**2. Considerations on cognitive science of human learning and cognitive modeling of students**

learning processes during learning [1], many of these systems are developed mainly by considering users´ cognitive styles, learning style [2–5], previous knowledge before or during an

Typically, an AHS approach demands two types of information processing to achieve two goals [10]. One process consists of gathering information (dependent variables typifying personal and psychological attributes of a user [6–8]), which is used to assign a user to one of several learner models (cognitive classification). Based on the results, a second process adapts the hypermedia instruction (e.g., adaptive content selection, adaptive presentation, and adap-

Note from **Figure 1** that achieving the second goal depends completely on achieving the first goal, that is, selecting a learner model. Thus, any weakness in achieving proper student classification demands urgent corrective behavior within the adaptation process to accurately infer the user goals and thus offer navigation support and content adaptation during instruction. Unfortunately, more often than not, the construction of user models is based on weak data collection (descriptive and/or psychological data), and this weakness leads to the implementation of mechanisms to enhance adaptation processes by minimizing the cost of adaptive behavior and increasing user control over adaptation [12], improving [13], and addressing user variability [14], etc. In other words, this process is driven by the corrective adaptivity of the system rather than adaptability in which the user can consciously participate in the

It is assumed that weaknesses in student modeling frequently stem from using cognitive tools that are controversial, either poorly structured or poorly developed, and many tools are famous for lacking robust empirical support (e.g., learning style/cognitive style instruments [16]).

Thus, from a cognitive science point of view, there is clearly much to say about student modeling. As we will discuss in the next sections, by digital implementation of more sophisticated cognitive science tools to study human learning and by introducing a third goal regarding assessment in typical adaptive instruction systems, research directions can

Generally, these tools do not have a good reputation in cognitive science.

be expanded to provide innovation in student modeling to enhance AHS.

**Figure 1.** Typical approach in developing adaptive hypermedia technologies.

AHS learning [6–8], or intellectual [9].

70 From Natural to Artificial Intelligence - Algorithms and Applications

adaptation [15].

tive navigation [11]). **Figure 1** illustrates these processes.

Common sense in formal education assumes that the better we understand learners cognitive functioning during learning, the more effective the instruction can be achieved. Inside the educational technology fields, many intelligent tutoring systems (ITSs) claim to do this by modeling the way students take decisions [17] and solve problems while they are socializing [18] or by considering users emotional states during instruction [19]. Even when this approach has many positive implications to research and development on cognitive ergonomics and engineering psychology [20], it is our strong belief that the current state on ITS is still far from inheriting positive implications from cognitive science research advances. For instance, AHS innovation, instead of considering cognitive research to innovate student modeling to improve error-type analysis of learner's performance during learning, has rested on corrective adaptability of instruction to support learning outcomes [17]. This kind of evaluating a learner's performance resembles summative assessment of learning where the goal is to specify what a student does not know at the end of a course rather than knowing what a student knows during and after learning like in formative assessment of learning approaches [21]. This approach to evaluate learning can be extrapolated to many fields of digital educational technology [22, 23].

To our current paper goals and to illustrate this point in a deeper way, we will introduce next a discussion inside the context of adaptive hypermedia systems (AHSs) to emphasize how education technology development is strengthened when contextualized by basic cognitive research. Here the main goal is to speak in favor of:


Thus, the following description of a formative-oriented AHS computational system is brought as an example on how improvement opportunities are at disposal for ITS research and development. This is achieved by focusing our attention on considering the human lexicon as the starting point to develop an AHS to support constructive learning outcomes.
