Examining student engagement in the organic chemistry laboratory

Abstract

This exploratory case study investigates the multifaceted dynamics of student interactions within an undergraduate organic chemistry laboratory. As efforts to improve curriculum design in this area continue, understanding how students engage with one another during lab activities is crucial. This study aims to offer insights into the intricate dynamics of student interactions within the laboratory environment. Observations were conducted in two laboratory sections, each during four distinct experiments. The content, depth, and extent of students’ interactions during lab activity were analyzed using content and interaction analysis. The analysis of student interactions during the experiments sought to generate insights into the relationship between different forms of engagement: behavioral, cognitive, social, and affective. Our findings reveal several factors that influence student engagement, including the nature and complexity of tasks and group composition and dynamics. Our results provide insight into how different types of engagement interact and affect the overall learning experience. While this study does not attempt to draw definitive conclusions, it provides a foundational understanding of the complex student engagement process within the laboratory setting. These insights can inform future investigations and support the development of more effective strategies to foster meaningful student interactions in organic chemistry labs.

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Introduction

Experiments and projects in laboratory courses create opportunities for students to develop and apply their knowledge and skills, gaining experience using various techniques and expanding their knowledge from textbook learning. To achieve the most from laboratory courses, students must be fully engaged. However, depending on the experimental tasks and group dynamics, different students will experience varying levels of engagement. In a complex learning environment, such as a laboratory course, one can expect a variety of factors to affect a student's behavioral, cognitive, social, and affective engagement. In this study, we explored the nature and extent of different forms of student engagement in an undergraduate organic chemistry laboratory. Our findings provide insights into various factors influencing student engagement in this learning environment, including the nature of the experiment, task type, and gender composition of the laboratory groups. Results from this study may aid chemistry educators in designing and implementing activities that enhance student engagement in experimental settings.

Student engagement in the laboratory

Student engagement is a multifaceted and complex construct linked to student outcomes such as performance, achievement, and persistence (Kahu, 2013). Different forms of student engagement have been categorized as behavioral, cognitive, social, and affective (Fredricks et al., 2004; Bowden et al., 2021). Behavioral engagement is rooted in participatory action in a task. Cognitive engagement involves higher-level thinking in which students actively process and apply complex concepts. This form of engagement requires deep mental involvement, encouraging students to analyze, synthesize, and evaluate information rather than merely memorize facts. Social engagement refers to the connections individuals establish with one another, including the sense of belonging developed during shared experiences. This form of engagement fosters collaboration and support, allowing students to build meaningful relationships that enhance their personal and academic growth. Lastly, affective engagement is related to the level of emotions experienced by students, which can be positive or negative and can be driven by a student's passion.

While a variety of educational researchers have explored student engagement in lecture or discussion settings (Hake, 1998; Lin et al., 2017; Venton and Pompano, 2021; Naibert and Barbera, 2022; Naibert et al., 2022; Reid et al., 2022), there are considerably fewer studies conducted in science laboratory courses or other practice-based learning environments. These investigations have predominantly focused on analyzing student work during pre-laboratory activities (Kelly and Finlayson, 2007; Cann, 2016; Rayment et al., 2023) and post-lab activities (Heslop, 2017; Petritis et al., 2022), as well as on the functional analysis of cognitive and social processing during experiments (Xu and Talanquer, 2013). There is, however, a lack of direct observations and characterization of different forms of student engagement while engaged in experimental activity, particularly in organic chemistry laboratories.

Observational research has been performed in other types of experimental settings, such as in biology (Winkelmann et al., 2007; Shumow et al., 2013; Loveys and Riggs, 2019), physics (Roychoudhury and Roth, 1996), and general chemistry (Xu and Talanquer, 2013; Wei et al., 2018; Smith and Alonso, 2020) labs. Smith and Alonso (2020) explored three forms of student engagement: behavioral, affective, and cognitive in general chemistry laboratories. In this study, behavioral engagement centered on student involvement in academic tasks and learning (effort, persistence, and attention), emotional engagement focused on students’ reactions in the laboratory, and cognitive engagement referred to investment in academic tasks and an effort toward learning, understanding, and mastering. This study revealed a high occurrence of behavioral engagement in the general chemistry laboratory.

Studying different forms of engagement when conducting laboratory experiments presents a considerable challenge. Previous efforts have used video and audio recording devices to monitor student interactions in laboratory environments (Galloway and Bretz, 2016). However, their focus was primarily on monitoring group interactions and did not emphasize identifying specific types of engagements and their relationships. Other studies have used surveys or written responses to analyze student perceptions of student engagement but without explicit observations of student work during the experiments (Iler et al., 2012). However, existing work in diverse areas provides insights into data and strategies that can be used to investigate behavioral, social, affective, and cognitive engagement in laboratory settings.

Behavioral engagement offers a structured lens to examine students' active participation in laboratory activities. Observable actions such as answering questions, completing tasks, and maintaining focus during experiments serve as indicators of behavioral engagement. Some studies connect these behaviors to meaningful task completion, emphasizing the value of hands-on activities in achieving learning outcomes (Marks, 2000). Meanwhile, other studies link behavioral engagement to task focus as a reflection of students' competence and connection to their learning environment (Skinner and Pitzer, 2012). Together, these frameworks provide valuable tools for analyzing and promoting behavioral engagement in laboratory settings.

