To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.

This study examined the dissatisfaction among Chinese medical students with online medical English courses, which overemphasize grammar yet fail to provide practical opportunities related to medical situations. This study compared co-teaching’s effects, involving native and non-native instructors, with a single-instructor (traditional) model on student satisfaction in online medical English courses. Using a qualitative design, pre- and post-course interviews were conducted with 49 second-year medical students across seven classes, exploring their perceptions of instruction, curriculum, and course satisfaction. The findings indicated that the co-teaching model improved student engagement and satisfaction, not specifically due to the native English-speaking instructor but likely because of the focus on more interactive and discussion-oriented strategies. In contrast, the single-instructor model maintained the traditional grammar-focused instruction, leading to lower satisfaction levels. Both instructional models faced limitations related to their reliance on textbooks for delivering core material needed for the course’s comprehensive exam. These results suggest that the instruction design and approach, rather than the native instructor alone, was the main driver of positive outcomes in co-teaching. The study’s findings suggest a need for curriculum reforms that reduce textbook dependence and incorporate more practical, interactive learning strategies. Future research should consider applying various research techniques, such as mixed-method approaches, longitudinal studies, and experimental designs, to comprehensively assess the long-term effects of instructional strategies and curriculum innovations on student outcomes.