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

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

As it has been the case for most cognitive constructs introduced to explain the human mind, to consider a human lexicon as part of our cognitive architecture has not been an easy task. After heated academic debates, several views (cognitive models) regarding the lexicon have emerged, leaving different research groups to enroll into different theoretical considerations or views ranging from the possibility of a mental dictionary-like system up to the possibility of a no-lexicon view. Thus, currently, three dominant views prevail to guide academic research on this topic [25]: the multiple lexicons view implying different system stores for different lexical information like sensorimotor information, emotion or spatial information [26, 27], the single-lexicon view where all lexical levels are integrated [28], and the no-lexicon view (lexical knowledge without a mental lexicon [29]).

**a.** Methodology is obtained to measure specific assumptions about how lexical 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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**b.** In contrast to other alternative lexicon views, the no-lexicon proposal is computational plausible under consideration of recent advances in computer science to model learners,

**c.** Most importantly, as it will be described, the use of artificial neural net classifiers (ANNs) allows researchers to deal with cognitive theoretical developments, suggesting that schemata to assimilate knew knowledge does not really exist in memory but knowledge

Finally, by embedding these cognitive precepts about the human lexicon into AHS development, a prominent role is given to articulate dynamic assessment of learning to adapt and support digital instruction. This requires another way to explore adaptability of an ITS.

Instruction and assessment are integral parts of teaching to improve students´ experiences [31]. For instance, learning-oriented assessment (also referred to as formative assessment or assessment for learning) requires active participation of students in using feedback and selfmonitoring from instruction and assessment as keys to successfully acquire appropriate new knowledge from a course [32]. It is assumed that assessment provides explicit and implicit

Let us first present a general framework of implementing dynamic assessment inside the context of AHS development. In this proposal, assessment is assumed to exert effects at various

An e-assessment system that complies with these evaluation requirements, implementation viability, and cognitive science principles was first presented by Morales and colleagues [33–35]. At the core of their assessment system (EVCOG, for cognitive evaluator), there is a neural net classifier capable of identifying students who have integrated schema-related concepts from a school course into their lexicon (this schema-related concepts are obtained by using natural semantic nets; **Figure 3A**). The neural net classification capacity is based on the cognitive fact that once a student has integrated new knowledge into her/his long-term memory, a semantic priming effect (in a semantic priming study) is obtained from schema-related words only if meaningful long-term learning has occurred (single-word schemata priming [36, 37]). Thus, the classifier uses a student's schema-related word-recognition times to assess whether the student has integrated new knowledge into long-term memory or has retained information in her/his short-term memory (e.g., to pass a test) or no new schemata were acquired at all. **Figure 3B** shows the role of this net classifier within a cognitive constructive-responsive/chronometric

levels, and it constitutes by itself a domain and a goal. **Figure 2** illustrates this point.

namely connectionism models of mental knowledge representation.

schema emerges as required for learning and thinking purposes.

**4. Adaptive instruction and the constructive/chronometric** 

messages to facilitate a student's academic performance.

is acquired.

**e-assessment approach**

assessment of learning [38, 39].

In spite of controversy regarding this topic, the concept of a human lexicon has been appealing enough to bring attention from education technology developers. For instance, Salcedo et al. [30] presented an adaptive hypermedia model (LEXMATH) that can be used as an opportunity to illustrate this point. Specifically, these authors argued that by considering a student's lexicon, learner modeling is optimized. In this AHS model, students´ lexicons regarding general or specific topics are obtained through surveys and are maintained in a database. An ideal lexical domain is obtained from teachers, and during instruction, an expert system optimizes learning paths by adapting navigation support and teaching activities to minimize differences between students´ lexicons and the provided ideal lexical domain in field of mathematics.

