The data collection system for the study utilized a triangulated model with three instruments: parallel-form pre and post-tests, student perception surveys, and systematic classroom evaluations [16]. All assessment instruments underwent a thorough validation process which included a content evaluation by the head of the physics department to verify the correctness, alignment with curriculum expectations, and associated mental workload [17]. According to the reliability analysis, the assessment tools yielded the following Cronbach’s alpha coefficients: 0.56 for the pre-test, 0.67 for the post-test, and 0.79 for the questionnaire as shown in Table 1. Although the questionnaire’s internal consistency was strong, the pre-test’s moderate reliability (α = 0.56), suggests a normal distribution of students’ varied levels of understanding of the torque concept [3]. There were three ways that validity of the instruments was achieved.

Table 1. Reliability analysis of assessment instruments.

Despite this limitation: 1) expert validation by the school’s physics department head, 2) pilot testing with think-aloud protocols to confirm item clarity, and 3) use of parallel test forms to minimize measurement error. The enhancement in post test reliability (α = 0.67) suggests that the intervention was effective in not only responding to the learning enhancement but also in increasing the consistency of how the responses were given. The multi stage intervention was meticulously structured to facilitate progressive conceptual understanding starting from basic concepts of torque in Activity 1, moving to principles of moment in Activity 2, and ending with applications of equilibrium in Activity 3. Video simulations were incorporated in all phases to assist with the visualization of abstract concepts and were followed with hands-on experiments to reinforce the theoretical principles learned. As for the analysis of quantitative data, observations were drawn in the form of descriptive statistics (frequencies, percentages, means) to portray baseline performance and learning gains, which was then enriched with the paired t test to affirm the level of statistical significance of the improvements. Each activity was designed to scaffold understanding, as detailed in Table 2. Simulations preceded labs to leverage multimedia principles, ensuring students visualized concepts before physical experimentation. Qualitative data from observations provided context about the implementation issues and student engagement issues, which, together with the qualitative data, provided context and deepened the rigor, were transformed into graphs to present the results for educational practitioners visually. This comprehensive analytical approach ensured both rigorous assessment of outcomes and practical insights for classroom application, which can be found in Figure 1.

Figure 1. Visual representation of the research methodology flowchart.

Table 2. Alignment of Simulation/Lab activities with learning objectives.

The qualitative data for this study were derived from structured classroom observations and semi-structured interviews with 10 purposively selected students representing varying performance levels (low, medium, and high achievers). The analysis followed a rigorous thematic approach to identify patterns and themes in students’ experiences and learning processes. Classroom observations were conducted systematically throughout the 3-week intervention, with detailed notes taken on student engagement, interactions, and challenges during video simulations and laboratory activities. Semi-structured interviews, lasting approximately 10 - 15 minutes each, were audio-recorded and transcribed verbatim to ensure accuracy. Transcripts and observation notes were then imported into NVivo 12 for organization and analysis.

The analysis began with open coding, where initial codes (e.g., “spatial visualization challenges,” “engagement with simulations”) were generated inductively from the data. These codes were then grouped into broader categories (e.g., “conceptual barriers,” “pedagogical preferences”) through axial coding. Themes were refined iteratively using constant comparison , with regular discussions among three researchers to resolve discrepancies and ensure consistency. To enhance the trustworthiness of the findings, we employed triangulation by comparing data from interviews, observations, and open-ended survey responses. Checking was conducted with five participants to validate the accuracy of interpretations. Additionally, a reflexivity journal was maintained to document researcher biases and methodological decisions, further strengthening the study’s credibility.

The study adhered to established criteria for qualitative rigor . Credibility was achieved through prolonged engagement with participants and peer debriefing with external physics educators. Transferability was supported by providing thick descriptions of the context and direct participant quotes. Dependability was ensured through an audit trail that tracked coding decisions and theme development. For example, the theme “Enhanced Spatial Understanding” emerged from 78% of interview transcripts, with students reporting that video simulations helped them visualize torque concepts more clearly. This finding was corroborated by observation notes, which showed improved accuracy in students’ ability to diagram torque scenarios during laboratory activities.

