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It is not effective to assume that all learners will have to follow the same instructional model. All learners should not be advised to read the same material and with the same order (Chrysafiadi & Virvou, 2013). Provision of the same instructional conditions to all students can be pedagogically ineffective. In contrast, achieving learning goals can be more effective if pedagogical procedures are geared to comply with the pupils’ individual needs (Akbulut & Cardak, 2012). In various investigations, the authors agree that it is very important to identify the student's characteristics, which in various studies are called the student's model. For example, Rongmei and Lingling mention that the student’s model is the record of the learner’s basic information and cognitive state of information, which accurately, objectively and truly reflects the learner’s current level of knowledge and cognitive ability (Rongmei & Lingling, 2009). On the other hand, in (Alsubait & Khamis, 2011), the authors state that among many characteristics that define the student’s model, there are characteristics that have a greater influence on the learning process such as:
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The student’s previous knowledge that determines the concepts that he can learn.
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Student’s skills and learning styles that determines the teaching approach that best suits him/her.
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Student’s emotions and motivations is another important characteristic that is usually forgotten in student modelling research.
Likewise, tailoring learning experiences to student needs can be accomplished with respect to several factors including intellectual ability, cognitive styles, learning styles, prior knowledge, anxiety, achievement motivation, and self-efficacy (Akbulut & Cardak, 2012).
In the mentioned works, the authors agree that one of the relevant characteristics in the student's model is the detection of the prior knowledge that the student has on the subject to be taught or learnt. In looking at the importance of the detection of the students’ prior knowledge as pedagogical support, the authors can mention the theory of cognitive load (CLT). One of its effects, the reversal expertise, affirms that many experimental studies generated by the cognitive load theory demonstrated that instructional procedures and techniques that were effective for novice learners appeared to be detrimental for more experienced learners (Kalyuga, Rikers, & Paas, 2012). In contrast to experts, novices should achieve higher levels of learning performance with this additional material that is essential due to their level of expertise, compared to novices lacking the written explanation (Leppink, Broers, Imbos, van der Vleuten, & Berger, 2012), (Rey & Andreas, 2013).
The process of detecting prior knowledge is closely related to the constant evaluation of students, which leads to detecting the cognitive skills that the student has to solve problems, and in this research, focuses on problems of algebra. This evaluation is the one that will give the guidelines to determine the type of instructional design that should be provided to the student. However, the process of constant evaluation is not easy to perform manually, so personalized education requires automated tools, but at the same time, as mentioned in (Cuendet, Dehler-Zufferey, Ortoleva, & Dillenbourg, 2015), classrooms using computer technology today are the exception rather than the norm. This is not due to the lack of potential of computer technology for learning. And even though the digital age is so advanced around the world, in Mexico, where this research is carried out, the necessary conditions do not exist for any institution to use fully automated tools. Disadvantages such as poor infrastructure, lack of computers, and inadequate internet access, are some of the most common obstacles that do not allow most Mexican educational institutions to use automated techno-pedagogical strategies.