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MathSpring is a math practice environment inside an adaptive tutor that provides math instruction embedded within extensive hints, help, and links to external instructional videos (Arroyo et al. 2014).
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The student model is the key component of any adaptive tutoring system, as it stores all the information about the student (for example, knowledge, interest, learning styles, etc).
In addition, this paper can be used as a guide for making decisions about the techniques that should be adopted when designing a student model for an adaptive tutoring system.
These well-known and commonly used evaluation techniques have been selected in order to design an accurate and correct evaluation methodology, since there are no clear guidelines in the literature for the evaluation of the student model of an adaptive tutoring system.
This is an adaptive tutoring system used to teach the Java programming language.
Integrating multiple adaptive tutoring systems with complementary strengths shows some potential to improve learning.
Pedagogical agents who act as tutors and mentors have been long used in intelligent and adaptive tutoring systems.
Advances in these core tutoring tasks will move state-of-the-art ITSs closer to implementing fully adaptive tutoring which means tailoring instruction to each individual student.
Assessment is a key element in education in general and in Intelligent Tutoring Systems (ITSs; (Rus et al. 2013)) in particular because fully adaptive tutoring presupposes accurate assessment (Chi et al. 2001; Woolf 2008).
The need for fully adaptive, intelligent tutoring systems has all the time developed.
Most proposed tools in literature use data from adaptive learning systems/Intelligent Tutoring Systems (ITS), Web-based Courses and LMS to achieve adaptation of learning (Arnold and Pistilli 2012; Cabada et al. 2011); with classification and prediction as the most used LA techniques.
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