales. These engagement patterns do not define the student's personality, nor are they immune to contextual constraints, such as laboratory curricula demanding complicated experiments be finished within 3 hours. Thus, even if students are motivated by certain experiments or parts of them, they may be less motivated by others, due to the lack of perceived relevance (Cetin, 2021; Finne et al., 2022) or competing goals and priorities (McCann and Turner, 2004; DeShon and Gillespie, 2005). Therefore, if epistemic conation is to be assessed, it should not be done as generic psychometrics of individual students, but rather viewed in relation to the task and curricular contexts. From an engagement perspective, it is related to such notion as “situational engagement”, in which students’ engagement is gauged within specific learning situations, allowing for detailed momentary assessment of their learning, typically identified with optimal learning moments (Kyynäräinen et al., 2024), when they “experience high levels of interest, skill, and challenge simultaneously” (p. 2). While situational engagement may concern cognitive, affective, or behavioural dimension (Lawrie, 2023), epistemic conation is primarily focussed on the noncognitive aspect of knowledge co-construction, which is arguably best assessed also on a momentary level. We will exemplify such an attempt with our group- and task-level investigation into students’ epistemic conation in the following.

2.4. Research question

Framed within the above discourse, the present study aims to address the following research question: How does epistemic conation manifest in the laboratory during students’ co-construction of knowledge?

3. Methods

3.1. Context and participants

The present study was conducted in the context of a pharmaceutical analytical chemistry teaching laboratory at a research-intensive university in Denmark. The purpose of the course is to enable the student to carry out reliable, quantitative determinations of active ingredients in pharmaceutical products using a suitable calibration method and to evaluate the reliability of the result. This includes an understanding of the theoretical principles for the analysis methods and the influence of experimental conditions on the result. Theoretical principles of chromatographic and electrophoretic separation methods, as well as atomic spectroscopic and mass spectroscopy detection methods, are reviewed. Emphasis is placed on the coupling of separation and detection methods used in the practical part of the course. The course provides a highly relevant context for this study, as the laboratory curriculum is scaffolded to varying levels of inquiry (Buck et al., 2008), allowing for analysis of inquiry laboratory that builds on a progression (Agustian, 2025b).

Student participants were divided into two data corpora. First, 30 students volunteered to be interviewed in pairs or groups of three, according to the grouping of laboratory work within the course. They responded to queries on learning in the laboratory (see Appendix 1), using their laboratory reports or snippets of video recordings of their experiment. Of these, two pairs of students doing an experiment on quantification of acetaminophen and caffeine (henceforth called Module 3) were recorded over the course of three days, each lasting four hours, corresponding to preparation (Day 1), experiment (Day 2), and data analysis (Day 3). During their conversations in the laboratory, 39 other students were also represented in the data, scaling up the participant number to 43 (see Table 1).

Table 1 Number of participants according to the data corpus

Data corpus	n	Note
Focus group transcripts	30	Using artefacts
Laboratory discourse	43	Focus on 4 students

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All participants volunteered in this study through an open call. Ethical considerations were secured through approval from the Institutional Review Board (case number 514-0278/21-5000) and as per the university's general data protection regulation. This partly entails that all sensitive data, including the video recordings, were stored in the university's secured folder, requiring institutional permissions to access. All names were anonymised with pseudonyms. Unless indicated by the participants themselves, the pseudonyms are generally referred to with gender-neutral pronouns.

3.2. Data sources and analyses

3.2.1. Focus group transcripts. Focus group interviews were conducted using two different artefacts. First, laboratory reports of the Module 3 experiment were used to engage students in a focused dialogue about their experiment, through explanation and argumentation of both epistemic and conative aspects of their learning. Second, excerpts of video recordings were used to elicit students’ reasoning and conative experiences during specific parts of their experiment. A complete protocol for interviewing students about their learning in the laboratory as epistemic practice is appended (see Appendix 1). Each focus group lasted around an hour and was done with 13 pairs or triads of students, as shown in Table 2. A professional transcriber transcribed the audio recordings.

Table 2 Focus group participants and the artefacts used to guide the interviews

Pair/triad	Pseudonym	Artefact
AB	Alexis, Brooks	Lab report
CDE	Cameron, Delaney, Eli	Lab report
FGH	Flynn, Gideon, Hunter	Lab report
IJ	Iman, Jo	Lab report
KLM	Keaton, Lashawn, Miller	Lab report
NO	Noel, Ozzy	Lab report
PQ	Parker, Quinn	Lab report
RS	Ryder, Shayan	Lab report
TU	Taryn, Ulf	Lab report
VWX	Visaya, Waverley, Xerxes	Lab report
YZ	Yllya, Zacharee	Lab report
EF	Eliana, Felix	Video recording
GH	Grace, Hayley	Video recording

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Subsequently, the focus group transcripts were analysed according to interpretative phenomenological analysis (Eatough and Smith, 2017), using ATLAS.ti (developed by Scientific Software Development GmbH), partly assisted by its integrated artificial intelligence. Following the hermeneutic framework, the qualitative data were reduced to descriptive categories to identify the most relevant and meaningful text passages [see also Agustian (2020)]. Each statement was carefully weighed with regard to significance for a description of learning in the laboratory. Any code suggested by AI within the software was critically evaluated by the authors for its accuracy and relevance. This process led to the omission, addition, revision, and reconfiguration of codes and groups of codes. All relevant statements were recorded and coded. Examples of codes include “Motivation” and “Monitoring”. Next, nonrepetitive, nonoverlapping statements were organised into so-called invariant horizons. Then, they were clustered into themes. For example, “Motivation” and “Enjoyment” were clustered into a theme called “Epistemic motivation”. Both invariant horizons and themes were then synthesised into a description of the textures of the experience, whereby verbatim excerpts were included. By reflecting on the researchers’ own textural description, a description of the structures of the researchers’ experience was constructed. Finally, the textural and structural descriptions of the meanings and essences as perceived by the researcher were combined into a model of conation in laboratory-related epistemic practices.

3.2.2. Laboratory reports. To guide the semi-structured focus group interviews, the laboratory reports were reviewed beforehand to get an overview of conative aspects, such as indicated experimental goals and attention to precision and accuracy. For example, a part of Alexis and Brooks's laboratory report was annotated to encourage argumentation on why they did six SST (system suitability test) analyses and what it meant for their experimental results, whereas Flynn, Gideon, and Hunter's laboratory report was annotated to focus the interview on the role of US pharmacopeia requirements in quality assurance and how the experiment was perceived in that perspective. As such, there were slight variations in how the focus group interviews were conducted, depending on the pertinent aspects of their laboratory report, but the protocols (see Appendix 1) were useful in keeping consistency with the research question.

Report writing is a skill that needs to be taught and developed. We scaffold this part of laboratory pedagogy across the study programme by starting with schematic, structured report in the first year's course. In the present second-year's course, students have to write the entire report from scratch. While they should keep it concise, all required elements need to be written for an approval by the instructors. Most importantly, the faculty teaching the course (typically at the associate professor level) discuss the feedback they give in the laboratory, to make sure students understand and take up the feedback, highlighting the formative aspect of this widely practiced assessment approach. See Jørgensen and colleagues (2023) for why it should not be assumed that feedback practice in the laboratory is always effective.

