An Evidential Reasoning Approach for College Students´ Autonomous Learning Ability Evaluation

The procedure for self-regulated learning ability evaluation inevitably involves human being´s subjective judgments and uncertainties. in order to model these uncertainties, this paper presents an interval evidential reasoning (IER) approach for autonomous learning ability assessment, which uses the analytical evidential reasoning (ER) algorithm to aggregate all evidence simultaneously. Two pairs of nonlinear optimization models are employed to estimate the upper and lower bounds of the combined belief degrees and to compute the maximum and the minimum expected utilities. the numerical example gives an application of IER, which demonstrates its validity for college students´ autonomous learning ability evaluation.