A Robotic-Based Instruction for Integrating Robotics in Chemistry Education: A case of stoichiometry and titration

A Robotic-Based Instruction for Integrating Robotics in Chemistry Education: A case of stoichiometry and titration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Robotic-Based Instruction for Integrating Robotics in Chemistry Education: A case of stoichiometry and titration thabo mhlongo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9419614/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The integration of robotics into chemistry education has attracted increasing interest as a means to enhance learner engagement and support conceptual understanding. Traditional instruction, often dependent on abstract concepts and symbolic representations, can result in misconceptions and difficulties in practical application. This study investigated the effects of robotics-based instruction on teaching stoichiometry and titration using a quasi-experimental design. Comparisons were made between learners taught through robotics-integrated methods and those receiving conventional instruction, with attention to engagement, conceptual change, and academic performance. The findings provide insights into how robotics can be implemented to support interactive, learner-centered learning, highlight potential challenges, and inform curriculum development and instructional strategies in chemistry education. Materials Engineering Robotics-based instruction Chemistry education Conceptual understanding Stoichiometry and titration Inquiry-based learning INTRODUCTION The integration of robotics into chemistry education has received growing scholarly and pedagogical attention as educators seek innovative approaches to enhance learner engagement, promote conceptual understanding, and strengthen the connection between theory and practice in STEM learning environments (Eguchi, 2017 ; Sapounidis & Alimisis, 2020 ). Robotics is increasingly viewed not only as a technological tool but also as a learning mediator that can support inquiry, experimentation, modelling, and data-driven reasoning in school science (Darmawansah et al., 2023 ). In chemistry classrooms, where abstract symbolic representations frequently dominate instruction, robotics-integrated learning may offer opportunities for learners to interact with concepts in more concrete, observable, and manipulable ways, potentially improving meaning-making and conceptual development (Mhlongo et al., 2025; Lotriet & Gouws, 2024 ). A persistent challenge in chemistry education is that many learners struggle to develop deep conceptual understanding of topics that require coordination of multiple representations, procedural steps, and quantitative reasoning (Benitti, 2012 ). Stoichiometry and titration are particularly demanding because they require learners to connect balanced chemical equations, mole ratios, concentration relationships, experimental procedures, and interpretation of endpoints or equivalence points (Sapounidis & Alimisis, 2020 ). Learners often approach these topics as algorithmic exercises focused on memorising steps rather than understanding underlying chemical principles, which can contribute to misconceptions and weak transfer of knowledge to practical contexts (Ching & Hsu, 2024 ). For example, learners may apply formula-based procedures without conceptualising the meaning of limiting reagents, the role of the balanced equation, or the chemical significance of the titration curve and neutralisation point. Such conceptual difficulties are intensified when practical experimentation is limited or absent, reducing opportunities for learners to validate calculations and interpretations through evidence-based investigation. In response to these challenges, contemporary science education increasingly emphasises learner-centred and inquiry-oriented pedagogies that encourage exploration, experimentation, collaboration, and the construction of knowledge through interaction with learning materials and peers (Ching & Hsu, 2024 ). Within this orientation, robotics has been identified as a promising tool for supporting experiential learning and problem-solving by allowing learners to design systems, test predictions, observe outcomes, and refine their thinking based on feedback from the learning environment (Eguchi, 2017 ). Robotics-integrated instruction may therefore support chemistry learning by enabling controlled manipulation of variables (e.g., reagent volume, concentration, dispensing rate), real-time data collection (e.g., pH readings), and iterative testing of hypotheses. These features can make invisible chemical processes more visible and measurable, strengthening the development of scientific reasoning and procedural competence. Empirical research on robotics in education has reported benefits such as improved motivation, collaboration, and engagement, as well as the development of computational and problem-solving skills (Benitti, 2012 ; Darmawansah et al., 2023 ). However, much of this work has focused on robotics in general STEM contexts rather than in chemistry-specific learning, and fewer studies have examined robotics applications for chemistry topics that require strong quantitative reasoning, such as stoichiometry and titration (Sapounidis & Alimisis, 2020 ). Furthermore, the successful integration of robotics into chemistry teaching is not guaranteed, as implementation depends on contextual factors including curriculum alignment, availability of resources, laboratory access, teacher preparedness, and classroom constraints (Lotriet & Gouws, 2024 ). These challenges are particularly relevant in under-resourced schooling contexts, where limited laboratory infrastructure and large class sizes restrict opportunities for practical work, even though such experiences are central to meaningful chemistry learning. In the South African context, many schools especially those in township and rural communities experience resource disparities that reduce access to laboratory equipment and consumables needed for practical chemistry activities. As a result, learners may encounter chemistry primarily through textbook-based instruction, which can limit conceptual understanding and engagement with scientific inquiry. Robotics-integrated instruction may provide a feasible pathway for strengthening practical engagement in such contexts by supporting low-cost simulations of experimental processes, structured measurement activities, and data-driven analysis. Nevertheless, there remains limited empirical evidence on how robotics-integrated instruction influences Grade 10 learners’ conceptual understanding and academic achievement in stoichiometry and titration, particularly in contexts characterised by constrained resources and limited opportunities for hands-on experimentation. To address this gap, the present study investigated the effects of robotics-integrated instruction on Grade 10 learners’ engagement, conceptual understanding, and academic performance in stoichiometry and titration. The study compared robotics-integrated teaching with conventional instructional approaches in a South African secondary school context. The study was guided by the following research question: How does the use of robotics influence Grade 10 learners’ conceptual understanding and academic achievement in stoichiometry and titration? METHODOLOGY Research Design The study adopted a quasi-experimental pre-test–post-test design involving an experimental group (robotics-integrated instruction) and a control group (conventional instruction). Random assignment was not feasible because intact classes were used; therefore, group comparability was strengthened through baseline diagnostic testing and matched group allocation. The study also incorporated qualitative classroom observations to support interpretation of quantitative learning outcomes, making the design suitable for examining both performance change and learning processes in authentic classroom conditions. School Context The study was conducted in a South African township secondary school with typical resource constraints affecting science teaching and learning. While the schools offered Physical Sciences in Grade 10, opportunities for sustained laboratory experimentation were limited due to infrastructure constraints and large class sizes. However, the school had access to a functional computer laboratory and basic digital learning resources, making it suitable for implementing and examining robotics-integrated instruction within a constrained context. Participants and Sampling The study involved 40 Grade 10 Physical Sciences learners, divided into: · Experimental group: n = 20 (robotics-integrated instruction) · Control group: n = 20 (conventional instruction) A purposive sampling approach was used to select a school and participants aligned to the study objectives. Learners were allocated into groups using pre-test scores to ensure balanced baseline performance distribution across the experimental and control conditions. Two Physical Sciences teachers participated: one facilitated the experimental group and one facilitated the control group. Teacher Training and Capacity Building Teacher preparation was treated as a critical implementation requirement to ensure that robotics integration functioned as a pedagogically meaningful intervention, rather than a technological add-on. Two Grade 10 Physical Sciences teachers participated in the study. One teacher facilitated the experimental group (robotics-integrated instruction), while the second facilitated the control group (conventional instruction). To strengthen implementation fidelity and reduce variability in instructional delivery, both teachers met with the researcher prior to the intervention to align lesson objectives, assessment expectations, and pacing in accordance with the Grade 10 CAPS content requirements for stoichiometry and titration. E1. Training Structure and Duration The teacher assigned to the experimental group received targeted professional development (PD) prior to the intervention. The PD programme was delivered over two training sessions (approximately 6 hours total) and focused on building both technical and pedagogical competence for classroom implementation. Training activities were designed to ensure the teacher could independently set up, operate, troubleshoot, and meaningfully integrate the robotics tools during instruction. E2. Training Content The teacher training programme addressed three integrated areas: 1. Technical Familiarisation and Robotics Operation The teacher was introduced to the robotics hardware and software used in the study (e.g., Engino robotics components, sensors, and programming interface). Practical training included assembling the robotics models, calibrating components, and running test trials of the robotics simulations used in stoichiometry and titration lessons. Emphasis was placed on classroom-ready competence, including basic troubleshooting (e.g., wiring errors, sensor reading instability, motor control errors) to minimise lesson disruptions. 2. Pedagogical Integration for Chemistry Learning Training focused on how robotics activities were aligned with curriculum concepts and used to support conceptual understanding. The teacher was guided to implement robotics through an inquiry-oriented structure comprising: o Concept Introduction: eliciting prior knowledge and introducing the chemistry concept; o Robotics Exploration: learners engaging with robotics models to generate observations and measurements; o Concept Application: learners interpreting results and linking patterns to chemical principles. Particular attention was given to ensuring that robotics tasks explicitly supported conceptual development (e.g., mole ratios, limiting reagents, neutralisation, and endpoint identification), rather than functioning as general STEM enrichment. 3. Classroom Facilitation and Learner Support The teacher was supported in planning strategies for managing group-based robotics activities, including structuring learner roles (builder, programmer/operator, recorder, presenter), promoting collaboration, and guiding learners through scientific reasoning discussions. The training also addressed facilitation practices such as questioning techniques, prompting prediction-making, supporting error analysis, and encouraging learners to justify conclusions using collected evidence. E3. Implementation Support During the Intervention Ongoing support was provided to ensure consistent delivery of the robotics-integrated lessons. This included scheduled check-ins between the researcher and the experimental-group teacher to review lesson progress, confirm readiness of robotics resources, and address technical or pedagogical challenges encountered during classroom sessions. This approach strengthened intervention consistency and ensured that learner engagement with robotics remained aligned to the intended chemistry learning outcomes. E4. Ensuring Fidelity of the Robotics-Based Instruction To improve reliability and reduce the risk of inconsistent implementation, the study used a structured lesson plan format and an observation rubric to document how robotics activities were conducted in practice. Fidelity indicators included: (i) completion of the planned robotics tasks, (ii) extent of learner participation in robotics operation and data collection, (iii) alignment between robotics activity outputs and chemistry concepts, and (iv) evidence of guided facilitation during learner sense-making discussions. These indicators supported systematic monitoring of whether robotics integration was enacted as intended. E5. Capacity Building Outcomes The teacher training component was intended not only to support the intervention but also to strengthen teacher capacity for sustainable robotics integration in chemistry instruction. By the end of the training phase, the experimental-group teacher demonstrated competence in operating the robotics tools, facilitating structured inquiry activities, and supporting learners in linking robotics-generated observations to chemical reasoning. This teacher development component was therefore treated as an enabling condition for effective robotics integration in resource-constrained classroom contexts Intervention: Robotics-Integrated Chemistry Instruction The experimental group received robotics-integrated lessons aligned to Grade 10 stoichiometry and titration content. Robotics tools were used to support interactive modelling, controlled simulation of procedural steps, and structured inquiry tasks. The control group received conventional instruction using teacher explanation, textbook-based examples, and standard classroom exercises aligned to the same curriculum outcomes. Robotics Setup Robotics activities were implemented using Engino robotics kits to construct models simulating controlled dispensing and reaction processes relevant to stoichiometry and titration learning outcomes. Where applicable, sensors were used to support real-time observation of changes during titration progression. Learner Robotics Actions and Learning Processes To ensure that robotics integration was not treated as a technological add-on, learner activities were designed to make robotics the central mechanism through which chemistry concepts were explored, tested, and explained. Learners in the experimental group worked in small groups (4–5 learners per group) and engaged in structured tasks that required them to build, operate, and interpret outputs from robotics-based simulations aligned to stoichiometry and titration learning outcomes. Each lesson followed a consistent inquiry sequence: prediction → robotics interaction → data capture → explanation → reflection. A. Group Organisation and Roles At the start of each robotics session, learners were assigned rotating roles to ensure participation and accountability: · Assembler/Builder: assembled or adjusted the robotics mechanism (e.g., dispensing system). · Operator/Programmer: controlled the robotic actions (e.g., motor steps, dispensing rate). · Recorder: captured measurements, observations, and outputs in a worksheet. · Analyst: performed calculations (e.g., mole ratios, concentration). · Presenter: reported the group’s reasoning and findings during class discussion. Role rotation occurred weekly to reduce passive participation and ensure all learners engaged with both the technical and conceptual aspects of the tasks. B. Stoichiometry Robotics Activities (Step-by-Step Learner Actions) During stoichiometry lessons, learners used the robotics model to represent the controlled addition and proportional combination of reactants. Learner actions included: 1. Prediction and Planning o Learners predicted expected reactant relationships using balanced chemical equations. o They identified mole ratios and wrote expected product quantities before using the robotics model. 