**6. Adaptive e-instruction through e-assessment in e-learning environments: a proposal**

Up to this point, the discussion of applying e-assessment to navigation support and adaptation of content has focused only on AHS. Note, however, that the same arguments can be applied to alternative e-instruction systems or alternative e-instruction. For instance, adaptive navigation support for AHS or in e-instruction can be implemented using the same assumptions by considering the model presented in **Figure 9**.

## **6.1. The student model**

This schema priming is assumed to occur when schema information is stored in long-term memory, which likely explains why the neural net (after training) is useful for discriminating

**Figure 7.** Students' word-recognition latency times corresponding to associative, schema-related, and nonrelated words (A). Comparison of schema priming effects obtained from this study and from similar studies involving other

This is relevant because even when we cannot see the existence of a schema in the lexicon, we can track its footsteps as evidence that long-term learning has occurred. On the other hand, it is not necessary to specify a lexicon; it is enough to say that lexical information is obtained and organized as proposed by a no-lexicon view. **Figure 7B** shows that this effect might vary

Several possibilities are introduced by considering a cognitive assessment of learning like the one just described. Let us consider a study [43] carried over 60 first-semester bachelor engineering students who took a course on computer usability. Here, 15 students failed to pass the course, but after a post-season corrective course, they succeeded and achieved the course credit. **Figure 8** shows the mental concept representations obtained by a constructive-

Note that at the beginning of the course, the EVCOG system shows that students have a mental representation with separated concept clusters (A). This leads to confusion in terms of meaning of a topic. After a corrective course, students presented a single unified schemata knowledge where DESIGN, INTERFACE, and USER showed meaningful centrality to knowledge representation (B). The teacher in charge of the course argued that after looking at the system cognitive report at the beginning of the course, she tried meaningful integration of topics by having the concept of DESIGN as the main reference for meaning formation. Chronometric assessment provided support to this learning process since schemata priming was not obtained at the beginning of the learning period but appeared at the end of classes supporting the idea that students not only successfully passed the course but also obtained long-term retention of schemata.

depending on the knowledge domain and the effect of instruction [34, 37, 39].

chronometric assessment of learning before and after the corrective course.

between successful and unsuccessful learners.

78 From Natural to Artificial Intelligence - Algorithms and Applications

knowledge domains (B).

In a functional adaptive instruction system such as the one shown in **Figure 8**, the student model is a domain-specific well-trained classifier. Empirical research in several knowledge domains has shown that this type of classifier yields successful classification in 95–98% of instances [38].

## **6.2. Expert model: determining concept organization of meaning formation**

During the defining of a target concept (in natural semantic net mapping), after a student decides which is the highest-ranking concept definer (indicated by its M value), the nexthighest-ranked concept from the set of definers depends on the concept frequency (F) in the definition task and the time required to produce it, that is, its interresponse time (ITR; see

**Figure 9.** Proposal for adaptive instruction/assessment instruction.

right column in the SAM group in **Figure 5A**). Thus, the M value of each definer can be correctly predicted (98% accuracy) using the following equation [41]:

$$M = \mathbf{A} \ast e^{(\mathbf{0}/\mathbf{F} + \mathbf{C}^\*/TR)} + \mathbf{D} \ast \ln(F) \tag{2}$$

where O(t) relates to a specific conceptual organization (defined by natural semantic net parameters), which in turns defines R. Furthermore, by using some basic notation from

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

(q, x), a) = T´

For example, consider a set of 10 highest-ranked concepts that provide most of connectivity in

requires going from q0 to q9. Now suppose that after information foraging, a user produces a

user obtained a valid homomorphic mental representation of the meaning implied inside the web page even when the concept path position (estimated M value) is high (by considering Eq. (2)).

, q10] such that:

, q<sup>1</sup> , q<sup>3</sup> , q<sup>4</sup> , q<sup>5</sup> , q<sup>6</sup> , q<sup>7</sup> , q<sup>8</sup> , q<sup>9</sup> ]

where meaning formation implies regulation of a transition rule ∂′(q, w) = T′ (**Figure 10**).

[R(S(t), O(t))], (4)

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

81

]. Here, proper meaning formation

), it is assumed the

automata theory [45], it is possible to specify a transition rule from 3 as follows:

(q, w) = ∂(∂′

, q<sup>1</sup> , q<sup>2</sup> , q<sup>3</sup> , q<sup>4</sup> , q<sup>5</sup> , q<sup>6</sup> , q<sup>7</sup> , q<sup>8</sup> , q<sup>9</sup>

Since user exploration of contents only missed one relevant semantic concept (q<sup>2</sup>

**Figure 10.** Building a mental model from web page contents through meaning formation [44].

∂′

a natural semantic network [q<sup>0</sup>

, q<sup>6</sup> , q<sup>0</sup> , q<sup>3</sup> , q<sup>5</sup> , q<sup>7</sup> , q<sup>4</sup> , q<sup>9</sup> , q<sup>8</sup>

Natural semantic net ∩ information foraging [q<sup>0</sup>

transition set like [q<sup>1</sup>

where A, B, C, and D are constants obtained from fit analysis. Here, word position in a SAM group is needed only to identify which definer ranks higher since the concept frequency has already been used to filter the SAM group.

