**6.3. Expert model: inference engine**

The expert system includes a PROLOG backward-chaining inference engine that allows the system to build a valid "mental representation (GOAL)" based on natural semantic net data structures in the knowledge domain (templates) by request of a decision rule. This rule system considers whether schema priming for a specific module has been achieved by consulting the neural net classifier and by comparing the obtained path M values against an ideal descending organization of M values. If a semantic effect is not obtained, then the following events occur:


module or an entire course is completed, the user can obtain a cognitive performance report (**Figure 11B**). This report serves as explicit assessment results that empower a user to adapt the searching of content (encouraging adaptability), whereas the link modification is an implicit message of corrective adaptability to improve proper meaning formation of the content. Selection of learning activities either using hypermedia or by modification of knowledge

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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The goal of the proposed system is to promote assessment tasks as learning tasks, student involvement in assessment, and forward-looking feedback in adaptive e-instruction systems [32]. This system reduces the enormous delay in e-assessment innovation: e-assessment has been limited to mere digitization of traditional, sometimes ancient, evaluation methods [35]. A new empirical research line is opened in which student modeling is improved by using tools of cognitive science in adaptive e-learning systems in ways that were not possible before. We believe that research exploring the human lexicon as a way to adapt instruction will be at

This study was supported by DGAPA-UNAM with a grant from the Support Program of

Research and Technological Innovation Projects (PAPIIT) <<TA400116>>.

content depends on the expert model's evaluation of the user's meaning formation.

**Figure 11.** Interface showing a menu of options of instruction (A), and assessment (B).

the center of future developments in AHS/hypertext.

**7. Conclusions**

**Acknowledgements**

Currently, research is being performed to achieve a dynamic optimization of search information by adapting navigational support based on minimization of differences between meaning values of the user and knowledge domains rather than waiting for the user to complete a knowledge module.
