Goodness of Fit of Skills Assessment Approaches: Insights from Patterns of Real vs. Synthetic Data Sets
نویسندگان
چکیده
This study investigates the issue of the goodness of fit of different skills assessment models using both synthetic and real data. Synthetic data is generated from the different skills assessment models. The results show wide differences of performances between the skills assessment models over synthetic data sets. The set of relative performances for the different models create a kind of “signature” for each specific data. We conjecture that if this signature is unique, it is a good indicator that the corresponding model is a good fit to the data.
منابع مشابه
Predictive performance of prevailing approaches to skills assessment techniques: Insights from real vs. synthetic data sets
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