Social engagement emerges from student interactions within collaborative learning settings, driven by group dynamics and approaches to problem-solving. Vygotsky's sociocultural theory of learning frames these interactions as essential for scaffolding knowledge, where peer collaboration fosters deeper conceptual understanding (Vygotsky, 1978). Studies in this area include: (a) demonstrations of how group discussions promote critical thinking and enable students to integrate diverse perspectives into their learning (Gokhale, 1995), (b) investigations of cooperative interactions as a mechanism to strengthen mutual accountability and interpersonal skills, contributing to both academic success and personal growth (Johnson, 1979), and (c) analyses of frequency and quality of peer interactions (Wu et al., 2013). Collectively, these studies illustrate that productive social engagement is characterized by dynamic exchanges, a shared working environment, and the establishment of a supportive classroom community that enhances the overall learning experience.

Affective engagement reflects students' emotional connections to their learning experiences, with key components being interest, enthusiasm, and motivation. Interest can be a driving force for fostering curiosity and sustaining focus and exploration of enthusiasm, transforming the learning environment into a more dynamic and stimulating space (Keller, 2014). Control-value theory of emotions ties these emotional elements together, showing how emotions like curiosity and anxiety influence students' cognitive and behavioral engagement (Pekrun, 2010). For instance, laboratory experiments perceived as novel or personally relevant can heighten emotional investment, while frustration or boredom may lead to disengagement. These frameworks are important in designing learning activities that evoke positive emotional experiences to sustain and enhance engagement.

Cognitive engagement refers to the depth of mental effort and critical thinking students apply to learning. Lesh et al. (2008) focus on the complexity of tasks that challenge students to apply higher-order thinking. These strategies often involve asking probing questions, refining hypotheses, or rethinking experimental approaches. Greene et al. (2004) further examine how the ability to use advanced cognitive processes, like synthesis and evaluation, can significantly enhance learning. This is especially important in laboratory settings, where students are encouraged to link theoretical principles with experimental results, fostering a deeper understanding of the subject matter. Cognitive engagement is vital for developing critical thinking skills and promoting intellectual independence, helping students apply their knowledge to real-world situations.

Various interventions have been developed to enhance student cognitive engagement in diverse learning environments. Structured activities such as conceptual worksheets or guided inquiry tasks are frequently used. These approaches promote deeper cognitive engagement by encouraging students to reflect on and analyze their learning process (Wolters, 2012). Real-world problem-solving tasks have proven effective in enhancing engagement. Integrating these interventions within laboratory settings has shown significant potential in improving overall engagement. Studies have demonstrated that targeted activities can address specific engagement challenges, promoting holistic involvement in the learning process, and thus enhancing their academic experiences (Reeve, 2013).

Conceptual understanding checkpoints have emerged as a valuable cognitive intervention to enhance students' grasp of complex concepts. These checkpoints, strategically placed throughout the curriculum, are designed to assess and reinforce students' comprehension before progressing to more advanced topics. For instance, some studies emphasize the importance of frequent conceptual assessments not only to help identify areas where students struggle but also to provide opportunities for targeted feedback and remediation (Cunningham-Nelson et al., 2017). Moreover, there is evidence that curriculum interventions can significantly improve students' ability to integrate and apply theoretical knowledge in practical contexts, thereby enhancing overall academic performance (Lo et al., 2020). By implementing these cognitive interventions, educators can foster a more robust understanding of chemical principles and support students in developing the critical thinking skills necessary for mastering the subject.

Research goals and questions

This study sought to identify and characterize different types of student engagement during an undergraduate organic chemistry laboratory class. Based on content and interaction analysis, observational data of student interactions were used to shed light on factors affecting student engagement. In particular, our work was guided by the following research questions:

(1) To what extent do different forms of student engagement (behavioral, social, affective, and cognitive) manifest across different student groups in an organic chemistry laboratory?

(2) How do different forms of engagement vary depending on type of lab experiment and student gender makeup?

(3) What impact does a conceptual understanding checkpoint intervention have on student engagement in the organic chemistry laboratory?

Methods

Context and participants

This investigation was carried out at the University of Arizona (UA), a public research-intensive university in the southwest of the USA. The Department of Chemistry and Biochemistry at this institution offers two one-credit organic chemistry labs to STEM majors every semester. The Organic Chemistry I Laboratory (OCI lab), where the present investigation occurred, introduces students to basic techniques for the analysis and synthesis of organic compounds. A total of 9 one-week experiments are completed in a semester. Each experiment focuses on a different technique or type of reaction (see Table 1). Students meet once a week for a 3-hour experiment, working in pairs. The course grade is awarded based on participation in the lab, pre-laboratory quizzes, and examinations.

Table 1 Experiment layout

Week	Experiment/activity
1	Introduction to lab safety
2	Thin-layer chromatography
3	Column chromatography
4	Infrared spectroscopy
5	Synthesis of fatty acid methyl ester
6	Midterm exam
7	Introduction to NMR
8	Nucleophilic substitution reactions
9	Elimination reactions
10	Synthesis of esters
11	Isolation of limonene
12	Final exam preparation
13	Final exam

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Eight two-student groups participated in this study across two lab sections. One lab section (N = 4 groups) completed worksheets designed to further cognitive engagement on various concepts related to the associated lab. In contrast, the other lab section (N = 4 groups) included no additional worksheet. The gender makeup of the groups included two male/female, one female/female, and one male/male groups within the intervention section, as well as one male/female, two female/female, and one male/male groups within the non-intervention section. Students in these groups were primarily second or third-year STEM majors.

