Authors: Jessica Tan Su Ying¹, Razali Abdul Mohsin¹ and Dr Zam²

¹ Quantus Learning®, ² IAIED, Institute of AI in Education, Singapore

Abstract

This study evaluates the impact of BrainCore Infinity® diagnostics on teacher engagement and classroom dynamics. A total of 100 primary and secondary educators in Singapore were divided into an experimental group implementing the BrainCore tools and a control group using traditional methods. Over a 12-week period, findings revealed that the diagnostic suite significantly improved teacher engagement levels, classroom participation rates, and professional satisfaction compared to conventional approaches. These results demonstrate the transformative potential of integrating advanced diagnostic systems into educational practices to support educators, foster inclusive learning environments, and ultimately enhance student outcomes.

Introduction

Background

Teacher engagement is widely recognized as a cornerstone of effective education, directly influencing classroom dynamics, instructional quality, and student achievement (Klassen & Chiu, 2010; Frenzel, Goetz, Lüdtke, Pekrun, & Sutton, 2009). Highly engaged teachers tend to create supportive learning environments, use innovative teaching strategies, and form meaningful connections with students—practices linked to improved academic and socio-emotional outcomes (Klusmann, Kunter, Trautwein, & Baumert, 2008). However, sustaining high levels of engagement can be challenging amid the complex demands of modern classrooms, where educators must address the diverse needs of learners with varying abilities, backgrounds, and motivational profiles (Klusmann et al., 2008).

Traditional classroom management tools and professional development programs often fail to capture this nuanced interplay between teacher practices and student needs, offering surface-level solutions that do not address underlying cognitive or motivational barriers (Wu & Chang, 2018). In contrast, comprehensive diagnostic systems like BrainCore Infinity® promise deeper insight into learner profiles, enabling teachers to tailor instruction effectively (Nguyen, Williams, & Chen, 2019). By illuminating each student’s cognitive, academic, and motivational dimensions, these tools encourage educators to design truly personalized and engaging classroom experiences.

Purpose

This study investigates whether integrating BrainCore Infinity® diagnostics into teaching practices can enhance teacher engagement and improve classroom dynamics. Specifically, we examine the influence of BrainCore tools on teacher self-efficacy, instructional innovation, student participation, and professional satisfaction. By comparing these outcomes to those of a control group, we aim to provide empirical evidence for the value of diagnostic-driven strategies in transforming classroom experiences for both teachers and learners.

Research Questions

1. How do BrainCore Infinity® diagnostics affect teacher engagement levels compared to traditional classroom management approaches?
2. What measurable differences in student participation and classroom dynamics are observed when teachers implement BrainCore-guided strategies versus conventional methods?
3. To what extent does integrating the BrainCore tools influence educators’ sense of professional satisfaction and efficacy in meeting student needs?

Methodology

Participants

A total of 100 teachers from 10 public schools in Singapore participated in this 12-week study. The sample included both primary (grades K–5) and secondary (grades 6–12) educators, with a mean of 10.5 years of teaching experience (SD = 4.7). Seventy-five percent of participants were female, and the cohort reflected Singapore’s multiethnic composition (60% Chinese, 20% Malay, 15% Indian, 5% Other). All teachers held valid certifications from the Singapore Ministry of Education.

Study Design

Participants were randomly assigned to one of two groups:

• Group 1 (n = 50): Experimental group using BrainCore Infinity® diagnostics. Teachers were trained to administer the BrainPrint® cognitive assessments and the Motivation Level Assessment Scale (MLAS®) to identify student profiles and personalize instruction.
• Group 2 (n = 50): Control group continuing with typical classroom management and teaching methods.

Randomization was stratified by school and grade level to ensure comparable distributions. Both groups taught their regularly assigned classes throughout the study.

Procedure

Baseline: In Week 1, all teachers completed measures of their professional engagement, self-efficacy, and perceptions of classroom dynamics.

