首页> 外文学位 >Inferring dynamic learner behavior for user modeling in continuously adapting hypermedia.
【24h】

Inferring dynamic learner behavior for user modeling in continuously adapting hypermedia.

机译:在连续调整超媒体中为用户建模推断动态学习者行为。

获取原文
获取原文并翻译 | 示例

摘要

Adaptive Hypermedia offers a technological solution for individualized and optimized online learning. However, effectively adapting web-based educational systems to individual learning traits remains an open issue. Inconclusive results and several criticisms to the popular approach of pre-screening individuals and adapting accordingly can be found in the literature.; This work investigated an alternative to pre-screening for adaptivity based on a learning style cycle. A learning cycle approach allows for the observation of learner interaction behavior over instructional events designed to attend to all different learning styles. The ADaPtor system was developed with the goal of optimizing learning efficiency and effectiveness by gradually and iteratively adapting the learning cycle based on learner behavior and performance. ADaPtor's adaptivity scheme personalizes presentation, content and navigation to satisfy individual learning needs.; Three learning cycles were developed for evaluating ADaPtor. Learners were split in two learning groups (control and adaptive) and adaptivity was gradually introduced for the adaptive group. Despite a reduction in the adaptive group effectiveness in the second cycle, when presentation was adapted, effectiveness was comparable in the third and fully adaptive learning cycle. Differences in effectiveness disappeared when more data was available for predictions and learners started following adaptive recommendations more often. Overall, learning efficiency was optimized.; This research provides the adaptive hypermedia community with a tool for using a learning style cycle in adapting to individual learning traits, namely ADaPtor. It emphasizes the need for a collaborative approach to better understand of how people learn online and how adaptive hypermedia can be employed for optimizing individual learning experiences.
机译:自适应超媒体为个性化和优化的在线学习提供了一种技术解决方案。然而,有效地使基于网络的教育系统适应个人学习特征仍然是一个悬而未决的问题。在文献中可以发现不确定的结果和对预先筛选个体并据此进行适应的流行方法的批评。这项工作研究了一种基于学习风格周期的适应性预筛查方法。学习周期方法允许观察学习者在旨在参加所有不同学习方式的教学事件上的互动行为。开发适配器系统的目的是通过根据学习者的行为和表现逐步并迭代地调整学习周期,从而优化学习效率和效果。 ADaPtor的适应性方案可以个性化表示,内容和导航,以满足个人的学习需求。开发了三个学习周期来评估ADaPtor。将学习者分为两个学习组(控制和适应性学习),并逐渐将适应性引入适应性学习组。尽管在第二个周期中适应性小组的有效性有所降低,但当对演示文稿进行调整时,在第三个和完全适应性学习周期中,有效性是可比的。当更多的数据可用于预测并且学习者开始更频繁地遵循适应性建议时,有效性差异就消失了。总体而言,学习效率得到了优化。这项研究为适应性超媒体社区提供了一种工具,用于利用学习风格周期来适应个体学习特征,即ADaPtor。它强调需要一种协作方法,以更好地了解人们如何在线学习以及如何利用自适应超媒体来优化个人学习体验。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号