Ethical considerations

This study was approved by the UA Institutional Review Board (IRB) and adhered to all ethical guidelines for research involving human subjects. Informed consent was obtained from all participants prior to data collection, ensuring their voluntary participation and the confidentiality of their responses. All data were anonymized to protect participant privacy, and no identifying information was included in the analysis or reporting of results.

Data collection

The results of this study are based on data collected in laboratory classes taught by the same graduate teaching assistant (GTA) during the OCI lab; this GTA was a PhD student who had multiple semester experiences teaching organic chemistry labs. The observed experiments included nucleophilic substitution reactions, elimination reactions, synthesis of esters, and isolation of a natural product. These experiments were selected based on their chemistry content and the nature of experimental work (see Table 2).

Table 2 Characteristics of lab experiments observed in this study

Experiment	Overall goal	Technique	Analysis	Description
Unimolecular nucleophilic substitution reactions	Compare reaction rates of t-butyl chloride and t-butyl bromide	Indicator based reaction timing	Reaction rates	With the use of a bromophenol indicator students were asked to time the reaction rate of the reaction between tert-butyl chloride with sodium hydroxide at room temperature (3 trials), tert-butyl chloride with sodium hydroxide at chilled temperature (1 trial), and tert-butyl bromide with sodium hydroxide (3 trials). The reaction was observed to be complete when the color of the reaction vessel changed from blue to yellow and the students were asked to time how long the reaction took to reach completion after adding sodium hydroxide.
Elimination reaction	Compare reaction products of dehydration of 2-butanol and dehydrohalogenation of 2-bromobutanol	Gas collection apparatus	Gas chromatography	Students were asked to setup a gas collection apparatus for either a dehydration of 2-butanol (E1) or a dehydrohalogenation of 2-bromobutanol (E2). After collecting the gas from the reaction vessel students performed gas chromatography and used their GC chromatogram to match with the products shown in the GC standards.
Esterification reaction	Identify starting alcohol of an esterification reaction	Reflux	IR/NMR Spectroscopy	Students were asked to setup a reflux reaction with acetic acid and unknown alcohol. The starting alcohols were hexanol, 2-pentanol, 4-methyl-2-pentanol, 1-pentanol, and isopentyl alcohol. After allowing the reaction to reflux, a workup was performed. Students were then asked to match their obtained IR and NMR spectra to standards in order to determine their starting alcohol.
Natural product isolation	Isolate of R-limonene from orange zest	Distillation	IR/NMR Spectroscopy	Students performed a distillation of orange zest and performed a liquid–liquid extraction to isolate R-limonene. IR and NMR were used to verify the presence/purity of R-limonene.

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Each student group participating in the study was observed using a GoPro camera or Amazon Fire tablet, strategically positioned to capture the majority of the groups’ bench work. Additionally, each student participant wore an audio recorder attached to their lab coat. While students occasionally moved to retrieve reagents or work in a fume hood, the primary focus of the recordings was on their interactions and engagement at the bench. Instances where students left the recording frame were supplemented by their audio recordings, allowing for a comprehensive analysis of their engagement throughout the experiment. Adobe Premier Pro was used to merge the video and audio and generate transcripts that were used as the base for data analysis.

The conceptual checkpoint intervention in this study was designed to promote cognitive engagement during laboratory experiments by incorporating structured worksheets and guided collaborative activities. The intervention groups were given one worksheet to complete as a group and students were asked to complete these worksheet activities during experiment downtime. The worksheets contained open-ended questions, prompts to connect experimental results to underlying concepts, and opportunities for reflection on the scientific process. The goal was to promote critical thinking about the chemical principles involved in the experiments by integrating structured opportunities for discussion and problem-solving within student groups. This approach aimed to create a more interactive and reflective laboratory environment.

For example, during the Week 9: elimination experiment, students were tasked with completing a worksheet that required them to predict reaction outcomes based on the structure and reactivity of the starting materials (see Appendix A–D). As the experiment progressed, students were prompted to compare their predictions with actual results and discuss any discrepancies within their group. Some worksheets included questions about the reaction mechanism, providing students with the opportunity to predict the molecular components of the reaction flask. This structured approach allowed for reinforcement of reaction mechanism concepts while creating space for collaborative problem-solving and peer-to-peer learning, giving all group members a chance to participate in the discussion. We include the worksheets used during the study in the appendix for reference.

Data analysis

Data collected was analyzed based on distinct moments in a group's experimental work. The analytical process started by dividing lab activity into different stages based on the nature of group work. In particular, we identified three major stages: (1) Procedural work, which entailed working on procedural activities related to applying a technique or carrying out a reaction, (2) Worksheet work, which involved generating answers to the questions included in the assigned worksheet (for the intervention groups), and (3) Wait time, which related to activities carried out outside the other two types of moments, such as waiting for a solution to drain or allowing a precipitation reaction to fully form solid products. Distinct interactions between students in a group during these three stages were then identified and categorized into four forms of engagement: behavioral, cognitive, social, and affective.