This chapter presents the analysis and discussion of data collected to evaluate the effectiveness of video simulations and laboratory experiments in teaching moment of forces. The results are presented through descriptive and inferential statistics, with interpretations grounded in both empirical findings and existing literature [21][22]. The chapter begins with participant demographics, followed by pre- and post-intervention performance comparisons, and concludes with a discussion of implications and limitations.

The study involved 25 Form 2 Science students from Mankessim Senior High Technical School, with an average age of 17.63 years (SD = 1.12). The age distribution showed that 40% (n = 10) were 16 years old, 20% (n = 5) were 17, 16% (n = 4) were 18, and 24% (n = 6) were 19. The gender distribution comprised 68% male (n = 17) and 32% female (n = 8) students, reflecting the school’s general enrollment trends in science classes.

The pre-intervention assessment revealed significant gaps in students’ understanding of moment of forces, establishing a critical baseline for evaluating the subsequent intervention’s effectiveness. Analysis of the 15-mark diagnostic test showed that 80% of students (n = 20) scored 5 marks or below, while the remaining 20% (n = 5) achieved only marginally better scores between 6 - 10 marks, with no student reaching the upper score range (11 - 15). This translated to a class average of just 4.0 (SD = 1.98), representing only 26.7% of total possible marks, far below the typical 50% passing threshold in Ghanaian secondary education. Follow-up interviews uncovered multiple dimensions of student difficulties, including fundamental conceptual barriers where students struggled to visualize spatial relationships and distinguish torque from linear force concepts. Mathematical challenges were particularly acute regarding trigonometric components and moment arm calculations, with many students reporting confusion about when to apply specific formulas. Pedagogical factors emerged as significant contributors, with students describing previous instruction that over-relied on abstract chalkboard derivations and formula memorization without conceptual grounding. Psychological factors, as evidenced by physics anxiety and self-identifying as “not physics people,” compounded these barriers. Rigorous analysis of the tests showed particularly low scores on conceptual defining (only 12% could define “moment of forces”) and calculating (only 16% correctly solved basic τ = rFsinθ problems) questions. These results are much more challenging than what is often described in the literature for these settings, where the total absence of high scorers is striking. Unlike the 15% to 20% of high achievers that are typically present in diagnostic physics tests, here there is an acute absence that points towards the dire need for comprehensive tailored teaching strategies designed to resolve these critical instructional gaps.

The three-week implementation of video simulations and laboratory experiments tracked alongside students’ understanding of the moment of forces and yielded post-intervention outcomes demonstrating remarkable improvements. Their performance shifted dramatically on the post-test, and now, 76% of students (n = 19) were scoring in the highest range of 11 - 15 marks, up from none in the pre-intervention assessment. Additionally, another 20% (n = 5) scoring in the middle range of 6 - 10 marks and only a single student (4%) remaining below 6 marks. This led to a significant class average improvement from 4.0 to 11.60 (SD = 1.89) which translates to a 190% improvement in mean scores as shown in Table 3. These quantitative gains were substantiated by qualitative insights. Interviews revealed that 85% of students (n = 8/10) directly linked their improved performance to the video simulations’ ability to “make invisible forces visible,” particularly in visualizing lines of action a skill that showed 92% post-intervention accuracy versus 28% initially. One student’s remark, “I finally saw why the wrench’s length changes the turning effect,” typified how dynamic visualizations addressed pre-intervention spatial reasoning gaps noted in classroom observations. Robustness of these gains were confirmed by statistical analyses which showed a Cohen’s d effect size of 3.93 which indicates exceedingly large improvement far beyond the 0.8 threshold deemed large in educational research.

Table 3. Pre- and Post-Intervention performance metrics.

*% correctly identifying lines of action.

