{'fraction_non_alphanumeric': 0.01937046004842615, 'fraction_numerical': 0.002421307506053269, 'mean_word_length': 5.138613861386139, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Assessment of communicative learning via Augmented Reality versus traditional method for aeronautical transportation

Advances in Augmented Reality (AR) applied to learning the assembly operation for Maintenance and Repair Operations (MRO), in terms of productivity, must certainly be evaluated. Having this in mind, we propose a congruent sequence of statistical procedures that lead us to our target of comparing the hypotheses for the mean time of training via Augmented Reality (AR) versus traditional method, in an aeronautical case of assembly of a component. We are going to do this because we think AR techniques provide an advanced and advantageous achievement in productivity for getting the training process. It has been endeavors to determine the work sample size for small sample cases at aeronautical sector with significance level (α) of 1%. Based on a good estimation of sample size (n), we determined the combined standard deviation ŝ which is required in the application of the Student t distribution. This distribution is used to perform our test of hypotheses: the learning via AR is faster than that acquired by mean of traditional method for a case of assembly of an aeronautical component using a RV-10 airplane kit.