Building a Scalable Natural Language Understanding System on AWS EKS for Higher Education: Personalizing Student Learning with BERT and AWS OpenSearch

1. Introduction

Higher education institutes are in a rapid state of change due to demand from the enterprises for personalized learning experiences and operational efficiency. The volume of information from students-essays, feedback forms, discussion forums, and digital engagement metrics, among others-are most visibly compelling opportunities to apply sophisticated technology for actionable insight.

Students increasingly expect personalized learning paths and real-time feedback, while educators seek tools to automate more mundane tasks-such as grading and analysis of student performance. Addressing these needs, Natural Language Understanding models are significant in the wake of recent progress in machine learning. NLU systems can fill roles that range from sentiment identification in student feedback to categorization coursework for targeted recommendations, playing a transformative role in creating personalized and scalable educational experiences.

Key Challenges in Processing Large-Scale Educational Data

Key Challenges in Large-Scale Processing of Educational Data While the potential benefits of NLU in education are huge, several challenges also come along with implementing such solutions.

1. Diversity and unstructured nature of data: Most educational datasets contain free-text essays, discussion forum threads, and survey responses. This makes it hard for the extraction of useful insights to be done efficiently.
2. Scalability: Workloads in educational systems are highly variable. For example, large spikes in student submissions during examination periods can overload static infrastructures and delay processing.
3. Latency and Real-Time Feedback: Students and educators expect near real-time feedback. Large volumes of text data require robust and low-latency infrastructure to process in real time.
4. Cost Management: NLP models, such as BERT, are expensive to deploy, especially in production for inference. Inference requires either GPUs or high-performance CPUs. There is a trade-off between performance and costs to allow scalability.
5. Search and Retrieval: Storing the processed insights and enabling educators to query them is not trivial to implement. It would involve ElasticSearch or similar setups that handle large datasets and allow low-latency search capabilities.

2. Solution Overview

The blog describes how higher education may develop an NLU system by following an end-to-end architecture scale. The current solution proposes leveraging the latest unsupervised techniques in machine learning and AWS cloud infrastructure as a means to overcome these challenges of scaling with latency and efficient querying of data.

Workflow:

1. In the workflow we ingests all kinds of student submissions, including essays, discussion forum posts, assignments entailing open-ended questions or feedback.
2. After that, the preprocessed text data passes through the fine-tuned BERT model to get the analyses.
3. Insights like sentiment scores and topic classifications generated by BERT are indexed into ElasticSearch.
4. The educators and administrators reach these insights through a user interface, thus allowing them to give individual feedback or make informed decisions.

This solution smooths the processing of texts, leverages scalability, cost efficiency, and real-time processing capabilities, hence very ideal for the dynamic and demanding environment of higher education.

Architecture:

Above is the architecture of a scalable, secure system that would provide the processing and analytics of students' submissions within higher education for real-time insights and personalized learning powered by AWS. The AWS Amplify-hosted frontend application, integrated with CloudFront and Amazon S3, is hosting, enabling students to submit essays or feedback through the Amazon API Gateway. The system will queue the submissions in Amazon SQS and will be processed by EKS Worker Nodes, running in a private subnet, executing the NLP tasks using the fine-tuned BERT model. The processed insights-sentiment scores and topic analysis-get indexed and stored in Amazon OpenSearch Service for real-time querying and visualization. Amazon S3 will be serving as a data lake for raw, preprocessed, and processed data.

3. Fine-Tuning BERT for Education-based Use Cases

Problem Statement

In the education domain, textual data are represented as essays, feedback forms, discussion posts, and other open-ended responses that require handling in a different way altogether. Though the pre-trained versions of BERT models are general-purpose, they lack domain-specific understanding to perform well on educational data. Fine-tuning BERT on educational datasets lets the model learn domain-specific nuances, such as the following:

• Sentiment in student feedback.
• Key topic extraction in essays.
• Classification of students' questions into academic, administrative, or both.
• Extracting the reasons behind students finding areas difficult from responses/feedback provided.

The goal is to adapt BERT to these specific use cases while ensuring scalability and efficiency.

Detailed Instructions: Fine Tuning BERT

Step 1: Data Preparation

Your dataset plays a very critical role in fine tuning BERT efficiently. Here are steps to take care of the data.

Gather datasets in education :

• Gather text data from essays, feedback forms, discussion forums, or academic research papers.
• Label the data for desired tasks, for example, sentiment, topic categories.

