Inquiry-Based Science Classes

• Inquiry-based science classes are educational environments that center on posing investigable questions, designing experiments, collecting data, and constructing evidence-based explanations.
• The pedagogy integrates technology-enhanced scaffolding, iterative feedback, and constructivist learning principles to develop robust scientific process skills and epistemic ownership.
• Empirical research shows significant improvements in student confidence, process skills, and inclusive engagement through innovative assessment strategies and structured inquiry activities.

Inquiry-based science classes are structured around the cyclical process of posing investigable questions, designing and carrying out experiments, collecting and analyzing data, and constructing evidence-based explanations. Rather than following prescriptive protocols, students take epistemic ownership of their inquiry, supported by technology-enhanced scaffolds and iterative feedback. The pedagogy combines constructivist learning theory, authentic research practices, and targeted interventions to develop both domain knowledge and inquiry skills.

1. Conceptual Foundations and Frameworks

Inquiry-based instruction in science education is rooted in constructivist theories (National Research Council, 2000; Yilmaz, 2008), emphasizing students as active constructors of knowledge via questioning, exploration, and data-driven sense-making (Stoeckel, 2020). Core research frameworks include:

• Harwood's 10 Inquiry Activities: Ask questions, define the problem, form the question, investigate the known, articulate expectations, carry out the paper, examine results, reflect, communicate, and make observations (Spencer et al., 2018).
• Technology Acceptance Model 2 (TAM2): In technology-rich inquiry environments, student acceptance is modeled as a function of perceived usefulness (PU), perceived ease of use (PEOU), subjective norm (SN), image (IM), job relevance (JR), output quality (OQ), result demonstrability (RD), and behavioral intention (BI). Equation:

$$BI \;=\;\beta_{1}\,\mathrm{PU}\;+\;\beta_{2}\,\mathrm{PEOU}\;+\;\beta_{3}\,\mathrm{SN}\;+\;\beta_{4}\,\mathrm{IM}\;+\;\dots$$

(Ga et al., 6 Nov 2025)

• Levels of Inquiry: Ranging from confirmation (problem, procedure, and conclusion given), structured inquiry (problem/procedure given, conclusion open), guided inquiry (problem given, procedure/conclusion open), to open inquiry (all elements open) (Bradbury et al., 2020).

Pedagogical objectives include mastery of the empirical research cycle, improvement of science process skills, and the development of positive attitudes and epistemologies toward science and experimentation (Riegle-Crumb et al., 2023, Panuluh, 2022, Doucette et al., 2021).

2. Instructional Models and Technology Integration

The design of inquiry-based science classes combines exemplar activities, guided experimentation, and technology-enhanced observation. Representative implementations:

• Arduino-Enhanced Inquiry: In a Korean high school R&E course, students generated research questions (e.g., air quality, heart-rate variability), built custom sensor devices using Arduino UNO, mBlock 5 block-coding environment (Gaduino extension), and an IoT platform for data visualization (Ga et al., 6 Nov 2025).
• Maker-Lab Model (Pandemic Resilience): Pairs of students conducted two consecutive research cycles using Arduinos for measurement, engaged in independent at-home experiments, and participated in flipped-classroom discussions (Bradbury et al., 2020).
• Easy Java Simulation (EJS): Open-source, customizable computer models allow manipulation of physical parameters, iterative hypothesis testing, and on-the-fly data analysis (e.g., spring-mass, projectile motion, collision carts) through guided inquiry sequences (Wee et al., 2012, Wee et al., 2013, Goh et al., 2013).
• Guided Inquiry Practicum: University physics students complete six lab activities (three highly scaffolded, three open-ended), targeting eight process skills: asking, hypothesizing, planning, observing, classifying, predicting, interpreting, communicating (Panuluh, 2022).
• Small-Group Discourse: Cluster analysis identifies group discourse roles (e.g., "high on-task/high social") and reveals alignment within groups, variation across activities, and inequities in DHH student engagement (Wan et al., 24 May 2024).

Key design principles include early access to hardware, contextually relevant projects, robust scaffolding (e.g., sensor datasheet workshops, troubleshooting guides), and peer review of device design and data quality (Ga et al., 6 Nov 2025, Bradbury et al., 2020).