3.2.3. Laboratory discourse. Chemistry education research in the laboratory settings that looks into detailed interactions and conversations is rare. A recent systematic review shows that it accounts for merely 1% of a bulk of more than 350 empirical studies in university chemistry education (Agustian et al., 2022a; Agustian, 2024). In studying epistemic practices in this context, understanding laboratory discourse is crucial, as it provides insights into how students “talk science” while “doing science” (Osborne, 2015). To do this, the video recordings were transcribed verbatim by a professional transcriber. To enrich the analysis, non-verbal cues and actions were also registered in the transcription process, performed in detail by the first and third authors. Registering actions is relevant because they have been theorised in the literature as a key indicator of conation (Hershberger, 1989). The resulting multimodal transcripts accounted for over 10 000 lines of analysis, tabulated in a spreadsheet for further examination.

The analysis of laboratory discourse followed a systematic approach combining deductive and inductive approaches within the theoretical framework described earlier. We employed microanalytic discourse analysis techniques (Gee, 2014) described in the previous work on laboratory education (Kelly and Crawford, 1997; Jiménez-Aleixandre and Reigosa, 2006). This involved closely examining the transcripts to identify interaction patterns and engagement.

The analysis proceeded through several stages. First, segmentation, in which the transcripts were divided into meaningful units of analysis called stanza (Shaffer, 2017), typically consisting of conversational turns or topically coherent segments (e.g. about the underlying principle of chromatography). Second, coding, in which a coding scheme was developed based on our theoretical framework and research question. This included codes for epistemic practices (e.g. “Asking/proposing explanations”, “Generating own questions”), conative elements (e.g. “Striving”, “Demonstrating persistence”), and social interactions (e.g. “Seeking help”, “Constructive disagreement”). Third, iterative coding and refinement, in which data segments were independently coded, continually compared, and discussed to resolve discrepancies and refine the scheme. Finally, pattern identification, in which the coded data were examined for recurring patterns and themes related to epistemic conation.

3.2.4. Reliability and trustworthiness. In addition to triangulating data sources, an intercoder agreement (ICA) analysis was also sought to increase the quality and rigour of our analysis. We base our approach to improving the credibility and reliability of the proposed model on Lincoln and Guba's (1986; 1985) trustworthiness techniques through member checking and Krippendorf's (2022) coefficient calculation. Both techniques are useful in addressing thorny reliability issues in naturalistic inquiries such as ours, as we continually pose critical questions pertaining to the truth value, consistency, and applicability (Lincoln and Guba, 1985). The member checking was carried out by incorporating the revisions into the findings to ensure they reflect participants’ perspectives. It was an iterative process that involved the four authors of this paper, the remaining researchers within the project (including an international expert in the field of laboratory education), and several university science education researchers. The first and third authors conducted the ICA analysis, also using ATLAS.ti software. The results of the ICA analysis of representative excerpts from the data are shown in Table 3 (see Appendix 2 for the analysis report). The four codes were selected to represent each conative construct in our model.

Table 3 Intercoder agreement analysis for the semantic domain of conation

Selected codes	Holsti index	Krippendorf's αbinary
Goal setting	83.6%	0.830
Relevance	86.5%	0.849
Seeking help	83.8%	0.819
Striving	91.5%	0.908
All combined	86.5%	0.801

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The Holsti index represents a variation of percent agreement in which coders do not code precisely the same data segment (Friese, 2021). Similar to the simpler percent agreement measure, it does not consider chance agreement. To compare to this index calculation, we measured two Krippendorf's coefficients, which are sensitive to different sample sizes. First, the alpha coefficient for binary data, Krippendorf's α_binary, infers the reliability of dichotomous data (Krippendorf, 2022), for example, whether a certain segment is coded as Striving or not. Table 3 above shows that this value is high for all codes, especially Striving (0.908). Second, the Cu-α coefficient tests whether both coders were able to distinguish between the codes of the semantic domain of conation. The value for the overall performance is 1.000, which implies that both coders completely agree on the presence of the semantic domain.

3.3. Model building

To reconceptualise individual traits that drive inquiry in the chemistry laboratory, we built a model of epistemic conation in the laboratory, grounded in our multidimensional data and framed critically within relevant theoretical perspectives. The close link between the domains of theory and data is of paramount importance in testing our assumptions, identifying patterns, and uncovering new insights that may not be clear from extant theory. As such, it strengthens the foundation for the proposed model. In building the model, we are fully aware of the contextual factors others should consider in operationalising the constructs, as argued elsewhere (Darling-Hammond et al., 2020).

Essentially, the analytical process can be summarised as follows: the theoretical framework delineated in the front matter provides broad categories associated with epistemic conation, such as dispositional, motivational, and volitional aspects. Through coding development, these categories were used to analyse the focus groups and laboratory discourse data. A number of subordinate constructs were coded from these analyses. To provide an overview of how our analyses led to the proposed model, a chart of code frequency, or prevalence, is presented in Fig. 1. For instance, original codes such as ‘Precision’ and ‘Accuracy’ constitute a subconstruct called ‘Experimental goal orientation’ (abbreviated as EXG in our coding process, along with other subconstructs in Table 4). Together with ‘Epistemic goal orientation’ (EGO), the subconstructs constitute ‘Goal orientations,’ one of the four conative constructs within our model of epistemic conation in the laboratory. Through iterative deliberations between the authors and continual aligning between theories, our empirical data, and the proposed model, we introduce the concept of epistemic conation as a part of fundamental drivers of inquiry in the laboratory, alluding to Illeris's (2018) notion of incentive dimension in a multidimensional view of learning.

Fig. 1 Prevalence of original codes that constitute each conative (sub)construct in our model of epistemic conation in the laboratory (focus groups’ data, n = 30). Each code is broken down to show how it is distributed across pairs/triads (e.g. AB: Alexis & Brooks, PQ: Parker & Quinn, TU: Taryn & Ulf. See Table 2 for other students), categorised into a conative subconstruct (e.g. CPD: collaborative planning and decision-making, EMO: epistemic motivation, RES: resilience. See Table 4 for other abbreviated subconstructs), and modeled to constitute one of the four conative constructs (e.g. volitional strategies).

Table 4 List of abbreviations of conative (sub)constructs

Conative construct	Subconstruct	Abbreviation
Conative dispositions	Conscientiousness	CON
	Perseverance	PER
	Resilience	RES
Motivational factors	Epistemic motivation	EMO
	Relevance motivation	RMO
Goal orientation	Epistemic goal orientation	EGO
	Experimental goal orientation	EXG
Volitional strategies	Collaborative planning and decision making	CPD
	Self- and peer-regulation	SPR
	Active help-seeking	AHS

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4. Results

Employing both theoretical reconceptualisation and empirical investigation, we propose a model of epistemic conation in the laboratory, structured around four key constructs, namely, conative dispositions, motivational factors, goal orientations, and volitional strategies. Each of these constructs diverges into further specific notions, as visualised in Fig. 2 and elaborated in Table 5. The model was generated from our phenomenological and discourse analyses of student experiences and interactions in a pharmaceutical analytical chemistry laboratory, framed within the theory delineated in the front matter. It identifies three key conative dispositions that drive inquiry in the laboratory, namely, conscientiousness, perseverance, and resilience. Two distinct types of motivation emerge from this context, namely epistemic motivation and relevance motivation. Accordingly, the model distinguishes between epistemic and experimental goal orientations. In their engagement with epistemic practices in the laboratory, students use three volitional strategies to help translate motivation into action, namely, collaborative planning and decision-making, self- and peer-regulation, and active help-seeking.

Fig. 2 A model of epistemic conation in the laboratory based on theoretical reconceptualisation and empirical investigation.