Preprocess the Text:

• Clean and normalize the text, for example, remove HTML tags, special characters.
• Tokenize the text into subwords using BERT's tokenizer.

from transformers import BertTokenizer

# Load BERT tokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

# Sample texts
texts = ["This course is amazing!", "The assignment was unclear."]

# Tokenize the text
inputs = tokenizer(
    texts,
    padding=True,           # Add padding to match the longest sequence
    truncation=True,        # Truncate sequences longer than the model's max length
    return_tensors="pt"     # Return PyTorch tensors
)

# Display tokenized input
print("Tokenized Inputs:", inputs)

        

Split the Dataset: Split the dataset into training, validation, and testing subsets:

• Training: 70%
• Validation: 15%
• Testing: 15%

Step 2: Fine-Tune BERT

Fine-tuning entails training the already pre-trained BERT model on one's domain-specific dataset for specific tasks; these include classification tasks like sentiment or topic modeling.

Load the Pre-Trained Model: Load the pre-trained model, BERTForSequenceClassification from Hugging Face Transformers.

Define Training Parameters: Define the output directory, batch size, learning rate, and every other hyperparameter.

Fine-Tune the Model: This is actually training the model by using a Trainer object, which abstracts most of the work for the user.

from transformers import BertForSequenceClassification, Trainer, TrainingArguments

# Load pre-trained BERT model for sequence classification
model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)

# Define training arguments
training_args = TrainingArguments(
    output_dir="./results",           # Output directory for model checkpoints
    evaluation_strategy="epoch",     # Evaluate the model at each epoch
    per_device_train_batch_size=16,  # Batch size per GPU/CPU
    num_train_epochs=3,              # Number of epochs
    learning_rate=2e-5,              # Learning rate
    logging_dir="./logs",            # Directory for logs
    save_steps=500,                  # Save checkpoint every 500 steps
)

# Define the Trainer object
trainer = Trainer(
    model=model,                      # The pre-trained model
    args=training_args,               # Training arguments
    train_dataset=train_data,         # Training dataset
    eval_dataset=val_data             # Validation dataset
)

# Start fine-tuning
trainer.train()

        

Step 3: Evaluate the Model

After Training, the Fine-Tuned Model Has to Be Evaluated on the Test Set:

• Use metrics like accuracy, precision, recall, and F1-score for classification tasks.

Evaluation:

from sklearn.metrics import accuracy_score, precision_recall_fscore_support

# Make predictions
predictions = trainer.predict(test_data)
preds = predictions.predictions.argmax(-1)

# Evaluate predictions
accuracy = accuracy_score(test_data["labels"], preds)
precision, recall, f1, _ = precision_recall_fscore_support(test_data["labels"], preds, average="binary")

print(f"Accuracy: {accuracy}, Precision: {precision}, Recall: {recall}, F1-Score: {f1}")

        

Step 4: Inference Optimization

Fine-tuned BERT models are resource-intensive when it comes to inference. It is always recommended to apply an optimization technique:

Model Quantization: Decrease the precision of weights, for instance, float32 → int8, with the help of tools like Hugging Face Optimum or ONNX Runtime.

from optimum.onnxruntime import ORTModelForSequenceClassification

# Load optimized model
optimized_model = ORTModelForSequenceClassification.from_pretrained("path_to_fine_tuned_model")        

DistilBERT: Replace BERT with DistilBERT, a smaller and faster variant of BERT, where some loss in accuracy is acceptable.

4. Efficient Data Storage and Search with Amazon Managed OpenSearch

In handling large-volume education data through BERT, efficient storage and querying are required for actionable insights. Amazon OpenSearch Service provides the end-to-end management of a scalable solution in indexing, storing, and querying processed data. This section describes how one would deploy OpenSearch, integrate with BERT, and query into insights that could lead to better educational outcomes.

Setting Up Amazon OpenSearch

Amazon OpenSearch provides a managed service to deploy OpenSearch clusters without having to manage any infrastructure. It brings built-in scalability, security, and monitoring out of the box.

Deploying Amazon OpenSearch

• Go to the Amazon OpenSearch Service console.
• Create OpenSearch domain: Choose an instance type, such as t3.small for testing, r6g.large for production workloads.
• Configure the number of nodes and storage according to your workload.
• Turn on fine-grained access control for secure access and integrate with IAM.

Configure Indices for Processed Student Data

After the OpenSearch domain becomes active, create an index to store the processed data for student feedback and sentiment scores.

