Learning Evaluation Methods in University based on Data Mining
AUTHORS
Golshah Abawajy,Charles Sturt University, Wagga Wagga, Australia
ABSTRACT
One of the current frontier points of learning evaluation is to focus on whether and how to use powerful digital technology to analyze digital data. This paper proposes a learning evaluation method based on big data. This paper constructs a new standard for the development of evaluation tools—metrolytic standards. The combination of standards used in the field of learning analytics and commonly used methods in educational measurement provides a framework for ensuring the reliability and validity of all educational evaluations. Measurement and analysis standards include quality requirements for the reliability, validity, accuracy, or interpretability of the test. These requirements are usually only applicable to high-risk, large-scale evaluations, such as PISA, SAT, or GMAT. The application of measurement analysis standards is based on a solid understanding of evaluation and its role in learning, combined with the advantages of learning analysis, artificial intelligence, and measurement science, and provides a choice for researchers in the frontier field of evaluation.
KEYWORDS
Digital technology, Artificial intelligence, Learning evaluation tools, Learning evaluation methods
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