2. Robotics Setup and Calibration o Learners assembled the dispensing mechanism and ensured correct alignment. o They calibrated dispensing steps (e.g., fixed motor rotations representing fixed reactant quantities). 3. Robotics Execution o Learners ran repeated trials where the robotic system dispensed reactant “units” in controlled ratios. o They adjusted input quantities to model scenarios such as excess reactant and limiting reactant conditions. 4. Data Capture o Learners recorded trial outputs (dispensed quantities and resulting ratios) in structured tables. o They compared predicted vs observed proportional outcomes. 5. Conceptual Interpretation o Learners explained outcomes using mole ratio reasoning. o They justified limiting reagent identification using evidence from robotics trials. 6. Reflection o Learners wrote short reflections on how the robotics activity changed or strengthened their understanding of proportional reasoning in chemical equations. C. Titration Robotics Activities (Step-by-Step Learner Actions) During titration lessons, learners used a motorised dispensing system to simulate controlled titrant addition and observe changes associated with neutralisation. Learner actions included: 1. Prediction and Planning o Learners predicted the relationship between titrant volume and neutralisation progress. o They identified what “endpoint” means conceptually and procedurally. 2. System Preparation o Learners set up the titration model (dispensing mechanism, beaker/flask setup, and sensor where applicable). o They ensured consistent starting conditions across trials. 3. Controlled Dispensing o Learners initiated titrant addition in incremental steps using the motor control. o They paused dispensing at intervals to observe changes and record values. 4. Observation and Measurement o Learners observed indicator behaviour or sensor feedback (where available). o They recorded titrant volume and corresponding observations at each stage. 5. Graphing and Interpretation o Learners plotted titration progression (volume vs response trend). o They identified the endpoint region and linked it to neutralisation concepts. 6. Calculation and Chemistry Reasoning o Learners calculated unknown concentrations using recorded volumes and stoichiometric relationships. o They compared their results across groups and justified discrepancies. 7. Whole-Class Sense-Making Discussion o Learners presented findings, explained their endpoint decisions, and responded to teacher prompts that required chemical justification. D. How Robotics Supported Conceptual Change To explicitly connect robotics interaction to learning outcomes, learners were required to: · explain observations using chemical language (moles, ratios, concentration), · correct misconceptions through evidence-based argumentation, · connect procedural actions (dispensing, measuring, identifying endpoint) to conceptual meanings (neutralisation, proportionality, limiting reagent). This structure ensured that learner engagement with robotics directly contributed to measurable chemistry understanding rather than functioning as general activity-based learning. Implementation Timeline and Procedure The intervention was conducted over six weeks, with two sessions per week (approximately 90 minutes per session). Implementation proceeded as follows: · Week 1: Baseline engagement observations and administration of the pre-test · Weeks 2–5: Robotics-integrated instruction (experimental group) and conventional instruction (control group) · Week 6: Post-test administration and post-intervention engagement observations Throughout the intervention period, classroom observations were conducted to document learner participation patterns, collaboration, and task engagement during instructional activities. Data Collection Instruments Pre-test and Post-test (Academic Performance and Conceptual Understanding) Learners completed a pre-test and post-test aligned to stoichiometry and titration learning outcomes. The tests measured conceptual understanding and procedural application through structured questions requiring chemical reasoning, calculations, and interpretation of scenarios. Classroom Observation Schedule (Engagement and Learning Processes) An observation rubric was used to document learner engagement indicators during lessons, including: · participation in group tasks, · interaction with learning resources (robotics tools in the experimental group), · collaboration and peer discussion, · persistence and problem-solving behaviour. Video recordings and field notes were used to support the observation rubric and strengthen descriptive accuracy of classroom events. Data Analysis Quantitative Analysis Pre-test and post-test scores were analysed to determine changes in academic performance between the experimental and control groups. Comparative analyses were conducted to examine learning gains and assess differences in achievement outcomes attributable to the instructional approach. Qualitative Analysis Observation notes and supporting evidence were analysed thematically to identify patterns related to learner participation, collaborative learning, engagement behaviours, and how learners interacted with robotics activities to construct chemistry understanding. Qualitative findings were used to explain and contextualise quantitative performance outcomes. Ethical Considerations Ethical clearance and permissions were obtained prior to data collection. Participation was voluntary, with informed consent procedures followed. Learner anonymity and confidentiality were maintained, and all data were securely stored and used only for research purposes. RESULTS AND ANALYSES This section presents the findings of the study in relation to the research question: How does the use of robotics influence Grade 10 learners' conceptual understanding and academic achievement in stoichiometry and titration? The analysis draws on both quantitative data (pre- and post-test scores) and qualitative data (learner responses and classroom observations) to provide a comprehensive account of learners’ performance and understanding. Given that teaching and learning are shaped by the interaction between instructional practices and learner engagement, the study examined how robotics-based instruction influenced learners’ ability to demonstrate both procedural competence and conceptual understanding. However, instructional approaches do not necessarily produce uniform learning outcomes, as learners’ performance is influenced by differences in prior knowledge, cognitive processing, and problem-solving strategies (Tóthová et al., 2021 ; Tóthová et al., 2021). Academic Achievement and Conceptual Performance The findings presented in Table 1 (Descriptive Statistics and Paired-Samples t-Test Results) summarise learners’ performance in the pre- and post-tests for both the robotics (experimental) and traditional (control) groups. The table provides a comparative overview of mean scores, standard deviations, and statistical significance, offering an empirical basis for evaluating changes in learner achievement. The results indicate that learners in the robotics group showed a substantial increase in mean scores from pre-test to post-test, whereas the traditional group demonstrated minimal change. This difference in performance provides an initial indication of variation in learning outcomes across the two instructional approaches. In addition to performance gains, the results also provide indirect evidence of learners’ ability to engage with conceptual aspects of stoichiometry and titration. As noted by Howard Gardner, the distinction between acquired knowledge and demonstrated understanding lies in the ability to apply knowledge in meaningful contexts (Gardner, 1997 ). In this study, learners’ post-test performance particularly in problem-solving tasks serves as an indicator of their capacity to activate and apply learned concepts. Table 1 Descriptive Statistics and Paired-Samples t-Test Results for Pre- and Post-Test Scores Group Pre-test Mean (M) Pre-test SD Post-test Mean (M) Post-test SD Mean Gain t-value p-value Robotics 51.2 9.1 80.5 7.5 29.3 10.76 < .001 Traditional 36.8 11.5 37.8 9.0 1.0 0.98 0.337 The results presented in Table 1.1 indicate a statistically significant improvement in academic achievement among learners in the robotics group compared to those in the traditional instruction group. The substantial mean gain observed in the experimental group suggests that robotics-based instruction may provide a more effective learning environment for topics such as stoichiometry and titration, which are often perceived as abstract and cognitively demanding. However, this improvement should be interpreted with caution. The observed gains are unlikely to be attributable to robotics as a standalone tool; rather, they may reflect the combined effect of structured instructional support and interactive learning opportunities embedded within the robotics activities. This aligns with socio-cultural perspectives on learning, particularly the concept of scaffolding proposed by Lev Vygotsky, which emphasizes the role of guided support in enabling learners to achieve higher levels of cognitive performance (Vygotsky, 1978 ). Furthermore, the limited improvement observed in the traditional group highlights the constraints of teacher-centred approaches that rely heavily on procedural instruction. Similar findings have been reported in science education literature, where passive learning environments are associated with lower conceptual gains compared to interactive and inquiry-based approaches (Hake, 1998 ). While the statistical significance of the findings is notable, the absence of reported effect sizes and sample size limits the ability to fully assess the magnitude and generalizability of the impact. Future studies should incorporate these measures to provide a stronger evaluation of instructional effectiveness. Table 2 Learners’ knowledge representations Topic Group Learner Level Learner Response Analysis Stoichiometry (Mole Calculations & Mass Relationships) Experimental Group L1: High-performing "Using the robotics tool, I visually observed the reaction. For 5.0 g of Na, I calculated moles as 5.0 g / 23 g/mol = 0.217 mol. Using the mole ratio of Na:NaCl (2:2), moles of NaCl = 0.217 mol. Converting this to mass: 0.217 mol × 58.5 g/mol = 12.8 g. The robotics simulation clarified each step, enhancing my understanding of mole conversions." High-performing learners in the experimental group effectively integrated robotics tools with theoretical calculations, showcasing strong conceptual clarity and detailed reasoning. L2: Middle "The robotics helped me see how much Na and Cl 2 were needed. I calculated moles of Na = 0.217 and used it to find NaCl mass = 12.8 g." Middle-performing learners in the experimental group demonstrated partial integration of robotics tools, with some gaps in detailing calculations. L3: Low-performing "I used robotics to find NaCl. My answer was 12.8 g." Low-performing learners relied on robotics but showed limited understanding of calculations or mole concepts. Control Group L4: High-performing "Moles of Na = 5.0 / 23 = 0.217. Using mole ratio (1:1), moles of NaCl = 0.217. Mass of NaCl = 0.217 × 58.5 = 12.8 g." High-performing learners in the control group demonstrated accurate procedural calculations without referencing applied learning tools. L5: Middle "I calculated moles of Na as 0.217 and found NaCl mass to be 12.8 g." Middle-performing learners in the control group provided correct answers but lacked detail in explaining steps. L6: Low-performing "Na = 0.217 mol; NaCl = 12.8 g." Low-performing learners in the control group exhibited limited reasoning and minimal explanation of processes. Limiting Reagent Experimental Group L1: Highest "H 2 and O 2 react in a 2:1 ratio. I calculated moles: H 2 = 50 / 2 = 25 mol, O 2 = 50 / 32 = 1.56 mol. O 2 is the limiting reagent. Water produced = 2 × 1.56 = 3.12 mol. The robotics bar graph helped me visualise the reaction progress." High-performing learners in the experimental group displayed strong conceptual understanding and effectively used robotics visualisations to support their reasoning. L2: Middle "Using robotics, I found O 2 was the limiting reagent because it ran out first. Water produced = 3.12 mol." Middle-performing learners used robotics tools but provided less detail in their calculations and reasoning. L3: Low-performing "Robotics showed O 2 was the limiting reagent." Low-performing learners relied on robotics visualisations without fully articulating the underlying concepts. Control Group L4: High-performing "Moles of H 2 = 25; O 2 = 1.56. Limiting reagent = O 2 . Water = 3.12 mol." High-performing learners in the control group performed accurate calculations but did not reference applied learning tools. L5: Middle "I found O 2 was limiting and calculated water as 3.12 mol." Middle-performing learners correctly identified the limiting reagent but lacked depth in explanation. L6: Low-performing "O 2 is limiting; water = 3.12 mol." Low-performing learners provided minimal responses with no conceptual elaboration. Titration