Deep Learning vs. Traditional Machine Learning: Choosing the Right Approach for EdTech Applications

At the core of the modern AI revolution, two primary approaches to machine learning have emerged: traditional machine learning and deep learning. While both stem from the same goal of making computers think, they are fundamentally different in execution and application. This can offer multiple applications for EdTech. As of 2023, 38% of education organizations surveyed by HolonIQ had successfully embedded machine learning within their operations, making it the most widely used technology in the segment.

For EdTech, where the future of education is being reimagined even as we write this article, the choice between these two types of machine learning becomes crucial. Selecting the right approach can mean the difference between a transformative educational tool and a mediocre one.

Diving into Traditional Machine Learning

Traditional machine learning is a method where algorithms learn from a given set of data, drawing patterns and making decisions based on predefined features. It requires significant expertise in selecting and designing these features to optimize performance.

EdTech applications often use algorithms like decision trees, linear regression, and support vector machines to predict student performance, automate assessments, and personalize learning paths. For example, two such applications are:

• Adaptive Learning Platforms: Systems that adjust content in real-time based on student performance, often using algorithms like decision trees or support vector machines. Given its immense benefits, the global market for adaptive learning is forecasted to expand at a CAGR of 24.20% between 2023 and 2030, despite high production costs.
• Learning Analytics: Tools that analyze student data and provide feedback using clustering or regression techniques.

Deep Dive into Deep Learning

Deep learning is a subset of machine learning that mimics the functioning of the human brain through neural networks. Unlike its traditional counterpart, deep learning doesn’t rely on handcrafted features; instead, it autonomously learns from raw data.

Deep learning predominantly utilizes deep neural networks, structures with multiple layers that process information hierarchically. In EdTech, deep learning aids in:

• Personalized Content Recommendation: By analyzing past interactions, deep learning models can predict and recommend tailored content for each student. Due to the growing demand for personalized learning, the AI market in the education segment is projected to grow at a CAGR of 10% between 2023 and 2032. This is not just for K12 and higher education. 77% of L&D professionals consider personalization beneficial for corporate education because it boosts employee engagement.
• Automated Content Creation: Neural networks can generate quiz questions or interactive exercises based on the course material.

Contrasting Traditional ML and Deep Learning

Feature Engineering and Representation

1. Handcrafted in Traditional ML: Traditional ML’s success is deeply intertwined with the expertise of feature selection. This can be time-consuming and lacks flexibility.

2. Automated in Deep Learning: Deep learning models autonomously derive significant features from raw data, eliminating manual intervention.

Navigating Complexity and Size

3. Scalability Woes of Traditional ML: With larger and more complex datasets, traditional ML’s efficiency can diminish, necessitating more computational power.

4. Deep Learning’s Affinity for Big Data: Deep learning thrives on large datasets, evolving with greater accuracy as data size increases.

Considerations for Choosing the Right Approach in EdTech

The Role of Data

One factor dictating the choice between traditional ML and deep learning is the volume of available data. Deep learning thrives on big data, often requiring vast datasets to achieve accurate results. But what if your EdTech application has limited data? Traditional ML might be your best bet.

Task Complexity Decides the Approach

The nature of the EdTech challenge also influences the choice. For simple prediction tasks, traditional ML might suffice. But for intricate problems, like understanding student emotions from voice data, deep learning may offer superior results. Certain EdTech problems, like curriculum design, might require a mix of both approaches. A 2020 study used both machine learning and deep learning to predict learning failure and found that deep learning was more accurate in predictions and could be leveraged to improve prediction performance due to its autonomous means of self-enrichment.

Hybrid Models: The Best of Both Worlds

Why not get the best of both worlds? Some EdTech applications combine traditional ML and deep learning, leveraging the strengths of both. For instance, a hybrid model might use traditional algorithms for data preprocessing and then feed the refined data into a deep learning network for final predictions.

EdTech Successes with Hybrids

Intelligent Tutoring Systems: These systems might use traditional ML to assess a student’s current skill level and deep learning to predict future performance, thereby personalizing content delivery.

While traditional machine learning depends heavily on feature engineering and is apt for specific, simpler tasks, deep learning excels with vast datasets, handling intricate operations autonomously.

In the burgeoning world of EdTech, it’s paramount to understand both the nature of the problem and the available data. Making an informed choice between traditional machine learning and deep learning – or even a hybrid approach – can be the deciding factor in an application’s success. Contact Integra to learn more about leveraging ML to transform your business.