**1. Introduction**

A significant number of cognitive oriented adaptive hypermedia systems (AHSs) for learning have been developed. Due to the alternative formative character of AHSs emphasizing

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

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

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

Formative E-Assessment of Schema Acquisition in the Human Lexicon as a Tool in Adaptive…

http://dx.doi.org/10.5772/intechopen.81623

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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

**a.** Innovating education technology by constantly binding basic cognitive science research

**b.** Considering new empirical directions to integrate assessment of learning and instruction

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

**3. The human lexicon as a potential cognitive construct to implement** 

The human mental lexicon is considered a memory capacity to store and meaningfully organize single concepts by connecting them through different types of semantic relations (a mental dictionary). This definition of one of our mental capacities was first appointed by Treisman in 1961 [24], and it is considered a central cognitive structure for language description and

into single parallel formulations to support adaptability of instruction.

starting point to develop an AHS to support constructive learning outcomes.

**cognitive modeling of students**

digital educational technology [22, 23].

**AHSs**

research. Here the main goal is to speak in favor of:

advances to develop education technology.

human learning (e.g., learning a language).

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

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 adaptation [15].

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

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

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