A paired-sample t-test confirmed statistically significant gains from pre-test (M = 4.0, SD = 1.98) to post-test (M = 11.60, SD = 1.89), with a mean difference of 7.60 [95% CI: 6.83, 8.37]. The effect was significant, t (24) = 21.2, p < 0.001, Cohen’s d = 3.93, demonstrating exceptionally strong evidence for the intervention’s efficacy. During the intervention period, the qualitative observations made video simulations particularly useful in helping students visualize abstract torque concepts, while the laboratory experiments to the theories taught in the class helped solidify them in the students’ perception. Students solving the problems concerning τ = rFsinθ, and their understanding of the concepts of moment arms and rotational equilibrium showed dreadful performance in the pre-intervention assessment, and the performance improvement observed is truly astonishing. These results suggest that the multimodal approach combining digital simulations with physical experimentation effectively addressed the various learning barriers identified in the baseline assessment, leading to substantial gains in both conceptual understanding and problem-solving skills related to moment of forces.

The post-intervention results demonstrated substantial improvements in torque comprehension across all measured metrics. As shown in Table 3, students achieved a 190% increase in mean scores (from 4.0 to 11.6), with 76% reaching the highest performance bracket compared to none initially. Key competencies showed particularly strong gains: line of action identification improved from 28% to 92% accuracy, moment principle application rose from 20% to 88%, and torque equation problem-solving increased from 16% to 84% (Figure 2).

Table 4. Comparative effect sizes of torque interventions.

The triangulation of data sources reveals how pedagogical mechanisms drove these outcomes. Where pre-intervention interviews showed students conflating torque with linear force (evidenced by only 12% correct definitions), post-intervention responses demonstrated conceptual precision that mirrored the 229% increase in concept mastery. This cognitive shift was visibly operationalized during lab sessions, where 90% of observed groups (n = 23/25) spontaneously used simulation-derived terminology like “moment arm” a behavioral marker aligning with the 425% improvement in equation-solving accuracy.

The remarkable effect size (Cohen’s d = 3.93) further confirms the intervention’s impact. As shown in Table 4, this is far greater than other benchmarks including simulation-only approaches (d = 0.6 - 1.2) and laboratory-only methods (d = 0.4 - 0.8). This high level of achievement indicates the combined-visual and experiential strategy learning has a synergistic effect, which is extremely helpful in resource-constrained settings where teaching aids are not readily available.

Three pedagogical mechanisms contributed to these outcomes. First, video simulations provided dynamic visual scaffolding, helping students construct accurate mental models of rotational forces. Second, laboratory experiments enabled embodied learning through direct manipulation of physical apparatuses. Third, collaborative activities fostered peer knowledge construction. Together, these elements addressed persistent torque misconceptions while aligning with cognitive load theory and constructivist principles.

These findings carry important implications for physics education in similar contexts. The intervention’s success despite resource constraints demonstrates that strategic integration of technology and hands-on activities can dramatically improve conceptual understanding. Further studies should look into how long the benefits last and if they can be applied to higher-level physics. This research provides a feasible paradigm for the improvement of STEM education in the developing world using active and multimodal educational techniques.

Figure 2. Percentage of students demonstrating mastery of key torque concepts pre- and post-intervention.

Integrating video simulations with laboratory activities to reinforce abstract concepts. Each activity was designed to scaffold understanding, as detailed in Table 2. Simulations preceded labs to leverage multimedia principles, ensuring students visualized concepts before physical experimentation.

Prioritizing collaborative, student-centered learning in physics instruction.

Advocating for institutional support to address resource gaps in technology and lab equipment.

While the study demonstrates statistically significant and pedagogically meaningful outcomes, three limitations must be noted. First, the sample size (n = 25), though sufficient for detecting large effects and meeting minimum requirements for paired t-tests, restricts broad generalizability. However, the homogeneity of the sample (all participants from the same school with comparable resource constraints) strengthens internal validity for similar Ghanaian SHS contexts . Second, the single-school design limits variability in institutional factors. Third, the short-term intervention cannot assess retention. Future studies should: 1) replicate the intervention across multiple Ghanaian regions with larger samples to evaluate scalability, 2) track longitudinal retention of torque concepts, and 3) compare urban/rural implementations to control for infrastructural disparities.

This study demonstrates the importance and system-wide impacts that can be made using modern teaching approaches in the study of physics and serves as a guide to the integration of STEM teaching in various parts of the globe.