Automatic learning: Definition, algorithms, and applied artificial intelligence

Automatic learning helps maximize the personalization of educational itineraries, optimizes the evaluation process, and predicts needs. 

This subset of artificial intelligence (AI) allows machines to learn from data and improve their performance without requiring explicit programming.

Some examples of resources and tools powered by this technology include smart educational chatbots, virtual simulations, and learning management platforms.

In this article, we will explain what automatic learning is and cover the key algorithms that support it, as well as its main applications in education.

What is automatic learning?

Automatic learning is a cognitive computing process considered a subset of artificial intelligence that simulates human thought processes through self-learning algorithms.

Throughout this article, we will use automatic learning as a synonym for machine learning, as they are both widely used in EdTech (Educational Technology) contexts.

However, it is important to note that automatic learning is a much broader term that does not always refer to the specific, advanced algorithms used in machine learning. 

These algorithms identify patterns in data that allow them to make predictions, becoming increasingly accurate as they process more information, mirroring the way humans naturally learn.

One of its most significant features is adaptability, making it a valuable tool for tasks or requests that continuously evolve. 

In ever-changing educational environments, automatic learning can enhance personalization by dynamically adapting to meet the unique needs of individual students. 

Adaptive learning systems, for example, use automatic learning to analyze student performance and suggest the most relevant materials based on the learners´ preferences. 

Automatic learning, however, should not be confused with autonomous learning. 

What is autonomous learning?

Autonomous learning refers to a student-centered approach, where learners take responsibility for their own educational process. 

In this context, students are encouraged to set goals, manage their time, and evaluate their progress independently. 

The term can also be used in technological settings, but always referring to an independent way of learning, whereas automatic or machine learning is considered to be a field of study in AI. 

Although these concepts are frequently mistaken for one another, understanding their differences is key. 

How does automatic learning work? 

The functioning of machine learning mimics the automatic activities our brain performs without us consciously noticing them.

Below are the three basic steps machine learning systems follow: 

Data collection

The data collection process focuses on obtaining the necessary information to address the given situation.

This involves verifying the source of the data, resolving any integrity issues, identifying potential anomalies, and structuring the dataset.

Depending on its intended use, the information may come from customer or user records, sales reports, training cycle logs, and more.

Once the system ensures the data is accurate, it is transformed into a suitable format to facilitate classification and analysis.

Data classification

The next step involves extracting the specific information needed to meet the demand, identifying patterns and links between data and segmenting it based on the objectives.

For example, depending on the context, customer demographics, user activity histories, and so on can be created.

There are many models that can be used for this purpose, including Support Vector Machines, decision trees, and neural networks. 

At this stage, and if you are not familiar with machine learning, these technical concepts might sound overwhelming. However, we will go into more detail later in this article. 

Model training and validation

Once selected and validated based on the problem and the available data corpus, the machine learning model must be trained using preprocessed information.

This training can be either unsupervised or supervised by a person or another system, depending on the needs.

Regarding validation, there are different approaches, such as the Hold-Out method. 

This involves splitting the dataset into two parts: one is used to train the model, and the other tests it to verify that it performs correctly with new data.

These evaluative procedures ensure reliability and help confirm that the model is generalizable or, in other words, that it does not only work well, but it can also make accurate predictions in real-world situations it has not encountered yet.

Types of automatic learning

Machine learning can be classified into several types: supervised, unsupervised, semi-supervised, and reinforcement learning – depending on the techniques and algorithms used for systems to learn and adapt.

These types are detailed in the following sections:

Supervised learning

This type of algorithm is the most commonly used. With the help of a data technician who acts as a teacher, the system learns from a set of pre-labeled data with a predefined outcome.

This process teaches the algorithm the conclusions it should reach and enhances its ability to make predictions or decisions.

As a result, it is often incorporated into intelligent tutoring systems or regression models that predict future grades. Supervised learning algorithms are also used  to detect early school dropout. 

Additionally, they can be found in predictive models that adjust the difficulty of exercises for each user, allowing them to progress at their own pace. 

Unsupervised learning 

In contrast to supervised learning, unsupervised learning deals with datasets that have no structure or labels, so the machine learning system must identify complex patterns and relationships between the data, grouping them into clusters on its own.

In this way, the model organizes the data independently, grouping it based on similarities and labeling it using association rules and k-means clustering.