Table 5 Contextual definition of the conative constructs that constitute epistemic conation in the laboratory

Conative construct	Subconstruct	Contextual definition informed by theory and empirical data
Conative dispositions	Conscientiousness (CON)	Inner drive to excel and maintain high standards in students’ laboratory activities, e.g. by going the extra mile
	Perseverance (PER)	Ability to continue pursuing a laboratory goal or task despite challenges or obstacles along the way
	Resilience (RES)	Capacity to bounce back from setbacks or stressors, and to adapt in the face of difficult circumstances
Motivational factors	Epistemic motivation (EMO)	Drive to engage with laboratory work for the inherent satisfaction, enjoyment derived from epistemic experience and objectives
	Relevance motivation (RMO)	Drive to engage in laboratory work for its relevance to students’ career and real world applications
Goal orientations	Epistemic goal orientation (EGO)	Desire to pursue deeper understanding of scientific concepts underlying the experiment and pursue valid, reliable results
	Experimental goal orientation (EXG)	Deliberate and purposeful direction of students’ efforts towards specific outcomes or targets within the experiment
Volitional strategies	Collaborative planning and decision-making (CPD)	Process by which students work together to strategically plan, coordinate, and make informed decisions about the experiment.
	Self- and peer-regulation (SPR)	Individual and collaborative processes by which students regulate their own and peers’ cognition, affect, and behaviour in the laboratory, through monitoring and evaluation
	Active help-seeking (AHS)	Proactive behaviour of seeking guidance or support from relevant sources to overcome challenges, clarify concepts, or enhance understanding in the laboratory setting

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To describe how epistemic conation manifests in the laboratory and address the research question, we define each construct within the context of laboratory-related epistemic practices (see Table 5). All definitions are discerned from our data considering the espoused theories. As such, we highlight the social aspects of engaging with experimental work and knowledge coconstruction. Appendix 3 reports additional evidence and supporting evidentiary codes to further substantiate our claims. To increase rigour (Agustian, 2024), we triangulate our phenomenological data with observational and discourse analysis of students’ experimental work in the laboratory, wherever relevant and meaningful.

4.1. Conative dispositions

We started this article by problematising grit as a psychological construct that tends to be overly individualised and overlooks systemic barriers. While this study does not focus on how such barriers bear on marginalised and underrepresented students, we keep in mind that our student populations consist of both white Caucasian majority and students with immigrant backgrounds, mainly from the Middle East. We also consider our initial reference to Rosalind Franklin's struggle as a female chemist in the 20th-century masculinised science, as we interpret our data. In this section, we provide several phenomenological and observational accounts of such dispositions that are more socially embedded in laboratory-related epistemic practices. In our series of studies that investigate learning processes in the laboratory (Agustian, 2022, 2024; Agustian et al., 2025), we de-emphasise the focus on the individual student and increase the granularity to a group level. Three specific conative dispositions are substantiated, namely, conscientiousness, perseverance, and resilience.

4.1.1. Conscientiousness, when students express their inner drive to excel and maintain high standards in their laboratory activities. Students seemed to uphold a certain standard in their laboratory work, which was reflected in the report they delivered following the experiment. In our data, this conative construct is centred on the code ‘Striving’. The following quotes may best represent how conscientiousness manifests in the laboratory. Cameron and Delaney strove to submit the best version of their laboratory report, in this case, by going the extra mile.

Yea… There was… a goal we would like to [achieve]… Not because we didn’t want to do it again, but because… that makes us at least want to go [the] extra mile to make it… better in the first go, instead of handing in a half…, in quality terms, half-ready report. (Cameron, in a conversation with Delaney)

In a similar vein, Yllya sand Zacharee seemed to agree that they learned better when they did their utmost to achieve good results. They emphasised the importance of being constantly aware of their learning.

You can’t just, you know, doze [off]… and just forget what you are doing. You have to be more conscious about what you are doing… And, you know, if you want to make it as well as you can, it just…, you know, you also learn better, I think. (Yllya, in a conversation with Zacharee)

The juxtaposition between consciousness and conscientiousness points to Marton and Booth's (2013) notion of awareness and learning. By focusing on what matters in their laboratory work, such as precision and accuracy, these students maintain some form of rigour, which is a part of socially constructed criteria for epistemic practice. A multitude of previous studies on conscientiousness reveal how this noncognitive trait has been a useful construct in analysing performance (Hong and Lin, 2011; Dumfart and Neubauer, 2016; Wilmot and Ones, 2019). In a laboratory setting, we maintain that such a conative disposition is enhanced and stimulated by the social context in which students engage with experimental work. In a way, the social context provides them with accountability, by which they take responsibility for their learning because they know they depend on each other: one strives to be conscientious because they expect their lab partner to do the same. We substantiate this claim with a stanza between Eliana and Felix below, where Eliana demonstrated being conscientious, as she kept shaking a flask to make sure the solute (acetaminophen) was entirely dissolved and used a pipette to make sure that she hit the mark. As she strove to do this, she kept involving Felix in her effort, including exploring the alternative using an ultrasound.

Eliana: Do you think I should put it on ultrasound, Felix? (shakes the solution in a volumetric flask, shows it to Felix.)

Felix: Is this not shaken mechanically? (looks at the flask.)

Eliana: No, but when… It should not be shaken, it is just because there is a white powder that has not been dissolved, it is not ultrasound. uhm… Filtering. (holds up the flask high at Felix's eye level.)

Felix: Filtering? (suggests a solution, walks to his table.)

Eliana: Yes, good old-fashioned gravity. (continues mixing solution. Felix walks away.)

Eliana: What is the point of ultrasound? (looks at the flask closely, continues mixing.)

Felix: I don’t know, you can also try it… (works in his fume hood, looks at his manual.)

Eliana: What…?

Felix: I say I just need to have some volumes in anyway, so it's actually very good. (makes solutions in his fume hood.)

Eliana: Mmm. Why did I use a pipette? Because I just wanted to be sure that I hit the mark. (continues mixing solution, turns around.)

4.1.2. Perseverance, when students demonstrate an ability to continue pursuing a laboratory goal or task despite encountering challenges or obstacles along the way. Doing chemistry in the laboratory certainly entails intrinsic challenges. In the laboratory education research literature, these challenges are often associated with cognitive (over)load, where students have to process so much information while they make meaning of the experiment (Agustian and Seery, 2017; Kelley, 2021; Tenney et al., 2024). In our data, we can discern how students persevered in doing chemistry from their accounts of staying late in the laboratory, either because they wanted to finish their report and have it accepted, as illustrated by a quote from Hayley below, or because they wanted to avoid having to redo the experiment, as stated by Ozzy and Noel (see Appendix 3).

Well,… we were like almost the only persons in the laboratory, because we really wanted to finish our report. We wanted to have it accepted,… So, we stayed there pretty late. We were one of the groups left there. But we finished our report. We made it. And we did all the Excel sheets that we needed to make, and Theo [the teacher] said go for it, and we… decided to stay in the laboratory… because we wanted to get the proper help from Theo, and we know that he [was] there. And in case we go home. If we are stuck with something, it might take some while, before we can get help, and um… and yes, and then we will forget what we were really doing. So it's… We like to do our reports in the laboratory. (Hayley, in a conversation with Grace)

Perseverance has been defined as a core element of grit (Kirchgasler, 2018; Audley and Donaldson, 2022). As such, research on this conative construct has also been problematised. Datu (2021) maintains that social agents such as teachers and peers influence an individual's perseverance. This effect is amplified in a collectivist culture that values social relations and a sense of belonging (Datu et al., 2024), which may be true for the Danish context in this study. In our phenomenological accounts of student learning, Theo's role as a teacher cannot be understated in providing help and validating students’ laboratory work, especially when things get tough, as quoted by Hayley above. Likewise, the role of peers in fostering perseverance can also be inferred from an excerpt below, where Grace kept trying to dissolve the sample in a volumetric flask, following an obstacle of “powder [getting] stuck in the funnel” (see below). Throughout the process, partly involving Theo, she kept communicating with Hayley to share her concerns and collectively solve the problem.