Experimental Group L1: High-performing "Volume of NaOH = 50 mL = 0.050 L. Moles of NaOH = 0.050 × 0.10 = 0.005 mol. HCl concentration = 0.005 / 0.025 = 0.20 M. Robotics visualisations made it easier to track equivalence points and understand mole relationships." High-performing learners in the experimental group used robotics tools to enhance their understanding and clearly articulated titration concepts. L2: Middle "Robotics showed when NaOH and HCl were equal. I calculated HCl concentration as 0.20 M." Middle-performing learners understood the role of robotics in identifying equivalence points but provided less detail in their calculations. L3: Low-performing "Robotics helped me see the endpoint. HCl = 0.20 M." Low-performing learners relied on robotics for visual cues but lacked depth in explaining calculations. Control Group L4: High-performing "Moles of NaOH = 0.005; HCl = 0.20 M." High-performing learners in the control group performed accurate calculations but did not connect their understanding to practical applications. L5: Middle "I calculated HCl concentration as 0.20 M." Middle-performing learners provided correct answers but lacked conceptual depth. L6: Low-performing "HCl = 0.20 M." Low-performing learners provided minimal responses with no explanation of calculations. Table 2 reveals qualitative differences in how learners represented and articulated their understanding of chemical concepts. Learners in the robotics group demonstrated more elaborated responses, often integrating visual representations, procedural reasoning, and conceptual explanations. This suggests that robotics-based instruction may support the development of deeper conceptual understanding by enabling learners to engage with content through multiple representational forms. In contrast, learners in the traditional group, while occasionally producing correct answers, tended to rely on procedural recall with limited conceptual explanation. This distinction highlights the limitation of assessing learning outcomes based solely on correctness, as it may obscure differences in the depth of understanding. These findings are consistent with constructivist perspectives on learning, particularly those advanced by Jean Piaget, which posit that learners actively construct knowledge through interaction with their environment (Piaget, 1970). The robotics activities provided opportunities for learners to manipulate variables, visualize relationships, and test their understanding, thereby facilitating the construction of meaning. Additionally, the use of multiple representations aligns with Howard Gardner’s theory of multiple intelligences, which suggests that learners benefit from engaging with content through diverse modalities (Gardner, 1983). The integration of visual, kinesthetic, and symbolic elements in robotics-based instruction may therefore have contributed to the richer explanations observed among learners in the experimental group. However, it is important to note that not all learners benefited equally. Lower-performing learners in the robotics group still exhibited gaps in conceptual understanding, indicating that access to multiple representations alone is insufficient without adequate instructional scaffolding. This reinforces the importance of teacher guidance in mediating the learning process. Table 3 Learner- teacher robotics interaction Description Observation Analysis I. Lesson Design and Implementation 1. Instructional strategies respected learners' prior knowledge and preconceptions. Experimental group: Robotics tools were integrated, building on learners' existing understanding of chemical reactions. Robotics allowed learners to connect prior knowledge to chemical concepts in a hands-on way, making the learning process more relevant and engaging. 2. The lesson was designed to engage learners as members of a learning community. Experimental group: Collaborative group tasks with robotics tools encouraged peer engagement. Control group: Traditional lectures with minimal peer collaboration. Robotics fostered a sense of community through teamwork, whereas the control group had less opportunity for peer interaction. 3. Learner exploration preceded formal presentation. Experimental group: Learners explored chemical phenomena using robotics tools before formal explanations. Control group: Theoretical explanations were provided before practical activities. Learners in the experimental group were given opportunities for exploration, sparking curiosity, while the control group focused on theory first, reducing exploration. 4. The lesson encouraged alternative modes of investigation or problem-solving. Experimental group: Robotics allowed for diverse experimental methods, including simulations and visualisations. Control group: Problem-solving was formulaic. Robotics encouraged a variety of investigative approaches, enhancing creativity and critical thinking, which was limited in the traditional methods used by the control group. 5. Lesson focus/direction was determined by learner ideas. Experimental group: Learners’ ideas and experiments shaped the focus of the lesson. Control group: Teachers followed a strict lesson plan. The robotics group allowed learners to shape the lesson, which enhanced engagement and autonomy, contrasting with the rigid structure in the control group. II. Content Propositional Knowledge 6. Lesson involved fundamental concepts of the subject. Both groups: Core concepts like stoichiometry and titration were introduced. While both groups engaged with fundamental content, the robotics group facilitated deeper understanding through interactive tools. 7. Lesson promoted strongly coherent conceptual understanding. Experimental group: Robotics helped learners understand the relationships between chemical concepts. Control group: Concepts were taught in isolation. Robotics-enhanced learning facilitated the integration of concepts, while the traditional approach presented topics in isolation, affecting coherence. 8. Teacher had a solid grasp of subject matter content. Both groups: Teachers demonstrated a strong understanding of chemistry content. Teacher expertise contributed to the quality of instruction in both groups, but robotics-enabled teachers could better demonstrate concepts in action. 9. Elements of abstraction were encouraged. Experimental group: Robotics tools were used to visualise abstract concepts such as reaction mechanisms. Control group: Limited abstraction, mainly focused on equations. Robotics allowed for dynamic visualisation of abstract concepts, helping learners grasp them more concretely compared to the traditional, static approach. 10. Connections with other disciplines/real-world phenomena were explored. Experimental group: Robotics demonstrated how chemistry applies in industry and technology. Control group: Real-world applications were discussed but not demonstrated. Robotics offered a tangible link to real-world applications, enhancing relevance, while the control group relied on verbal descriptions, which lacked immediate connection. Procedural Knowledge 11. Learners used varied means to represent phenomena. Experimental group: Robotics tools enabled learners to represent phenomena through models, simulations, and graphs. Control group: Textbook diagrams and verbal explanations were the primary representations. The robotics group employed multimodal representations, fostering diverse learning styles, while the control group was limited to traditional representations. 12. Learners made predictions/hypotheses and tested them. Experimental group: Learners tested hypotheses using robotics tools in real-time. Control group: Predictions were made theoretically. Robotics provided immediate feedback on predictions, encouraging deeper engagement and inquiry compared to the theoretical predictions in the control group. 13. Learners were actively engaged in the critical assessment of procedures. Experimental group: Robotics activities required learners to adjust and optimise experiments. Control group: Limited opportunities for critical assessment. Robotics activities encouraged learners to assess and refine their methods, while the traditional approach did not offer the same level of engagement in critical evaluation. 14. Learners were reflective about their learning. Experimental group: Learners regularly reflected on their work post-robotics activities. Control group: Reflections were sporadic. The experimental group incorporated regular reflection into the learning process, promoting metacognition, while the control group missed out on these opportunities. 15. Intellectual rigor, constructive criticism, and idea-challenging were valued. Experimental group: Robotics-based activities promoted critical thinking and problem-solving discussions. Control group: Discussions were more teacher-driven and less critical. Robotics interventions promoted a dynamic environment where learners were encouraged to challenge ideas, whereas the control group’s discussions were more passive and teacher-dominated. III. Classroom Culture Communicative Interactions 16. Learners communicated ideas using varied media. Experimental group: Robotics allowed learners to use visual tools and verbal presentations. Control group: Limited to verbal and written communication. The robotics group benefited from multimodal communication, enhancing expression and comprehension, unlike the traditional methods in the control group. 17. Teacher’s questions triggered divergent thinking. Experimental group: Robotics-based questions encouraged exploration and divergent thinking. Control group: Questions were mostly factual and recall-based. The robotics approach stimulated divergent thinking, encouraging learners to explore multiple perspectives, while the control group’s questions were focused on recall. 18. High proportion of learner talk among peers. Experimental group: Learners engaged in active discussions during robotics tasks. Control group: Learner talk was minimal. Robotics activities created opportunities for meaningful peer-to-peer communication, while the control group limited peer interaction. 19. Learner questions/comments determined the focus of discourse. Experimental group: Learner-driven questions shaped class discussions. Control group: Teacher dominated discourse. Robotics empowered learners to direct the focus of class discussions, whereas in the control group, learners had little influence over the lesson's direction. 20. Climate of respect for others’ ideas. Both groups: A respectful atmosphere was maintained in both classrooms. Respect for learners’ contributions was evident in both groups, though the robotics group demonstrated greater collaborative engagement. Learner/Teacher Relationships 21. Active participation of learners was encouraged. Experimental group: Robotics activities required active participation. Control group: Participation was largely dependent on teacher prompting. The robotics-based lesson structure promoted active learner participation, whereas the control group followed a more passive model. 22. Learners were encouraged to generate alternative strategies. Experimental group: Robotics encouraged creative problem-solving and the exploration of alternative solutions. Control group: Learners followed fixed procedures. Robotics supported the generation of alternative strategies, which was not a central feature in the control group. 23. Teacher was patient with learners. Both groups: Teachers showed patience with learners. Patience was observed in both groups, creating a positive and supportive learning environment. 24. Teacher acted as a resource person. Experimental group: Teachers facilitated learner inquiries and supported robotics tasks. Control group: Teachers provided direct instruction. The teacher’s role in the robotics group was more as a facilitator, contrasting with the directive teaching style in the control group. 25. “Teacher as listener” metaphor was characteristic. Experimental group: Teachers actively listened and responded to learner ideas. Control group: Teacher listening was less evident. The teacher’s role in the robotics group involved greater listening and adapting to learner input, promoting learner agency and engagement. The findings presented in Table 3 highlight notable differences in classroom interaction and instructional practices between the robotics and traditional groups. The robotics group was characterized by increased learner participation, collaboration, and engagement in exploratory activities, whereas the traditional group exhibited more teacher-directed instruction and limited interaction. These patterns suggest that robotics-based instruction may facilitate more interactive and learner-centred classroom environments. Such environments are consistent with socio-constructivist theories of learning, which emphasize the importance of social interaction and collaborative knowledge construction. In particular, the work of Lev Vygotsky highlights the role of dialogue and shared activity in cognitive development (Vygotsky, 1978 ). The increased opportunities for hypothesis testing, peer discussion, and multimodal engagement observed in the robotics group may have contributed to the enhanced learning outcomes reported in Table 1. Similar findings have been documented in studies of inquiry-based science instruction, where active engagement is associated with improved conceptual understanding (Hmelo-Silver et al., 2007 )). However, it is important to distinguish between engagement and effective learning. While the robotics group demonstrated higher levels of interaction, the quality of this engagement likely depended on the structure of the activities and the role of the teacher. Without appropriate guidance, exploratory learning environments may lead to superficial engagement rather than meaningful understanding (Kirschner, Sweller, & Clark, 2006 ). The shift in teacher roles from knowledge transmitter to facilitator also emerges as a critical factor in the successful implementation of robotics-based instruction. This transition requires not only technological resources but also pedagogical expertise, highlighting the need for targeted professional development to support teachers in integrating innovative tools effectively. CONCLUSION This study investigated the impact of integrating robotics into the teaching of stoichiometry and titration among Grade 10 learners. The findings indicate that robotics-based instruction is associated with improved learner performance and enhanced conceptual engagement, particularly when implemented alongside structured instructional support. Learners in the experimental group demonstrated greater gains in academic achievement and showed increased ability to connect procedural calculations with underlying chemical concepts. Importantly, the findings suggest that the effectiveness of robotics is not inherent to the technology itself, but rather dependent on how it is pedagogically integrated into the learning environment. Consistent with socio-constructivist