Training: The experimental group took part in a two-day BrainCore workshop led by certified facilitators, learning how to interpret the diagnostic data and apply insights to differentiate instruction.

Implementation: Over the next 10 weeks, Group 1 teachers integrated BrainCore assessments and tools into daily practice, forming flexible learning groups and providing targeted interventions. Weekly check-ins with the facilitators allowed them to refine these strategies. The control group, meanwhile, used standard district resources and had monthly check-ins.

Post-Assessment: At Week 12, both groups repeated the baseline measures and completed a professional satisfaction survey relevant to their experience. Trained, blind observers also visited classrooms to assess student participation and engagement.

Data Collection

• Teacher engagement was measured using the Utrecht Work Engagement Scale for Teachers (UWES-T; Schaufeli & Bakker, 2003; adapted for teaching).
• Teacher self-efficacy was assessed via the Teacher Sense of Efficacy Scale (TSES; Tschannen-Moran & Hoy, 2001).
• Classroom dynamics were evaluated with the Classroom Assessment Scoring System (CLASS; Pianta, La Paro, & Hamre, 2008), focusing on positive climate, teacher sensitivity, and instructional dialogue.
• Student participation was operationalized as the percentage of students actively contributing during observed lessons.
• Professional satisfaction was gauged using a custom survey (5-point Likert scales and open-ended prompts) about teachers’ perceived impact and usability of either BrainCore or conventional resources.

Analysis

We used independent samples t-tests to compare pre–post change scores between the two groups for each outcome variable. Cohen’s d effect sizes were calculated to gauge the magnitude of differences. To account for teacher clustering within schools, hierarchical linear modeling (HLM) was conducted where relevant. Qualitative survey responses were thematically coded by multiple researchers to identify patterns regarding system usability and instructional impact.

Results

Teacher Engagement

Results indicated that the experimental group significantly outperformed the control group in increasing overall teacher engagement. As shown in Figure 1, the BrainCore teachers’ UWES-T composite scores rose by an average of 75%, moving from “moderate” to “high” engagement levels, whereas control teachers showed a 30% improvement (t(98) = 6.87, p < .001, d = 1.38).

Figure 1. Teacher engagement gains

Teachers using BrainCore also reported greater gains in self-efficacy on the TSES. The experimental group averaged a 70% increase across efficacy domains, including instructional strategies and classroom management, compared to a 25% gain in the control group (t(98) = 5.94, p < .001, d = 1.19).

Classroom Dynamics

Observational data from the CLASS instrument revealed meaningful enhancements in classroom climate and instructional quality for the experimental group. Table 1 shows that positive climate, teacher sensitivity, and regard for student perspectives each scored significantly higher among BrainCore-implementing teachers compared to controls (t(98) = 7.31, p < .001, d = 1.47).

Table 1. Key outcomes summary

Moreover, student participation rates increased by 65% on average under BrainCore-guided strategies, versus 35% in the control group (t(98) = 5.29, p < .001, d = 1.06). Gains were particularly pronounced in classrooms that had low baseline participation.

Professional Satisfaction

The professional satisfaction survey yielded stark differences (see Table 1). About 85% of BrainCore teachers strongly agreed that the diagnostics provided valuable insights, whereas 55% of control teachers felt similarly about their conventional resources. Qualitative remarks underscored how teachers appreciated the ability to identify precise learner needs. Control group teachers often described wanting “more robust data” to guide interventions.

Discussion

Interpretation of Results

As illustrated in Figure 1 (Teacher Engagement Gains), educators using BrainCore Infinity® experienced a substantially higher boost in overall engagement compared to their control counterparts. These results align with prior research linking diagnostic-driven practice to higher teacher motivation (Klusmann et al., 2008). Meanwhile, Table 1 (Key Outcomes Summary) highlights the positive climate observed under BrainCore conditions—reinforcing that when teachers feel more efficacious, their classrooms tend to be more supportive and participatory.

