Learning style recognition method based on fusion label and stacking machine learning model
A machine learning model and learning style technology, applied in the field of information recognition, can solve problems such as time cannot guarantee data interpretability, and achieve the effects of reducing poor prediction performance, difficulty, and time
Pending Publication Date: 2021-09-17
SHANGHAI NORMAL UNIVERSITY
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But the deep learning method needs a lot of data to train the model, which will take a lot of time and cannot guarantee the interpretability of the data
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[0044] The purpose of the present invention is aimed at the application field of learning style recognition, and the proposed method based on fusion labels and stacked machine learning models can implicitly and dynamically identify learners' learning styles in the online learning process. Learners' learning styles are divided into four types according to Kolb's learning style theory: divergent, concentrated, adaptive, and assimilative, because learning styles will change with the age of learners, cognitive level, and environment. This variability makes the scale's static approach to capturing learning styles unreliable. In addition, because there is no guarantee that the collected data samples have a balanced learning style label, in order to ensure the accuracy of the recognition, a resampling technique is used to improve the imbalance of the data samples while using the stacked machine learning model to identify the learning style. , to obtain a higher recognition rate.
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The invention relates to a learning style recognition method based on a fusion label and a stacking machine learning model, and the method comprises the steps: 1), calculating learning styles corresponding to a learner through a Kolb learning style scale and an online learning behavior survey scale, and obtaining an intersection of two calculation results, and obtaining divided and undivided learning styles; 2) clustering the divided and undivided learning styles, determining the learning styles, labeling the learning styles, and supplementing the undivided learning styles; 3) performing correlation test on the obtained learning style label and online learning behavior characteristics collected by an online learning platform; 4) selecting training data and test data, and training by using the stacking machine learning model to obtain a complete stacking model; and 5) carrying out comprehensive performance evaluation on the trained stacking model. Compared with the prior art, the invention has the advantages of reducing the model training difficulty, improving the recognition accuracy and the like.
Description
technical field [0001] The invention relates to the technical field of information recognition, in particular to a learning style recognition method based on fusion labels and stacked machine learning models. Background technique [0002] Online education eliminates the time and space constraints of traditional education, allowing teachers and students to communicate anytime and anywhere, which brings the possibility of realizing the "teaching students in accordance with their aptitude" proposed by Confucius. Scholars such as Dunn, Kolb, Felder, and Keefe have long recognized that students have different styles of learning new knowledge, and the differences among students include personality characteristics, knowledge levels, learning abilities, and learning styles. Among them, learning style includes learning preference and learning characteristics. Finding suitable learning styles for students can guide students' learning, so the task of automatic identification of learni...
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Login to View More IPC IPC(8): G06K9/62G06N20/00
CPCG06N20/00G06F18/23213G06F18/254
Inventor 倪琴徐宇辉张波樊卓魏廷江
Owner SHANGHAI NORMAL UNIVERSITY