Integration of Cognitive Technology in Learning Assessment and Evaluation

Abstract

The integration of technology in education, especially cognitive technology, is important to increase the efficiency and effectiveness of the learning assessment and evaluation process. Cognitive technology, which combines artificial intelligence and machine learning, offers new possibilities for adapting and personalizing learning experiences. However, its use in learning assessment is still limited and requires further investigation to identify its effectiveness. This research aims to analyze and evaluate the role of cognitive technology in learning assessment and evaluation. The main objective is to determine the extent to which this technology can improve the accuracy and relevance of feedback provided to students and explore the potential for improving learning outcomes through adapting learning content based on assessment data. This research uses quantitative methods with an experimental design. The research sample involved students at a university who used a cognitive technology-based assessment system during one academic semester. The results show that using cognitive technology in assessment can improve accuracy in diagnosing learning weaknesses and provide more personalized and timely feedback. Additionally, students who took assessments with cognitive technology showed greater improvements in academic achievement compared to a control group that did not use similar technology. This research concludes that integrating cognitive technology in learning assessment and evaluation offers great potential to improve the educational process by providing more relevant and personalized feedback. This technology supports teachers in identifying individual learning needs and helps students understand their weaknesses, facilitating more effective and efficient learning. Keywords: The integration of technology in education, especially cognitive technology, is important to increase the efficiency and effectiveness of the learning assessment and evaluation process. Cognitive technology, which combines artificial intelligence and machine learning, offers new possibilities for adapting and personalizing learning experiences. However, its use in learning assessment is still limited and requires further investigation to identify its effectiveness. This research aims to analyze and evaluate the role of cognitive technology in learning assessment and evaluation. The main objective is to determine the extent to which this technology can improve the accuracy and relevance of feedback provided to students and explore the potential for improving learning outcomes through adapting learning content based on assessment data. This research uses quantitative methods with an experimental design. The research sample involved students at a university who used a cognitive technology-based assessment system during one academic semester. The results show that using cognitive technology in assessment can improve accuracy in diagnosing learning weaknesses and provide more personalized and timely feedback. Additionally, students who took assessments with cognitive technology showed greater improvements in academic achievement compared to a control group that did not use similar technology. This research concludes that integrating cognitive technology in learning assessment and evaluation offers great potential to improve the educational process by providing more relevant and personalized feedback. This technology supports teachers in identifying individual learning needs and helps students understand their weaknesses, facilitating more effective and efficient learning.