APPROACH TO LEARNING BEHAVIOUR ANALYSIS FOR HIGHER-ORDER THINKING DEVELOPMENT: EDUCATIONAL BIG DATA PERSPECTIVE
Volume 2, Issue 10, Pp 1-7, 2024
DOI: https://doi.org/10.61784/tsshr3093
Author(s)
Ji Li1, Jin Lu2*
Affiliation(s)
1Research Management Office, Shenzhen Polytechnic University, Shenzhen 518055, Guangdong, China.
2Guangdong Key Laboratory of Big Data Intelligence for Vocational Education, Shenzhen Polytechnic University, Shenzhen 518055, Guangdong, China.
Corresponding Author
Jin Lu
ABSTRACT
With the rapid development of information technology, educational big data gradually become an important resource in the field of education. Characteristic by large data volume, diverse types and low value density, educational big data can provide rich information support for education and teaching. Higher-order thinking, as a necessary quality of innovative talents, has been increasingly valued in the field of education. Educational big data is closely linked to the development of higher-order thinking, and through the analysis of educational big data, it can provide an in-depth understanding of students’ learning behaviors and provide a basis for the cultivation of students’ higher-order thinking. The purpose of this paper is to explore the mechanism of promoting the development of students‘ higher-order thinking through the analysis of students’ learning behaviors in the perspective of education big data. After training, the machine automatically annotated text used in the examples of this paper achieves a score of 4.5 or more, with an accuracy rate close to 0.98, which can meet the needs of classroom applications. It can be seen that when this paper is applied to actual classroom evaluation, it lays an important foundation for carrying out large-scale comparative research on classroom teaching as well as digging into the laws of classroom dialogue, and it can break down the barriers of mathematics teaching between districts and schools, break down the effect of data silos, and provide a relatively uniform basis for the comparison and evaluation of the level of classroom teaching.
KEYWORDS
Big data in education; Higher order thinking; Learning behaviour analysis
CITE THIS PAPER
Ji Li, Jin Lu. Approach to learning behaviour analysis for higher-order thinking development: educational big data perspective. Trends in Social Sciences and Humanities Research. 2024, 2(10): 1-7. DOI: https://doi.org/10.61784/tsshr3093.
REFERENCES
[1] Hasan R, Palaniappan S, Raziff A R A, et al. Student academic performance prediction by using decision tree algorithm//2018 4th international conference on computer and information sciences (ICCOINS). IEEE, 2018: 1-5.
[2] Burgos C, Campanario M L, de la Pe?a D, et al. Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout. Computers & Electrical Engineering, 2018, 66: 541-556.
[3] Amrieh E A, Hamtini T, Aljarah I. Mining educational data to predict student’s academic performance using ensemble methods. International journal of database theory and application, 2016, 9(8): 119-136.
[4] Cobb J A. Relationship of discrete classroom behaviors to fourth-grade academic achievement. Journal of Educational Psychology, 1972, 63(1): 74.
[5] McKinney J D, Mason J, Perkerson K, et al. Relationship between classroom behavior and academic achievement. Journal of Educational Psychology, 1975, 67(2): 198.
[6] Allen J, Gregory A, Mikami A, et al. Observations of effective teacher–student interactions in secondary school classrooms: Predicting student achievement with the classroom assessment scoring system—secondary. School psychology review, 2013, 42(1): 76-98.
[7] Bidwell J, Fuchs H. Classroom analytics: Measuring student engagement with automated gaze tracking. Behav Res Methods, 2011, 49(113).
[8] Thomas C, Jayagopi D B. Predicting student engagement in classrooms using facial behavioral cues//Proceedings of the 1st ACM SIGCHI international workshop on multimodal interaction for education. 2017: 33-40.
[9] TS A, Guddeti R M R. Automatic detection of students’ affective states in classroom environment using hybrid convolutional neural networks. Education and information technologies, 2020, 25(2): 1387-1415.
[10] Wang X, Zhao S ,Guo L , et al. Graph CA: Learning From Graph Counterfactual Augmentation for Knowledge Tracing. IEEE/CAA Journal of Automatica Sinica, 2023, 10(11): 2108-2123.
[11] Gong J, Wang S, Wang J, et al. Attentional graph convolutional networks for knowledge concept recommendation in moocs in a heterogeneous view//Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 2020: 79-88.
[12] Hongwei Wang, Miao Zhao, Xing Xie, et al. Knowledge Graph Convolutional Networks for Recommender Systems. In The World Wide Web Conference (WWW '19). Association for Computing Machinery, New York, NY, USA, 2019, 3307-3313.
[13] Shimizu R, Matsutani M, Goto M. An explainable recommendation framework based on an improved knowledge graph attention network with massive volumes of side information. Knowledge-Based Systems, 2022, 239: 107970.
[14] Bing Q, Zhu Q, Dou Z. Cognition-aware knowledge graph reasoning for explainable recommendation//Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 2023: 402-410.
[15] Corbett A T, Anderson J R. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, 1994, 4: 253-278.
[16] Piech C, Bassen J, Huang J, et al. Deep knowledge tracing. Advances in neural information processing systems, 2015, 28.
[17] Nakagawa H, Iwasawa Y, Matsuo Y. Graph-based knowledge tracing: modeling student proficiency using graph neural network//IEEE/WIC/ACM International Conference on Web Intelligence. 2019: 156-163.
[18] Yang Y, Shen J, Qu Y, et al. GIKT: a graph-based interaction model for knowledge tracing//Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part I. Springer International Publishing, 2021: 299-315.
[19] TONG H, WANG Z, ZHOU Y, et al. HGKT: Introducing hierarchical exercise graph for knowledge tracing. 2006. https://arxiv.org/abs/2006.16915.