The unit of analysis for this study was defined as distinct episodes within the laboratory experiments. Episodes were segmented based on changes in student activity, such as transitions between experimental steps (e.g., set-up, data collection, and analysis) or shifts in interaction patterns (e.g., task-oriented discussion vs. exploratory questioning). Each episode was further broken down into moments reflecting observable engagement behaviors or interactions. During the analytical process, sub-categories were created within each form of engagement to better capture the nature of student activity. Behavioral engagement was categorized as procedural (handling experimental tasks), verification (relying on others to double check the procedure), overseeing (checking or controlling procedural actions of a partner), and multitasking (performing multiple tasks at once). Cognitive engagement was characterized as reaction-focused (thinking about chemical reactions), mechanism-focused (thinking about reaction mechanisms), structure-focused (thinking about molecular structure), property-focused (thinking about properties of chemical substances), and interaction-focused (thinking about molecular interactions). Social engagement was categorized as with-partner (interacting with group partner), with-TA (interacting with TA), or with-others (interacting with members of other groups). Affective engagement was characterized as insecurity (lacking confidence in decisions or actions), confusion (revealing confusion during an experiment), frustration (exhibiting anxiety or annoyance), and happiness (demonstrating excitement or joy). See Table 3 for examples in each of these categories.

Table 3 Coding scheme and examples

Type of engagement	Definition	Subcategory	Description	Transcript example
Behavioral	On-task involvement or participation	Procedure	Involvement focused on procedure of the experiment	The student locates and obtains ice bucket for chilled experiment
				The student sets up distillation apparatus
		Verification	Utilization of resources/others to double check work	The student locates five different bottles of reagent and then turns back to their bench to review the procedure, to double check which reagent.
		Overseeing	Managing group tasks or delegating tasks	The first student comes back from the fume hood with a vial of acetic acid, the second student proceeds to ask “Is that the 2 mL of acetic acid” and directs them to put the acetic acid slowly into the round bottom flask.
		Multitasking	Performing multiple tasks at once or aware of future tasks	The student obtains reagent from fume hood and then afterwards obtains glassware from the drawer for future experimental steps while looking at the workbook in order to set up the apparatus for the reaction.
Cognitive	Participation in chemistry conceptual reasoning	General reaction	Discussion of the steps of a reaction, or the identity of the reactants and products	The students discuss if t-butyl chloride/bromide are the reactants for the reaction
		Chemical structure	Discussion of the structure of any chemical used in the experiment	The students states: “Do you think the reaction would be faster because of the tertiary carbocation formation”
		Periodic properties	Discussion of periodic table trends of any of the associated experimental chemicals	The student suggest that they should be looking at the size of the atoms, like if bromine is bigger than chlorine.
		Mechanism	Discussion of the mechanistic steps of the reaction	The students say: “Since our reaction was an E2 reaction and it has 1 step shouldn't that mean our reaction should be faster than the E1 reaction which has two steps.”
		Molecular interaction	Discussion of how the chemicals in the system interact on a molecular scale	The student insinuates that maybe the reaction progresses faster because the reactants are colliding with each other more frequently.
Social	Participation in the community of learners	Partner	Use of partner to aid in learning or performance	The student asks their partner, “Which unknown are you choosing?”
		TA	Use of TA to aid in learning or performance	The students ask their TA, “Do you understand why our flask is not bubbling?”. The TA responds, “It may take awhile for the heat plate to come up to temperature”.
		Other groups	Use of other groups to aid in learning or performance	The students turn around and ask the group working behind them, “Do you guys know if we should be looking at the chemical structure of the reactants or the size of the halogens.”
Affective	Emotional expression	Lack of confidence	Not confident in their response or questioning their thoughts	The student claims that the reaction is faster because bromine is bigger, but then says “But I am not sure if that's what we should be looking at.”
		Confusion	Procedural confusion, looking around to see what to do next	The student says to their partner, “I am confused if the graduated cylinder goes under the first or second opening.”
		Frustration	Verbal expression of frustration or frustrated body language	The students expressively states, “Ugh, why didn’t we get the product.”
		Happiness	Verbal expression of happiness or excitement	The student says, “Yay, we finally did the reaction successfully.”

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Both verbal and non-verbal cues were included in the coding process to capture student engagement. Observable, non-verbal actions such as gestures, facial expressions, checking measurements, referring to lab manuals, and handling equipment were manually coded from video recordings. These non-verbal engagements were categorized alongside verbal engagement to ensure a full representation of student interactions in the laboratory setting. While behavioral and affective engagement included both verbal and non-verbal indicators, verbal evidence was primarily required for identifying social and cognitive engagement to ensure accurate interpretation of interaction and conceptual reasoning.

The transcripts from each episode was independently coded by two researchers for each individual student and discrepancies were resolved through discussion to achieve consensus. When discrepancies were discussed video footage of the episode was used to reach consensus. This multi-dimensional coding approach provided a comprehensive view of engagement, allowing for a nuanced understanding of how the behavioral, social, cognitive, and affective dimensions interacted during laboratory experiments. This analysis offered insights into the dynamic nature of student engagement in chemistry laboratory settings.

