Enhancing Teacher Self-Efficacy through CLT-Aligned Formative Assessment Tools: A Cognitive Load-Optimized Approach for High School Instruction

We propose a new system aimed at improving high school educators’ self-efficacy by refining their cognitive handling of formative assessment data with tools aligned to Cognitive Load Theory (CLT). Traditional evaluation frameworks frequently burden educators with unnecessary cognitive tasks, shifting focus away from the improvement of teaching practices. The proposed framework bridges this gap by introducing three essential elements: a simplified data visualization engine converting intricate assessment data into understandable dashboards, alongside a module prioritizing contextually relevant metrics that employs attention mechanisms to rank pedagogical importance, and built-in instructional prompts producing actionable recommendations derived from established heuristics. The system merges effortlessly with current workflows and substitutes manual data analysis with automated, intellectually streamlined procedures. Moreover, empirical validation focuses on measuring improvements in teacher self-efficacy and reductions in cognitive load during data-analysis tasks. The microservices architecture guarantees scalability and low-latency performance, whereas the focus on CLT principles sets this method apart from conventional formative assessment tools. Our contributions are centered on explicitly modeling and reducing teachers’ cognitive load, thus supporting more effective instructional decision-making. The results suggest potential for widespread adoption in high school settings, where teacher self-efficacy is critical for student success.