In education, this approach can be useful for detecting plagiarism or identifying students with difficulties. In educational data mining, it can be used to identify learning patterns, among many other applications. 

Semi-supervised learning

Semi-supervised learning combines both supervised and unsupervised data to train machine learning models through label propagation or self-training.

This approach is more accurate than unsupervised learning and more efficient than supervised learning.

This type of system can be found in voice recognition or spam detection solutions, for example. 

Reinforcement learning 

In this case, a computer program replaces the human operator and helps determine the outcome based on a feedback loop of trial and error.

Thus, the model continuously updates itself by feeding back the information it receives to improve its performance over time.

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Most commonly used automatic learning algorithms in education

AI has revolutionized education, especially when it comes to personalizing the individual educational journey.

Machine learning algorithms help analyze students´ performance and allow content and pace to be adapted to their particular needs.

They can not only be used to support students who need reinforcement, but also to provide specific resources for advanced students who want to go further and improve.

In any case, these algorithms have transformed the classroom, enriching and enhancing the student experience thanks to their adaptive and evolving capabilities.

Below are some examples of the most commonly used algorithms in the educational field.

Logistic regression

Logistic regression is a binary classification statistical model that determines the probability of a student passing a subject based on various variables such as participation, study habits, or attendance.

It also helps predict dropout rates based on factors like socio-economic background, level of commitment, and academic history.

Decision trees

Decision trees are systems based on the interpretability of data that also help predict academic success by analyzing decisions and their possible outcomes.

These are supervised learning models in the form of a tree, with branches that split based on the choices made by students.

For example, by analyzing factors like grades, the type of interactions on educational platforms, and/or the study methods used, the system can predict academic results.

In other words, it identifies various variables that affect student performance and is able to segment them effectively.

Neural networks 

Neural networks consist of interconnected artificial neurons known as nodes or units, organized in layers. 

These neurons mimic the behavior of the brain: they capture data, analyze it, obtain a result, and transmit the information to neurons in the following layers.

This enables them to learn different patterns, helping them make predictions and even decisions about data they have not previously encountered.

They are an essential component of deep learning, and can easily be found in applications on educational platforms that offer intelligent automated tutoring systems, as they are capable of handling complex tasks such as speech recognition or natural language processing.

Support Vector Machines 

Support Vector Machines (SVMs) are supervised algorithms used for classification and regression. They offer great potential for optimizing the learning process.

Some of its applications in education include the following:

• Prediction of academic performance: They can detect students with low performance or those with the potential to excel, allowing for preventive measures to be taken.
  
• Detection of dropout risk: They help identify students at risk of dropping out, enabling teachers to act quickly with emotional and academic support for those who need it.
  
• Classification of students by learning profile: They help categorize students based on their learning styles (kinesthetic, visual, etc.) and enhance the personalization of learning processes.
  
• Identification of academic fraud and problematic behaviors: They can even detect cases of cyberbullying. Their ability to identify unusual behavior justifies their use in proctoring solutions, which must ensure academic integrity while providing respectful supervision that fosters student trust.

Artificial intelligence, automatic learning, and deep learning

Let´s explain the difference between AI, machine learning, and deep learning before exploring the applications of artificial intelligence in education. 

A simple way to understand this is to think of these concepts as a set of Russian dolls. 

With this example in mind, artificial intelligence is the outer doll that encompasses the other two concepts, and deep learning is a specialized subset of machine learning. 

Let’s review their definitions:

• Artificial intelligence comprises techniques and systems aimed at achieving a level of understanding, reasoning, and problem-solving by the machine autonomously, based on data. 
• Automatic learning is a subset of AI that, through training, uses a series of algorithms to enable the system to learn, either supervised or unsupervised. The goal is for the system to make predictions, decisions, and more. All of this is done through the analysis of data, labeled or not, in a process that mimics the human brain. 
• Deep learning is a specialization of machine learning that uses neural network algorithms, which replicate the structure of the human brain to provide answers.

Automatic learning and inclusive classrooms 

While technology has been used in classrooms for some time, artificial intelligence and machine learning are innovative resources with great potential to help create inclusive learning environments. 

A clear example of this is educational chatbots, which provide real-time feedback to students, allowing them to receive personalized guidance exactly when they need it.