During the data analysis, we paid attention to differences in forms of engagement depending on the nature of the experiment and gender. Gender dynamics were examined to identify potential patterns in participation, collaboration, and leadership roles within groups, particularly whether students of certain genders exhibited tendencies toward specific forms of engagement, such as behavioral leadership or social facilitation. These factors were analyzed to uncover potential disparities or unique trends that could inform tailored interventions for fostering equitable and meaningful engagement across diverse student populations. The interplay between these variables and the nature of the experiment, such as its complexity or perceived relevance, was explored to better understand how specific experimental designs might elicit different engagement patterns.

Major findings

General patterns

To examine engagement patterns across all four experiments, we analyzed the overall distribution of engagement behaviors between the intervention and non-intervention groups (see Fig. 1). A Chi-square test of independence revealed significant differences in student engagement between the two conditions (χ²(3, N = 1431) = 47.7, p < 0.0001, Cramér's V = 0.18). These differences were primarily driven by lower-than-expected behavioral engagement and higher-than-expected cognitive and social engagement in the intervention group. Further analysis comparing different forms of engagement within the intervention groups during worksheet activities and at other times (see Fig. 2) showed a significant relative increase in cognitive, social, and affective engagement during work on the worksheet. A comparison of the data in Fig. 1 and 2 also indicates that the intervention influenced engagement beyond the time spent working on the worksheet. Notably, there were marked increases in social and affective engagement, accompanied by a decrease in cognitive engagement, during other parts of the lab work compared to the non-intervention groups.

Fig. 1 Overall student engagement.

Fig. 2 Student engagement during worksheet activity and at other times in the intervention groups.

Both intervention and non-intervention groups exhibited high levels of behavioral engagement, primarily consisting of procedural actions such as measuring, pipetting, and assembling apparatus (see Table 4). However, this subcategory of behavioral engagement was significantly less prevalent in the intervention group (46% in the intervention group vs. 53% in the non-intervention group). In contrast, verification behaviors, where students double-checked their work and reasoning, and multitasking behaviors, where students performed multiple tasks simultaneously, were more prominent in the intervention group (24% and 17%, respectively) compared to the non-intervention group (23% and 11%). The role of task oversight within groups was similar across both conditions (13% in each group).

Table 4 Distribution of each engagement type. Observed instances in the intervention group are split into instances during the worksheet activity (n_ws) and instances at other times (n_ot)

Engagement form	Subcategory	Intervention (nws + not, %)	Non-intervention (n, %)
Behavioral	Procedure	0 + 169 (46%)	222 (53%)
	Verification	9 + 79 (24%)	95 (23%)
	Overseeing	0 + 49 (13%)	54 (13%)
	Multitasking	0 + 65 (17%)	47 (11%)
Cognitive	General reaction	25 + 4 (34%)	9 (26%)
	Chemical structure	13 + 3 (19%)	8 (24%)
	Periodic properties	9 + 4 (15%)	10 (29%)
	Mechanism	12 + 5 (19%)	3 (9%)
	Molecular interaction	10 + 1 (13%)	4 (12%)
Social	Partner	21 + 57 (48%)	53 (60%)
	TA	9 + 38 (29%)	23 (25%)
	Other groups	8 + 28 (23%)	13 (15%)
Affective	Lack of confidence	12 + 41 (32%)	42 (40%)
	Confusion	10 + 38 (29%)	28 (28%)
	Frustration	9 + 33 (24%)	18 (17%)
	Happiness	4 + 21 (15%)	16 (15%)

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Cognitive engagement was the least frequent type of engagement in both groups but was significantly more prevalent in the intervention group (11% compared to 5% in the non-intervention group). The worksheet activity appeared to prompt deeper discussions about certain conceptual aspects of the experiments. In the intervention group, discussions often focused on reaction steps (34%) and reaction mechanisms (19%). In contrast, the non-intervention group more frequently engaged in discussions centered on chemical structures (24%) and periodic properties (29%).

Social engagement patterns differed between the intervention and non-intervention groups. Logistical coordination between lab partners, such as discussing experimental details, was the most common form of social engagement in both groups (48% in the intervention group vs. 60% in the non-intervention group). However, the intervention group exhibited a more evenly distributed pattern of social engagement. While students in the non-intervention group primarily interacted with their lab partners, those in the intervention group engaged more frequently with the teaching assistant (29%) and with members of other groups (23%). This suggests that, in the presence of a structured cognitive activity, students may have been more inclined to seek support through broader peer discussions beyond their immediate lab partner.

Across all groups, affective engagement was predominantly characterized by negative emotions, with lack of confidence (32% in the intervention group vs. 40% in the non-intervention group), confusion (29% vs. 28%), and frustration (24% vs. 17%) frequently observed (see Table 4). These emotions were often associated with procedural challenges or unexpected experimental results. Although the overall frequency of negative affect was similar between groups, the intervention group exhibited slightly higher levels of frustration, particularly during worksheet discussions, where students appeared to struggle with the conceptual material introduced. In contrast, frustration in the non-intervention group was more commonly tied to procedural tasks. Positive affective responses, such as happiness or satisfaction (15% in both groups), were also present, though typically brief and occurring after successful task completion.

