Integration of Cognitive Technology in Learning Assessment and Evaluation
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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.
Abidin, Moh. A., Fithriyah, N. N., Nirwana, R., Achmad, W., & Ummah, R. (2023). Implementation of The Independent Curriculum In The Post-Pandemic Period. Syekh Nurjati International Conference on Elementary Education, 1(0), 263. https://doi.org/10.24235/sicee.v1i0.14586
Akram, H., Abdelrady, A. H., Al-Adwan, A. S., & Ramzan, M. (2022). Teachers’ Perceptions of Technology Integration in Teaching-Learning Practices: A Systematic Review. Frontiers in Psychology, 13, 920317. https://doi.org/10.3389/fpsyg.2022.920317
Al-Dokhny, A., Drwish, A., Alyoussef, I., & Al-Abdullatif, A. (2021). Students’ Intentions to Use Distance Education Platforms: An Investigation into Expanding the Technology Acceptance Model through Social Cognitive Theory. Electronics, 10(23), 2992. https://doi.org/10.3390/electronics10232992
Anthonysamy, L., Choo, K. A., & Hin, H. S. (2020). Impact of cognitive and metacognitive strategies on learning performance in digital learning: What’s working and what’s not in the age of brilliant technology. Journal of Physics: Conference Series, 1529(5), 052019. https://doi.org/10.1088/1742-6596/1529/5/052019
Bahari, A. (2023). Challenges and Affordances of Cognitive Load Management in Technology-Assisted Language Learning: A Systematic Review. International Journal of Human–Computer Interaction, 39(1), 85–100. https://doi.org/10.1080/10447318.2021.2019957
Bio-inspired Computational Intelligence and Deep Learning Algorithms, 3D Modeling and Cognitive Neuro-Engineering Technologies, and Immersive Visualization and Internet of Things-based Decision Support Systems in the Virtual Environment of the Metaverse. (2023). Review of Contemporary Philosophy, 22(0), 278. https://doi.org/10.22381/RCP22202316
Chen, K., Cheng, X., Zhang, R., Cillo, G., & Ragusa, A. (2022). Unveiling the Role of Cross-Cultural and Cognitive Differences in Organizational Learning Mechanism of Technology-Acquiring Cross-Border Mergers and Acquisitions: Evidence From Emerging Market Enterprises. Frontiers in Psychology, 13, 863442. https://doi.org/10.3389/fpsyg.2022.863442
Chen, S., Shen, W., & Tan, X. (2023). Entrepreneurial Education Model Based on Interactive Technology and Cognitive Psychology. International Journal of Human–Computer Interaction, 1–13. https://doi.org/10.1080/10447318.2023.2189816
Corrigendum to The Interaction of Cognitive Profiles and Text-to-Speech Software on Reading Comprehension of Adolescents With Reading Challenges. (2021). Journal of Special Education Technology, 36(4), 336–336. https://doi.org/10.1177/01626434211046829
Deep Learning-based Sensing Technologies, Artificial Intelligence-based Decision-Making Algorithms, and Big Geospatial Data Analytics in Cognitive Internet of Things. (2021). Analysis and Metaphysics, 20(0), 159. https://doi.org/10.22381/AM20202111
Dugarova, T. T., Filippovich, Y. N., Bulanova, M. N., Bagramyan, E. R., Aigumova, Z. I., & Dugarov, T.-Z. T. (2020). Info-cognitive technologies in language education. Journal of Physics: Conference Series, 1691(1), 012085. https://doi.org/10.1088/1742-6596/1691/1/012085
George, F. J. P., & Vinay, M. (2019). Cognitive Technology for the Indian Higher Education: A Language Teaching and Learning Application. Journal of Computational and Theoretical Nanoscience, 16(5), 2368–2371. https://doi.org/10.1166/jctn.2019.7901
Herlambang, A. D., Wijayanti, N., & Zulvarina, P. (2023). Project-Based Learning Implementation Effect Comparison on the Students’ Cognitive and Psychomotor Learning Outcomes at Indonesian Vocational High School Majoring in Information Technology. Proceedings of the 8th International Conference on Sustainable Information Engineering and Technology, 348–353. https://doi.org/10.1145/3626641.3627019
Holographic Telepresence and Digital Twin Simulation Technologies, 3D Virtual Space Networking and Machine Learning-based Image Recognition Tools, and Environment Mapping and Cognitive Artificial Intelligence Algorithms in a Fully Connected Metaverse. (2023). Review of Contemporary Philosophy, 22(0), 119. https://doi.org/10.22381/RCP2220237
Hsu, C.-C., Chen, Y.-L., Lin, C.-Y., & Lien, W. (2022). Cognitive development, self-efficacy, and wearable technology use in a virtual reality language learning environment: A structural equation modeling analysis. Current Psychology, 41(3), 1618–1632. https://doi.org/10.1007/s12144-021-02252-y
Jalal, A., & Mahmood, M. (2019). Students’ behavior mining in e-learning environment using cognitive processes with information technologies. Education and Information Technologies, 24(5), 2797–2821. https://doi.org/10.1007/s10639-019-09892-5
Khan, A., Zeb, I., Zhang, Y., & Tahir. (2023). Impact of emerging technologies on cognitive development: The mediating role of digital social support among higher education students. IJERI: International Journal of Educational Research and Innovation, 20, 1–15. https://doi.org/10.46661/ijeri.8362