Using OpenSearch REST API:

curl -X PUT "https://<your-domain-endpoint>/student-insights" -H 'Content-Type: application/json' -d'
{
  "mappings": {
    "properties": {
      "student_id": { "type": "keyword" },
      "feedback": { "type": "text" },
      "sentiment_score": { "type": "float" }
    }
  }
}'        

Index Field Explanation:

• student_id: Keyword type for exact matches, like querying students with specific IDs.
• feedback: Text type for full-text search and analysis.
• sentiment_score: Float type for numerical comparisons, such as filtering students who have low sentiment scores.

Integration with BERT for Data Storage

Processed insights from BERT-for example, the results of sentiment analysis-can be stored directly in OpenSearch for real-time querying and analytics.

Python Example for Storing Data in OpenSearch

Use the OpenSearch REST API to index data into the student-insights index.

import requests

# OpenSearch endpoint
opensearch_url = "https://<your-domain-endpoint>/student-insights/_doc"

# Example processed data from BERT
payload = {
    "student_id": "12345",
    "feedback": "Great course!",
    "sentiment_score": 0.95
}

# Send data to OpenSearch
response = requests.post(opensearch_url, headers={"Content-Type": "application/json"}, json=payload)

# Print the response
if response.status_code == 201:
    print("Data successfully indexed:", response.json())
else:
    print("Failed to index data:", response.status_code, response.text)        

Features:

• Stores processed feedback, making it searchable and analyzable.
• Each document in the student-insights index represents a student's feedback and sentiment score.

Querying for Insights in OpenSearch

After indexing data, you can query OpenSearch to derive actionable insights, such as identifying students who might need additional support based on their sentiment scores.

Example Query: Finding Students Needing Support

Search the student-insights index for feedback that has less than 0.5 sentiment score.

Using the OpenSearch REST API:

curl -X GET "https://<your-domain-endpoint>/student-insights/_search" -H 'Content-Type: application/json' -d'
{
  "query": {
    "range": {
      "sentiment_score": { "lt": 0.5 }
    }
  }
}'        

Response:

{
  "hits": {
    "total": {
      "value": 2
    },
    "hits": [
      {
        "_source": {
          "student_id": "67890",
          "feedback": "The assignment was unclear.",
          "sentiment_score": 0.3
        }
      },
      {
        "_source": {
          "student_id": "45678",
          "feedback": "The course material was difficult to follow.",
          "sentiment_score": 0.4
        }
      }
    ]
  }
}        

Python Example for Querying OpenSearch

You would commonly use Python scripts for automating querying and integrating analytics with notification systems and/or dashboards.

Python Code:

# Query OpenSearch for low sentiment scores
query = {
    "query": {
        "range": {
            "sentiment_score": { "lt": 0.5 }
        }
    }
}

response = requests.get(f"{opensearch_url}/_search", headers={"Content-Type": "application/json"}, json=query)

if response.status_code == 200:
    results = response.json()
    for hit in results['hits']['hits']:
        print(f"Student ID: {hit['_source']['student_id']}, Feedback: {hit['_source']['feedback']}, Sentiment: {hit['_source']['sentiment_score']}")
else:
    print("Failed to query data:", response.status_code, response.text)        

Visualizing Data with OpenSearch Dashboards

Use OpenSearch Dashboards to create visualizations and dashboards for educators and administrators.

Getting Your Dashboards ready

• Log in to OpenSearch Dashboard,
• Create Visualisation, such as:

Pie Charts: Pie chart of the general distribution of students' sentiment across feedback submitted.

Line Graphs: Time-series sentiment of the performance of a course.

Tables: Student-specific feedback with sentiment scores.

5. Real-Time Use Case: Personalized Student Feedback

Personalized feedback is one of the keys to enhancing student learning experiences. By using real-time processing of students' submissions in the form of essays, queries, feedback, etc., instructors are quickly provided with insight into areas to better help elevate the problem and enhancement of course material, as well as specific individual student counsel. The insights from this workflow would range from, but would not be limited to sentiment analysis, topic modeling presented in an easy web interface by using Amazon OpenSearch Service.

Workflow Overview

Student Submission:

• A student submits an essay, question, or comment through a front-end application hosted on AWS Amplify or similar.
• This text, through Amazon API Gateway, is routed into the processing pipeline and queued in Amazon SQS.

Text Processing:

• Sentiment Analysis: EKS pods running a fine-tuned BERT model process the text for sentiment analysis to identify if the text is positive, negative, or neutral.
• Topic Modeling: It identifies major topics or themes within the text.

Storing Insights:

• Processed insights such as sentiment score and topics are kept in Amazon OpenSearch Service for real-time querying and visualization.