Qualitative insights on engagement

Qualitative excerpts from video and audio data capture student interactions during the experiments, providing insight into their engagement. The following examples illustrate how students navigated procedural tasks, collaborated with peers, and responded to cognitive challenges. While behavioral engagement was most prevalent, instances of social interaction, affective responses, and cognitive struggle emerged, shaping students’ experiences. Students primarily focused on completing tasks efficiently, emphasizing procedural accuracy. They frequently checked lab manuals and double-checked measurements. For example, while setting up a gas collection apparatus, one student hesitated:

“I think we attach this tube here, but I’m not sure.”

(Both students check the manual to verify the setup.)

Similarly, another student, watching their reaction mixture, questioned:

“It says it should turn yellow… this is sort of yellow? Oh, wait, it faded. Should I add another drop?”

These moments illustrate the dominance of behavioral engagement, where students prioritized following instructions over discussing underlying chemistry. Social engagement often manifested in collaborative partner reliance and peer reassurance. In one instance, two students confirmed their next steps:

Student 1: “Do I add the indicator now?”

Student 2: “Yeah, I think so. The indicator is right here.”

Student 1: “Do we put it on ice?”

Student 2: “Let me check the procedure… Yes, five minutes on ice.”

Affective engagement was evident during challenging parts of the experiment as well as successes the student may have had. When an experiment failed, a frustrated student muttered:

“I just want some product so we can do IR/NMR!”

Conversely, after successfully isolating R-limonene, another student celebrated:

“Yay, we actually got it right! That means we weren’t totally lost!”

Cognitive engagement was primarily observed during the worksheet intervention, as students discussed reaction mechanisms and chemical properties, topics rarely mentioned during procedural work. For example, while completing the worksheet after running a nucleophilic substitution reaction, a student questioned:

“Wait, so it is S_N1 because we are using a t-butyl reactant, right?”

Another student, worked through the regioselectivity of their elimination reaction with their lab partner:

Student 1: “So hypothetically the major product was the one where the Br ends up on the more substituted carbon. That's Markovnikov's rule, right?”

Student 2: “Yeah, because the carbocation forms first, and the more substituted one is more stable.”

Student 1: “Then why did we get a little bit of the other product?”

These discussions suggest that students engaged with reaction concepts only when prompted by structured activities, rather than integrating them into their lab work organically. Procedural engagement was the dominant form of student interaction in the laboratory. However, cognitive, social, and affective engagement also played significant roles and contributed to the students' overall lab experience.

Engagement across experiments

Engagement patterns varied across the four experiments, with differences observed across all engagement categories (see Fig. 3). These variations were more pronounced in the intervention groups (χ²(9, N = 786) = 39.4, p < 0.0001, Cramér's V = 0.13) than in the non-intervention groups (χ²(9, N = 645) = 16.32, p = 0.064, Cramér's V = 0.09). Behavioral engagement was the most common form of engagement across all experiments and groups, though its prevalence fluctuated with task complexity. The nucleophilic substitution (S_N1) experiment showed the highest levels of behavioral engagement, as students were primarily focused on following multiple procedural steps. In contrast, the other three experiments, which involved fewer procedural steps, showed lower levels of behavioral engagement, suggesting that students were less occupied with hands-on tasks. A consistent reduction in behavioral engagement was observed across all lab types when the intervention was implemented.

Fig. 3 Engagement (Behavioral, Cognitive, Social, and Affective) across experiments (S_N1, E1/2, Esterification, and Limonene Natural Product Isolation) within Intervention and non-intervention groups.

Cognitive engagement was the least frequent type of engagement overall but showed notable increases in the intervention groups, particularly in the elimination (E1/E2) and limonene experiments. The differences in cognitive engagement between the intervention and non-intervention groups were smaller in the S_N1 and esterification experiments. Across all labs, students engaged less frequently in discussions about underlying chemical principles when additional scaffolding was not provided, tending instead to focus on simply completing the task.

Social engagement varied depending on the experimental design and the level of required student collaboration. Experiments with more complex procedures, such as the elimination experiment, prompted more instances of collaboration compared to those with simpler procedures, like the limonene experiment. The intervention consistently increased social engagement across all lab types, with the most notable increases observed in the elimination and esterification experiments.

Affective engagement varied depending on the complexity of the experiment. The elimination experiment elicited the highest levels of affective engagement, as its procedural complexity and demand for precision often led to moments of both frustration and satisfaction. In contrast, affective engagement was lower in the S_N1 experiment, where the procedure was more straightforward and followed a predictable sequence. The intervention consistently increased affective engagement across all experiments although moderately.

Engagement based on gender

Analysis of engagement patterns by gender revealed similar trends across categories for female and male students within both the intervention and non-intervention groups, although females demonstrated slightly higher level of engagement across all categories and groups (see Fig. 4). There were no major differences observed in the engagement of female/female, male/male, of female/male groups with or without intervention. However, when comparing each gender across intervention and non-intervention conditions, chi-square analysis indicated that the intervention had a stronger impact on female students (χ²(3, N = 791) = 48.6, p < 0.0001, Cramér's V = 0.24) than on male students (χ²(3, N = 620) = 15.6, p = 0.0013, Cramér's V = 0.16). Overall, the intervention led to a greater reduction in behavioral engagement and a larger increase in social engagement among female students compared to male students.

Fig. 4 Engagement based on Gender (9 females and 7 males total).

