Emotion Classification and Achievement of Students in Distance Learning Based on the Knowledge State Model
To sum up, there have been significant advances in the automatic perception of emotion in distance learning. The learning state of learners can be predicted based on the real situation of each facial feature. Finally, according to the predicted results, an adaptive learning system is adopted to automatically adjust learning strategies, recommend learning resources, and provide early warnings. However, there are still many problems in the research on emotion classification of distance-learning students based on knowledge state model. For example, emotion recognition models are less accurate in identifying emotions such as pleasure, concentration, confusion, and boredom. Facial video-based emotion recognition methods have a lot of room for improvement. The use of single mode instead of fusion multi-mode signals leads to weak generalization ability of the model, and the knowledge state model is not widely used. In this study, a learner knowledge state model integrating learning emotions under the background of digital teaching is proposed, and a multi-dimensional online classification model of students’ emotions is constructed based on ResNet 18 neural network. Two modules of feature embedding and feature aggregation are added to identify students’ cognitive emotional states in distance learning. Finally, this study analyzes the correlation between the emotion classification of distance learning students and their grades, and verifies the validity of the emotion classification model in distance learning.
2. Related Works
The above studies contain some innovations in content and perspective, but they represent preliminary exploratory research, in terms of a knowledge state model that integrates learning emotions, and there are still many deficiencies. Due to the complexity of learning emotions and the diversity of learner models, this study focuses on the following three issues:
How to effectively classify students’ emotions according to facial images in distance learning?
How to build a knowledge state model integrating learning emotion?
How to explore the characteristics of knowledge state model integrating learning emotion?
4. Knowledge State Model with Learning Emotion
This study establishes a learner knowledge state model that takes learners’ learning emotions into consideration, which can effectively simulate real learning situations, be closer to learning reality, make more accurate predictions of learners’ knowledge state, reflect students’ current learning state, provide a more accurate basis for action for real educators to conduct academic early warnings and adjust teaching strategies, promote the realization of personalized teaching methods, and provide future education big data. The development of an intelligent teaching assistant system provides new modeling methods and practical reference.
In this model, students’ emotion classification modeling in distance learning is used as a weight variable for learning results.
represents one of the emotions of pleasure, focus, confusion, and boredom, and
represents the weight of the corresponding emotion. Emotion
uses Boolean representation to indicate the possible influence of emotion on learning results in the learning process. For example, the learners’ confusion emotion
(1 = confusion; 0 = no confusion) in the learning process is expressed as
in the learner model. W means that the possible influence of learners’ confusion on knowledge point learning will change with the changes in learners’ individual differences, number of learning items, and learning state.
Worcester Polytechnic Institute. This data set contains data on learning behavior and learning emotion. This data set was used to test the effectiveness of the proposed model. Students’ behavior data and emotional data in the learning process were refined, and five learning characteristics were selected.
The names and corresponding descriptions of the learning characteristics are as follows:
Analytical view: this feature indicates that learners actively click during the learning process;
Correct or incorrect first attempt: this feature indicates whether the learner’s first attempt at solving problems is correct or not;
Number of learning knowledge points: this feature represents the number of different knowledge points learned by learners;
Total attempts: this feature represents the total attempts of learners in the learning process;
Confusion emotion: this indicates the average probability of each confusion emotion.
After defining the above learning characteristics, this study compiled statistics on the frequency of the above five characteristics and learners’ learning time respectively, and finally obtained a data set containing information such as student number, five learning characteristics, and learning duration.
Through the analysis of 543 pieces of data, we can see that students felt confusion 197 times in the process of answering questions. The average number of perplexities of a single student was 7.04, and the average number of correct answers was 9.04. These two values are relatively low. This may be because the students do not fully understand the knowledge points investigated, do not feel confused, or do not choose the correct answer in the process of answering. However, this does not rule out the possibility that that the students are in a negative state in the process of learning.
is the batch size,
is the learning rate. It can be seen that learning rate and batch size are two very important parameters that affect gradient descent. In order to get the best model, this study conducted parameter adjustment experiments, and trained 100 epochs for each parameter setting. In order to save the best model in the training, each epoch was trained, and the verification set was used to verify once. When the accuracy of the verification set increased, the model was saved, and the best model obtained from the training was selected for evaluation on the test set. For learning rate, 1 × 10−4, 4 × 10−4, and 1 × 10−5 were set successively. For batch size, 8, 32, and 64 were set. The result of parameter adjustment is shown in Figure 3.
The abscissa represents different learning rates, and the ordinate represents different batch sample sizes. It can be seen that when the learning rate is set to 1 × 10−5 and the batch size is 32, the training model has the highest accuracy. After parameter adjustment, the best model is saved.
On the basis of the classical knowledge state model, this study proposes a knowledge state model that integrates learning emotions, adjusts the parameter composition in the algorithm, and uses the linear combination of parameters to represent the characteristics of learning emotions, making up for the lack of consideration of the basic knowledge state model in the learners’ learning emotions.
Under the background of digital teaching, the rapid development of data mining technology has laid a good technical foundation for personalized teaching. This study constructs an online classification model of students’ multi-dimensional emotions based on ResNet 18 neural network, innovatively adds two modules of feature embedding and feature aggregation, and uses frame attention network to extract frame level features closely related to students’ emotions. The average recognition accuracy of four cognitive emotions, namely pleasure, focus, confusion and weariness, reaches 88.76%. This study uses a mathematical modeling method to add emotional factors as parameters to the modeling of learners’ knowledge state, analyzes the correlation between students’ emotional classification and their scores in distance learning, verifies the effectiveness of students’ emotional classification model in the application of distance learning, and finds that there is a significant correlation between focus, confusion, boredom and learning results. In addition, this paper puts forward a new direction of thinking about teaching intervention. The study provides technical support for distance learning emotion classification and early warning, which is of great significance in helping teachers understand students’ emotional state in distance learning promptly, and in promoting students’ in-depth participation in the distance learning process.
In order to improve the research findings, future work will include:
Further analysis and discussion of the integration of other learning emotions and learner models, and finding more good emotional features that can predict learning results in the learning process;
Learning more methods to improve models, realizing the potential of models that comprehensively consider multiple learning emotions, enhancing the completeness of models that are more suitable for online learning in secondary schools, improving the reliability of learner models that integrate learning emotions, and developing learner knowledge.
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