TY  - RPRT
U1  - Arbeitspapier
A1  - Bekmoldayeva, Assel
T1  - An Intelligent learning management platform for Data-Driven course improvement
N2  - Modern online courses often replicate traditional instruction as static artifacts, failing to reveal the cognitive causes of learner errors. This paper proposes a self-improving educational ecosystem integrating interactive modules, diagnostic assessments, and AI-driven analytics in a closed feedback loop.
The model is implemented on a real platform using WordPress as a flexible application framework. Each module combines theory, interactive practice (H5P), and diagnostic assessment. Natural-language queries to an AI assistant serve as diagnostic signals, revealing hidden cognitive barriers. A three-level management model separates operational support (AI tutor), pedagogical quality assurance, and strategic product development. Continuous improvement follows a four-stage cycle: signal collection, pattern analysis, targeted instructional adjustments, and impact verification.
This approach demonstrates that intelligent, evidence-based learning management can transform courses into self-correcting systems, where each cohort improves the experience for the next.
AB  - Modern online courses often replicate traditional instruction as static artifacts, failing to reveal the cognitive causes of learner errors. This paper proposes a self-improving educational ecosystem integrating interactive modules, diagnostic assessments, and AI-driven analytics in a closed feedback loop.
The model is implemented on a real platform using WordPress as a flexible application framework. Each module combines theory, interactive practice (H5P), and diagnostic assessment. Natural-language queries to an AI assistant serve as diagnostic signals, revealing hidden cognitive barriers. A three-level management model separates operational support (AI tutor), pedagogical quality assurance, and strategic product development. Continuous improvement follows a four-stage cycle: signal collection, pattern analysis, targeted instructional adjustments, and impact verification.
This approach demonstrates that intelligent, evidence-based learning management can transform courses into self-correcting systems, where each cohort improves the experience for the next.
T3  - Constructor University Technical Reports - 61 
KW  - Intelligent learning platform
KW  - Learning analytics
KW  - Artificial intelligence in education
KW  - Data-Driven course management
Y1  - 2026
U6  - https://nbn-resolving.org/urn:nbn:de:gbv:579-opus-1013559
UN  - https://nbn-resolving.org/urn:nbn:de:gbv:579-opus-1013559
ER  -