Cognitive Analysis of Meaning and Acquired Mental Representations as an Alternative Measurement Method Technique to Innovate E-Assessment

Autor/inn/en	Morales-Martinez, Guadalupe Elizabeth; Lopez-Ramirez, Ernesto Octavio; Castro-Campos, Claudia; Villarreal-Treviño, Maria Guadalupe; Gonzales-Trujillo, Claudia Jaquelina
Titel	Cognitive Analysis of Meaning and Acquired Mental Representations as an Alternative Measurement Method Technique to Innovate E-Assessment
Quelle	In: European Journal of Educational Research, 6 (2017) 4, S.455-464 (10 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2165-8714
Schlagwörter	Evaluation Methods; Computer Assisted Testing; Cognitive Processes; Long Term Memory; Artificial Intelligence; Knowledge Level; Student Evaluation; Schemata (Cognition); Educational Technology; Technology Uses in Education; Semantics; Priming; Word Recognition + Suchen Sie Ihr Suchwort? Cognitive process; Kognitiver Prozess; Langzeitgedächtnis; Künstliche Intelligenz; Wissensbasis; Schulnote; Studentische Bewertung; Cognition; Schema; Kognition; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Semantik; Worterkennung
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01