This reduces student anxiety, as they can overcome learning obstacles by accessing the necessary resources, information, or assistance to keep progressing.

In the same context, these cutting-edge technological models facilitate the creation of simulations and virtual environments for experiential learning, making the educational process more meaningful in a safe and controlled setting.

Moreover, since advanced algorithms can analyze vast amounts of data, they can identify whether a student or group of students is struggling with a specific concept or unit. 

With this insight, teachers can adjust their educational strategies, modify the level of difficulty, and actively participate in the continuous improvement of their teaching methods.

In addition to fostering inclusive education, AI also adds value for teachers through tools that automatize administrative tasks, freeing up more time for them to focus on their students’ academic development.

Lastly, when it comes to inclusivity, the multidisciplinary approach of STEM education plays a key role in preparing students to tackle real-world challenges. 

AI can enhance this approach and make it more accessible to all learners. This methodology focuses on students solving problems in real-world contexts by applying science, technology, engineering, and mathematics. 

Artificial intelligence in education: use cases 

Through the following applications of AI in the classroom, you will see why it is becoming an essential tool for educators.

This is because AI not only contributes to automatizing administrative tasks, as we have seen, but also helps identify student behavior patterns, allowing teachers to adjust their approach and improve student performance. 

Personalized learning

By conducting a detailed analysis of large amounts of data, AI enables real-time adaptation of instruction to meet students’ needs.

This high level of personalization is based on analyzing task completion rates and grades to generate insights that help tailor educational pathways.

Intelligent tutoring

Machine learning systems can assist students in completing their tasks or learning new concepts through interactive feedback.

Additionally, the pace and difficulty of instruction can be adjusted based on individual performance.

Predictive analysis

AI-driven academic performance predictions enable instructors to take proactive steps to prevent students from dropping out. 

Design of learning pathways

We have already discussed the major advantages of AI in personalizing students’ learning paths. 

This level of flexibility and adaptability to meet students’ real needs is especially valuable for methodologies like learning stations. 

This educational approach breaks down a unit into different spaces or stations, allowing a topic to be explored from various angles and perspectives, making learning more meaningful.

In this case, AI not only reduces the teacher´s workload by assisting with the creation of activities, roadmaps, and other materials but also significantly engages digital-native students. 

Adaptive evaluations

To provide a more accurate assessment of students’ knowledge and skills, evaluation solutions that incorporate automatic learning can adjust the difficulty level of questions and tasks based on the user’s profile.

This approach integrates students into a dynamic of continuous and progressive development while helping to prevent fear or frustration in the classroom.

When combined with AI’s ability to predict learning difficulties, teachers can offer targeted interventions to students who need support before they become disengaged or demotivated.

You might be wondering how to access these adaptive learning tools and resources. Fortunately, there are many options available.

One of the most comprehensive solutions is Learning Management Systems (LMS) that incorporate AI models through plugins or built-in features. Examples include Moodle, Blackboard, or Canvas. 

Advantages, challenges, and future trends of automatic learning in the classroom

By now, you are well aware of the advantages that machine learning can offer in the classroom, with its key benefits including a high degree of personalization and the ability to identify patterns and trends in student performance.

Additionally, by optimizing the integration and analysis of structured and unstructured data, it reinforces data integrity and enables fairer, evidence-based assessments.

A key factor in learning success is how machine learning improves student experience with adaptive interfaces, targeted content, interactive chatbots, and real-time virtual assistants.

By automatizing administrative processes, automatic learning frees up teachers’ time, allowing them to dedicate more resources to guiding and supporting students. 

This, in turn, improves learning outcomes and strengthens the instructor-student relationship.

Taking all these benefits into consideration, future trends indicate an increasing integration of machine learning models into classrooms and educational programs.

However, this transformation also presents significant challenges, such as the need to adopt accessible and effective tools that help overcome resistance to change in more traditional environments or institutions that lack the necessary resources to implement these solutions.

Another crucial challenge is ensuring data security and privacy.

At Smowltech, this issue is of great importance to us, which is why we have focused on it while developing our proctoring plans and our scalable exam supervision solution. 

Our proctoring software incorporates the latest technological advancements, enabling you to provide students with secure environments that comply with current regulations on data integrity and protection.

If you are interested in discovering how AI can enhance your digital assessments, request a free demo and we will walk you through all the details.