perspectives, particularly those of Lev Vygotsky, robotics functioned as a mediating tool that supported interaction, collaboration, and guided knowledge construction. However, the benefits were not uniformly experienced across all learners. Variations in prior knowledge and levels of engagement influenced learning outcomes, with some learners demonstrating passive participation or limited conceptual understanding despite exposure to robotics-based activities. The study also highlights the role of the teacher as a critical factor in the successful implementation of robotics-based instruction. The shift from teacher-centred to facilitative pedagogies requires not only access to technological resources but also the development of pedagogical strategies that effectively scaffold learner engagement and understanding. Without such support, the potential of robotics to enhance learning may not be fully realised. Despite these promising findings, several limitations must be acknowledged. The study was conducted over a relatively short period, and therefore does not provide insight into the long-term retention of knowledge. Additionally, the absence of longitudinal data limits the ability to determine whether the observed improvements are sustained over time. The study also did not compare robotics-based instruction with laboratory-based practical work, which remains an important area for future investigation. In light of these limitations, future research should examine the long-term impact of robotics on knowledge retention and conceptual development, as well as explore its integration within broader, interdisciplinary STEM frameworks. Further studies should also investigate the comparative effectiveness of robotics and traditional hands-on laboratory approaches, particularly in resource-constrained educational contexts. In addition, there is a need to explore targeted teacher professional development models that support the effective integration of robotics into classroom practice. This study contributes to the growing body of research on technology-enhanced science education by providing empirical evidence on the potential and limitations of robotics as a pedagogical tool. While robotics shows promise in supporting learner engagement and conceptual understanding, its effectiveness remains contingent on thoughtful instructional design, adequate support structures, and contextual considerations. Declarations ETHICS STATEMENT This study was conducted in accordance with the ethical guidelines for research involving human participants and was approved by the University of South Africa. Ethical clearance was obtained prior to data collection. Informed consent was secured from all participants, with additional parental or guardian consent obtained for learners under the age of 18. Participation was voluntary, and participants were informed of their right to withdraw from the study at any stage without penalty. To ensure confidentiality and anonymity, all personal identifiers were removed, and data were systematically coded. All data were used solely for research purposes and stored securely in accordance with institutional data protection policies. The study was designed to minimise potential risk, and no physical or psychological harm was anticipated. All efforts were made to ensure that participants benefited educationally from the intervention. Any identifying information related to individuals or institutions has been anonymised. References Benitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Computers & Education, 58(3), 978–988. https://doi.org/10.1016/j.compedu.2011.10.006Getrightsandcontent Ching, Y. H., & Hsu, Y. C. (2024). Educational robotics for developing computational thinking in young learners: A systematic review. TechTrends, 68(3), 423–434. https://doi.org/10.1007/s11528-023-00841-1 Darmawansah, D., Hwang, G. J., Chen, M. R. A., & Liang, J. C. (2023). Trends and research foci of robotics-based STEM education: a systematic review from diverse angles based on the technology-based learning model. International Journal of STEM Education, 10(1), 12. https://doi.org/10.1186/s40594-023-00400-3 Eguchi, A. (2017). Bringing robotics in classrooms. In: Khine, M. (eds) Robotics in STEM education (pp. 3–31). Springer. https://doi.org/10.1007/978-3-319-57786-9_1 Gardner, H. (1997). The disciplined mind: What all students should understand . Simon & Schuster. Hake, R. R. (1998). Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66 (1), 64–74. https://doi.org/10.1119/1.18809 Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42 (2), 99–107. https://doi.org/10.1080/00461520701263368 Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41 (2), 75–86. https://doi.org/10.1207/s15326985ep4102_1 Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41 (2), 75–86. https://doi.org/10.1207/s15326985ep4102_1 Lotriet, H., & Gouws, P. (2024). Using educational robotics in chemistry education: A systematic review. International Journal of Innovation in Science and Mathematics Education, 32(4). https://doi.org/10.30722/ IJISME.32.04.004 Piaget, J. (1972). The psychology of the child. New York: Basic Books Sapounidis, T., & Alimisis, D. (2020). Educational robotics for STEM: A review of technologies and some educational considerations. In L. Leite, E. Oldham, A. Floriano Viseu, L. Dourado, M. Martinho (Eds) Science and mathematics education for 21st century citizens: Challenges and ways forward (pp. 167–190). Nova Science. Tóthová, M., Rusek, M., & Chytrý, V. (2021). Students’ procedure when solving problem tasks based on the periodic table: An eye-tracking study. Journal of Chemical Education, 98 (6), 1831–1840. https://doi.org/10.1021/acs.jchemed.1c00167 Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9419614","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623201040,"identity":"a830a999-d338-47b5-8206-86c340466980","order_by":0,"name":"thabo mhlongo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYNCCCon6fgkwS0KGoGIeMHnGhnHmDAbGBqAWHuK0MLalMW64AdbCQFiLPQN34ufCtsPMxrebjz+6UWPBw8B++OgG/Lbwbpaece4wm9mdY4nNOceADuNJS7tBQMsGaZ6ywzxmN3IMm3PYgFokgGxCtvzmYTssYTwDpOUfcVq2SfO0pRkYSAC15LYRo+Uw7zZrnjM2CRI30hJn5/ZJ8LAR8gt7e+/m2zwVEgn8M5IPfM75VifHz374GF4tDMzoAmx4lY+CUTAKRsEoIAoAAFWGQb68n9qyAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-9814-5691","institution":"University of South Africa","correspondingAuthor":true,"prefix":"","firstName":"thabo","middleName":"","lastName":"mhlongo","suffix":""}],"badges":[],"createdAt":"2026-04-14 21:20:03","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9419614/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9419614/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107705118,"identity":"e3033d15-261d-46e5-9513-c3136ba910e9","added_by":"auto","created_at":"2026-04-24 09:08:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":391605,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9419614/v1/a522cbd6-541f-4fe3-a427-c12536de4fea.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA Robotic-Based Instruction for Integrating Robotics in Chemistry Education: A case of stoichiometry and titration\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe integration of robotics into chemistry education has received growing scholarly and pedagogical attention as educators seek innovative approaches to enhance learner engagement, promote conceptual understanding, and strengthen the connection between theory and practice in STEM learning environments (Eguchi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sapounidis \u0026amp; Alimisis, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Robotics is increasingly viewed not only as a technological tool but also as a learning mediator that can support inquiry, experimentation, modelling, and data-driven reasoning in school science (Darmawansah et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In chemistry classrooms, where abstract symbolic representations frequently dominate instruction, robotics-integrated learning may offer opportunities for learners to interact with concepts in more concrete, observable, and manipulable ways, potentially improving meaning-making and conceptual development (Mhlongo et al., 2025; Lotriet \u0026amp; Gouws, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA persistent challenge in chemistry education is that many learners struggle to develop deep conceptual understanding of topics that require coordination of multiple representations, procedural steps, and quantitative reasoning (Benitti, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Stoichiometry and titration are particularly demanding because they require learners to connect balanced chemical equations, mole ratios, concentration relationships, experimental procedures, and interpretation of endpoints or equivalence points (Sapounidis \u0026amp; Alimisis, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Learners often approach these topics as algorithmic exercises focused on memorising steps rather than understanding underlying chemical principles, which can contribute to misconceptions and weak transfer of knowledge to practical contexts (Ching \u0026amp; Hsu, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For example, learners may apply formula-based procedures without conceptualising the meaning of limiting reagents, the role of the balanced equation, or the chemical significance of the titration curve and neutralisation point. Such conceptual difficulties are intensified when practical experimentation is limited or absent, reducing opportunities for learners to validate calculations and interpretations through evidence-based investigation.\u003c/p\u003e \u003cp\u003eIn response to these challenges, contemporary science education increasingly emphasises learner-centred and inquiry-oriented pedagogies that encourage exploration, experimentation, collaboration, and the construction of knowledge through interaction with learning materials and peers (Ching \u0026amp; Hsu, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Within this orientation, robotics has been identified as a promising tool for supporting experiential learning and problem-solving by allowing learners to design systems, test predictions, observe outcomes, and refine their thinking based on feedback from the learning environment (Eguchi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Robotics-integrated instruction may therefore support chemistry learning by enabling controlled manipulation of variables (e.g., reagent volume, concentration, dispensing rate), real-time data collection (e.g., pH readings), and iterative testing of hypotheses. These features can make invisible chemical processes more visible and measurable, strengthening the development of scientific reasoning and procedural competence.\u003c/p\u003e \u003cp\u003eEmpirical research on robotics in education has reported benefits such as improved motivation, collaboration, and engagement, as well as the development of computational and problem-solving skills (Benitti, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Darmawansah et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, much of this work has focused on robotics in general STEM contexts rather than in chemistry-specific learning, and fewer studies have examined robotics applications for chemistry topics that require strong quantitative reasoning, such as stoichiometry and titration (Sapounidis \u0026amp; Alimisis, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, the successful integration of robotics into chemistry teaching is not guaranteed, as implementation depends on contextual factors including curriculum alignment, availability of resources, laboratory access, teacher preparedness, and classroom constraints (Lotriet \u0026amp; Gouws, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These challenges are particularly relevant in under-resourced schooling contexts, where limited laboratory infrastructure and large class sizes restrict opportunities for practical work, even though such experiences are central to meaningful chemistry learning.\u003c/p\u003e \u003cp\u003eIn the South African context, many schools especially those in township and rural communities experience resource disparities that reduce access to laboratory equipment and consumables needed for practical chemistry activities. As a result, learners may encounter chemistry primarily through textbook-based instruction, which can limit conceptual understanding and engagement with scientific inquiry. Robotics-integrated instruction may provide a feasible pathway for strengthening practical engagement in such contexts by supporting low-cost simulations of experimental processes, structured measurement activities, and data-driven analysis. Nevertheless, there remains limited empirical evidence on how robotics-integrated instruction influences Grade 10 learners\u0026rsquo; conceptual understanding and academic achievement in stoichiometry and titration, particularly in contexts characterised by constrained resources and limited opportunities for hands-on experimentation.\u003c/p\u003e \u003cp\u003eTo address this gap, the present study investigated the effects of robotics-integrated instruction on Grade 10 learners\u0026rsquo; engagement, conceptual understanding, and academic performance in stoichiometry and titration. The study compared robotics-integrated teaching with conventional instructional approaches in a South African secondary school context. The study was guided by the following research question: \u003cb\u003eHow does the use of robotics influence Grade 10 learners\u0026rsquo; conceptual understanding and academic achievement in stoichiometry and titration?