International Journal of New Developments in Education, 2025, 7(1); doi: 10.25236/IJNDE.2025.070111.
Wei Liu
School of Statistics, Shandong Technology and Business University, Yantai, Shandong, 264100, China
This study aims to explore the teaching method of combining educational statistics with computers based on big data fusion, aiming to improve the quality of education and optimize the allocation of teaching resources. The article first analyzes the profound impact of computer technology on the field of education in the era of big data, and points out that the teaching design combining educational statistics with computers has become an important research direction at present. Then, the characteristics of big data and its transformative role in the education industry are elaborated in detail, and it is proposed to design an intelligent teaching platform through big data technology. The study adopts a decision tree algorithm to ensure the consistency of system selection and user information based on the matching of user needs and teaching resources. In addition, the study also uses unsupervised learning methods to perform cluster analysis on user information, and realizes self-optimization of user information through the semantic description of ontology. Finally, the effectiveness of the method is verified through a series of teaching experiments, and the results show that the platform can provide a more personalized learning experience and teaching resource configuration. This study provides new ideas for educational practice, especially in the field of teaching combining educational statistics with computer technology, which has important reference value.
Big Data, Educational Statistics, Computer Teaching, Teaching Research
Wei Liu. Teaching Research of the Combination of Educational Statistics and Computer Based on Big Data Fusion. International Journal of New Developments in Education (2025), Vol. 7, Issue 1: 71-80. https://doi.org/10.25236/IJNDE.2025.070111.
[1] Huang Z, Fu Y, Dai F. Study for Multi-Resources Spatial Data Fusion Methods in Big Data Environment [J]. Intelligent automation and soft computing, 2018, 24(1):29-34.
[2] Dazhi, Yang. Educational research Instructional strategies Online course design Online pedagogy Statistics Teaching STEM online[J]. International journal of STEM education, 2017, 4(1):34-34.
[3] Kocaman-Karoglu A. Personal voices in higher education: A digital storytelling experience for pre-service teachers[J]. Education & Information Technologies, 2016, 21(5):1-16.
[4] Kmen B, Kl A. A Research about the Level of Using Language Teaching Methods and Its Effect on Some Variables: in Turkey[J]. Universal Journal of Educational Research, 2016, 4(9):1994-2001.
[5] Ruz F, Molina-Portillo E, Contreras J M. Attitudes Towards Descriptive Statistics And Its Teaching In Prospective Teachers[J]. Cadernos de Pesquisa, 2020, 50(178):964-980.
[6] Ritzhaupt A D, Valle N, Sommer M. Design, Development, and Evaluation of an Online Statistics Course for Educational Technology Doctoral Students: a Design and Development Case[J]. Journal of Formative Design in Learning, 2020, 4(2):119-135.
[7] Liu X, Wang W, Zhu G. Research and analysis of big data based on hadoop[J]. Boletin Tecnico, 2017, 55(4):382-386.
[8] Tnsing K M. Supporting the Production of Graphic Symbol Combinations by Children with Limited Speech: A Comparison of Two AAC systems[J]. Journal of Developmental and Physical Disabilities, 2016, 28(1):5-29.
[9] Cohen-Mimran R, Reznik-Nevet L, Korona-Gaon S. An Activity-Based Language Intervention Program for Kindergarten Children: A Retrospective Evaluation[J]. Early Childhood Education Journal, 2016, 44(1):1-10.
[10] Ben-Zvi D, Makar K. A Framework for Assessing Statistical Knowledge for Teaching Based on the Identification of Conceptions of Variability Held by Teachers[J]. The Teaching and Learning of Statistics, 2016, 10.1007/978-3-319-23470-0(Chapter 37):315-325.
[11] Umugiraneza O, Bansilal S, North D. Investigating teachers' formulations of learning objectives and introductory approaches in teaching mathematics and statistics[J]. International Journal of Mathematical Education in Science & Technology, 2018, 49(7-8):1148-1164.
[12] Saglimbene V, Strippoli G, Craig J C, et al. Statistics and data analyses—a new educational series for nephrologists - ScienceDirect[J]. Kidney International, 2020, 97(2):233-235.
[13] Ho, A. D. The New (Educational) Statistics: Properties of Scales That Matter[J]. Journal of Educational & Behavioral Statistics, 2016, 41(1):94-99.
[14] Carl M. Excel 2013 for educational and psychological statistics: a guide to solving practical problems [J]. Computing Reviews, 2016, 57(10):595-596.
[15] Nye J, Bryukhanov M, Polyachenko S. Descriptive statistics and regressions of 2D:4D and educational attainment based on RLMS data[J]. Data in Brief, 2017, 12(C):552-583.
[16] George, B, Macready, et al. The Use of Probabilistic Models in the Assessment of Mastery[J]. Journal of Educational Statistics, 2016, 2(2):99-120.
[17] Dennis, M, Roberts, et al. Reliability and Validity of a Statistics Attitude Survey[J]. Educational and Psychological Measurement, 2016, 40(1):235-238.
[18] Mcneish D M. Using Data-Dependent Priors to Mitigate Small Sample Bias in Latent Growth Models: A Discussion and Illustration Using Mplus[J]. Journal of Educational & Behavioral Statistics, 2016, 41(1):7-8.
[19] Bolsinova M, Tijmstra J. Posterior predictive checks for conditional independence between response time and accuracy[J]. Journal of Educational & Behavioral Statistics, 2016, 41(2):123-145.
[20] Tutz G, Berger M. Response Styles in Rating Scales: Simultaneous Modeling of Content-Related Effects and the Tendency to Middle or Extreme Categories[J]. Journal of Educational & Behavioral Statistics, 2016, 41(3):239-268.
[21] Liu Y, Tian W, Xin T. An Application of M2 Statistic to Evaluate the Fit of Cognitive Diagnostic Models[J]. Journal of Educational and Behavioral Statistics, 2016, 41(1):3-26.
[22] Mistler S A, Enders C K. A Comparison of Joint Model and Fully Conditional Specification Imputation for Multilevel Missing Data[J]. Journal of Educational and Behavioral Statistics, 2017, 42(4):432-466.
[23] Ackerman, T. Discussion of David Thissens Bad Questions: An Essay Involving Item Response Theory[J]. Journal of Educational & Behavioral Statistics, 2016, 41(1):90-93.
[24] Naumann A, Hartig J, Hochweber J. Absolute and Relative Measures of Instructional Sensitivity[J]. Journal of Educational and Behavioral Statistics, 2017, 42(6):678–705.
[25] Jeon M, Boeck P D, Linden W. Modeling Answer Change Behavior: An Application of a Generalized Item Response Tree Model. [J]. Journal of Educational and Behavioral Statistics, 2017, 42(4):467-490.
[26] Zhang J, Wang J S, Du W, et al. Big data analysis reveals the truth of lumbar fusion: gender differences[J]. Spine Journal, 2017, 17(5):754-755.