用于学习管理系统可用性评估的量子神经系统数据分析体系结构

Raul Valverde, Beatriz Torres, H. Motaghi
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引用次数: 3

摘要

NeuroIS使用脑电图(EEG)等工具来测量可能与人类焦虑有关的高脑电波频率。过去的研究表明,计算机焦虑会影响用户如何感知学习管理系统(LMS)的易用性。虽然计算机焦虑已被成功地用于评估LMS的可用性,但其评估的主要数据收集机制是问卷调查。调查问卷可能存在一些问题,比如不足以理解某些形式的信息,比如情绪和诚实的回答。基于量子的意识研究方法在过去几年非常流行,包括彭罗斯和哈默洛夫(1995)的微管量子模型缩减。本章的目标是提出一种基于NeuroIS的架构,该架构通过使用用户的EEG收集数据,然后使用收集到的数据进行分析,使用为LMS的可用性测试的计算机焦虑测量提出的量子意识模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Quantum NeuroIS Data Analytics Architecture for the Usability Evaluation of Learning Management Systems
NeuroIS uses tools such as electroencephalogram (EEG) that can be used to measure high brainwave frequencies that can be linked to human anxiety. Past research showed that computer anxiety influences how users perceive ease of use of a learning management system (LMS). Although computer anxiety has been used successfully to evaluate the usability of LMS, the main data collection mechanisms proposed for its evaluation have been questionnaires. Questionnaires suffer from possible problems such as being inadequate to understand some forms of information such as emotions and honesty in the responses. Quantum-based approaches to consciousness have been very popular in the last years including the quantum model reduction in microtubules of Penrose and Hameroff (1995). The objective of the chapter is to propose an architecture based on a NeuroIS that collects data by using EEG from users and then use the collected data to perform analytics by using a quantum consciousness model proposed for computer anxiety measurements for the usability testing of a LMS.
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