\u003c/b\u003e\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cp\u003e\u003cstrong\u003eResearch Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study adopted a quasi-experimental pre-test\u0026ndash;post-test design involving an experimental group (robotics-integrated instruction) and a control group (conventional instruction). Random assignment was not feasible because intact classes were used; therefore, group comparability was strengthened through baseline diagnostic testing and matched group allocation. The study also incorporated qualitative classroom observations to support interpretation of quantitative learning outcomes, making the design suitable for examining both performance change and learning processes in authentic classroom conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSchool Context\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in a South African township secondary school with typical resource constraints affecting science teaching and learning. While the schools offered Physical Sciences in Grade 10, opportunities for sustained laboratory experimentation were limited due to infrastructure constraints and large class sizes. However, the school had access to a functional computer laboratory and basic digital learning resources, making it suitable for implementing and examining robotics-integrated instruction within a constrained context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants and Sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study involved 40 Grade 10 Physical Sciences learners, divided into:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Experimental group: \u003cem\u003en = 20\u003c/em\u003e (robotics-integrated instruction)\u003c/p\u003e\n\u003cp\u003e\u0026middot; Control group: \u003cem\u003en = 20\u003c/em\u003e (conventional instruction)\u003c/p\u003e\n\u003cp\u003eA purposive sampling approach was used to select a school and participants aligned to the study objectives. Learners were allocated into groups using pre-test scores to ensure balanced baseline performance distribution across the experimental and control conditions. Two Physical Sciences teachers participated: one facilitated the experimental group and one facilitated the control group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTeacher Training and Capacity Building\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTeacher preparation was treated as a critical implementation requirement to ensure that robotics integration functioned as a pedagogically meaningful intervention, rather than a technological add-on. Two Grade 10 Physical Sciences teachers participated in the study. One teacher facilitated the experimental group (robotics-integrated instruction), while the second facilitated the control group (conventional instruction). To strengthen implementation fidelity and reduce variability in instructional delivery, both teachers met with the researcher prior to the intervention to align lesson objectives, assessment expectations, and pacing in accordance with the Grade 10 CAPS content requirements for stoichiometry and titration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE1. Training Structure and Duration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe teacher assigned to the experimental group received targeted professional development (PD) prior to the intervention. The PD programme was delivered over two training sessions (approximately 6 hours total) and focused on building both technical and pedagogical competence for classroom implementation. Training activities were designed to ensure the teacher could independently set up, operate, troubleshoot, and meaningfully integrate the robotics tools during instruction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE2. Training Content\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe teacher training programme addressed three integrated areas:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eTechnical Familiarisation and Robotics Operation\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The teacher was introduced to the robotics hardware and software used in the study (e.g., Engino robotics components, sensors, and programming interface). Practical training included assembling the robotics models, calibrating components, and running test trials of the robotics simulations used in stoichiometry and titration lessons. Emphasis was placed on classroom-ready competence, including basic troubleshooting (e.g., wiring errors, sensor reading instability, motor control errors) to minimise lesson disruptions.\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003ePedagogical Integration for Chemistry Learning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTraining focused on how robotics activities were aligned with curriculum concepts and used to support conceptual understanding. The teacher was guided to implement robotics through an inquiry-oriented structure comprising:\u003c/p\u003e\n\u003cp\u003eo Concept Introduction: eliciting prior knowledge and introducing the chemistry concept;\u003c/p\u003e\n\u003cp\u003eo Robotics Exploration: learners engaging with robotics models to generate observations and measurements;\u003c/p\u003e\n\u003cp\u003eo Concept Application: learners interpreting results and linking patterns to chemical principles.\u003c/p\u003e\n\u003cp\u003eParticular attention was given to ensuring that robotics tasks explicitly supported conceptual development (e.g., mole ratios, limiting reagents, neutralisation, and endpoint identification), rather than functioning as general STEM enrichment.\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eClassroom Facilitation and Learner Support\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The teacher was supported in planning strategies for managing group-based robotics activities, including structuring learner roles (builder, programmer/operator, recorder, presenter), promoting collaboration, and guiding learners through scientific reasoning discussions. The training also addressed facilitation practices such as questioning techniques, prompting prediction-making, supporting error analysis, and encouraging learners to justify conclusions using collected evidence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE3. Implementation Support During the Intervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOngoing support was provided to ensure consistent delivery of the robotics-integrated lessons. This included scheduled check-ins between the researcher and the experimental-group teacher to review lesson progress, confirm readiness of robotics resources, and address technical or pedagogical challenges encountered during classroom sessions. This approach strengthened intervention consistency and ensured that learner engagement with robotics remained aligned to the intended chemistry learning outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE4. Ensuring Fidelity of the Robotics-Based Instruction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo improve reliability and reduce the risk of inconsistent implementation, the study used a structured lesson plan format and an observation rubric to document how robotics activities were conducted in practice. Fidelity indicators included: (i) completion of the planned robotics tasks, (ii) extent of learner participation in robotics operation and data collection, (iii) alignment between robotics activity outputs and chemistry concepts, and (iv) evidence of guided facilitation during learner sense-making discussions. These indicators supported systematic monitoring of whether robotics integration was enacted as intended.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE5. Capacity Building Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe teacher training component was intended not only to support the intervention but also to strengthen teacher capacity for sustainable robotics integration in chemistry instruction. By the end of the training phase, the experimental-group teacher demonstrated competence in operating the robotics tools, facilitating structured inquiry activities, and supporting learners in linking robotics-generated observations to chemical reasoning. This teacher development component was therefore treated as an enabling condition for effective robotics integration in resource-constrained classroom contexts\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntervention: Robotics-Integrated Chemistry Instruction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental group received robotics-integrated lessons aligned to Grade 10 stoichiometry and titration content. Robotics tools were used to support interactive modelling, controlled simulation of procedural steps, and structured inquiry tasks. The control group received conventional instruction using teacher explanation, textbook-based examples, and standard classroom exercises aligned to the same curriculum outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRobotics Setup\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRobotics activities were implemented using Engino robotics kits to construct models simulating controlled dispensing and reaction processes relevant to stoichiometry and titration learning outcomes. Where applicable, sensors were used to support real-time observation of changes during titration progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLearner Robotics Actions and Learning Processes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure that robotics integration was not treated as a technological add-on, learner activities were designed to make robotics the central mechanism through which chemistry concepts were explored, tested, and explained. Learners in the experimental group worked in small groups (4\u0026ndash;5 learners per group) and engaged in structured tasks that required them to build, operate, and interpret outputs from robotics-based simulations aligned to stoichiometry and titration learning outcomes. Each lesson followed a consistent inquiry sequence: prediction \u0026rarr; robotics interaction \u0026rarr; data capture \u0026rarr; explanation \u0026rarr; reflection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. Group Organisation and Roles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the start of each robotics session, learners were assigned rotating roles to ensure participation and accountability:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Assembler/Builder: assembled or adjusted the robotics mechanism (e.g., dispensing system).\u003c/p\u003e\n\u003cp\u003e\u0026middot; Operator/Programmer: controlled the robotic actions (e.g., motor steps, dispensing rate).\u003c/p\u003e\n\u003cp\u003e\u0026middot; Recorder: captured measurements, observations, and outputs in a worksheet.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Analyst: performed calculations (e.g., mole ratios, concentration).\u003c/p\u003e\n\u003cp\u003e\u0026middot; Presenter: reported the group\u0026rsquo;s reasoning and findings during class discussion.\u003c/p\u003e\n\u003cp\u003eRole rotation occurred weekly to reduce passive participation and ensure all learners engaged with both the technical and conceptual aspects of the tasks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Stoichiometry Robotics Activities (Step-by-Step Learner Actions)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring stoichiometry lessons, learners used the robotics model to represent the controlled addition and proportional combination of reactants. Learner actions included:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003ePrediction and Planning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Learners predicted expected reactant relationships using balanced chemical equations.\u003c/p\u003e\n\u003cp\u003eo They identified mole ratios and wrote expected product quantities before using the robotics model.\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eRobotics Setup and Calibration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Learners assembled the dispensing mechanism and ensured correct alignment.\u003c/p\u003e\n\u003cp\u003eo They calibrated dispensing steps (e.g., fixed motor rotations representing fixed reactant quantities).\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eRobotics Execution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Learners ran repeated trials where the robotic system dispensed reactant \u0026ldquo;units\u0026rdquo; in controlled ratios.\u003c/p\u003e\n\u003cp\u003eo They adjusted input quantities to model scenarios such as \u003cstrong\u003eexcess reactant\u003c/strong\u003e and \u003cstrong\u003elimiting reactant\u003c/strong\u003e conditions.\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eData Capture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Learners recorded trial outputs (dispensed quantities and resulting ratios) in structured tables.\u003c/p\u003e\n\u003cp\u003eo They compared predicted vs observed proportional outcomes.\u003c/p\u003e\n\u003cp\u003e5. \u003cstrong\u003eConceptual Interpretation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Learners explained outcomes using mole ratio reasoning.\u003c/p\u003e\n\u003cp\u003eo They justified limiting reagent identification using evidence from robotics trials.\u003c/p\u003e\n\u003cp\u003e6. \u003cstrong\u003eReflection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Learners wrote short reflections on how the robotics activity changed or strengthened their understanding of proportional reasoning in chemical equations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. Titration Robotics Activities (Step-by-Step Learner Actions)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring titration lessons, learners used a motorised dispensing system to simulate controlled titrant addition and observe changes associated with neutralisation. Learner actions included:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003ePrediction and Planning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Learners predicted the relationship between titrant volume and neutralisation progress.\u003c/p\u003e\n\u003cp\u003eo They identified what \u0026ldquo;endpoint\u0026rdquo; means conceptually and procedurally.\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eSystem Preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Learners set up the titration model (dispensing mechanism, beaker/flask setup, and sensor where applicable).\u003c/p\u003e\n\u003cp\u003eo They ensured consistent starting conditions across trials.\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eControlled Dispensing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Learners initiated titrant addition in incremental steps using the motor control.\u003c/p\u003e\n\u003cp\u003eo They paused dispensing at intervals to observe changes and record values.\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eObservation and Measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Learners observed indicator behaviour or sensor feedback (where available).\u003c/p\u003e\n\u003cp\u003eo They recorded titrant volume and corresponding observations at each stage.\u003c/p\u003e\n\u003cp\u003e5. \u003cstrong\u003eGraphing and Interpretation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Learners plotted titration progression (volume vs response trend).\u003c/p\u003e\n\u003cp\u003eo They identified the endpoint region and linked it to neutralisation concepts.\u003c/p\u003e\n\u003cp\u003e6. \u003cstrong\u003eCalculation and Chemistry Reasoning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Learners calculated unknown concentrations using recorded volumes and stoichiometric relationships.\u003c/p\u003e\n\u003cp\u003eo They compared their results across groups and justified discrepancies.\u003c/p\u003e\n\u003cp\u003e7. \u003cstrong\u003eWhole-Class Sense-Making Discussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Learners presented findings, explained their endpoint decisions, and responded to teacher prompts that required chemical justification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD. How Robotics Supported Conceptual Change\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explicitly connect robotics interaction to learning outcomes, learners were required to:\u003c/p\u003e\n\u003cp\u003e\u0026middot; explain observations using chemical language (moles, ratios, concentration),\u003c/p\u003e\n\u003cp\u003e\u0026middot; correct misconceptions through evidence-based argumentation,\u003c/p\u003e\n\u003cp\u003e\u0026middot; connect procedural actions (dispensing, measuring, identifying endpoint) to conceptual meanings (neutralisation, proportionality, limiting reagent).