Data Mining of Online Teaching Evaluation Based on Deep Learning
Abstract
:1. Introduction
- Detailed introduction of dictionary-based sentiment analysis methods, the BERT-LSTM model, and association rule mining.
- Calculation of sentiment scores for unannotated text data using sentiment lexicon methods, determining sentiment tendencies through calculated scores combined with manual judgment.
- Development of a teaching evaluation sentiment analysis model using BERT-LSTM for predicting and analyzing sentiment trends.
- Application of the Apriori algorithm to mine association rules from objective teaching evaluation data, followed by analysis based on the mining results.
2. Related Work
2.1. Sentiment Analysis
2.1.1. Lexicon-Based Methods
2.1.2. Machine Learning Methods
2.1.3. Deep Learning Methods
2.2. Association Rule Mining
3. Method
3.1. Sentiment Analysis Based on Sentiment Lexicon
3.1.1. Text Preprocessing
3.1.2. Calculating Sentiment Scores Using a Sentiment Lexicon
3.2. Sentiment Analysis Based on BERT-LSTM Model
3.2.1. BERT
3.2.2. LSTM
3.2.3. BERT-LSTM
3.3. Association Rule Mining Based on the Apriori Algorithm
3.3.1. Basic Concept of Association Rules
3.3.2. Apriori Algorithm
4. Subjective Evaluation of Teaching Analysis
4.1. Experimental Data Preparation
4.1.1. Preprocessing of Subjective Evaluation Data
4.1.2. Addressing Category Imbalance
4.2. Performance Metrics
4.3. Experimental Environment and Parameter Settings
4.4. Results
5. Objective Evaluation of Teaching Analysis
5.1. Objective Evaluation Data Preprocessing
5.1.1. Data Cleaning
5.1.2. Data Conversion
5.2. Association Rule Mining for Objective Assessment Data
5.2.1. Association Rule Mining Results
5.2.2. Association Rule Analysis
6. Conclusions and Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Type | Score |
---|---|
most | 8 |
very | 6 |
more | 4 |
over | 2 |
ish | 0.6 |
insufficiently | −1.5 |
Type | Score |
---|---|
positive | 1 |
negative | −1 |
no attitude | 0 |
Evaluation | Segment | Score |
---|---|---|
A very responsible teacher, Chaoxing and MOOC have his own time to record microclasses, as well as for the examination and non-examination students of different levels of difficulty of the knowledge points of the explanation, the class will also be uploaded to the Chaoxing so that we can review after class, on the homework of the explanation is also very important, he will also record the course for us to speak, Mr. Tan is the most responsible teacher I met in the university. | [‘very’, ‘responsible’, ‘class’, ‘time’, ‘record’, ‘micro classes’, ‘different ‘, ‘difficult’, ‘knowledge’, ‘explanation’, ‘learning’, ‘lesson’, ‘will’, ‘homework’, ‘explanation’, ‘very’, ‘important’, ‘university’, ‘met’, ‘the most’, ‘responsible’] | 23 |
Index | Evaluation | Score | Tendency | Label |
---|---|---|---|---|
1 |
| 49 | positive | 1 |
2 | The instruction was clearly delineated and organized, it just got in the way of the overall level being a little easy for me, but that’s not the teacher’s problem. | 0.4 | positive | 1 |
3 | Some particularly simple topics can be briefly summarized and less homework can be assigned. | −12 | negative | 2 |
4 | Classes are slightly boring. | −0.6 | negative | 2 |
Positive Simple in Forecast | Negative Simple in Forecast | |
---|---|---|
Positive simple in reality | TP | FN |
Negative simple in reality | FP | TN |
Hyperparameter | Conversion Result |
---|---|
learning rate | 0.00002 |
max_length | 75 |
batch_size | 16 |
LSTM units | 128 |
dropout rate | 0.2 |
optimizer | Adam |
loss function | SparseCategoricalCrossentropy |
epochs | 30 |
Item | Label | Precision | Recall | Accuracy |
---|---|---|---|---|
Neutral | 0 | 100.00% | 92.50% | 92.76% |
Positive | 1 | 87.80% | 88.89% | |
Negative | 2 | 90.48% | 92.68% |
Index | Item | Word |
---|---|---|
1 | At the beginning of the semester, the teacher clearly told us the course requirements and assessment methods. | Information |