Educator Access:

• Educators access the insights through a web interface powered by OpenSearch Dashboards in order to analyze the trends and address the concerns of students individually.

How the Text Gets Processed by BERT

Here’s how the text is processed using the Hugging Face Transformers library:

Sentiment Analysis :

from transformers import pipeline

# Load the pre-trained BERT model for sentiment analysis
nlp_pipeline = pipeline("sentiment-analysis")

# Example feedback
feedback = "This assignment was very confusing."

# Process feedback
result = nlp_pipeline(feedback)

# Display the result
print(result)        

Output:

[{'label': 'NEGATIVE', 'score': 0.978}]        

Storing and Retrieving Results from Amazon OpenSearch

Storing Results : After processing the text, insights will be stored in the Amazon OpenSearch Service.

Indexing Data in OpenSearch:

import requests

# OpenSearch endpoint
opensearch_url = "https://<your-domain-endpoint>/student-insights/_doc"

# Processed insights from BERT
payload = {
    "student_id": "67890",
    "feedback": "This assignment was very confusing.",
    "sentiment_score": 0.978,
    "sentiment_label": "NEGATIVE",
    "topics": ["assignment", "confusion"]
}

# Send data to OpenSearch
response = requests.post(opensearch_url, headers={"Content-Type": "application/json"}, json=payload)

if response.status_code == 201:
    print("Data successfully indexed:", response.json())
else:
    print("Failed to index data:", response.status_code, response.text)        

Key Points:

• student_id: Ties feedback to a student
• sentiment_score and sentiment_label: Convey sentiment insights to educators.
• topics: Emphasize some key themes in the submission.

Querying Insights

Using this stored data, educators will query it to see if there is any trending or students in need. Sample Query Negative Feedback Retrieval All negative feedback where the sentiment score is less than 0.5.

Using the OpenSearch REST API:

curl -X GET "https://<your-domain-endpoint>/student-insights/_search" -H 'Content-Type: application/json' -d'
{
  "query": {
    "range": {
      "sentiment_score": { "lt": 0.5 }
    }
  }
}'        

Querying OpenSearch:

query = {
    "query": {
        "range": {
            "sentiment_score": { "lt": 0.5 }
        }
    }
}

response = requests.get(f"{opensearch_url}/_search", headers={"Content-Type": "application/json"}, json=query)

if response.status_code == 200:
    results = response.json()
    for hit in results['hits']['hits']:
        print(f"Student ID: {hit['_source']['student_id']}, Feedback: {hit['_source']['feedback']}, Sentiment Score: {hit['_source']['sentiment_score']}")
else:
    print("Failed to query data:", response.status_code, response.text)        

Response:

{
  "hits": {
    "hits": [
      {
        "_source": {
          "student_id": "67890",
          "feedback": "The course material was very hard to understand.",
          "sentiment_score": 0.3,
          "sentiment_label": "NEGATIVE"
        }
      }
    ]
  }
}        

Dashboard for Educators

With Amazon OpenSearch Dashboards, educators can visualize and analyze students' feedback in real time. Dashboards may include:

Sentiment Distribution: A pie chart showing the percentage distribution of positive, negative, and neutral sentiment.

Feedback Trends: A line graph plotting sentiment trends over time for particular courses or instructors.

Student-Specific Insights: A table listing the students with low sentiment scores, their respective feedback, and associated topics.

6. Conclusion

This solution showcases how BERT and Amazon OpenSearch Service together can revolutionize education by providing near real-time, actionable insights into student feedback. It allows educators to respond in a timely manner to student concerns, track sentiment trends, and make data-driven improvements to course content. OpenSearch's efficient querying enables institutions to analyze large volumes of processed data with ease, fostering personalized education at scale. Future plans for enhancement include adding multilingual support through models such as mBERT, addressing diverse linguistic needs, and expanding the use cases with conversational AI using AWS Lex or Amazon Bedrock. These can be further enhanced toward inclusive, scalable, and automated solutions that can increase student engagement and support even further.

Author

Clement Pakkam Isaac

Clement Pakkam Isaac is a Specialist Senior at Deloitte Consulting and an accomplished cloud infrastructure architect with 15 AWS certifications. With over 12 years of experience in technical consulting and leadership, he has architected and delivered large-scale cloud solutions for higher education and consumer industries. Clement’s expertise encompasses automation, infrastructure as code, resilience, observability, security, risk management, migration, modernization, and digital transformation. A trusted advisor to clients, he empowers organizations to adopt cutting-edge cloud practices and drive innovation through scalable and secure infrastructure solutions.

References:

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