Differences in engagement between male and female students become more nuanced when examining their behaviors in the context of structured (intervention) versus unstructured (non-intervention) laboratory activities. While both groups displayed a range of engagement types, the presence of intervention materials, such as worksheets designed to prompt conceptual thinking and collaboration, prompted important distinctions. A central finding was the gendered distribution of behavioral roles: female students were more likely to act as overseers, while male students frequently adopted multitasking roles.

Across both intervention and non-intervention conditions, female students were significantly more likely to engage in task-overseeing behaviors, focusing on procedural accuracy, group coordination, and attention to detail (see Fig. 5). This often included directing group members and managing timelines. Male students, in contrast, exhibited higher frequencies of multitasking, simultaneously executing multiple procedural steps and often working independently of peers. Structured intervention materials promoted increased cognitive engagement similarly for all students. In non-intervention settings, cognitive engagement was minimal for both genders, as the focus remained primarily on task completion rather than on theoretical understanding.

Fig. 5 A closer look at gender within multitasking and overseeing roles.

The overseeing behavior commonly adopted by female students correlated with higher levels of social engagement, particularly in the intervention groups. These students actively guided partners, confirmed procedures, and verbalized next steps, resulting in ongoing dialogue and coordination. In contrast, male students' multitasking approach led to more limited, task-focused interactions. Even in the intervention groups, their social engagement remained brief and oriented around procedural efficiency rather than collaborative reasoning. Male students in non-intervention groups demonstrated the lowest levels of social engagement overall. Female students displayed more visible emotional engagement, often reacting to procedural success or failure with enthusiasm or frustration. Male students exhibited affective engagement more subtly, although these emotional expressions increased in the intervention group.

Discussion

Our findings reveal critical patterns in student engagement within organic chemistry laboratory settings, with behavioral engagement emerging as the most prevalent form. The emphasis on task completion and procedural accuracy over deeper conceptual understanding reflects the procedural focus typical of laboratory learning environments, highlighting the need to explore strategies for better integrating conceptual thinking into laboratory tasks. Disaggregating engagement during worksheet activity showed that cognitive, social, and affective engagement increased during the worksheet. Notably, elevated social and affective engagement persisted throughout the rest of the lab, suggesting the worksheet may have had broader, sustained impact beyond its immediate use.

Interestingly, cognitive engagement remained relatively low across all experiments and was primarily associated with the intervention involving the conceptual worksheet. The overall prevalence of low cognitive engagement in other parts of the experiments suggests that our intervention had a limited impact on fostering deeper conceptual processing. Without additional scaffolding or opportunities to explore the underlying scientific principles beyond procedural tasks, cognitive engagement remained consistently low and constrained to the worksheet. This finding suggests that, in the absence of structured opportunities for conceptual engagement, students may default to procedural participation. Moreover, the lack of variability in cognitive engagement across experiments further underscores the need for targeted interventions that incorporate more opportunities for conceptual thinking within experiments.

When comparing experiments, some required more intensive procedural tasks or provided fewer opportunities for other forms of engagement. For instance, experiments with complex setups or multiple steps demanded meticulous attention to procedural details, often limiting students' ability to engage socially or cognitively. Conversely, simpler experiments with straightforward procedures tended to result in less diverse engagement patterns, as students completed tasks with minimal collaboration or critical thinking. Despite these minor variations, the similar percentages of engagement across experiments suggest that students consistently invested comparable levels of focus and effort, regardless of task complexity or demands. Overall, the findings indicate that while behavioral engagement predominates in all experiments, social and affective engagement are more sensitive to the experimental design and the specific opportunities it provides for interaction or the challenges that it poses.

Gender-based analysis revealed broadly similar engagement trends between male and female students across both intervention and non-intervention groups, with females demonstrating slightly higher levels of engagement in all categories. Interestingly, no major differences were observed in engagement patterns among different gender group compositions, female/female, male/male, or mixed-gender, with or without the intervention. However, when comparing each gender across intervention conditions, the intervention had a stronger overall impact on female students. Female students exhibited a more substantial decrease in behavioral engagement and a greater increase in social engagement relative to male students, suggesting that structured supports were particularly effective in shifting their focus from task completion toward collaborative interaction.

The presence of intervention materials introduced nuanced differences in how male and female students approached lab work. While both groups showed increased cognitive engagement in structured settings, gendered patterns in task roles emerged: female students more often assumed overseeing roles such as coordinating group efforts, managing timelines, and maintaining procedural accuracy, while male students tended to multitask independently. These distinct behaviors influenced the type and depth of social engagement. Female students’ overseeing approach was associated with richer collaborative dialogue, especially in intervention settings, while male students’ interactions were generally brief and centered around procedural execution. Emotional engagement also varied by gender; female students were more openly expressive, whereas male students showed more subtle affective responses that became more apparent under structured conditions. Overall, the intervention appeared to foster deeper collaboration and emotional involvement, particularly among female students, while helping increase cognitive focus for all.

Described trends provide valuable insights into how students navigate complex laboratory environments and may inform strategies to support both detailed and efficiency-driven engagement styles. Furthermore, they align with existing literature that emphasizes the role of gendered learning preferences and collaborative dynamics in STEM education.