Hayley: Mmm. What's the name there… uhm… (sits, uses hand gestures to describe a funnel.)

Grace: Funnel… (grabs a volumetric flask of stock solution and walks away.)

Hayley: Yes, thank you. I just write just something

that the powder was stuck in the funnel. (writes on her notebook. Grace walks back to their table.)

Grace: Yes. (shakes the volumetric flask.)

Hayley: And that there may have been spills here. (writes on her notebook.)

Grace: Yes. I’m getting started with stock solution. (keeps shaking the volumetric flask of stock solution.)

Hayley: Yes.

Grace: Uh, I don’t actually know, try to ask one of the supervisors up there. (keeps shaking the volumetric flask of stock solution and looks at the solution closer sometimes.)

4.1.3. Resilience, when students demonstrate a capacity to bounce back from setbacks or stressors, and to adapt positively in the face of difficult circumstances. As a conative construct, resilience is a step further from but closely related to striving and persevering. In a Swedish study, Thorsen and colleagues (2021) found that resilient students “showed a significantly higher level of perseverance and interest than the non-resilient students in each school grade” (p. 1492). Their longitudinal study also reveals that these differences increased when students got older. The notion of resilience in an academic context is contingent on positive adaptation in the face of adversity (Theron, 2021; Thorsen et al., 2021). In science and engineering, laboratory work lends itself to both successful and disastrous experiences (Firestein, 2016; Wylie, 2019). While teaching laboratories may be designed to provide students with successful experiences in science, failures can still happen. As a form of adversity, failures and mistakes can lead to negative affective experiences in the laboratory (Agustian et al., 2025), but it can also be turned into an opportunity for nurturing resilience, partly by adapting to changes, as stated by Shayan:

Shayan: I think also when I say open-mindedness, I mean it in that kind of senses, where if something goes wrong, your day is not completely ruined…

Ryder: No, and no blame, and…

Shayan:… and you can adapt to the changes. And yea, no blame, and just try to carry on, and if you can do it in the time you have left, then do it, and if not, then we take it from there. I think that kind of openness.

In our pursuit of further evidence of resilience in doing chemistry, we look into the laboratory discourse. Resilience was difficult to substantiate from the discourse, as it might entail more significant setbacks or challenges. However, we found how Hayley copes with an injury to her hand caused by an accident before Day 1 of their laboratory work. Our observations from a previous study indicated a performance disparity between Grace and Hayley due to Hayley's injury (Agustian et al., 2025). This injury led to a mistake in mixing the solution, resulting in incorrect results. The following stanza highlights their resilience as Hayley acknowledged her error and Grace reacted positively to overcoming the setback. Eventually, Grace and Hayley managed to deliver their laboratory report, despite the physical challenges. The way this pair boosted each other's resilience shows how it is far from being an isolated phenomenon in the context of laboratory work. A similar account of personal circumstances that bear on resilience in the laboratory can also be discerned from Yllya's experience of navigating loss, commitment, and exams (see Appendix 3).

Hayley: It must have been me who made the mistake, I think I was a little too stressed, in the end. (looks at the screen, avoiding eye contact.)

Grace: It’ll be fine, then we just get some data. (looks at the screen.)

Hayley: It was after those filtrations that we made. (looks at the screen.)

Grace: Yes. (confirms.)

Hayley: Then I think I might have just put it directly on it after that. (starts looking at Grace.)

Grace: Yes, I think so, I think it came directly in vials. (looks at Hayley.)

Hayley: There I should have paid more attention. (looks at Grace.)

Grace: Yes, it will be fine. We just need to take note of it, so we know for next time. (touches her head.)

4.2. Motivational factors

The second element of epistemic conation in the laboratory is concerned with motivational factors. It is closely related to conative dispositions as it involves the intentional pursuit of knowledge and understanding, as well as their projected use beyond the limited scope of laboratory learning. Drawing on theories in the learning sciences and science education, we substantiate two distinctive constructs useful in considering laboratory work as epistemic practice: epistemic and relevance motivation. These conative constructs should be seen as situational, contextual, and socially enhanced, as described in previous studies (Lau et al., 2002; Lu et al., 2022), rather than a static trait attributable to an individual student.

4.2.1. Epistemic motivation, when students engage with laboratory work for the inherent satisfaction and enjoyment derived from epistemic experience and objectives. Epistemic motivation has been studied in the context of primary science education (Jaber and Hammer, 2016; Davidson et al., 2020) and higher education (Lee, 2019; Baker and Anderman, 2020). As a conative construct, it is entangled with affective notions such as epistemic affect, indicated by “inherent satisfaction” and “enjoyment” in our contextual definition. It is concerned with the reason why students engage with laboratory work. In the present study, we substantiate this entanglement with data from both sources. Below, Delaney and Cameron described how doing chemistry in the laboratory where the inquiry level (or open-endedness) was higher provided them with epistemic motivation, by which they had the liberty to generate their own questions and explain the experimental results, both of which characterise inquiry-based science education (Bybee, 2014; Madsen et al., 2020). In self-determination theory, epistemic motivation is most aligned with intrinsic motivation (Ryan and Deci, 2020). In the context of laboratory work as epistemic practice, engagement with knowledge and knowing is the driving force, particularly in situations where experimental results are incomplete, unexpected, or inconsistent.

Hendra: What motivates you in this module compared to the previous one?

Delaney: Yea, cannot remember it. Um… But I think the difference is that in Module 2 you have questions to answer, and in this one, it's totally [up to] yourself what you want to [formulate and] explain…

Cameron:… and present…

Delaney:… and present. While in Module 2, you are given questions, and it's obvious what you need to present, and how.

Hendra: Ok. Yea. Ok. So… in terms of the openness, is this more open?

Cameron: Oh, yea, definitely.

Contextual factors, such as prescribed vs. more open-ended inquiry laboratory curricula, may foster or hamper epistemic motivation. The scientific practice of explaining experimental results also requires meaningful social and interactional processes, where group members engage in a dialogue about experimental processes, which can be guided and prompted by the teacher (Seery et al., 2024b). To corroborate this evidentiary support, we look into the laboratory discourse.

The following stanza illustrates how Hayley and Grace demonstrated epistemic motivation. Upon instruction from a teacher (Tristan), they discussed how making a triplicate in doing an experimental analysis was not only important to ensure accuracy but also “interesting” and “exciting”. Their use of these words suggests that they were motivated by an inherently epistemic reason: to pursue satisfaction and enjoyment derived from epistemic experiences and objectives, which in this case included working precisely and accurately. However, this realisation and verbalisation of interest and excitement did not occur in a vacuum. The teacher's role in promoting higher-order thinking and discussion of the notion of “triplicate” in sample analysis cannot be understated. In the following discourse, Hayley also expressed her doubt, a construct associated with epistemic affect (Agustian et al., 2025), that motivated her to investigate further.

Hayley: Uhm… Well, I didn’t really think we had to make triplicate, it was very interesting. (looks at her computer, is surprised at Tristan's suggestion. They only express this after Tristan walks away.)

