A Cognitive Level Evaluation Method Based on a Deep Neural Network for Online Learning: From a Bloom's Taxonomy of Cognition Objectives Perspective.

Yan Cheng,Gang Wu,Jing Huang,Haomai Chen,Zhuang Cai,Yingying Cai

doi:10.3389/fpsyg.2021.661235

Yan Cheng, Gang Wu + Show 4 more

Open Access

https://doi.org/10.3389/fpsyg.2021.661235

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Abstract

The evaluation of the learning process is an effective way to realize personalized online learning. Real-time evaluation of learners’ cognitive level during online learning helps to monitor learners’ cognitive state and adjust learning strategies to improve the quality of online learning. However, most of the existing cognitive level evaluation methods use manual coding or traditional machine learning methods, which are time-consuming and laborious. They cannot fully mine the implicit cognitive semantic information in unstructured text data, making the cognitive level evaluation inefficient. Therefore, this study proposed the bidirectional gated recurrent convolutional neural network combined with an attention mechanism (AM-BiGRU-CNN) deep neural network cognitive level evaluation method, and based on Bloom’s taxonomy of cognition objectives, taking the unstructured interactive text data released by 9167 learners in the massive open online course (MOOC) forum as an empirical study to support the method. The study found that the AM-BiGRU-CNN method has the best evaluation effect, with the overall accuracy of the evaluation of the six cognitive levels reaching 84.21%, of which the F1-Score at the creating level is 91.77%. The experimental results show that the deep neural network method can effectively identify the cognitive features implicit in the text and can be better applied to the automatic evaluation of the cognitive level of online learners. This study provides a technical reference for the evaluation of the cognitive level of the students in the online learning environment, and automatic evaluation in the realization of personalized learning strategies, teaching intervention, and resources recommended have higher application value.

Highlights

Compared to traditional classroom teaching, online learning breaks the traditional teaching form and provides learners with abundant learning resources, diversified learning methods, and an accessible learning space, making learners the learning leaders
This paper proposes a bidirectional gated recurrent convolutional neural network model based on the attention mechanism (AM-bidirectional gate recurrent unit (BiGRU)-convolutional neural networks (CNNs)), which can extract the cognitive level features of the discussion posts to realize the automatic evaluation of the cognitive level of online learners
Interactive text data of learners were taken from the online learning platform and preprocessed, the automatic cognitive evaluation methods for BiGRU, CNN, BiGRU-CNN, and AM-BiGRU-CNN deep neural network were constructed

Summary

Introduction

Compared to traditional classroom teaching, online learning breaks the traditional teaching form and provides learners with abundant learning resources, diversified learning methods, and an accessible learning space, making learners the learning leaders. It requires that learners have a clearer understanding of the individual and the environment, to be able to clarify their. A timely evaluation of the cognitive level of learners helps them understand their cognitive level and adjust learning strategies in time (Feng et al, 2016). It can help teachers obtain learners’ cognitive level information in time, implement teaching strategies more accurately, and provide personalized teaching interventions

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