Construction of dynamic education evaluation model integrating traditional and digital dimensions: exploration of multi-dimensional index system based on big data and real-time data flow

Research Article
Open access

Construction of dynamic education evaluation model integrating traditional and digital dimensions: exploration of multi-dimensional index system based on big data and real-time data flow

Nianqi Wei 1*
  • 1 Taizhou Polytechnic College, Taizhou, China    
  • *corresponding author nianqiwei666@163.com
Published on 12 June 2025 | https://doi.org/10.54254/3049-7248/2025.23846
JEEPS Vol.3 Issue 2
ISSN (Print): 3049-7256
ISSN (Online): 3049-7248

Abstract

This study proposes a new educational assessment framework to realize the organic integration of traditional assessment methods and digital technology. The model builds a three-dimensional assessment system by integrating dynamic data flow and educational big data. Considering the limitation of traditional assessment methods relying on periodic summary, a continuous dynamic feedback mechanism and a personalized learning tracking module are innovatively introduced. The technical program deeply integrates the educational theoretical system and intelligent algorithm technology to establish a panoramic assessment model in multiple dimensions such as academic development, class participation, and learning emotion. The system integrates classroom recording data, online assessment results, learning behavior logs, and other multi-source information to form a holographic portrait of learners' growth. In the research process, real teaching areas are selected to conduct technical verification. Experimental data show that the framework can effectively capture the dynamic characteristics of the learning process and provide real-time decision support for adapting teaching strategies. The technology optimization direction focuses on fine-tuning the granularity of data acquisition and explores the possibility of integrating new data inputs such as wearable devices. The practical application of this evaluation framework marks an important breakthrough in the direction of an intelligent and real-time education quality monitoring system.

Keywords:

dynamic education evaluation, traditional education, digital education, big data, real-time data flow

Wei,N. (2025). Construction of dynamic education evaluation model integrating traditional and digital dimensions: exploration of multi-dimensional index system based on big data and real-time data flow. Journal of Education and Educational Policy Studies,3(2),88-92.
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References

[1]. Tetzlaff, L., Schmiedek, F., & Brod, G. (2021). Developing personalized education: A dynamic framework. Educational Psychology Review, 33, 863-882.

[2]. Faura-Martínez, Ú., & Cifuentes-Faura, J. (2022). Building a dynamic indicator on inclusive education in higher education. European Journal of Special Needs Education, 37(4), 690-697.

[3]. AlGerafi, M. A., Zhou, Y., Oubibi, M., & Wijaya, T. T. (2023). Unlocking the potential: A comprehensive evaluation of augmented reality and virtual reality in education. Electronics, 12(18), 3953.

[4]. Troussas, C., Krouska, A., Mylonas, P., & Sgouropoulou, C. (2023, September). Personalized learner assistance through dynamic adaptation of chatbot using fuzzy logic knowledge modeling. In 2023 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP) 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2023) (pp. 1-5). IEEE.

[5]. Al-Adwan, A. S., & Al-Debei, M. M. (2024). The determinants of Gen Z's metaverse adoption decisions in higher education: Integrating UTAUT2 with personal innovativeness in IT. Education and Information Technologies, 29(6), 7413-7445.

[6]. Pratikno, Y., Hermawan, E., & Arifin, A. L. (2022). Human resource ‘Kurikulum Merdeka’from design to implementation in the school: What worked and what not in Indonesian education. Jurnal Iqra': Kajian Ilmu Pendidikan, 7(1), 326-343.

[7]. Cahapay, M. (2021). Kirkpatrick model: Its limitations as used in higher education evaluation. International Journal of Assessment Tools in Education, 8(1), 135-144.

[8]. Alrakhawi, H. A., Jamiat, N., & Abu-Naser, S. S. (2023). Intelligent tutoring systems in education: a systematic review of usage, tools, effects and evaluation. Journal of Theoretical and Applied Information Technology, 101(4), 1205-1226.

[9]. Marks, B., & Thomas, J. (2022). Adoption of virtual reality technology in higher education: An evaluation of five teaching semesters in a purpose-designed laboratory. Education and information technologies, 27(1), 1287-1305.

[10]. Gravina, A. G., Pellegrino, R., Palladino, G., Imperio, G., Ventura, A., & Federico, A. (2024). Charting new AI education in gastroenterology: cross-sectional evaluation of ChatGPT and perplexity AI in medical residency exam. Digestive and liver disease, 56(8), 1304-1311.

Cite this article

Wei,N. (2025). Construction of dynamic education evaluation model integrating traditional and digital dimensions: exploration of multi-dimensional index system based on big data and real-time data flow. Journal of Education and Educational Policy Studies,3(2),88-92.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Journal：Journal of Education and Educational Policy Studies

Volume number: Vol.3
Issue number: Issue 2
ISSN：3049-7248(Print) / 3049-7256(Online)

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References

[1]. Tetzlaff, L., Schmiedek, F., & Brod, G. (2021). Developing personalized education: A dynamic framework. Educational Psychology Review, 33, 863-882.

[2]. Faura-Martínez, Ú., & Cifuentes-Faura, J. (2022). Building a dynamic indicator on inclusive education in higher education. European Journal of Special Needs Education, 37(4), 690-697.

[3]. AlGerafi, M. A., Zhou, Y., Oubibi, M., & Wijaya, T. T. (2023). Unlocking the potential: A comprehensive evaluation of augmented reality and virtual reality in education. Electronics, 12(18), 3953.

[4]. Troussas, C., Krouska, A., Mylonas, P., & Sgouropoulou, C. (2023, September). Personalized learner assistance through dynamic adaptation of chatbot using fuzzy logic knowledge modeling. In 2023 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP) 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2023) (pp. 1-5). IEEE.

[5]. Al-Adwan, A. S., & Al-Debei, M. M. (2024). The determinants of Gen Z's metaverse adoption decisions in higher education: Integrating UTAUT2 with personal innovativeness in IT. Education and Information Technologies, 29(6), 7413-7445.

[6]. Pratikno, Y., Hermawan, E., & Arifin, A. L. (2022). Human resource ‘Kurikulum Merdeka’from design to implementation in the school: What worked and what not in Indonesian education. Jurnal Iqra': Kajian Ilmu Pendidikan, 7(1), 326-343.

[7]. Cahapay, M. (2021). Kirkpatrick model: Its limitations as used in higher education evaluation. International Journal of Assessment Tools in Education, 8(1), 135-144.

[8]. Alrakhawi, H. A., Jamiat, N., & Abu-Naser, S. S. (2023). Intelligent tutoring systems in education: a systematic review of usage, tools, effects and evaluation. Journal of Theoretical and Applied Information Technology, 101(4), 1205-1226.

[9]. Marks, B., & Thomas, J. (2022). Adoption of virtual reality technology in higher education: An evaluation of five teaching semesters in a purpose-designed laboratory. Education and information technologies, 27(1), 1287-1305.

[10]. Gravina, A. G., Pellegrino, R., Palladino, G., Imperio, G., Ventura, A., & Federico, A. (2024). Charting new AI education in gastroenterology: cross-sectional evaluation of ChatGPT and perplexity AI in medical residency exam. Digestive and liver disease, 56(8), 1304-1311.

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