Implications

Our findings have several practical implications for laboratory instruction and curriculum design. First, the predominance of behavioral engagement emphasizes the need to reimagine laboratory tasks in ways that prioritize conceptual understanding without compromising procedural rigor. Prior research highlights that traditional laboratory instruction often emphasizes procedures over deeper cognitive engagement (Holmes and Wieman, 2016), however instructors could consider incorporating guided reflection questions, real-time prompts, or scenario-based challenges that encourage students to think critically about experimental outcomes and underlying scientific principles. Providing opportunities for students to predict results, analyze data, and draw connections between theory and practice can help shift the focus from procedural tasks to a more balanced and meaningful engagement with the material (Weaver et al., 2008).

The limited cognitive engagement observed in our study highlights a broader issue in laboratory education: the need for interventions that extend beyond procedural mastery to cultivate chemical thinking skills. Research suggests that students often struggle to connect laboratory work with theoretical concepts, necessitating instructional strategies that promote metacognition and problem-solving skills (Cooper and Stower, 2018). Conceptual worksheets and similar tools should be integrated into laboratory curricula as standard practice, accompanied by instructor training to facilitate discussions and provide formative feedback. Additionally, scaffolding activities that connect laboratory procedures to real-world applications or complex problem-solving scenarios can help students recognize the material's relevance and inspire deeper engagement. These interventions, combined with intentional efforts to foster a supportive and reflective classroom environment, can better prepare students to navigate the complexities of scientific inquiry beyond the laboratory.

Finally, the gender differences observed in this study highlight the importance of designing laboratory environments that are inclusive and responsive to diverse learners. Research on gender disparities in STEM suggests that female students often excel in collaborative learning environments, whereas traditional laboratory settings may not always support their strengths (Eddy et al., 2014). For example, incorporating collaborative tasks, peer-teaching opportunities, or structured group roles can leverage female students’ strengths by fostering affective and social engagement while also encouraging male students to participate more meaningfully. Offering diverse modes of engagement—such as discussions, hands-on problem-solving, and collaborative decision-making—can help create, more equitable learning environments that accommodate varied preferences and approaches.

Limitations

Our sample size may not fully capture the diversity of student experiences or demographic representation, particularly regarding factors such as cultural background, prior educational experiences, and varying levels of familiarity with laboratory work. Future studies could address this limitation by analyzing engagement patterns in larger and more diverse cohorts, including students from different institutions, disciplines, and educational levels. Additionally, longitudinal studies that track changes in engagement over time or across multiple courses could offer deeper insights into how students' engagement evolves.

Additionally, the reliance on observational data introduces potential biases in measuring engagement. Certain forms of engagement, particularly the cognitive and affective dimensions, may not have been easily observable during the experiments. For example, internal cognitive processes such as critical thinking and problem-solving, as well as subtle emotional responses like satisfaction or confusion, may have been underestimated or misinterpreted. Incorporating complementary data sources, such as student interviews, surveys, or reflective journals, could help capture these less observable aspects of engagement and provide a more comprehensive understanding.

Another limitation is the focus on a specific intervention, such as the conceptual worksheet, which provided valuable insights into cognitive engagement but did not account for the broader range of strategies that could enhance other dimensions of engagement. While this intervention demonstrated the benefits of structured activities in promoting cognitive engagement, it may not have fully captured the complexity of engagement as a whole. Future research could explore alternative interventions—such as collaborative, group-based inquiry tasks, interactive digital tools, or real-time scaffolding by instructors—to assess their effectiveness in fostering behavioral, social, affective, and cognitive engagement.

While this study aimed to explore the effects of the conceptual worksheet intervention, we did not collect baseline data on group sociability, expressiveness, or engagement prior to the intervention. As a result, we cannot definitively rule out the possibility that the observed differences in engagement, particularly the higher frequencies of affective, social, and cognitive engagement in intervention groups, may be partially influenced by inherent differences in group dynamics or communication styles rather than solely by the worksheet. Future studies could incorporate pre-intervention measures or matched-group designs to better isolate the effects of instructional tools on engagement behaviors.

Moreover, the study's context within an organic chemistry laboratory may limit the generalizability of its findings to other settings or disciplines. Engagement dynamics in other STEM fields, interdisciplinary courses, or non-laboratory environments could differ significantly, underscoring the need for comparative studies that examine engagement across diverse educational settings. Such investigations could help develop more universal strategies for designing engaging and inclusive learning experiences.

Concluding remarks

While this study provides important insights into the nature of engagement in organic chemistry laboratories, its findings point to the need for continued innovation in laboratory teaching practices. By addressing the identified limitations and building on the implications, educators and researchers can work towards creating laboratory environments that foster equitable, meaningful, and conceptually rich learning experiences.

Author contributions

Devin Pontigon contributed to the investigation through study conceptualization, data collection and analysis, as well as writing the original draft. Vicente Talanquer contributed to study conceptualization, project supervision, and writing (reviewing and editing).

Data availability statement

Due to ethical confidentiality requirements, the recorded data have not been made publicly available. Our research participants have consented to share their data only with the researchers directly involved in this project.

Conflicts of interest

There are no conflicts to declare.

Appendices

Appendix A: unimolecular nucleophilic substitution experiment worksheets

Appendix B: elimination experiment worksheets

Appendix C: esterification experiment worksheets

Appendix D: natural product (limonene) isolation experiment worksheets

Acknowledgements

The authors would like to thank all students who allowed us to observe their work in the laboratory. We would also like to thank those that aided in ensuring interrater reliability during the data analysis.

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