通过通用设计的增强现实增强学习:对增强和传统学习材料的比较研究
Attila Bekkvik Szentirmai1, Pietro Murano2
1Department of Design, Norwegian University of Science and Technology - NTNU, Bygg 118, 315, Gjøvik, Norway. attila.b.szentirmai@ntnu.no.
Studies in health technology and informatics
|November 19, 2024
概括
全面设计 (UD) 增强现实 (AR) 应用程序增强了各种用户的可访问性和学习能力,包括残疾人. 与传统方法相比,UD AR提高了可用性和结果,促进了数字包容.
科学领域:
- 教育技术的教育技术
- 人与计算机的交互
- 可访问性研究 可访问性研究
背景情况:
- 传统的教育材料往往会给不同需求的用户带来可访问性障碍.
- 增强现实 (AR) 提供了沉浸式学习潜力,但需要仔细设计包容性.
- 全面设计 (UD) 的原则旨在创造出可以被所有人使用的产品,尽可能多.
研究的目的:
- 将普遍设计 (UD) 增强现实 (AR) 应用程序与传统纸质材料的有效性进行比较.
- 评估UD AR对不同用户群体的可访问性,可用性,用户体验和学习成果的影响.
- 调查UD AR在支持具有特定需求的学习者,如视力障碍和阅读障碍等方面的潜力.
主要方法:
- 一项涉及36名具有不同能力和残疾的参与者的比较研究.
- 两个实验条件:UD设计的AR应用程序和传统的纸质材料.
- 评估可访问性,可用性,用户体验和海洋生物学,人体解剖学和文化历史方面的短期学习成果.
主要成果:
- 用UD设计的AR应用程序显著改善了特殊需求用户的可访问性和可用性.
- 视觉用户通过UD AR增强了学习表现,特别是在空间任务中.
- 阅读障碍的用户表现出有效的信息回忆;盲人用户在没有外部辅助技术的情况下与AR内容互动.
结论:
- 在AR开发中纳入UD原则对于提高可访问性和可用性至关重要.
- 像AR这样的沉浸式技术,当被设计为通用时,可以显著改善教育体验并促进数字包容.
- UD AR为传统的学习材料提供了可行和有效的替代方案,特别适用于多元化的学习者.
相关概念视频
Study Design in Statistics
7.5K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
7.5K
Experimental Designs
11.3K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
11.3K
Comparing Experimental Results: Student's t-Test
5.8K
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
5.8K
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
419
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
419
Metacognition
1.2K
Metacognition is a conscious process where individuals are aware of their cognitive and executive processes, such as planning before solving a problem or self-monitoring during reading. For instance, a writer may need help with composing a piece. The situation involves a writer who is working on a piece of writing, but while doing so, they realize that something is missing. They notice that their characters lack depth or details. This realization occurs because the writer is reflecting on their...
1.2K
Methods of Medium Optimization
70
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
70


