Teaching Quality Evaluation by Using Big Data in Guangdong University of Finance
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The objectives of this research were (1) to study big data-based teaching evaluation indicators for teaching quality evaluation at Guangdong University of Finance and (2) to explore how these indicators can be used to improve the validity and reliability of teaching quality evaluation. This study employed a convergent mixed-methods design. The sample were 286 faculty members selected by simple random sampling and 6 participants selected by purposive sampling for semi-structured interviews. The instruments were a structured questionnaire and a semi-structured interview guide. The data were analyzed by frequency, percentage, mean, standard deviation, and content analysis.
The results revealed that 1) the big data-based teaching evaluation indicators consisted of four dimensions, namely teaching process (M=3.72), learning effectiveness (M=3.80), course quality (M=3.89), and interaction quality (M=3.87), all
of which were rated at a high level. Within these, "clarity of teaching objectives" (M=4.01) and "timeliness of feedback" (M=4.02) were perceived as the most important items. 2) The application of big data-based teaching evaluation indicators was perceived by faculty and administrators as capable of improving the validity and reliability of teaching quality evaluation by providing more objective, timely, processoriented, and evidence-based information to support teaching improvement and educational management.
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