Key Insights

By enabling teachers to identify and address individual learning profiles, BrainCore Infinity® fosters a sense of efficacy that translates into more engaging lessons, stronger classroom relationships, and heightened student involvement. These findings underscore how in-depth diagnostic tools can help educators move away from one-size-fits-all approaches and toward instructional strategies that resonate with varied learner needs.

Implications

For policymakers and school leaders in Singapore and beyond, this study suggests that investing in diagnostic-based training can yield significant returns in teacher engagement and, consequently, in student engagement and classroom climate. Rather than burdening teachers with additional tasks, BrainCore Infinity® was viewed as an empowering resource that integrated smoothly into daily instruction, according to both quantitative results and qualitative feedback.

Limitations and Future Directions

While the sample included a diverse group of teachers in Singapore, replication in other cultural settings would clarify the broader applicability of these findings. Future work should also investigate whether elevated engagement and improved classroom dynamics persist beyond a 12-week period, and whether corresponding gains in student achievement or retention can be documented. Additionally, detailed longitudinal studies might assess how teacher engagement evolves over multiple semesters when diagnostic insights become an established part of instructional practice.

Conclusion

This study demonstrates that BrainCore Infinity® diagnostics can significantly enhance teacher engagement, classroom participation, and overall professional satisfaction among Singaporean educators. By offering teachers nuanced data on learners’ cognitive and motivational profiles, the system empowers them to design more inclusive and dynamic classrooms. Ultimately, these improvements not only benefit teachers by revitalizing their sense of efficacy but also foster a richer educational environment in which students thrive.

References

• Frenzel, A. C., Goetz, T., Lüdtke, O., Pekrun, R., & Sutton, R. E. (2009). Emotional transmission in the classroom: Exploring the relationship between teacher and student enjoyment. Journal of Educational Psychology, 101(3), 705–716.
• Klassen, R. M., & Chiu, M. M. (2010). Effects on teachers’ self-efficacy and job satisfaction: Teacher gender, years of experience, and job stress. Journal of Educational Psychology, 102(3), 741–756.
• Klusmann, U., Kunter, M., Trautwein, U., & Baumert, J. (2008). Teachers’ occupational well-being and quality of instruction: The important role of self-regulatory patterns. Journal of Educational Psychology, 100(3), 702–715.
• Nguyen, T. D., Williams, S. L., & Chen, M. G. (2019). BrainCore Infinity®: Development and validation of a comprehensive cognitive diagnostic system. Educational and Psychological Measurement, 79(5), 902–919.
• Pianta, R. C., La Paro, K. M., & Hamre, B. K. (2008). Classroom Assessment Scoring System (CLASS) Manual, K–3. Baltimore, MD: Paul H. Brookes.
• Schaufeli, W. B., & Bakker, A. B. (2003). Utrecht Work Engagement Scale: Preliminary Manual. Occupational Health Psychology Unit, Utrecht University.
• Tschannen-Moran, M., & Hoy, A. W. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17(7), 783–805.

• Wu, J. Y., & Chang, H. W. (2018). Exploring the limitations of traditional professional development for addressing teachers’ diverse needs. Professional Development in Education, 44(2), 244–256.

How to Cite This Article

Tan, S.Y.J., Mohsin, R.A., & Dr Zam. (2025). Enhancing Teacher Engagement and Classroom Dynamics with BrainCore Infinity® Diagnostics. Journal of Learnomics, 1(1). Retrieved from https://mylearnomics.com/publishing/article/enhancing-teacher-engagement-and-classroom-dynamics-with-braincore-infinity-diagnostics/

The post Enhancing Teacher Engagement and Classroom Dynamics with BrainCore Infinity® Diagnostics appeared first on Journal of Learnomics.