\u003c/p\u003e\n\u003cp\u003eThis structure ensured that learner engagement with robotics directly contributed to measurable chemistry understanding rather than functioning as general activity-based learning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplementation Timeline and Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intervention was conducted over six weeks, with two sessions per week (approximately 90 minutes per session). Implementation proceeded as follows:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Week 1: Baseline engagement observations and administration of the pre-test\u003c/p\u003e\n\u003cp\u003e\u0026middot; Weeks 2\u0026ndash;5: Robotics-integrated instruction (experimental group) and conventional instruction (control group)\u003c/p\u003e\n\u003cp\u003e\u0026middot; Week 6: Post-test administration and post-intervention engagement observations\u003c/p\u003e\n\u003cp\u003eThroughout the intervention period, classroom observations were conducted to document learner participation patterns, collaboration, and task engagement during instructional activities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePre-test and Post-test (Academic Performance and Conceptual Understanding)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLearners completed a pre-test and post-test aligned to stoichiometry and titration learning outcomes. The tests measured conceptual understanding and procedural application through structured questions requiring chemical reasoning, calculations, and interpretation of scenarios.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClassroom Observation Schedule (Engagement and Learning Processes)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn observation rubric was used to document learner engagement indicators during lessons, including:\u003c/p\u003e\n\u003cp\u003e\u0026middot; participation in group tasks,\u003c/p\u003e\n\u003cp\u003e\u0026middot; interaction with learning resources (robotics tools in the experimental group),\u003c/p\u003e\n\u003cp\u003e\u0026middot; collaboration and peer discussion,\u003c/p\u003e\n\u003cp\u003e\u0026middot; persistence and problem-solving behaviour.\u003c/p\u003e\n\u003cp\u003eVideo recordings and field notes were used to support the observation rubric and strengthen descriptive accuracy of classroom events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePre-test and post-test scores were analysed to determine changes in academic performance between the experimental and control groups. Comparative analyses were conducted to examine learning gains and assess differences in achievement outcomes attributable to the instructional approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eObservation notes and supporting evidence were analysed thematically to identify patterns related to learner participation, collaborative learning, engagement behaviours, and how learners interacted with robotics activities to construct chemistry understanding. Qualitative findings were used to explain and contextualise quantitative performance outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical clearance and permissions were obtained prior to data collection. Participation was voluntary, with informed consent procedures followed. Learner anonymity and confidentiality were maintained, and all data were securely stored and used only for research purposes.\u003c/p\u003e"},{"header":"RESULTS AND ANALYSES","content":"\u003cp\u003eThis section presents the findings of the study in relation to the research question: \u003cem\u003eHow does the use of robotics influence Grade 10 learners' conceptual understanding and academic achievement in stoichiometry and titration?\u003c/em\u003e The analysis draws on both quantitative data (pre- and post-test scores) and qualitative data (learner responses and classroom observations) to provide a comprehensive account of learners\u0026rsquo; performance and understanding.\u003c/p\u003e \u003cp\u003eGiven that teaching and learning are shaped by the interaction between instructional practices and learner engagement, the study examined how robotics-based instruction influenced learners\u0026rsquo; ability to demonstrate both procedural competence and conceptual understanding. However, instructional approaches do not necessarily produce uniform learning outcomes, as learners\u0026rsquo; performance is influenced by differences in prior knowledge, cognitive processing, and problem-solving strategies (T\u0026oacute;thov\u0026aacute; et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; T\u0026oacute;thov\u0026aacute; et al., 2021).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAcademic Achievement and Conceptual Performance\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe findings presented in Table\u0026nbsp;1 (Descriptive Statistics and Paired-Samples t-Test Results) summarise learners\u0026rsquo; performance in the pre- and post-tests for both the robotics (experimental) and traditional (control) groups. The table provides a comparative overview of mean scores, standard deviations, and statistical significance, offering an empirical basis for evaluating changes in learner achievement. The results indicate that learners in the robotics group showed a substantial increase in mean scores from pre-test to post-test, whereas the traditional group demonstrated minimal change. This difference in performance provides an initial indication of variation in learning outcomes across the two instructional approaches.\u003c/p\u003e \u003cp\u003eIn addition to performance gains, the results also provide indirect evidence of learners\u0026rsquo; ability to engage with conceptual aspects of stoichiometry and titration. As noted by Howard Gardner, the distinction between acquired knowledge and demonstrated understanding lies in the ability to apply knowledge in meaningful contexts (Gardner, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In this study, learners\u0026rsquo; post-test performance particularly in problem-solving tasks serves as an indicator of their capacity to activate and apply learned concepts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics and Paired-Samples t-Test Results for Pre- and Post-Test Scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-test Mean (M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-test SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost-test Mean (M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePost-test SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean Gain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobotics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraditional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results presented in Table\u0026nbsp;1.1 indicate a statistically significant improvement in academic achievement among learners in the robotics group compared to those in the traditional instruction group. The substantial mean gain observed in the experimental group suggests that robotics-based instruction may provide a more effective learning environment for topics such as stoichiometry and titration, which are often perceived as abstract and cognitively demanding.\u003c/p\u003e \u003cp\u003eHowever, this improvement should be interpreted with caution. The observed gains are unlikely to be attributable to robotics as a standalone tool; rather, they may reflect the combined effect of structured instructional support and interactive learning opportunities embedded within the robotics activities. This aligns with socio-cultural perspectives on learning, particularly the concept of scaffolding proposed by Lev Vygotsky, which emphasizes the role of guided support in enabling learners to achieve higher levels of cognitive performance (Vygotsky, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1978\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, the limited improvement observed in the traditional group highlights the constraints of teacher-centred approaches that rely heavily on procedural instruction. Similar findings have been reported in science education literature, where passive learning environments are associated with lower conceptual gains compared to interactive and inquiry-based approaches (Hake, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). While the statistical significance of the findings is notable, the absence of reported effect sizes and sample size limits the ability to fully assess the magnitude and generalizability of the impact. Future studies should incorporate these measures to provide a stronger evaluation of instructional effectiveness.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLearners\u0026rsquo; knowledge representations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLearner Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLearner Response\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eStoichiometry (Mole Calculations \u0026amp; Mass Relationships)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eExperimental Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL1: High-performing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Using the robotics tool, I visually observed the reaction. For 5.0 g of Na, I calculated moles as 5.0 g / 23 g/mol\u0026thinsp;=\u0026thinsp;0.217 mol. Using the mole ratio of Na:NaCl (2:2), moles of NaCl\u0026thinsp;=\u0026thinsp;0.217 mol. Converting this to mass: 0.217 mol \u0026times; 58.5 g/mol\u0026thinsp;=\u0026thinsp;12.8 g. The robotics simulation clarified each step, enhancing my understanding of mole conversions.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-performing learners in the experimental group effectively integrated robotics tools with theoretical calculations, showcasing strong conceptual clarity and detailed reasoning.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL2: Middle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"The robotics helped me see how much Na and Cl\u003csub\u003e2\u003c/sub\u003e were needed. I calculated moles of Na\u0026thinsp;=\u0026thinsp;0.217 and used it to find NaCl mass\u0026thinsp;=\u0026thinsp;12.8 g.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMiddle-performing learners in the experimental group demonstrated partial integration of robotics tools, with some gaps in detailing calculations.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL3: Low-performing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"I used robotics to find NaCl. My answer was 12.8 g.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow-performing learners relied on robotics but showed limited understanding of calculations or mole concepts.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eControl Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL4: High-performing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Moles of Na\u0026thinsp;=\u0026thinsp;5.0 / 23\u0026thinsp;=\u0026thinsp;0.217. Using mole ratio (1:1), moles of NaCl\u0026thinsp;=\u0026thinsp;0.217. Mass of NaCl\u0026thinsp;=\u0026thinsp;0.217 \u0026times; 58.5\u0026thinsp;=\u0026thinsp;12.8 g.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-performing learners in the control group demonstrated accurate procedural calculations without referencing applied learning tools.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL5: Middle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"I calculated moles of Na as 0.217 and found NaCl mass to be 12.8 g.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMiddle-performing learners in the control group provided correct answers but lacked detail in explaining steps.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL6: Low-performing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Na\u0026thinsp;=\u0026thinsp;0.217 mol; NaCl\u0026thinsp;=\u0026thinsp;12.8 g.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow-performing learners in the control group exhibited limited reasoning and minimal explanation of processes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eLimiting Reagent\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eExperimental Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL1: Highest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"H\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e2\u003c/sub\u003e react in a 2:1 ratio. I calculated moles: H\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;50 / 2\u0026thinsp;=\u0026thinsp;25 mol, O\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;50 / 32\u0026thinsp;=\u0026thinsp;1.56 mol. O\u003csub\u003e2\u003c/sub\u003e is the limiting reagent. Water produced\u0026thinsp;=\u0026thinsp;2 \u0026times; 1.56\u0026thinsp;=\u0026thinsp;3.12 mol. The robotics bar graph helped me visualise the reaction progress.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-performing learners in the experimental group displayed strong conceptual understanding and effectively used robotics visualisations to support their reasoning.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL2: Middle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Using robotics, I found O\u003csub\u003e2\u003c/sub\u003e was the limiting reagent because it ran out first. Water produced\u0026thinsp;=\u0026thinsp;3.12 mol.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMiddle-performing learners used robotics tools but provided less detail in their calculations and reasoning.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL3: Low-performing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Robotics showed O\u003csub\u003e2\u003c/sub\u003e was the limiting reagent.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow-performing learners relied on robotics visualisations without fully articulating the underlying concepts.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eControl Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL4: High-performing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Moles of H\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;25; O\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.56. Limiting reagent\u0026thinsp;=\u0026thinsp;O\u003csub\u003e2\u003c/sub\u003e. Water\u0026thinsp;=\u0026thinsp;3.12 mol.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-performing learners in the control group performed accurate calculations but did not reference applied learning tools.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL5: Middle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"I found O\u003csub\u003e2\u003c/sub\u003e was limiting and calculated water as 3.12 mol.