**4. Adaptive instruction and the constructive/chronometric e-assessment approach**

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 messages to facilitate a student's academic performance.

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

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 assessment of learning [38, 39].

**Figure 2.** Diagram of how continuous assessment of student knowledge acquisition affects various levels of processing in an AHS learning session.

A constraint satisfaction neural net (CSNN) is developed from concept cooccurrence through SAM groups such that the probability that two concepts cooccur or do not cooccur becomes their weight association in a rectangular matrix, with k possible connections with N concepts such that k = N(N − 1)/2. Thus, the weight association between two concepts (W) is calculated

**Figure 4.** The schema-related concepts (Left) used to train a neural net through semantic priming studies are obtained from simulated connectionist schemata behavior (b), (c), that is based on teachers' and students' conceptual semantic nets (a).

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where X represents one concept in a pair of concepts to be associated, and Y represents another concept. In determining association values among concepts in a natural semantic network such as the one selected earlier, the joint probability value p(X = 1 and Y = 0) can be obtained by calculating how often the definer X of a pair of concepts appears in a list of definers in which Y does not appear, and likewise for the other probability values. These association values are used as an input matrix to the CSNN to simulate schemata of interest [42] (**Figure 4B** and **C**), and a large set of metrics for concept organization and structure can be obtained [41]. From schema simulations and semantic net analysis, schema-related word pairs are selected to implement semantic priming studies. Thus, students' word-recognition latencies to these word

pairs are presented to the classifier for student classification (**Figure 4**, left).

**5. Empirical support for e-assessment based on the human lexicon**

To better describe these concepts, we will describe data resulting from application of constructive-chronometric assessment in an undergraduate psychology course on the computational mind. **Figure 5A** and **B** shows partial instances of definitions obtained from a set of 10 schema-related target concepts relevant to this course before learning (**Figure 5**, top panels) and after learning (**Figure 5**, bottom panels). The following target concepts were provided by the teacher of the course: mind, computation, von Neumann, Turing machine, connectionism, memory, computational mind, working memory, long-term memory, and HPI (human

] [p(X = 1 & Y = 1) p(X = 0 & Y = 0)

]1, (1)

using the following derivative of the Bayesian formula:

Wij = −ln [p(X = 0 & Y = 1) p(X = 1 & Y = 0)

information processing).

**Figure 3.** Concepts related to schema course content are selected during a constructive evaluation (A). These concepts can be used to assess whether a student integrated new information into her/his long-term memory using digitized cognitive semantic priming techniques (chronometric evaluation; B). Word-recognition latency patterns are used by the neural net to discriminate between successful and unsuccessful learners.

To train a classifier, hundreds of successful and unsuccessful learners´ schema-related wordrecognition patterns are presented to it. Achieving this requires first obtaining schema-related concepts from students before and after a course (after learning).

**Figure 4A** shows a computer system for obtaining students' and teachers' concept definers to target schema-related concepts using a technique called natural semantic net mapping. This technique produces definitions (using single concept definers such as nouns and adjectives) for represented objects based on their meanings and not on free associations or pure semantic category memberships [40, 41].

In this technique, the 10 highest-ranked definers of each target concept (SAM group) can be used to draw a semantic net, if desired. Some concepts serve as definers for more than one target concept. These are common definers, and other definers and target concepts are interconnected through them. Numerous common definers tend to emerge whenever there are close links among target concepts (schemata).

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**Figure 4.** The schema-related concepts (Left) used to train a neural net through semantic priming studies are obtained from simulated connectionist schemata behavior (b), (c), that is based on teachers' and students' conceptual semantic nets (a).

A constraint satisfaction neural net (CSNN) is developed from concept cooccurrence through SAM groups such that the probability that two concepts cooccur or do not cooccur becomes their weight association in a rectangular matrix, with k possible connections with N concepts such that k = N(N − 1)/2. Thus, the weight association between two concepts (W) is calculated using the following derivative of the Bayesian formula:

$$\text{Wii} = -\ln\left[\mathbf{p}(\mathbf{X}=0 \& \mathbf{Y}=1)\right] \mathbf{p}(\mathbf{X}=1 \& \mathbf{Y}=0) \right] \left[\mathbf{p}(\mathbf{X}=1 \& \mathbf{Y}=1) \ \mathbf{p}(\mathbf{X}=0 \& \mathbf{Y}=0) \right] \mathbf{1},\tag{1}$$

where X represents one concept in a pair of concepts to be associated, and Y represents another concept. In determining association values among concepts in a natural semantic network such as the one selected earlier, the joint probability value p(X = 1 and Y = 0) can be obtained by calculating how often the definer X of a pair of concepts appears in a list of definers in which Y does not appear, and likewise for the other probability values. These association values are used as an input matrix to the CSNN to simulate schemata of interest [42] (**Figure 4B** and **C**), and a large set of metrics for concept organization and structure can be obtained [41]. From schema simulations and semantic net analysis, schema-related word pairs are selected to implement semantic priming studies. Thus, students' word-recognition latencies to these word pairs are presented to the classifier for student classification (**Figure 4**, left).