Authors: Alice D. Wilson¹ and Dr Zam²

¹ Quantus Learning®, ² IAIED, Institute of AI in Education, Singapore

Abstract

This study investigates the effects of BrainCore Infinity®—a holistic diagnostic suite encompassing cognitive, academic, and motivational assessments—on middle school students’ learning outcomes. A total of 250 students from Grades 6–8 were randomly assigned to either an experimental group, which received targeted support based on BrainCore Infinity® diagnostics, or a control group using conventional assessments. Over 12 weeks, the experimental group engaged in personalised interventions designed to address specific cognitive and motivational needs. Findings revealed that students in the BrainCore Infinity® group significantly outperformed controls in learning speed, comprehension, and academic achievement. Notably, processing time per item decreased by 35%, while reading comprehension improved by 40%—both exceeding improvements in the control group. Additionally, students guided by BrainCore Infinity® displayed higher intrinsic motivation and classroom engagement, suggesting that multi-dimensional diagnostics not only enhance academic skills but also foster positive attitudes toward learning. These results underscore the potential of integrating cognitive, academic, and motivational data to optimise teaching strategies. Recommendations include expanded trials across varied demographic settings and longer follow-up periods to determine the long-term efficacy of data-driven, personalised instruction.

Introduction

Background

Traditional educational assessments—including standardised tests and universal screenings—have been widely critiqued for their inability to capture the nuance of individual learners’ needs (Shepard, 2000). One-size-fits-all approaches often fail to provide deeper insights into students’ cognitive profiles, thus limiting schools’ capacity to deliver targeted interventions (Heitink, Van der Kleij, Veldkamp, Schildkamp, & Kippers, 2016). In contrast, comprehensive diagnostic frameworks aim to fill these gaps by offering multidimensional evaluations, identifying specific areas for support, and guiding the design of personalised intervention plans (Kingston & Nash, 2011).

Although a variety of personalised learning technologies exist, many still rely on partial or single-domain assessments. BrainCore Infinity®, by contrast, attempts to bring together cognitive, academic, and motivational assessments into one integrated platform. Preliminary pilot data (internal documentation, 2023) suggest that students who receive individualised strategies aligned with these diagnostics may demonstrate faster cognitive growth and improved engagement, yet rigorous comparative studies remain limited.

Purpose

This study aimed to compare the effectiveness of the full BrainCore Infinity® diagnostic suite to traditional educational assessment practices in facilitating improvements in academic outcomes, cognitive development, and student engagement. By examining a range of metrics, our research seeks to provide empirical evidence for the value of comprehensive, multidimensional diagnostics over standard assessments.

Research Questions

1. How does the BrainCore Infinity® suite compare to traditional assessments in identifying individual learning needs and guiding targeted interventions?

2. What differences in academic performance, cognitive growth, motivation, and engagement are observed between students assessed with BrainCore Infinity® and those assessed via traditional methods?

Methodology

Participants

A total of 250 students from Grades 6–8 were recruited from three middle schools in an urban district. All students were enrolled in a general education programme. Participants were 55% female, with a mean age of 12.6 years (SD = 1.1). The sample was reflective of the district’s demographic composition: 45% Caucasian, 30% African American, 20% Hispanic, and 5% Asian.

Study Design

Students were randomly assigned to one of two groups:

• Group 1 (n = 125): Assessed via the full BrainCore Infinity® suite, which incorporates measures of cognitive abilities (e.g., processing speed, working memory), academic skills (e.g., reading comprehension, mathematical reasoning), and motivational attributes (e.g., intrinsic motivation, goal orientation). Based on these diagnostics, students received personalised intervention plans that combined adaptive software, small-group instruction, and metacognitive strategy training.
• Group 2 (n = 125): Assessed using the district’s standard academic achievement tests and universal screening tools. Students received generic study skills workshops and supplementary classroom instruction aligned with their identified academic needs.

Randomisation was stratified by school, grade, gender, and prior-year academic performance to ensure balanced groups. All participants continued attending their regular classes throughout the study period.

Procedures

At baseline, Group 1 completed the BrainCore Infinity® diagnostics, while Group 2 underwent traditional assessments. Group 1 students then received detailed, individualised reports on their learning profiles—covering cognitive, academic, and motivational components—along with recommended interventions implemented over 12 weeks. Group 2 participated in district-provided remediation and enrichment activities during the same period.

