Applied System Innovation

In the evolving landscape of digital education, there is an increasing need to enhance traditional Learning Management Systems (LMSs) by integrating innovative pedagogical practices that promote active participation and learner autonomy. This study presents the transformation of a Greek LMS platform into an open learning ecosystem, incorporating three key educational innovations: collaborative hackathons, microcredentials, and blended learning support. The primary goal was to modernize the LMS in a way that encourages deeper engagement, social learning, collaboration, and mixed learning. To accomplish this objective, the system integrated advanced innovative tools designed to facilitate structured collaborative processes including hackathons, microcredentials aligned with specific learning objectives, and blended learning through flexible content delivery and student learning tracking tools. The use of these tools in the educational process contributes to the creation of a more dynamic and participatory learning environment, where knowledge is co-shaped and learning acquires a social character. In addition, the tools promote differentiated learning, allowing students to engage at their own pace and in their own way.

The paper presents methods of applying AI to generate mathematical and programming exercises for the purpose of creating courses on an educational platform. Various challenges and advantages are highlighted and discussed in the context of a new interactive platform—Compass. The proposed learning methods based on user–platform interaction are described, along with the results of evaluations conducted among university students who learned with Compass.

The acquisition of images of road surfaces not only establishes a theoretical foundation for road maintenance by relevant departments but also is instrumental in ensuring the safe operation of highway transportation systems. To address the limitations of traditional road surface image acquisition systems, such as low collection speed, poor image clarity, insufficient information richness, and prohibitive costs, this study has developed a high-speed binocular-vision-based system. Through theoretical analysis, we developed a complete system that integrates hybrid anti-shake technology. Specifically, a hardware device was designed for stable installation at the rear of high-speed vehicles, and a software algorithm was implemented to develop an electronic anti-shake module that compensates for horizontal, vertical, and rotational motion vectors with sub-pixel-level accuracy. Furthermore, a road surface image fusion algorithm that combines the stationary wavelet transform (SWT) and nonsubsampled contourlet transform (NSCT) was proposed to preserve multi-scale edge and textural details by leveraging their complementary multidirectional characteristics. Experimental results demonstrate that the fusion algorithm based on SWT and NSCT outperforms those using either SWT or NSCT alone across quality evaluation metrics such as QAB/F, SF, MI, and RMSE: at 80 km/h, the SF value reaches 4.5, representing an improvement of 0.088 over the SWT algorithm and 4.412 over the NSCT algorithm, indicating that the fused images are clearer. The increases in QAB/F and MI values confirm that the fused road surface images retain rich edge and detailed information, achieving excellent fusion results. Consequently, the system can economically and efficiently capture stable, clear, and information-rich road surface images in real-time under high-speed conditions with low energy consumption and outstanding fidelity.