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMiddle-performing learners correctly identified the limiting reagent but lacked depth in explanation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL6: Low-performing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"O\u003csub\u003e2\u003c/sub\u003e is limiting; water\u0026thinsp;=\u0026thinsp;3.12 mol.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow-performing learners provided minimal responses with no conceptual elaboration.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eTitration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eExperimental Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL1: High-performing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Volume of NaOH\u0026thinsp;=\u0026thinsp;50 mL\u0026thinsp;=\u0026thinsp;0.050 L. Moles of NaOH\u0026thinsp;=\u0026thinsp;0.050 \u0026times; 0.10\u0026thinsp;=\u0026thinsp;0.005 mol. HCl concentration\u0026thinsp;=\u0026thinsp;0.005 / 0.025\u0026thinsp;=\u0026thinsp;0.20 M. Robotics visualisations made it easier to track equivalence points and understand mole relationships.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-performing learners in the experimental group used robotics tools to enhance their understanding and clearly articulated titration concepts.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL2: Middle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Robotics showed when NaOH and HCl were equal. I calculated HCl concentration as 0.20 M.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMiddle-performing learners understood the role of robotics in identifying equivalence points but provided less detail in their calculations.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL3: Low-performing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Robotics helped me see the endpoint. HCl\u0026thinsp;=\u0026thinsp;0.20 M.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow-performing learners relied on robotics for visual cues but lacked depth in explaining calculations.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eControl Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL4: High-performing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Moles of NaOH\u0026thinsp;=\u0026thinsp;0.005; HCl\u0026thinsp;=\u0026thinsp;0.20 M.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-performing learners in the control group performed accurate calculations but did not connect their understanding to practical applications.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL5: Middle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"I calculated HCl concentration as 0.20 M.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMiddle-performing learners provided correct answers but lacked conceptual depth.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL6: Low-performing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"HCl\u0026thinsp;=\u0026thinsp;0.20 M.\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow-performing learners provided minimal responses with no explanation of calculations.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;2 reveals qualitative differences in how learners represented and articulated their understanding of chemical concepts. Learners in the robotics group demonstrated more elaborated responses, often integrating visual representations, procedural reasoning, and conceptual explanations. This suggests that robotics-based instruction may support the development of deeper conceptual understanding by enabling learners to engage with content through multiple representational forms. In contrast, learners in the traditional group, while occasionally producing correct answers, tended to rely on procedural recall with limited conceptual explanation. This distinction highlights the limitation of assessing learning outcomes based solely on correctness, as it may obscure differences in the depth of understanding.\u003c/p\u003e \u003cp\u003eThese findings are consistent with constructivist perspectives on learning, particularly those advanced by Jean Piaget, which posit that learners actively construct knowledge through interaction with their environment (Piaget, 1970). The robotics activities provided opportunities for learners to manipulate variables, visualize relationships, and test their understanding, thereby facilitating the construction of meaning. Additionally, the use of multiple representations aligns with Howard Gardner\u0026rsquo;s theory of multiple intelligences, which suggests that learners benefit from engaging with content through diverse modalities (Gardner, 1983). The integration of visual, kinesthetic, and symbolic elements in robotics-based instruction may therefore have contributed to the richer explanations observed among learners in the experimental group.\u003c/p\u003e \u003cp\u003eHowever, it is important to note that not all learners benefited equally. Lower-performing learners in the robotics group still exhibited gaps in conceptual understanding, indicating that access to multiple representations alone is insufficient without adequate instructional scaffolding. This reinforces the importance of teacher guidance in mediating the learning process.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLearner- teacher robotics interaction\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI. Lesson Design and Implementation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Instructional strategies respected learners' prior knowledge and preconceptions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Robotics tools were integrated, building on learners' existing understanding of chemical reactions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobotics allowed learners to connect prior knowledge to chemical concepts in a hands-on way, making the learning process more relevant and engaging.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. The lesson was designed to engage learners as members of a learning community.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Collaborative group tasks with robotics tools encouraged peer engagement. Control group: Traditional lectures with minimal peer collaboration.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobotics fostered a sense of community through teamwork, whereas the control group had less opportunity for peer interaction.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Learner exploration preceded formal presentation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Learners explored chemical phenomena using robotics tools before formal explanations. Control group: Theoretical explanations were provided before practical activities.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLearners in the experimental group were given opportunities for exploration, sparking curiosity, while the control group focused on theory first, reducing exploration.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. The lesson encouraged alternative modes of investigation or problem-solving.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Robotics allowed for diverse experimental methods, including simulations and visualisations. Control group: Problem-solving was formulaic.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobotics encouraged a variety of investigative approaches, enhancing creativity and critical thinking, which was limited in the traditional methods used by the control group.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Lesson focus/direction was determined by learner ideas.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Learners\u0026rsquo; ideas and experiments shaped the focus of the lesson. Control group: Teachers followed a strict lesson plan.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe robotics group allowed learners to shape the lesson, which enhanced engagement and autonomy, contrasting with the rigid structure in the control group.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eII. Content\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePropositional Knowledge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Lesson involved fundamental concepts of the subject.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoth groups: Core concepts like stoichiometry and titration were introduced.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhile both groups engaged with fundamental content, the robotics group facilitated deeper understanding through interactive tools.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Lesson promoted strongly coherent conceptual understanding.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Robotics helped learners understand the relationships between chemical concepts. Control group: Concepts were taught in isolation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobotics-enhanced learning facilitated the integration of concepts, while the traditional approach presented topics in isolation, affecting coherence.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Teacher had a solid grasp of subject matter content.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoth groups: Teachers demonstrated a strong understanding of chemistry content.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTeacher expertise contributed to the quality of instruction in both groups, but robotics-enabled teachers could better demonstrate concepts in action.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. Elements of abstraction were encouraged.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Robotics tools were used to visualise abstract concepts such as reaction mechanisms. Control group: Limited abstraction, mainly focused on equations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobotics allowed for dynamic visualisation of abstract concepts, helping learners grasp them more concretely compared to the traditional, static approach.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. Connections with other disciplines/real-world phenomena were explored.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Robotics demonstrated how chemistry applies in industry and technology. Control group: Real-world applications were discussed but not demonstrated.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobotics offered a tangible link to real-world applications, enhancing relevance, while the control group relied on verbal descriptions, which lacked immediate connection.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcedural Knowledge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. Learners used varied means to represent phenomena.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Robotics tools enabled learners to represent phenomena through models, simulations, and graphs. Control group: Textbook diagrams and verbal explanations were the primary representations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe robotics group employed multimodal representations, fostering diverse learning styles, while the control group was limited to traditional representations.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12. Learners made predictions/hypotheses and tested them.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Learners tested hypotheses using robotics tools in real-time. Control group: Predictions were made theoretically.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobotics provided immediate feedback on predictions, encouraging deeper engagement and inquiry compared to the theoretical predictions in the control group.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13. Learners were actively engaged in the critical assessment of procedures.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Robotics activities required learners to adjust and optimise experiments. Control group: Limited opportunities for critical assessment.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobotics activities encouraged learners to assess and refine their methods, while the traditional approach did not offer the same level of engagement in critical evaluation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14. Learners were reflective about their learning.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Learners regularly reflected on their work post-robotics activities. Control group: Reflections were sporadic.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe experimental group incorporated regular reflection into the learning process, promoting metacognition, while the control group missed out on these opportunities.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15. Intellectual rigor, constructive criticism, and idea-challenging were valued.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Robotics-based activities promoted critical thinking and problem-solving discussions. Control group: Discussions were more teacher-driven and less critical.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobotics interventions promoted a dynamic environment where learners were encouraged to challenge ideas, whereas the control group\u0026rsquo;s discussions were more passive and teacher-dominated.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIII. Classroom Culture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunicative Interactions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16. Learners communicated ideas using varied media.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Robotics allowed learners to use visual tools and verbal presentations. Control group: Limited to verbal and written communication.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe robotics group benefited from multimodal communication, enhancing expression and comprehension, unlike the traditional methods in the control group.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17. Teacher\u0026rsquo;s questions triggered divergent thinking.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Robotics-based questions encouraged exploration and divergent thinking. Control group: Questions were mostly factual and recall-based.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe robotics approach stimulated divergent thinking, encouraging learners to explore multiple perspectives, while the control group\u0026rsquo;s questions were focused on recall.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18. High proportion of learner talk among peers.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Learners engaged in active discussions during robotics tasks. Control group: Learner talk was minimal.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobotics activities created opportunities for meaningful peer-to-peer communication, while the control group limited peer interaction.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19. Learner questions/comments determined the focus of discourse.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Learner-driven questions shaped class discussions. Control group: Teacher dominated discourse.