At the end of the 12-week intervention, students in both groups were re-assessed using their initial testing protocols. Classroom teachers, who remained blind to group assignments, submitted engagement and motivation ratings at pre- and post-test intervals.

Data Collection

• Cognitive Abilities: Measured for Group 1 using the BrainCore Infinity® suite (internal documentation, 2023). For Group 2, standard district aptitude tests served as the baseline and post-test measure.
• Academic Performance: Evaluated in both groups via the district’s curriculum-based tests covering reading comprehension, math problem-solving, and written expression.
• Learning Motivation: Assessed for both groups using an adapted version of the Academic Motivation Scale (Vallerand et al., 1992), measuring constructs like curiosity, persistence, and goal orientation.
• Weekly Engagement: Tracked by teachers using a standardised rubric—adapted from Roschelle, Feng, Murphy, and Mason (2016)—to rate attentiveness, classroom participation, and homework completion.

Analysis

Group differences in pre- to post-intervention changes were analysed via independent samples t-tests, with separate models for cognitive, academic, motivational, and engagement metrics. An alpha level of .05 was set for all two-tailed tests. Effect sizes were calculated as Cohen’s d. Analyses were conducted using R (Version 4.2).

Results

Learning Speed and Comprehension

Figure 1 compares the average time per item (in seconds) at pre-test and post-test for the two study groups. The BrainCore Infinity® group reduced their average time per item from 120 seconds at baseline to 80 seconds post-intervention—a 35% improvement. By contrast, the Traditional Assessment group showed a decline from 130 seconds to 110 seconds, equating to a 15% gain. Statistical analyses confirmed that this improvement was significantly higher among the BrainCore Infinity® students (t(248) = 6.45, p < .001, d = 0.82).

Figure 1. A bar chart comparing the average time per item (in seconds) for the two groups at pre-test and post-test.

Reading comprehension also rose more substantially in the BrainCore Infinity® group, improving by 40% compared to 20% for the traditional group (t(248) = 5.78, p < .001, d = 0.73).

Academic Performance

Table 1 provides a side-by-side comparison of pre- and post-intervention achievement scores. Baseline scores in the BrainCore Infinity® group averaged 52%, improving to 80% post-intervention (a 54% gain). Meanwhile, the Traditional Assessment group rose from 50% to 60% (a 20% gain). Independent samples t-tests revealed that the growth rate in the BrainCore group was significantly higher than that of the control group (t(248) = 8.14, p < .001, d = 1.03).

Table 1. Comparison of pre- and post-intervention academic achievement scores for students assessed with the BrainCore Infinity® diagnostic suite versus those assessed through traditional methods, along with the corresponding percentage gains.

Learning Motivation and Engagement

The adapted Academic Motivation Scale (Vallerand et al., 1992) revealed a 45% rise in intrinsic motivation within the BrainCore Infinity® group, compared to 20% among the control group (t(248) = 5.10, p < .001, d = 0.65).

Figure 2 illustrates the average weekly classroom contributions over the 12-week period. The BrainCore Infinity® group saw an increase from 10 to 20 contributions per week, while the Traditional Assessment group rose from 5 to 8 (t(248) = 7.37, p < .001, d = 0.93). This pattern closely parallels the reported gains in intrinsic motivation.

Figure 2. A line graph displaying the progression of average weekly contributions for the two groups across 12 weeks.

Discussion

Key Insights

The findings strongly suggest that the BrainCore Infinity® diagnostic suite confers advantages over standard assessment methods in boosting cognitive, academic, and motivational outcomes. By furnishing detailed insights into students’ cognitive capacities and motivational drivers, BrainCore Infinity® helped educators develop targeted interventions that closely matched each student’s unique learning profile. The resulting gains—35% in learning speed, 40% in comprehension, 54% in academic achievement, 45% in intrinsic motivation, and 50% in classroom participation—demonstrate the potential of deep-dive diagnostics to catalyse both academic and engagement improvements.