Grace: Uhm… Yes. Ok. I didn’t make it any longer, but these dilutions because I think there might be some-thing exciting about this. (looks at her manual.)

Hayley: That didn’t really happen in addition to that. Grace: Ok.

Hayley: uhm… Mmmm… But I get a bit in doubt, so it says the USP acetaminophen RS. (gives the pills to Grace.)

Grace: Is it for me.… Uh. What we expect now is the standard solution. That's what we expect. (reads the information on the label of the pills’ containers and looks at her computer, repeatedly. Hayley agrees, but her intonation indicates doubt.)

Hayley: Yes…

4.2.2. Relevance motivation, when students engage in laboratory work for its relevance to students’ future career and real-world applications. Students are also motivated by external factors. When describing this motivational factor, students often refer to how the skills and competencies they develop through laboratory work are useful for their future careers, primarily in the pharmaceutical industry. As such, their motivation was shaped by the prospect of applying their knowledge and skills in a projected trajectory beyond the scope of laboratory learning. A conversation with Taryn and Ulf below exemplifies how pharmacy education in Denmark may differ from that of other countries in that it allows the graduates to enter the job market not only as pharmacists but also as pharmaceutical scientists, a career choice that is viewed as more lucrative (Anderson, 2002; Traulsen and Druedahl, 2018). As they engaged with epistemic practices in the laboratory, these students were driven by their understanding of career paths and industry needs, both of which entailed social perceptions and norms, as well as positioning in a larger social context. In describing what motivated them to engage in laboratory work, a number of students highlighted its future relevance and real-world applications.

Taryn: Yea. So… So I think our education is…

Ulf: It is…, yea…

Taryn:… is broad, you know, there is so much stuff that you [can use in the future]

Hendra: What do you think about that?

Taryn: I think it's a great thing.… Like, I wouldn’t have been here, if it was only to be… [a pharmacist]

Ulf: Yea, that wouldn’t be for me, I’m taking the education for the industry, yea, so…

4.3. Goal orientations

Whereas motivational factors are concerned with the underlying reasons that drive students to engage in epistemic practice, goal orientations in the laboratory are more specific and concretely formulated. As psychological constructs, they have been studied widely in relation to each other (DeShon and Gillespie, 2005; Kaplan and Maehr, 2007; Cook and Artino, 2016) and to grit (Datu et al., 2024; Zhao et al., 2024). Likewise, neuroscience associates goal-oriented behaviour with conation (Heilman, 2002). The current framing of epistemic conation in the laboratory emphasises how motivational factors underlie goal orientations. We substantiate two ways students orientate themselves in laboratory-related epistemic practices, namely, epistemic and experimental goal orientations.

4.3.1. Epistemic goal orientation, when students demonstrate a desire to pursue a deeper understanding of chemical concepts and theories underlying the experiment and pursue valid, reliable results. Conceptually, epistemic goal orientation is underpinned by epistemic motivation, akin to how it has been theorised in goal orientation theory… When students are motivated by inherent satisfaction and enjoyment associated with epistemic experiences, such as glorious Aha! moments (Odden and Russ, 2019; Dini et al., 2021), they tend to set their learning goals around conceptual understanding and scientific practices that lead to those experiences. These learning goals have been previously conceptualised as epistemic aims (Barzilai and Chinn, 2018; Kelley, 2021), which are supposedly shared among the students and reinforced by the teachers, hence emphasising the social aspect. We provide evidence for how students verbalise their epistemic goal orientation, as described by a conversation between Hunter, Gideon, and Flynn below. Here, they maintained that the notion of “connection” was key to understanding why it was so important to work precisely when making standard solutions.

Hunter: Yes, so just have to work really precise, you know, and how… important it is to be very… specific with things when you work. But also to understand that actually… there is a “connection” in it, yes, when you work with it. So, measuring like the right solutions with the pipettes and you know, take the right volumes.

Gideon: Also, like work precisely enough, because we made a lot of solutions, so if we missed one of the… yes… so we are learning very accurately.

Flynn: Yes, especially for our standard concentration, as you probably saw in the report, we had to measure the concentration of 5 different things, to get a final concentration. So they all depend on each other, and if you get one thing wrong, it kind of messes up the entire experiment, so we really wanted to be as precise as possible, and get the best results possible. Um. So yes…

We looked further into the laboratory discourse to identify situations and conversations in which students translated their desire to pursue a deeper understanding of chemical concepts and theories underlying the experiment into observable actions and interactions. One example of epistemic goal orientation was observed when Felix and Eliana discussed the relationship between pyridoxine measurements, UV spectrum analysis, and the reliability of absorption readings (see below). They considered the absorption at different wavelengths and reflected on how it affected the interpretation of the results. This scenario shows that Eliana and Felix were engaged in understanding the chemical concepts of the results and ensuring that they obtained valid and reliable results.

Eliana: It would mean that a greater response would be seen from wavelengths where there are peaks… Looking at different ones, peaks are set at different places… But if you also see here that at 290 it has a high peak, but it doesn’t actually have that. Uh… No, but I think… what you… Yes, at 290 for pyridoxine. (looks at Felix's computer.)

Felix: Yes, it has… yes, response to nothing, almost. (looks at his computer.)

Eliana: I’ll write 60 instead.

Felix: Yes, that's where it has the biggest, but when you look at the UV spectrum, it gives… isn’t it right, because it's actually believed to have it around those 210 approx. (types on his computer.)

Felix: Pyridoxine has at 210, so… No, it… yes it's actually at 200 I can see here. (types on his computer, thinks out loud.)

Eliana: Yes yes, but that's how you… yes yes… but…

Felix: Uh… But it's also a way to be selective with what you’re dealing with.

Felix: But it's also mostly about whether they match, if they are a bit consistent, but… Well, they are consistent, because we have for example again with pyridoxine here, it is measured at 290, because none of the others really have absorption at 290, and they do not interfere with the signal, so to speak.

4.3.2. Experimental goal orientation, when students deliberately and purposefully direct their efforts towards achieving specific outcomes or targets within the experiment. Students doing chemistry in the laboratory are also guided by experimental goals. These are often formulated by the laboratory course designers and stated in the manual or compendium. In the pharmaceutical analytical chemistry laboratory course in which this study was conducted, these goals were set as, among others, “Perform a quantitative determination using the calibration curve, internal standard, and state the uncertainty of the result” and “Explain the principles of chromatographic separation method”. Our argument for epistemic conation is that students still need to transpose these formal goals into their own formulation, through group deliberation and prioritisation. In such a transposition process, they may focus on what they deem most important and relevant, which may be different for each group member, but these goals need to be negotiated to achieve the shared experimental goals, which characterise a community of inquiry (Garrison, 2016). Alluding to Kaplan and Maehr's (2007) categorisation of goal orientation theory, experimental goal orientation in the laboratory perhaps leans more towards performance rather than mastery. We substantiate this conative construct with a conversation between Delaney and Cameron below on their goal setting. Their focus on how the instruments work mirrors the course formulation described above. But they also orientated themselves in communication competence, which is not necessarily formulated in the course but is sure essential for a successful collaborative inquiry in the laboratory (Agustian et al., 2022a; Connor et al., 2023). Cameron's reference to writing a good laboratory notebook also foreshadows a volitional strategy that he implemented in order to achieve their goals, as described in the next section.

Delaney: So, yea, I also set my goals to understand how an HPLC computer works, how the gas chromatography works, how the mass spectrophotometry works and how you prepare, how you function in the lab, and how you…

Cameron:… communicate…

Delaney:… communicate with the team. Because in the Bachelor's [project], you would be four persons.

