Performance Evaluation of SeisTutor Using Cognitive Intelligence-Based "Kirkpatrick Model"

Area of Science:

• Artificial Intelligence in Education
• Cognitive Science
• Educational Technology

Background:

• Human tutors adapt learning materials based on student psychology and prior knowledge.
• Replicating human tutor cognitive intelligence in intelligent tutoring systems (ITS) is challenging.
• Personalized learning experiences are crucial for effective education.

Purpose of the Study:

• To develop and evaluate SeisTutor, an ITS designed to mimic human tutor cognitive intelligence.
• To assess the effectiveness of SeisTutor in providing a personalized curriculum.
• To evaluate SeisTutor using Kirkpatrick's four-phase evaluation model.

Main Methods:

• Developed SeisTutor, an intelligent tutoring system incorporating cognitive intelligence.
• Utilized Kirkpatrick's four-phase evaluation model to assess the system's efficacy.
• Compared learning gains between SeisTutor with and without incorporated intelligence.

Main Results:

• SeisTutor demonstrated enhanced learning gains compared to a non-intelligent version.
• The cognitive intelligence incorporated in SeisTutor positively impacted student learning.
• Kirkpatrick's model provided a framework for evaluating the ITS's effectiveness.

Conclusions:

• Mimicking human tutor cognitive intelligence in ITS is feasible and beneficial.
• SeisTutor shows promise as an effective tool for personalized education.
• Further evaluation using Kirkpatrick's model confirms the value of intelligent tutoring systems.