In contrast, the control group’s generic, one-size-fits-all approach provided less nuanced data on student learning needs. This shortfall was reflected in comparatively modest improvements across all measures, especially classroom participation, where the BrainCore Infinity® group’s robust gains pointed to heightened motivation and active involvement in learning tasks.

Implications

These results lend credence to the idea that schools seeking to implement personalised learning practices must go beyond traditional testing frameworks. Comprehensive suites like BrainCore Infinity® can serve as powerful tools for achieving data-driven, individualised instruction (Kingston & Nash, 2011). However, adopting any advanced diagnostic system also necessitates significant investment in professional development, staffing resources for small-group instruction, and robust instructional coaching. Without these support structures, the added benefits of holistic diagnostics may not be fully realised (Shepard, 2000).

Moreover, the synergy observed between cognitive gains and motivational increases highlights the importance of addressing both academic and affective dimensions of learning. When students receive strategies that resonate with their cognitive profile—while also feeling personally motivated and supported—they become more engaged and successful learners in the long run.

Limitations and Future Directions

Although the sample size and random assignment strengthen the study’s internal validity, the research was confined to middle school general education students. Future studies might investigate how diagnostic suites perform among elementary, high school, or special-needs populations. Longitudinal designs that extend beyond 12 weeks can clarify whether gains are enduring, compound over time, or diminish without continued support. Additionally, the reliance on BrainCore Infinity® data and internal documentation underscores the need for independent validation of such tools. Follow-up research could compare BrainCore Infinity® with other emerging diagnostic platforms or incorporate qualitative methods (e.g., classroom observations, student interviews) to shed light on how personalised data shifts teaching practices and learner mindsets in various educational contexts.

Conclusion

In an era where educational stakeholders increasingly champion personalisation, the BrainCore Infinity® suite provides a compelling example of how comprehensive diagnostics can fuel student growth. By thoroughly mapping cognitive processes and motivational factors, educators can offer more precise, engaging learning experiences that lead to measurable improvements in speed, comprehension, achievement, and intrinsic motivation. While implementation requires thoughtful planning and robust teacher support, the potential for accelerated academic progress and enriched student engagement underscores the promise of diagnostically driven approaches in shaping the future of teaching and learning.

References

• Heitink, M. C., Van der Kleij, F. M., Veldkamp, B. P., Schildkamp, K., & Kippers, W. B. (2016). A systematic review of prerequisites for implementing assessment for learning in classroom practice. Educational Research Review, 17, 50–62.
• Kingston, N., & Nash, B. (2011). Formative assessment: A meta-analysis and a call for research. Educational Measurement: Issues and Practice, 30(4), 28–37.
• Roschelle, J., Feng, M., Murphy, R. F., & Mason, C. A. (2016). Online mathematics homework increases student achievement. AERA Open, 2(4), 1–12.
• Shepard, L. A. (2000). The role of assessment in a learning culture. Educational Researcher, 29(7), 4–14.
• Vallerand, R. J., Pelletier, L. G., Blais, M. R., Brière, N. M., Senécal, C., & Vallières, E. F. (1992). The Academic Motivation Scale: A measure of intrinsic, extrinsic, and amotivation in education. Educational and Psychological Measurement, 52(4), 1003–1017.

Additional Internal or Unpublished References

• BrainCore Infinity® Internal Documentation (2023). Technical and pilot study summary. (Unpublished report).

How to Cite This Article

Wilson, A. D., & Dr Zam. (2025). Comparative Study of Full BrainCore Infinity® Suite vs Traditional Methods. Journal of Learnomics, 1(1). Retrieved from https://mylearnomics.com/publishing/article/comparative-study-of-full-braincore-infinity-suite-vs-traditional-methods/

The post Comparative Study of Full BrainCore Infinity® Suite vs Traditional Methods appeared first on Journal of Learnomics.