Cameron: And for me personally, also I’m not a guy that goes heavy into writing notes and writing stuff down for lectures, so by making a really good report, you force yourself to make these notes in that sense. So the better report you have, the better notes. The better reports I have, always correlates to how good you will get to the exam, also.

4.4. Volitional strategies

We have theorised that volition is a conative construct that bridges motivation and actions that students actually take in their engagement with epistemic practices in the laboratory. In science education research, Ammoneit and colleagues (2024) have studied volition in the context of modeling competence development. Their findings suggest that volitional strategies such as self-control and self-regulation are beneficial for developing such competence. In our study, some of these notions also manifest in the laboratory. Particularly, these strategies are continually negotiated and monitored. We substantiate three volitional strategies by which students translated their epistemic and experimental goals into actions and sustained their commitment to achieving these goals through conative dispositions, namely, collaborative planning and decision-making, self- and peer-regulation, and active help-seeking. As we argue throughout this study, while these strategies are performed by individuals, they are highly contextualised in a sociomaterial, interactional, and intertextual character of doing chemistry, demonstrating a dynamic interplay.

4.4.1. Collaborative planning and decision-making, when students work together to plan, coordinate activities, and make informed decisions about the experiment. While discussions on conation in general often refer to the decision-making process (Hershberger, 1989; Nagelsmith et al., 2012; Lee, 2019), in the context of laboratory-related epistemic practices, the process is, by and large, collaborative. This volitional strategy is palpable, particularly during the planning and preparatory stage of their experiment. The structure of Module 3, in which the experiment on quantification of acetaminophen and caffeine is spread over three days, affords plenty of opportunities for students to develop this volitional strategy, especially on Day 1. A conversation between Flynn, Gideon, and Hunter below also suggests that collaborative planning and decision-making were crucial to get everyone equally prepared and on the same base prior to the experiment. In a context where every group member's contribution matters, students seem to value equal footing and egalitarian roles in successfully performing laboratory work.

Flynn: I think the preparation actually works pretty well…

Gideon & Hunter: Yes…

Flynn:… because we all just sit down on a computer, and do it together, so I don’t really have anything [off] with the preparation.

Hunter: No, it's just…

Gideon: I think Day 1, when we leave, we are all on the same base…

Flynn: Yes.

Hunter: Exactly.

Gideon:… We are equally prepared.

Hunter: Yes… Yes…

On the first day of the lab, we observed numerous instances of collaborative planning and decision-making among students as they developed their experimental protocols based on the provided manual. This crucial phase involved determining precise quantities of solutions and tablets required for their experiments. A notable example of this collaboration is a discussion between Felix and Eliana regarding the optimal number of tablets to be used in their solution preparation (see below). The pair actively interpreted the manual to formulate their experimental plans. They also focused on precise measurements and calculations through continual peer-to-peer interactions. The process of seeking and providing explanations was essential in ensuring mutual understanding and effective coordination within the lab groups. Such collaborative approaches not only enhanced the quality of experimental design but also fostered a supportive learning environment conducive to scientific inquiry and problem-solving.

Eliana: Did you get an answer to your question? Let me hear it. (works on her computer.)

Felix: Yes, there's no need for us to use 20, ha ha ha. (walks back and sits down.)

Eliana: No no no no. Okay. So we only need… (works on her computer.)

Felix: So what should we say, maybe 4 tablets, does that sound reasonable? We still have to do it in triplicate. (looks at Eliana.)

Eliana: Umm… Yes, but… but… (reads the manual, rests her hand on her cheek.)

Felix: Yes… 4 tablets might be a bit too much after all…? (reads the manual.)

Eliana: But we need it to be equivalent to. half a tablet per solution. (rests her hand on her cheek.)

Felix: Half a tablet per solution. What did they write, oh, now he obviously doesn’t have it anymore. (looks at Eliana.)

Eliana: Uh, what… It was just, what did they write in the other, in theirs…, in…? You don’t know, it doesn’t matter now. (looks at Felix.)

Felix: No, I don’t know, I mean. But I’ve just been told it's only within industry, or when you industrially test it, to get batch documentation on it, and not just for…

Eliana: Okay. Fine. Okay. (reads the manual, rests her hand on her chin.)

4.4.2. Self- and peer-regulation, when students regulate their own and peers’ cognition, affect, and behaviour in the laboratory, through monitoring and evaluation. Self-regulation is a key volitional strategy in science education and is particularly useful in a collaborative, investigative learning activities (Reyna et al., 2021; Ammoneit et al., 2024). While the literature often frames self-regulation within a (meta)cognitive context (Zohar and Barzilai, 2013; Fleur et al., 2021), a considerable volume of research also demonstrates its function in relation to noncognitive factors such as in regulating emotions (Tomas et al., 2016) and, indeed, motivation (Smit et al., 2017). In our focus on epistemic conation, we have ample evidence of how self-regulation unfolds in the laboratory, centred on evidentiary codes such as “Monitoring” and “Increased awareness” (see Appendix). Furthermore, our data indicate that regulatory processes also take place at a group level, with students serving as each other's friendly reminder of continual monitoring and reflection at different points in their experiment. We substantiate this claim with accounts of students using artefacts such as laboratory notebooks to monitor the progress of their experiment, as described by Zacharee below. Using laboratory notebooks is a common practice in chemical research (Kelley, 2023), but our data suggest we should not assume that students always follow this good practice.

So, in Module 2, we didn’t write a laboratory notebook. So that was our first mistake, and then we accidentally mixed the solution, so we ran our sample solution instead of our standard solution, 6 times, and then we had the wrong data, and yea, it was a very, very big chaos. So what we did differently in Module 3, was to use the laboratory notebook, write everything down, also name every solution correctly, and be very precise about what we are putting in the vials and where we put the vials. (Zacharee, in a conversation with Yllya)

In a group context, students may also monitor each other's work verbally, by emphasising being minds-on while doing hands-on activity, lest they should be getting bad experimental results, as Grace asserts in a stanza below. Verbal peer monitoring has been shown elsewhere to be an effective regulatory strategy (Brown et al., 1999; Delgado, 2005), and in a context in which precision is a must, especially when working with intricate parts of the experiment, it complements artefacts such as the aforementioned laboratory notebooks in their function to help ensure successful translation of motivation and goal orientation to meaningful actions in the laboratory.

Grace: We need to be aware that when we make a standard solution, we need to use a stock solution, so we have to make sure it's right, because otherwise we’ll have to f*** the next one. (emphasises her concern, Hayley looks at her.)

Hayley: Ok. (continues her work on her computer.) Grace: Yes. (reads the manual.)

Hayley: And we have to… So we also need to use the standard solution, sample solution… Sample stock solution…

Grace: I think so.

Students also regulate their learning by reflecting on their own and their peer's laboratory performance, understanding, or progress towards the learning goals they set for the experiment. This volitional strategy, coded with ‘Reflection’, is palpable in our data, as indicated by its high prevalence (see Appendix 3). We illustrate this part of epistemic conation in the laboratory with two examples. First is Eliana's account of her continual reflection and wondering if she was good enough at performing experimental techniques and working with epistemic notions such as precision and accuracy. From an epistemic conation perspective, she regulated her own learning by translating her epistemic and experimental goals into actions that met her standard of apt performance, which in itself refers to socially constructed criteria (Barzilai and Chinn, 2018). Viewed as such, her volitional strategy mirrors “conscientiousness” in doing laboratory work, as described previously.

