AI Predicts and Prevents Learning Roadblocks

A prominent US university’s research team developed an AI system to address the challenges of limited support in online learning. The system monitors student progress, predicting potential difficulties for early intervention and personalized support. This AI-powered approach shows promise in enhancing student outcomes.

Vendor:

Stanford University is a world-renowned private institution consistently ranked among the global elite.

Problem Statement:

● Severe Lack of Individual Support: Limited resources prevent providing necessary assistance to each student.
● Inability to Predict Difficulties: Existing systems cannot timely identify students requiring additional support.
● Сomplexity of Analyzing Failure Causes: Determining the root causes of learning problems demands extremely complex analytical methods.
● Difficulty Extracting Useful Information from Data: Large volumes of student performance data are challenging to process without artificial intelligence.

Results:

☑️ Early Intervention: AI accurately predicts student struggles, allowing for timely support.
☑️ Personalized Support: Targeted interventions address specific learning challenges.
☑️ Enhanced Success: AI-driven support boosts overall student performance.

AI Solution:

The research team developed an AI-powered predictive and intervention system to address the urgent need for timely and effective support for students engaged in self-paced online learning.

The AI system monitors student progress on online learning platforms and predicts potential study difficulties. By processing information such as the number of attempts a student makes on a question or the time spent on a particular task, the model could predict when a student was likely to become stuck or “wheel spin.”

Once a student is identified as at risk, the AI system recommends specific interventions tailored to the individual’s needs. These interventions could range from revisiting previous lessons to providing additional instructional resources or seeking teacher assistance.

The researchers utilized performance data from 1,170 Ugandan schoolchildren to train the AI model. This real-world data allowed the system to learn and adapt to the challenges faced by students in a low-resource setting.

Interesting Fact:

AI’s recommendations for intervention matched those of a human expert in four out of six cases.

References:

1. Using Artificial Intelligence to Understand Why Students are Struggling

Industry: Education
Vendor: Stanford University
Client: War Child Holland