2 | I can grasp the teacher’s main points. | Emphasis |
3 | The teacher’s content is moderate in difficulty and ease. | Moderate |
4 | The teacher can explain the knowledge points through practical cases. | Case |
5 | I feel the teacher is organized in class. | Orderliness |
6 | The teaching method adopted by the teacher works well for me. | Effectivity |
7 | Teachers can inspire me to think. | Enlighten |
8 | I think the teacher makes good use of the class time. | Efficiency |
9 | The teacher’s explanation made it easier for me to understand and master the course content. | Explain |
10 | The teacher’s lectures are full of spirit and not boring. | Interesting |
11 | I have the opportunity to participate in classroom interaction. | Interaction |
12 | The teacher often assigns us homework. | Homework |
13 | Teachers timely comment homework, answer questions carefully. | Answer |
14 | Teachers respect us and care about us. | Respect |
15 | The teacher is strict with his students. | Strictness |
16 | If I need to, I can contact the teacher to communicate. | Communication |
17 | The teachers are well-prepared and proficient in their lessons. | Preparation |
18 | Through the teacher’s guidance, I have mastered the main content of this course. | Guidance |
19 | The teacher’s words and deeds give me inspiration in life. | Edification |
20 | I think the teacher is one of the teachers teaching this semester. | Evaluation |
Item | Score | Level | Conversion Result |
---|---|---|---|
Information | 5 | 1 | Information1 |
Information | 4.5 | 2 | Information2 |
Information | 4 | 3 | Information3 |
Information | 3.5 | 4 | Information4 |
Information | 3 | 5 | Information5 |
Index | Antecedents | Consequents | Support | Confidence | Lift |
---|---|---|---|---|---|
1 | Orderliness1, Preparation1, Case1 | Explain1 | 0.83 | 0.98 | 1.08 |
2 | Enlighten1, Preparation1 | Explain1 | 0.84 | 0.98 | 1.09 |
3 | Preparation1 | Enlighten1, Orderliness1 | 0.84 | 0.90 | 1.05 |
4 | Orderliness1, Preparation1 | Efficiency1, Explain1 | 0.82 | 0.93 | 1.09 |
5 | Information1, Preparation1 | Emphasis1 | 0.85 | 0.96 | 1.08 |
6 | Preparation1, Case1 | Orderliness1 | 0.85 | 0.97 | 1.07 |
7 | Moderate1 | Explain1, Emphasis1 | 0.81 | 0.95 | 1.11 |
8 | Respect1 | Answer1 | 0.81 | 0.96 | 1.12 |
9 | Homework1 | Emphasis1 | 0.85 | 0.92 | 1.02 |
10 | Interesting1, Preparation1, Orderliness1 | Emphasis1 | 0.80 | 0.98 | 1.10 |
11 | Information1, Preparation1 | Guidance1 | 0.81 | 0.91 | 1.09 |
12 | Homework1, Information1 | Answer1 | 0.80 | 0.92 | 1.07 |
13 | Information1, Preparation1, Orderliness1 | Emphasis1 | 0.84 | 0.97 | 1.09 |
14 | Answer1, Explain1 | Enlighten1 | 0.80 | 0.97 | 1.10 |
15 | Preparation1 | Efficiency1, Emphasis1, Orderliness1 | 0.83 | 0.90 | 1.06 |
16 | Orderliness1, Case1 | Effectivity1 | 0.85 | 0.97 | 1.07 |
17 | Interesting1 | Emphasis1 | 0.82 | 0.97 | 1.08 |
18 | Enlighten1 | Efficiency1, Explain1, Emphasis1 | 0.82 | 0.93 | 1.10 |
19 | Orderliness1, Case1 | Efficiency1, Explain1, Emphasis1 | 0.81 | 0.93 | 1.11 |
20 | Enlighten1, Case1, Preparation1 | Emphasis1 | 0.81 | 0.98 | 1.09 |
21 | Case1 | Interesting1, Explain1 | 0.81 | 0.89 | 1.07 |
22 | Answer1 | Explain1, Emphasis1 | 0.81 | 0.94 | 1.09 |
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Qi, F.; Gao, Y.; Wang, M.; Jiang, T.; Li, Z. Data Mining of Online Teaching Evaluation Based on Deep Learning. Mathematics 2024, 12, 2692. https://doi.org/10.3390/math12172692
Qi F, Gao Y, Wang M, Jiang T, Li Z. Data Mining of Online Teaching Evaluation Based on Deep Learning. Mathematics. 2024; 12(17):2692. https://doi.org/10.3390/math12172692
Chicago/Turabian StyleQi, Fenghua, Yuxuan Gao, Meiling Wang, Tao Jiang, and Zhenhuan Li. 2024. "Data Mining of Online Teaching Evaluation Based on Deep Learning" Mathematics 12, no. 17: 2692. https://doi.org/10.3390/math12172692