Yes, yes, yes.… But of course I wouldn’t care if I had made a competition. In other words, I also always make a competition with myself. So that's what it's all about, a lot about how good I really am at balancing this properly, that is. Am I good enough to be accurate and stuff like that, no. So that's the way it is… So… yes… it probably also comes out a bit in a larger whole, but I do that… So there is such a general thing, I think more for me, anyway. (Eliana, in a conversation with Felix)

Although reflection is largely an introspective, individual process, recent work has also highlighted its effectiveness and higher accessibility when done in a group as a part of a learning design, or co-reflection (Yukawa, 2006; Vittrup, 2024). Several students’ accounts of this volitional strategy, such as Gideon's below, illustrate how doing chemistry collaboratively in the laboratory enhances learning.

Like this time it was maybe, I won’t call it easy for us to conclude or to discuss, and come up with points we can write. But maybe in something more complicated, it would really be hard for us to maybe even discuss, because we would question ourselves, like have we done something wrong…, like what could have gone wrong. (Gideon, in a conversation with Flynn and Hunter)

4.4.3. Active help-seeking, when students proactively seek guidance or support from relevant sources to overcome challenges, clarify concepts, or enhance understanding within the laboratory setting. The last volitional strategy in our model of epistemic conation is concerned with how students actively seek assistance when needed to move their inquiry forward. As in professional science, experimental work in teaching laboratories is teemed with situations in which students “get stuck” (Cooper and Kerns, 2006; Reeves et al., 2021) in their experiments. This could either relate to technical failures (such as instrumental defects that prevent reliable analysis), problem-solving processes (as in students not being able to solve a problem themselves), or something more of an epistemic nature (like in a situation where they cannot explain peculiar data). In any of these situations, they need to seek help actively. The wealth of research in self-regulated learning suggests that help-seeking is required, even in the context of self-regulation, signifying a close relation between these conative constructs. Karabenick and Gonida (2018) describe that this volitional strategy is also facilitated by social and emotional competencies. In our study, both phenomenological accounts and laboratory discourse provide plenty of evidence for active helpseeking, partly represented by its high prevalence (see Fig. 1).

For instance, Shayan and Ryder describe below how they address uncertainties by seeking help not only from teachers or technicians but also from other students. Different possible constellations in which students’ need for help, epistemic or otherwise, are met by various interlocutors represent the laboratory as a community of inquiry. In that regard, active help-seeking also reflects the social aspect of epistemic conation.

Shayan: Mmm. I think we talk, first of all to each other, and then our teammates, who have done the same, and if we are very unsure about for example an instrument or something like that, then we talk to the…

Ryder:… technicians, or… yea…

Shayan: Yea, the technicians, or the laboratory instructor. Um…

Ryder: So I mean, it's just… I mean, if you are insecure of something, you ask you classmates or your technician, and then…, you will be fine, right.

We also observed how students implement active help-seeking as a volitional strategy. One example of this is when Hayley asked Theo, a laboratory instructor, to review their work to ensure that nothing was missed (see below). Using informal language conducive to help-seeking behaviour, both the instructor and the students made (inter)textual reference to the laboratory manual and experimental plan, and demonstrated active listening. The stanza also indicated students’ epistemic state of uncertainty (“we are a little unsure”) that drove them to seek confirmation from each other and the instructor.

Hayley: So, um… We can just show the calculations afterwards, but we are a little unsure about how much mobile phase we need to make. (points out something on her computer.)

Theo: Yep. (looks at Hayley's computer.)

Grace: One has 2 ml per minute. (looks at Theo.)

Theo: Yep. (confirms.)

Grace: And how long it will take, I think it was unclear.

Theo: So you’ll be able to run for four hours… (looks at Grace.)

Grace: Ok…

Hayley: Yes…

Grace: So if we just expect the four hours. (looks at Theo.)

Grace: Yep, and then there has to be a bit more in the bottle. (looks the other way.)

Grace: So there is 120 per hour, it is 240… 480… So 500… (looks at Theo.)

Theo: Yep… Absolutely perfect. (confirms.)

5. Further discussion and limitations

Inspired by Rosalind Franklin's illustrious scientific career as she persevered in her pursuit of knowledge despite enormous challenges as a female chemist in a predominantly masculine science, this study has reconceptualised constructs for describing noncognitive processes within an epistemic context. By introducing the notion of “epistemic conation”, we have argued that the novel construct better captures both individual and social aspects of doing chemistry, by which responsibility for successfully conducting an experiment and overcoming intrinsic challenges is re-attributed to a supportive curricular, instructional, and relational structure, rather than solely at the hand of the individual student. This is aligned with the recent deliberations on epistemic practices in science and engineering education, highlighting dynamic, interactional, contextual, and intertextual characteristics (Cunningham and Kelly, 2017; Jiménez-Aleixandre and Crujeiras, 2017; Agustian, 2025a;), but also with considerations of potential barriers to engagement, particularly concerning marginalised and underrepresented students. Zooming out, the present study strengthens the pedagogical foundations for collaborative inquiry in the chemistry laboratory (Wheeler et al., 2015; Keen and Sevian, 2022). Akin to our arguments and findings, Keen and Sevian (2022) frame struggles in the laboratory as a part of learning processes, but they should be leveraged by supportive socioemotional support. Wheeler and colleagues (2015) maintain that such a support can be promoted by an effective pedagogical training of laboratory teaching assistants in which they themselves are engaged in collaborative inquiry.

Our theoretical reconceptualisation and empirical investigation evince how individual conative processes during knowledge coconstruction in the laboratory are continually influenced by and enacted within sociomaterial and cultural contexts. On the one hand, individual differences in motivation and volition recognise that students may approach scientific tasks in the laboratory with varying levels of conscientiousness, perseverance, and resilience. Our study indicates that these variations also apply to any individual student, depending on the task or curricular context. One could also argue that they may occasionally be affected by personal circumstances beyond the laboratory, as observed in the case of Hayley and Yllya. While the manifestations of epistemic conation in this study may appear at high level, they were not so all the time. The precise extent to which they varied and fluctuated is beyond the scope of this paper, but future empirical studies may focus on how variations within and between groups could be substantiated.

On the other hand, students’ engagement in the laboratory is shaped by peer interactions, pedagogical approaches (e.g. dialogic vs. authoritative teaching (Dohrn and Dohn, 2018; Seery et al., 2024b)), and the broader epistemic culture of the discipline. We draw attention to curricular progression in terms of inquiry level (Buck et al., 2008) and experiment design competency (Seery et al., 2019). This second-year laboratory course was perceived to have ahigh utility value, reflected in relevance motivation, but it may not be the case in general. Prior work shows that students may primarily be guided by affective goals of completing laboratory work quickly, so they can feel relieved (DeKorver and Towns, 2016). However, we have shown that there is a value in scaffolding inquiry level, also within the same course, to give students opportunity to deliberate experiment design and the rationale behind experimental procedures. We also emphasise the importance of allocating dedicated feedback session in the lab and taking a more dialogic approach to eliciting conceptual understanding. Akin to doing chemistry in professional science, making mistakes should be framed as a part of learning processes in the laboratory (Kyynäräinen et al., 2024), and that potentially negative emotions that it may cause should be normalised (Agustian et al., 2025), provided that they are resolved and attended to.