Kilag, O. K. T., Ignacio, R., Lumando, E. B., Alvez, G. U., Abendan, C. F. K., Quiñanola, N. M. P., & Sasan, J. M. (2022). ICT Integration in Primary School Classrooms in the time of Pandemic in the Light of Jean Piaget’s Cognitive Development Theory. International Journal of Emerging Issues in Early Childhood Education, 4(2), 42–54. https://doi.org/10.31098/ijeiece.v4i2.1170
Kornreich?Leshem, H., Benabentos, R., Hazari, Z., Potvin, G., & Kramer, L. (2022). The cognitive and affective roles of learning assistants in science, technology, engineering, and mathematics college classrooms: An exploration of classroom experiences and students’ metacognitive awareness and disciplinary identity. Science Education, 106(3), 545–572. https://doi.org/10.1002/sce.21703
Machine and Deep Learning Technologies, Location Tracking and Obstacle Avoidance Algorithms, and Cognitive Wireless Sensor Networks in Intelligent Transportation Planning and Engineering. (2022). Contemporary Readings in Law and Social Justice, 14(1), 41. https://doi.org/10.22381/CRLSJ14120223
Maniglio, F., & Barragán, V. (2022). Education, Science, and Technology Policies during Ecuador’s Citizens’ Revolution: Between Cognitive Dependency and the Struggle for the Social State. Latin American Perspectives, 49(3), 18–30. https://doi.org/10.1177/0094582X221083000
Metaverse Decentralized Governance and Networked Immersive Virtual Reality Systems, Machine Learning-based Image Recognition and Predictive Modeling Tools, and Cognitive Automation and Multisensor Fusion Technologies in Digital Hyper-Realistic Worlds. (2023). Review of Contemporary Philosophy, 22(0), 68. https://doi.org/10.22381/RCP2220234
Pan, M. A., Marfu’ah, S., & Dasna, I. W. (2021). The effect of the argument-driven inquiry (ADI) based on science, environment, technology, and society (SETS) to students’ concept understanding and scientific argument skill in buffer solution learning: Studied from cognitive style. 020038. https://doi.org/10.1063/5.0043621
Rajan, S., Bindu, G. V., & Mukherjee, S. (2021). Adopting Evolving Technologies to Aid Cognitive Abilities in Classroom Learning-Teaching. In K. A. Thomas, J. V. Kureethara, & S. Bhattacharyya (Eds.), Neuro-Systemic Applications in Learning (pp. 483–506). Springer International Publishing. https://doi.org/10.1007/978-3-030-72400-9_24
Remote Sensing and Edge Artificial Intelligence Computing Systems, Environment Perception and Geospatial Mapping Technologies, and Simulation Modeling and Machine Learning-based Image Recognition Tools in the 3D Cognitive Digital Twin Metaverse. (2023). Review of Contemporary Philosophy, 22(0), 208. https://doi.org/10.22381/RCP22202312
Saarinen, A. I. L., Lipsanen, J., Hintsanen, M., Huotilainen, M., & Keltikangas-Järvinen, L. (2021). The Use of Digital Technologies at School and Cognitive Learning Outcomes: A Population-Based Study in Finland. International Journal of Educational Psychology, 10(1), 1. https://doi.org/10.17583/ijep.2021.4667
Salman, O. H., Taha, Z., Alsabah, M. Q., Hussein, Y. S., Mohammed, A. S., & Aal-Nouman, M. (2021). A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work. Computer Methods and Programs in Biomedicine, 209, 106357. https://doi.org/10.1016/j.cmpb.2021.106357
Shi, Y., Yang, H., MacLeod, J., Zhang, J., & Yang, H. H. (2020). College Students’ Cognitive Learning Outcomes in Technology-Enabled Active Learning Environments: A Meta-Analysis of the Empirical Literature. Journal of Educational Computing Research, 58(4), 791–817. https://doi.org/10.1177/0735633119881477
Van Wart, M., Ni, A., Medina, P., Canelon, J., Kordrostami, M., Zhang, J., & Liu, Y. (2020). Integrating students’ perspectives about online learning: A hierarchy of factors. International Journal of Educational Technology in Higher Education, 17(1), 53. https://doi.org/10.1186/s41239-020-00229-8
Wang, C. C., Chen, H. C., & Hung, J. C. (2022). Employing Portable Eye Tracking Technology in Visual Attention of Cognitive Process: A Case Study of Digital Game-Based Learning. In Y.-M. Huang, S.-C. Cheng, J. Barroso, & F. E. Sandnes (Eds.), Innovative Technologies and Learning (Vol. 13449, pp. 480–490). Springer International Publishing. https://doi.org/10.1007/978-3-031-15273-3_53
Wang, J., Tigelaar, D. E. H., Zhou, T., & Admiraal, W. (2023). The effects of mobile technology usage on cognitive, affective, and behavioural learning outcomes in primary and secondary education: A systematic review with meta?analysis. Journal of Computer Assisted Learning, 39(2), 301–328. https://doi.org/10.1111/jcal.12759
Wu, Y.-T., Shen, P.-D., & Lin, C.-H. (2022). The Effects of Cognitive Apprenticeship and Co-Regulated Learning on Improving Student Computer Problem-Solving Skills and Learning Motivation: A Quasi-Experiment in an “Applied Information Technology: Office Software” Course. International Journal of Technology and Human Interaction, 18(1), 1–16. https://doi.org/10.4018/IJTHI.299355
Ying, S. T., Mun, N. K., Ab Rahim, A. A., Mohd Addi, M., & Osman, M. (2023). An Instrument to Measure Perceived Cognitive, Affective, and Psychomotor (CAP) Learning for Online Laboratory in Technology and Engineering Courses. Pertanika Journal of Science and Technology, 31(4). https://doi.org/10.47836/pjst.31.4.28
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