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Causes Of Poor Academic Performance Of Pupils In Rural Primary Schools In Nsama District Of Northern Province – Zambia

Authors: Dionysius Makumba

Abstract: The Zambian education system has evolved over the years since independence. Zambia inherited it’s education system from Britain and has continued mostly in the same line of the British education system. The Zambian government has been working hard to improve on the education system and the education standards of it’s citizens. The performance of pupils is of great importance to government as well as the general citizenry. According to Nelson Mandela, “Education is the greatest weapon which you can use to change the world. “Thus, government has been doing everything possible to improve on the performance of pupils in schools. Measures such as free education policy by the United Party for National Development (UPND) government evidenced by overwhelming enrollments recorded in many schools for the past three years . Consequently, over enrollment has caused poor academic performance for example the number of pupils enrolled does not match with the infrastructure and number of teachers. Most teachers have gone to towns. Another cause is that most parents or guardians do not encourage their children to go to school because of their low educational attainment. They are the ones who send their children to do something else when they are suppose to be in school thereby contributing more to high rate of absenteeism. The nature of the district also is another cause to poor academic performance for instance, the road network is extremely bad which did not facilitate movement of standard officers to monitor schools. Vehicles break down on the way therefore schools that are far away from the district education board secretary’s (DEBS) office are often not monitored.

 

 

 

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Enhancing Contextual Emotion Recognition Using Large Vision-Language Models

Authors: Vaishnavi Chevale, Dr.Santosh Gaikwad, Dr. A. A. Khan, Dr. R. S. Deshpande§

 

 

Abstract: Contextual Emotion Recognition (CER) is crucial for human-computer interaction, requiring an understanding of emotions from linguistic and visual cues. This paper explores the integration of Large Vision- Language Models (LVLMs) to improve CER accuracy. The proposed framework employs multimodal learning to capture contextual dependencies, reduce biases, and enhance generalization. Experimental results demon- strate superior performance in real-world scenarios, de- creasing ambiguity and increasing robustness compared to traditional methods.

DOI: http://doi.org/

 

 

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