This study is a first attempt to theorise epistemic conation. The benefit of conceptualising what drives students to strive in their learning this way is twofold. First, context-specificity emphasises the situational nature of conative dispositions, signifying their dynamic, non-deterministic feature, thus challenging the deficit framing and innate, individual-focused assumptions of concepts like grit (Carter et al., 2024). It means that students’ persistence or failure in various processes within this epistemic space should not be solely attributed to their lack of willpower. Contextual factors such as feedback quality and social dynamics in the laboratory influence the extent to which students persevere and maintain their motivation. Critical literature demonstrates that when these factors are not addressed, they may exert such power that they could hamper learning (Credé, 2018; Audley and Donaldson, 2022). Second, the notion of epistemic conation as a meta-construct foregrounds the scientific and epistemic practices that characterise university chemistry education, compared to more general constructs such as motivation and volition. Although striving to do chemistry involves motivation and volition, it is also shaped by scientific ways of thinking, knowing, and practicing.

The study offers a rigorous analysis of two data corpora, substantiating how epistemic conation manifests in the chemistry laboratory. However, the following limitations should be considered. As discussed elsewhere (Winberg and Berg, 2007), reliance on self-reported data in focus groups may be subject to social desirability bias and recall errors. We minimised this by combining this analysis with observational data, but the video recordings only captured a small subset of participants (two pairs) during one specific module, even though 39 other students were captured in sporadic interactions. We were limited by our capacity to do a microanalytic discourse analysis of more than 10 000 lines of analysis, but more extensive observational data could provide richer insights into how epistemic conation manifests in real-time laboratory actions and interactions. The interpretative phenomenological analysis was first and foremost conducted by humans. We used integrated AI in the ATLAS.ti software as an additional perspective in the coding process. While we critically examined each and every code it suggested, we are aware of possible biases, as is the case with human coders.

6. Conclusion and implications

From the outset, this study aimed to reconceptualise how we understand the drivers of inquiry in laboratory-based science education. By introducing and substantiating the construct of “epistemic conation”, our analysis reveals that striving to learn and do chemistry in the laboratory is not simply a matter of individual grit or perseverance, but a dynamic, context-dependent, and socially enhanced process. The findings also show how conative dispositions, motivational factors, goal orientations, and volitional strategies are continually negotiated and enacted within social and epistemic contexts.

The most significant theoretical contribution of this study is the integration and expansion of the conative domain of learning in chemistry education research. Rather than treating perseverance, motivation, and volition as isolated, static, or primarily individual traits, the proposed model of epistemic conation situates these constructs within the lived, social practices of laboratory work. This approach is grounded in philosophical traditions and contemporary learning sciences, but it moves beyond them by explicitly demonstrating how striving to learn to do chemistry in the laboratory is shaped by group dynamics, shared goal-setting, collaborative decision-making, and the overall epistemic culture of the discipline. In that regard, conscientiousness, perseverance, and resilience are not merely personal virtues but are actively fostered, challenged, and sustained through peer interactions, joint decision-making, and negotiation of scientific standards and experimental goals. Likewise, motivational factors such as epistemic and relevance motivation emerged as contextually situated and socially enhanced, with students’ engagement driven not only by interest in scientific inquiry but also perceived relevance of laboratory work for future careers and real-world applications. Such a dynamic interplay is also reflected in students exhibiting both mastery and performance goal orientations, as delineated by Kaplan and Maehr (2007), by engaging with underlying concepts and navigating multiple, sometimes competing, goals in the laboratory context. In a similar vein, volitional strategies in the laboratory are embedded in the social, interactional, and intertextual character of laboratory learning, as indicated in a conative construct such as self- and peer-regulation. In this way, our conclusions extend prior work on volition (Husman and Corno, 2010; Ammoneit et al., 2024) by emphasising the social aspect of regulatory processes.

Based on these results, there are several implications for chemistry education research and practice. The findings open several promising avenues for future research in inquiry-based chemistry education. As the large volume of research in the field tends to focus on instructional structures—e.g. in terms of the 5E model: engage, explore, explain, elaborate, and evaluate (Bybee, 2014)—future research could delve into the noncognitive engagement in the epistemic context. Extending on our critique in this study, further work should examine how epistemic conation is shaped by and can help address issues of equity and inclusion, by focusing on experiences of underserved student populations and challenging the deficit, innate narratives. Different characteristics of laboratories across university science disciplines also call for comparative studies.

To foster epistemic conation, laboratory curricula should strike a balance between structure and autonomy, by providing enough curricular and instructional structure to guide students in their inquiry while allowing for autonomy in decision-making and problem-solving. There is ample evidence for the benefit of collaborative activities in the laboratory, particularly in developing students’ volitional strategies. While individual laboratory work may have its pros (Lagowski, 1989), modern chemistry education research seems to establish higher learning gains from smallgroup settings, either in pairs or triads (Smith and Alonso, 2020; Wei et al., 2021; Jørgensen et al., 2024), even though laboratory curriculum developers’ motivation for group work seems related to organisational constraints, rather than pedagogically driven by social learning objectives (Schwarz, 2025). Emphasising the epistemic goals of laboratory work, such as using relevant chemical concepts to explain experimental results, evaluating evidence, and engaging in authentic scientific practices, can foster deeper epistemic motivation.

In terms of teaching in the laboratory, instructors should intentionally design learning environments that support collaborative inquiry, in which there is sufficient time to make space for deliberation. Explicit discussion of scientific standards and norms to strive for is key to promoting epistemic conation, and students should be encouraged to articulate their learning processes and challenges. Within this context, fostering psychological safety is of paramount importance, whereby respectful group culture is encouraged and students feel safe to take intellectual risks and view mistakes as a part of the process. In that regard, it may be worthwhile for instructors to model epistemic conation, by articulating and demonstrating their own, such as their epistemic motivation and volitional strategies. Surely, everyone involved in laboratory education has been through their own journeys of striving, persevering, getting stuck, and seeking help in doing chemistry. These relatable experiences may have the power to inspire and motivate students in their learning (Lin-Siegler et al., 2016). In line with arguments on self-regulated learning, laboratory teaching should scaffold epistemic conation through explicit instruction for self- and peer-monitoring as well as co-reflection. This can be facilitated with prompts (Seery et al., 2024b) to engage students in reflection on their learning goals, anticipated challenges, and ways to adapt in the face of obstacles.

Finally, epistemic conation intertwines with epistemic affect—powerful emotions and feelings that arise during engagement with epistemic practices (Agustian et al., 2025). Our analyses demonstrate that both epistemic conation and epistemic affect are embedded in the social, interactional, and discursive fabric of laboratory work. The informal learning environment of the laboratory provides opportunities for dialogic and discursive teaching approaches, fostering rich epistemic experiences.

Author contributions

Conceptualisation: all authors; methodology (development of task and methodology for data collection): HYA; investigation and data curation (data collection and transcription): HYA and MAR; formal analysis (detailed coding scheme development, detailed data analysis, and development of analytical framework): HYA and MAR; validation (review and validation of data analysis): all authors; writing – original draft: HYA and MLR; writing – review and editing: all authors; and funding acquisition: BG.

Conflicts of interest

There are no conflicts to declare.

Data availability

The data are not publicly available as approval for this study did not include permission for sharing data publicly.

The supplementary information consists of (1) focus group interview protocols, (2) intercoder analysis report of the semantic domain “Conation”, and (3) additional evidence for epistemic conation in the laboratory. See DOI: https://doi.org/10.1039/d5rp00232j.

Acknowledgements

The authors are grateful for the students and teaching faculty who participated in this study. Thank you for helping us understand your processes of learning and becoming. The work presented in this article is supported by Novo Nordisk Foundation grant NNF 18SA0034990.

Notes and references

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