These types of models point to a more robust direction to innovate student modeling since it empowers the AHS technology with a developed theoretical framework regarding human mental representation but still incomplete. However, notice that LEXMATH does not subscribe to a specific view or specific model within an academic view of the human lexicon. This model seems to rest on a commonsense view of considering a dictionary-like view of the human lexicon. This excludes the system from using robust methodology to assess specific assumptions of lexical behavior (especially regarding learning) promoted by a cognitive model. Rather, LEXMATH again describes a kind of error-type analysis approach to minimize differences between an expert and a learner where lexical knowledge acquisition (modification) uses indicators unfamiliar to robust cognitive lexicon views. As pointed before, this is not so uncommon since this approach to support cognitive-based instruction based on minimizing differences is frequently used inside modern approaches of ITS or AHS.

As we will describe next, alternative new empirical research directions that impose a strongest connection between basic cognitive research and education technology implementation empower innovation without losing our old tricks to ITS and AHS development. Specifically, to continue with our lexicon model discussion, it is described a cognitive constructive-chronometric system to asses human lexical oriented learning and at the same time improving student modeling to minimize corrective adaptability.

Interestingly, this model subscribes to the third view of the human lexicon, which is the nolexicon view. As it is expected, whenever an academic effort subscribes to a specific view, it immediately inherits academic criticisms form alternative views. However, by taking this step forward, some advantages are obtained:

**a.** Methodology is obtained to measure specific assumptions about how lexical knowledge is acquired.

As it has been the case for most cognitive constructs introduced to explain the human mind, to consider a human lexicon as part of our cognitive architecture has not been an easy task. After heated academic debates, several views (cognitive models) regarding the lexicon have emerged, leaving different research groups to enroll into different theoretical considerations or views ranging from the possibility of a mental dictionary-like system up to the possibility of a no-lexicon view. Thus, currently, three dominant views prevail to guide academic research on this topic [25]: the multiple lexicons view implying different system stores for different lexical information like sensorimotor information, emotion or spatial information [26, 27], the single-lexicon view where all lexical levels are integrated [28], and the no-lexicon

In spite of controversy regarding this topic, the concept of a human lexicon has been appealing enough to bring attention from education technology developers. For instance, Salcedo et al. [30] presented an adaptive hypermedia model (LEXMATH) that can be used as an opportunity to illustrate this point. Specifically, these authors argued that by considering a student's lexicon, learner modeling is optimized. In this AHS model, students´ lexicons regarding general or specific topics are obtained through surveys and are maintained in a database. An ideal lexical domain is obtained from teachers, and during instruction, an expert system optimizes learning paths by adapting navigation support and teaching activities to minimize differences between students´ lexicons and the provided ideal lexical domain in

These types of models point to a more robust direction to innovate student modeling since it empowers the AHS technology with a developed theoretical framework regarding human mental representation but still incomplete. However, notice that LEXMATH does not subscribe to a specific view or specific model within an academic view of the human lexicon. This model seems to rest on a commonsense view of considering a dictionary-like view of the human lexicon. This excludes the system from using robust methodology to assess specific assumptions of lexical behavior (especially regarding learning) promoted by a cognitive model. Rather, LEXMATH again describes a kind of error-type analysis approach to minimize differences between an expert and a learner where lexical knowledge acquisition (modification) uses indicators unfamiliar to robust cognitive lexicon views. As pointed before, this is not so uncommon since this approach to support cognitive-based instruction based on mini-

mizing differences is frequently used inside modern approaches of ITS or AHS.

student modeling to minimize corrective adaptability.

step forward, some advantages are obtained:

As we will describe next, alternative new empirical research directions that impose a strongest connection between basic cognitive research and education technology implementation empower innovation without losing our old tricks to ITS and AHS development. Specifically, to continue with our lexicon model discussion, it is described a cognitive constructive-chronometric system to asses human lexical oriented learning and at the same time improving

Interestingly, this model subscribes to the third view of the human lexicon, which is the nolexicon view. As it is expected, whenever an academic effort subscribes to a specific view, it immediately inherits academic criticisms form alternative views. However, by taking this

view (lexical knowledge without a mental lexicon [29]).

72 From Natural to Artificial Intelligence - Algorithms and Applications

field of mathematics.


Finally, by embedding these cognitive precepts about the human lexicon into AHS development, a prominent role is given to articulate dynamic assessment of learning to adapt and support digital instruction. This requires another way to explore adaptability of an ITS.