Consider the case of a user searching for information on a web page (information foraging). This page must contain linked concepts sufficient for meaning formation (obtained using a natural semantic net). Then, after calculating the M values of selected concepts (considering the time taken by the user to select an available concept; ITR), a comparison can be made to check if the M values corresponding to searching for information on a web page correspond to a proper path of optimized M values corresponding to ideal meaning formation [44].

To illustrate this point, consider **Figure 9**. Here, a user has an initial representation state or initial meaning of web contents. This initial user conceptual organization is not assumed to be identical to the concept organization in a web page (isomorphic) but homomorphic. Information foraging through time (R) is based on a user cognitive strategy to obtain meaning from contents. Thus, transforming conceptual organization (T) and acquiring new concepts serve to obtain valid homomorphic representation of contents such that T'R = RT. A transformation path can be specified as:

$$\left[\mathbf{R}[\mathbf{T(S(t),O(t))}]\right] = \mathbf{T(R(S(t),O(t)))}\tag{3}$$

where O(t) relates to a specific conceptual organization (defined by natural semantic net parameters), which in turns defines R. Furthermore, by using some basic notation from automata theory [45], it is possible to specify a transition rule from 3 as follows:

$$\delta(\mathbf{q}\_{\nu}\mathbf{w}) = \delta(\delta(\mathbf{q}\_{\nu}\mathbf{x})\_{\nu}\mathbf{a}) = \text{T}\left[\mathbf{R}(\mathbf{S}(\mathbf{t}), \mathbf{O}(\mathbf{t}))\right] \tag{4}$$

where meaning formation implies regulation of a transition rule ∂′(q, w) = T′ (**Figure 10**).

For example, consider a set of 10 highest-ranked concepts that provide most of connectivity in a natural semantic network [q<sup>0</sup> , q<sup>1</sup> , q<sup>2</sup> , q<sup>3</sup> , q<sup>4</sup> , q<sup>5</sup> , q<sup>6</sup> , q<sup>7</sup> , q<sup>8</sup> , q<sup>9</sup> ]. Here, proper meaning formation requires going from q0 to q9. Now suppose that after information foraging, a user produces a transition set like [q<sup>1</sup> , q<sup>6</sup> , q<sup>0</sup> , q<sup>3</sup> , q<sup>5</sup> , q<sup>7</sup> , q<sup>4</sup> , q<sup>9</sup> , q<sup>8</sup> , q10] such that:

Natural semantic net ∩ information foraging [q<sup>0</sup> , q<sup>1</sup> , q<sup>3</sup> , q<sup>4</sup> , q<sup>5</sup> , q<sup>6</sup> , q<sup>7</sup> , q<sup>8</sup> , q<sup>9</sup> ]

right column in the SAM group in **Figure 5A**). Thus, the M value of each definer can be cor-

*M* = A∗*e*(B/*<sup>F</sup>* <sup>+</sup> <sup>C</sup> <sup>∗</sup>*ITR*) + D∗ln(*F*) (2)

where A, B, C, and D are constants obtained from fit analysis. Here, word position in a SAM group is needed only to identify which definer ranks higher since the concept frequency has

Consider the case of a user searching for information on a web page (information foraging). This page must contain linked concepts sufficient for meaning formation (obtained using a natural semantic net). Then, after calculating the M values of selected concepts (considering the time taken by the user to select an available concept; ITR), a comparison can be made to check if the M values corresponding to searching for information on a web page correspond to a proper path of optimized M values corresponding to ideal meaning

To illustrate this point, consider **Figure 9**. Here, a user has an initial representation state or initial meaning of web contents. This initial user conceptual organization is not assumed to be identical to the concept organization in a web page (isomorphic) but homomorphic. Information foraging through time (R) is based on a user cognitive strategy to obtain meaning from contents. Thus, transforming conceptual organization (T) and acquiring new concepts serve to obtain valid homomorphic representation of contents such that T'R = RT. A transfor-

[R(S(t), O(t))], (3)

rectly predicted (98% accuracy) using the following equation [41]:

**Figure 9.** Proposal for adaptive instruction/assessment instruction.

80 From Natural to Artificial Intelligence - Algorithms and Applications

already been used to filter the SAM group.

formation [44].

mation path can be specified as:

R[T(S(t), O(t))] = T′

Since user exploration of contents only missed one relevant semantic concept (q<sup>2</sup> ), it is assumed the user obtained a valid homomorphic mental representation of the meaning implied inside the web page even when the concept path position (estimated M value) is high (by considering Eq. (2)).

**Figure 10.** Building a mental model from web page contents through meaning formation [44].

The expert system control mechanisms adapt navigation links that minimize differences between information foraging values and meaning formation (determined by transition rules specified by Eq. (4)), as well as by using the neural net classifier information (successful vs. unsuccessful integration of information in the user's lexicon).