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobotics empowered learners to direct the focus of class discussions, whereas in the control group, learners had little influence over the lesson's direction.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20. Climate of respect for others\u0026rsquo; ideas.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoth groups: A respectful atmosphere was maintained in both classrooms.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRespect for learners\u0026rsquo; contributions was evident in both groups, though the robotics group demonstrated greater collaborative engagement.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLearner/Teacher Relationships\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21. Active participation of learners was encouraged.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Robotics activities required active participation. Control group: Participation was largely dependent on teacher prompting.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe robotics-based lesson structure promoted active learner participation, whereas the control group followed a more passive model.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22. Learners were encouraged to generate alternative strategies.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Robotics encouraged creative problem-solving and the exploration of alternative solutions. Control group: Learners followed fixed procedures.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobotics supported the generation of alternative strategies, which was not a central feature in the control group.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23. Teacher was patient with learners.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoth groups: Teachers showed patience with learners.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatience was observed in both groups, creating a positive and supportive learning environment.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24. Teacher acted as a resource person.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Teachers facilitated learner inquiries and supported robotics tasks. Control group: Teachers provided direct instruction.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe teacher\u0026rsquo;s role in the robotics group was more as a facilitator, contrasting with the directive teaching style in the control group.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25. \u0026ldquo;Teacher as listener\u0026rdquo; metaphor was characteristic.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group: Teachers actively listened and responded to learner ideas. Control group: Teacher listening was less evident.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe teacher\u0026rsquo;s role in the robotics group involved greater listening and adapting to learner input, promoting learner agency and engagement.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe findings presented in Table\u0026nbsp;3 highlight notable differences in classroom interaction and instructional practices between the robotics and traditional groups. The robotics group was characterized by increased learner participation, collaboration, and engagement in exploratory activities, whereas the traditional group exhibited more teacher-directed instruction and limited interaction.\u003c/p\u003e \u003cp\u003eThese patterns suggest that robotics-based instruction may facilitate more interactive and learner-centred classroom environments. Such environments are consistent with socio-constructivist theories of learning, which emphasize the importance of social interaction and collaborative knowledge construction. In particular, the work of Lev Vygotsky highlights the role of dialogue and shared activity in cognitive development (Vygotsky, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1978\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe increased opportunities for hypothesis testing, peer discussion, and multimodal engagement observed in the robotics group may have contributed to the enhanced learning outcomes reported in Table\u0026nbsp;1. Similar findings have been documented in studies of inquiry-based science instruction, where active engagement is associated with improved conceptual understanding (Hmelo-Silver et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eHowever, it is important to distinguish between engagement and effective learning. While the robotics group demonstrated higher levels of interaction, the quality of this engagement likely depended on the structure of the activities and the role of the teacher. Without appropriate guidance, exploratory learning environments may lead to superficial engagement rather than meaningful understanding (Kirschner, Sweller, \u0026amp; Clark, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe shift in teacher roles from knowledge transmitter to facilitator also emerges as a critical factor in the successful implementation of robotics-based instruction. This transition requires not only technological resources but also pedagogical expertise, highlighting the need for targeted professional development to support teachers in integrating innovative tools effectively.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study investigated the impact of integrating robotics into the teaching of stoichiometry and titration among Grade 10 learners. The findings indicate that robotics-based instruction is associated with improved learner performance and enhanced conceptual engagement, particularly when implemented alongside structured instructional support. Learners in the experimental group demonstrated greater gains in academic achievement and showed increased ability to connect procedural calculations with underlying chemical concepts.\u003c/p\u003e \u003cp\u003eImportantly, the findings suggest that the effectiveness of robotics is not inherent to the technology itself, but rather dependent on how it is pedagogically integrated into the learning environment. Consistent with socio-constructivist perspectives, particularly those of Lev Vygotsky, robotics functioned as a mediating tool that supported interaction, collaboration, and guided knowledge construction. However, the benefits were not uniformly experienced across all learners. Variations in prior knowledge and levels of engagement influenced learning outcomes, with some learners demonstrating passive participation or limited conceptual understanding despite exposure to robotics-based activities.\u003c/p\u003e \u003cp\u003eThe study also highlights the role of the teacher as a critical factor in the successful implementation of robotics-based instruction. The shift from teacher-centred to facilitative pedagogies requires not only access to technological resources but also the development of pedagogical strategies that effectively scaffold learner engagement and understanding. Without such support, the potential of robotics to enhance learning may not be fully realised.\u003c/p\u003e \u003cp\u003eDespite these promising findings, several limitations must be acknowledged. The study was conducted over a relatively short period, and therefore does not provide insight into the long-term retention of knowledge. Additionally, the absence of longitudinal data limits the ability to determine whether the observed improvements are sustained over time. The study also did not compare robotics-based instruction with laboratory-based practical work, which remains an important area for future investigation.\u003c/p\u003e \u003cp\u003eIn light of these limitations, future research should examine the long-term impact of robotics on knowledge retention and conceptual development, as well as explore its integration within broader, interdisciplinary STEM frameworks. Further studies should also investigate the comparative effectiveness of robotics and traditional hands-on laboratory approaches, particularly in resource-constrained educational contexts. In addition, there is a need to explore targeted teacher professional development models that support the effective integration of robotics into classroom practice.\u003c/p\u003e \u003cp\u003eThis study contributes to the growing body of research on technology-enhanced science education by providing empirical evidence on the potential and limitations of robotics as a pedagogical tool. While robotics shows promise in supporting learner engagement and conceptual understanding, its effectiveness remains contingent on thoughtful instructional design, adequate support structures, and contextual considerations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical guidelines for research involving human participants and was approved by the University of South Africa. Ethical clearance was obtained prior to data collection. Informed consent was secured from all participants, with additional parental or guardian consent obtained for learners under the age of 18. Participation was voluntary, and participants were informed of their right to withdraw from the study at any stage without penalty.\u003c/p\u003e\n\u003cp\u003eTo ensure confidentiality and anonymity, all personal identifiers were removed, and data were systematically coded. All data were used solely for research purposes and stored securely in accordance with institutional data protection policies. The study was designed to minimise potential risk, and no physical or psychological harm was anticipated. All efforts were made to ensure that participants benefited educationally from the intervention. Any identifying information related to individuals or institutions has been anonymised.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBenitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Computers \u0026amp; Education, 58(3), 978\u0026ndash;988. https://doi.org/10.1016/j.compedu.2011.10.006Getrightsandcontent\u003c/li\u003e\n\u003cli\u003eChing, Y. H., \u0026amp; Hsu, Y. C. (2024). Educational robotics for developing computational thinking in young learners: A systematic review. TechTrends, 68(3), 423\u0026ndash;434. https://doi.org/10.1007/s11528-023-00841-1\u003c/li\u003e\n\u003cli\u003eDarmawansah, D., Hwang, G. J., Chen, M. R. A., \u0026amp; Liang, J. C. (2023). Trends and research foci of robotics-based STEM education: a systematic review from diverse angles based on the technology-based learning model. International Journal of STEM Education, 10(1), 12. https://doi.org/10.1186/s40594-023-00400-3\u003c/li\u003e\n\u003cli\u003eEguchi, A. (2017). Bringing robotics in classrooms. In: Khine, M. (eds) Robotics in STEM education (pp. 3\u0026ndash;31). Springer. https://doi.org/10.1007/978-3-319-57786-9_1\u003c/li\u003e\n\u003cli\u003eGardner, H. (1997). \u003cem\u003eThe disciplined mind: What all students should understand\u003c/em\u003e. Simon \u0026amp; Schuster.\u003c/li\u003e\n\u003cli\u003eHake, R. R. (1998). Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. \u003cem\u003eAmerican Journal of Physics, 66\u003c/em\u003e(1), 64\u0026ndash;74. https://doi.org/10.1119/1.18809\u003c/li\u003e\n\u003cli\u003eHmelo-Silver, C. E., Duncan, R. G., \u0026amp; Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). \u003cem\u003eEducational Psychologist, 42\u003c/em\u003e(2), 99\u0026ndash;107. https://doi.org/10.1080/00461520701263368\u003c/li\u003e\n\u003cli\u003eKirschner, P. A., Sweller, J., \u0026amp; Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. \u003cem\u003eEducational Psychologist, 41\u003c/em\u003e(2), 75\u0026ndash;86. https://doi.org/10.1207/s15326985ep4102_1\u003c/li\u003e\n\u003cli\u003eKirschner, P. A., Sweller, J., \u0026amp; Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. \u003cem\u003eEducational Psychologist, 41\u003c/em\u003e(2), 75\u0026ndash;86. https://doi.org/10.1207/s15326985ep4102_1\u003c/li\u003e\n\u003cli\u003eLotriet, H., \u0026amp; Gouws, P. (2024). Using educational robotics in chemistry education: A systematic review. International Journal of Innovation in Science and Mathematics Education, 32(4). https://doi.org/10.30722/ IJISME.32.04.004\u003c/li\u003e\n\u003cli\u003ePiaget, J. (1972). The psychology of the child. New York: Basic Books\u003c/li\u003e\n\u003cli\u003eSapounidis, T., \u0026amp; Alimisis, D. (2020). Educational robotics for STEM: A review of technologies and some educational considerations. In L. Leite, E. Oldham, A. Floriano Viseu, L. Dourado, M. Martinho (Eds) Science and mathematics education for 21st century citizens: Challenges and ways forward (pp. 167\u0026ndash;190). Nova Science.\u003c/li\u003e\n\u003cli\u003eT\u0026oacute;thov\u0026aacute;, M., Rusek, M., \u0026amp; Chytr\u0026yacute;, V. (2021). Students\u0026rsquo; procedure when solving problem tasks based on the periodic table: An eye-tracking study. \u003cem\u003eJournal of Chemical Education, 98\u003c/em\u003e(6), 1831\u0026ndash;1840. https://doi.org/10.1021/acs.jchemed.1c00167\u003c/li\u003e\n\u003cli\u003eVygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of South Africa","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Robotics-based instruction, Chemistry education, Conceptual understanding, Stoichiometry and titration, Inquiry-based learning","lastPublishedDoi":"10.21203/rs.3.rs-9419614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9419614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe integration of robotics into chemistry education has attracted increasing interest as a means to enhance learner engagement and support conceptual understanding. Traditional instruction, often dependent on abstract concepts and symbolic representations, can result in misconceptions and difficulties in practical application. This study investigated the effects of robotics-based instruction on teaching stoichiometry and titration using a quasi-experimental design. Comparisons were made between learners taught through robotics-integrated methods and those receiving conventional instruction, with attention to engagement, conceptual change, and academic performance. The findings provide insights into how robotics can be implemented to support interactive, learner-centered learning, highlight potential challenges, and inform curriculum development and instructional strategies in chemistry education.\u003c/p\u003e","manuscriptTitle":"A Robotic-Based Instruction for Integrating Robotics in Chemistry Education: A case of stoichiometry and titration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 05:12:27","doi":"10.21203/rs.3.rs-9419614/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c244d2c3-8a83-4a80-a682-16c176465869","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66323323,"name":"Materials Engineering"}],"tags":[],"updatedAt":"2026-04-16T05:12:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 05:12:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9419614","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9419614","identity":"rs-9419614","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}