This study measured the structural and organizational changes in the knowledge schema of human cognition in response to the learning achieved by 48 students enrolled in the second year of a psychology degree. Two studies were carried out based on the Chronometric Constructive Cognitive Learning Evaluation Model. This article deals only with the first one, which consisted of a conceptual definition task designed in line with the Natural Semantic Network technique. Participants defined ten target concepts with verbs, nouns, or adjectives (definers), and then weighed the grade of the semantic relationship between the definers and the target concepts. The data indicate that the initial knowledge structures had been modified towards the end of the course. The participants’ human cognition schema presented changes in terms of content, organization, and structure. This evidence supports the idea that the acquisition and transformation of the schemata learned in academic environments may be observed through cognitive science indicators.

This article illustrates the application of the Chronometric Constructive Cognitive Learning Evaluation Model to measure the structural, organizational, and temporal properties of the anatomical knowledge schemata acquired by 52 first-year medical students enrolled for a second time in an anatomy course. The participants took part in a mental representation experiment as a part of which they carried out a conceptual definition task involving anatomy concepts based on the Natural Semantic Networks (NSN) technique. A computational simulation was performed on the NSN data, after which the students took part in a semantic priming experiment involving a lexical decision task which required them to classify words related or unrelated to their anatomy schema as word/non-word. Findings revealed that, although students stored the anatomy information in their memory, they struggled to structure, consolidate, and retrieve this information from their memory. These findings suggest that students who did not get the passing grade in anatomy course may struggle with integrating and consolidating pertinent information. Thus, results showed that the constructive-chronometric cognitive approach is useful to measure the properties of schemes medical students developed on the anatomy topic.

This chapter presents a comprehensive method of implementing e-assessment in adaptive e-instruction systems. Specifically, a neural net classifier capable of discerning whether a student has integrated new schema-related concepts from course content into her/his lexicon is used by an expert system with a database containing natural mental representations from course content obtained from students and teachers for adapting e-instruction. Mental representation modeling is used to improve student modeling. Implications for adaptive hypermedia systems and hypertext-based instructions are discussed. Furthermore, it is argued that the current research constitutes a new cognitive science empirical direction to evaluate knowledge acquisition based on meaning information.

Empirical directions to innovate e-assessments and to support the theoretical development of e-learning are discussed by presenting a new learning assessment system based on cognitive technology. Specifically, this system encompassing trained neural nets that can discriminate between students who successfully integrated new knowledge course content from students who did not successfully integrate this new knowledge (either because they tried short-term retention or did not acquire new knowledge). This neural network discrimination capacity is based on the idea that once a student has integrated new knowledge into long-term memory, this knowledge will be detected by computer-implemented semantic priming studies (before and after a course) containing schemata-related words from course content (which are obtained using a natural semantic network technique). The research results demonstrate the possibility of innovating e-assessments by implementing mutually constrained responsive and constructive cognitive techniques to evaluate online knowledge acquisition. Keywords: E-assessment, learning, knowledge representation, connectionism, educational technology, innovation, neural nets.To cite this article: Morales-Martinez, G. E., Lopez-Ramirez, E. O., Castro-Campos, Villarreal-Treviño, & Gonzales-Trujillo, C. J. (2017). Cognitive analysis of meaning and acquired mental representations as an alternative measurement method technique to innovate e-assessment.

<p>This study includes a sample of 112 high school students who provided self-efficacy<br />judgments to solve math problems. Thirty-six experimental conditions called scenarios were<br />created for this study by combining 4 factors regarding solving mathematical problems<br />(modality, degree of difficulty, structuring, and relevance of the task). Each scenario<br />described a hypothetical context that required the participant to imagine an activity to<br />reinforce the learning of math skills in the scenario. Thus, the experimental task was to read<br />each scenario and to judge how capable the participant felt to undertake each math task under<br />the hypothetical context. Results showed two levels of self-efficacy judgment among<br />participants. Students in the first level judged themselves as highly capable of performing<br />math activities, while those in the second level, judged themselves as moderate capable.<br />Regarding the first cluster factors regarding difficulty and the structure of the task had a<br />greater weight whereas in the second cluster task difficulty and task relevance factors<br />obtained the higher weight values. Finally, a cognitive summation rule used by participants to<br />integrate information from the different study factors was identified. Results implications on<br />education are discussed in this article.</p>