The inclusion of computer technology in education has led to increased attention for ­personalized learning and instruction. By means of personalized learning, or adaptive learning, learners are given instruction and support directly, adjusted to their cognitive and ­noncognitive needs.
This chapter aims at giving an overview of the current research that addresses advanced technologies, models, and approaches to establish personalized learning, instruction, and performance. In order to provide this, relevant learner and learning characteristics need to be measured or inferred and incorporated in learner models. These learner models provide the basis from which personalization can occur and have to be considered as the core of personalized learning environments.
In order to provide dynamic personalized learning, learner models need to be adjusted and updated with new information about the learner´s knowledge, affective states, and behavior. To do so, the fields of artificial intelligence and educational data mining provide advanced technologies that can be applied for fine-grained learner modeling. First, the field of artificial intelligence in education has largely supported the development of intelligent tutoring systems. Second, educational data mining is indispensable for providing information about the learning process and learner behavior.
The integration of artificial intelligence and educational data mining in the learner modeling research provides a firm basis for effectiveness research on personalized systems. This chapter is concluded with the call for educational technologists to use advanced technologies as a method to support personalized learning and not as a goal when developing adaptive learning environments.