Abstract
This paper presents a comprehensive method to implement e-assessment using computer simulated schemata behavior that is based on natural semantic networks obtained from students before and after an academic course. Specifically, a computer system capable of obtaining natural semantic nets from students (as opposed to idiosyncratic semantic nets designed by a researcher) empower implementation of a constrains satisfaction neural net capable of emulate schemata behavior that can be used to formative e-assessment of learning. Empirical support to this approach to evaluate students long term retention of knowledge is presented trough a set of semantic priming studies considering different knowledge domains. Implications to innovate the XXI digital classroom are discussed.
Original language | English |
---|---|
Title of host publication | ACM International Conference Proceeding Series |
Publisher | Association for Computing Machinery (ACM) |
Pages | 32-37 |
Number of pages | 6 |
ISBN (Print) | 9781450364317 |
DOIs | |
Publication status | Published - 26 May 2018 |
Event | 2018 International Conference on Distance Education and Learning, ICDEL 2018 - Beijing, China Duration: 26 May 2018 → 28 May 2018 |
Publication series
Name | ACM International Conference Proceeding Series |
---|
Conference
Conference | 2018 International Conference on Distance Education and Learning, ICDEL 2018 |
---|---|
Country/Territory | China |
City | Beijing |
Period | 26/5/18 → 28/5/18 |
Bibliographical note
Publisher Copyright:© 2018 Association for Computing Machinery.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Software