3. Empirical Research, Assessment Strategies, and Outcomes

Inquiry-based classes use diverse, multi-stage assessment regimes to capture both cognitive and attitudinal outcomes:

• Attitudinal Change: Hands-on-Science (HoS) model for pre-service teachers demonstrated statistically significant increases in confidence (Δ = +0.37, d ≈ 0.49), enjoyment (+0.27, d ≈ 0.32), and relevance (+0.08, d ≈ 0.12), with marked anxiety reduction (Δ = –0.48, d ≈ 0.67) on 5-point Likert scales (Riegle-Crumb et al., 2023).
• Science Process Skills: Paired-sample t-tests on 32-item Likert instruments revealed significant improvement in total score after six guided-inquiry labs (pre = 76.33, post = 82.40; t(14) = –2.305, p = 0.037, Cohen’s d ≈ 0.60), with largest gains in observation (d ≈ 0.80) and communication (d ≈ 0.65) (Panuluh, 2022).
• Experimental Epistemology (E-CLASS): Inclusion of explicit reflection prompts in inquiry-based introductory physics labs eliminated the typical loss in expert-like attitudes (Δ=–0.2, p=0.18 versus –1.1 to –1.3 in traditional formats) (Doucette et al., 2021).
• Quantitative Analysis: Data modeling, signal processing, calibration, and error propagation are embedded (e.g. median frequency for EMG: $$\int_{0}^{f_m} P(f)\,df = \int_{f_m}^{f_s/2} P(f)\,df$$; error propagation formula: $$\sigma_y = \sqrt{ \sum_i (\frac{\partial f}{\partial x_i})^2 \sigma_{x_i}^2 }$$) (Bradbury et al., 2020).

Assessment artifacts typically include research proposals, progress reports, full project manuscripts, poster presentations, process-skill surveys, and peer evaluations. Grades balance individual accountability and group products (Gray et al., 2015).

4. Sociocultural and Equity Dimensions

Inquiry-based science learning is sensitive to contextual, cultural, and equity factors:

• Admissions Pressure: In Korean secondary education, the hakgyosaenghwal girokbu and university admissions strongly motivate adoption of advanced technologies such as Arduino, shaping subjective norms (SN) and the perceived image (IM) of technology users (Ga et al., 6 Nov 2025).
• Gender and Ability: Studies report no systematic difference in discourse engagement between female and male students, but DHH students in mixed-ability groups exhibit lower levels of on-task engagement and instructor interaction, requiring group-level interventions (role rotation, interpreter co-location) for equity (Wan et al., 24 May 2024).
• Resource Access: Equitable inquiry requires universal hardware distribution (starter kits, loaner sensors), contextualization of phenomena (e.g., local air pollution), and explicit acknowledgment of pressure sources to prevent stratification or disengagement (Ga et al., 6 Nov 2025).
• Pedagogical Scaffolds: Layered supports such as peer review, datasheet workshops, structured reflection, and formative metacognitive prompts buffer against cognitive overload and promote inclusive participation (Bradbury et al., 2020, Ga et al., 6 Nov 2025).

5. Curriculum Design, Best Practices, and Implementation Recommendations

Optimal inquiry-based science curricula employ structured progression, iterative feedback, and integration of digital resources:

• Project Sequencing: Initiate with exemplar projects; introduce block-coding early; embed IoT modules for data visualization; allocate device-building and iterative troubleshooting time (Ga et al., 6 Nov 2025).
• Flipped and Independent Modalities: Pre-lab online instruction frees contact time for design critique and troubleshooting; independent, remote labs enable authentic ownership and resilience to disruptions (e.g. pandemic closures) (Bradbury et al., 2020).
• Open-Source Tools and Customization: Licensing under GPL/CC-BY, community-sharing, and modifiable code (EJS, Arduino sketches) support adaptation, reproducibility, and targeted remediation of misconceptions (Wee et al., 2012, Goh et al., 2013).
• Assessment Integration: Use of scalable rubrics aligned to process and epistemological outcomes, peer-review cycles, and iterative reporting foster depth, accountability, and transferrable inquiry skills (Gray et al., 2015, Spencer et al., 2018).
• Equity Interventions: Rotating roles, group reflection protocols, explicit interpreter assignment, and monitoring discourse metrics promote fair engagement of underrepresented students (Wan et al., 24 May 2024).

Representative activities include custom sensor devices for environmental monitoring, comparative evaluation of measurement systems, analogical modelling (e.g., Rutherford's gold foil via marble-blackbox apparatus), and domain-specific open inquiry projects (e.g., protein function determination via experimental/computational methods) (Ga et al., 6 Nov 2025, Tuveri et al., 14 Apr 2025, Gray et al., 2015).

6. Impact, Limitations, and Future Directions

Inquiry-based science classes have demonstrated robust improvements in process skills, conceptual understanding, and science attitudes. Longitudinal analyses indicate sustained attitudinal and skill gains among pre-service teachers and undergraduate majors (Riegle-Crumb et al., 2023, Gray et al., 2015). However, effective implementation depends on adequate scaffolding, equitable access, tailored group structures, and explicit connection to epistemological goals. Challenges include increased facilitator workload, need for technical training, and potential sociocultural barriers related to status or external pressures (Cantalupo, 2022, Ga et al., 6 Nov 2025).

Ongoing research focuses on optimizing group discourse, digital workflow integrations, culturally responsive curriculum adaptation, and measurement of long-term transfer to authentic research settings (Spencer et al., 2018, Wan et al., 24 May 2024).