学习型搜索中用户信息源选择和使用策略研究

Exploring Users Information Source Selection and Use Strategies in Learning Related Search

网络信息体量的激增,信息质量的参差不齐使用户利用搜索系统获取信息、增长知识面临巨大的挑战。在用户开展搜索的过程中,对于信息的选择和使用,很大程度上依赖于该信息的来源。本研究采用实验法,围绕不同的学习层面设计搜索任务,重点关注用户在信息源选择和使用过程中,类型分布上发生的变化,从而识别不同用户所采取的信息源策略的本质,并探索用户的策略与学习效果间的关系。研究发现,用户对自身选择阅读的信息源的依赖程度要高于对系统提供的用户可获得的信息源的依赖程度。根据对可获得的和自身选择的信息源的依赖程度不同,用户存在四种不同的信息源策略:全依赖型、非依赖型、选择依赖性和使用依赖型。并且,用户的信息源策略对学习效果有一定的影响,在接受性学习任务中采取非依赖型策略的用户,与在评价性学习任务中采取全依赖型策略的用户,搜索后能够获得更好的学习效果。这一发现有助于深入理解用户在信息源选择和使用过程中的相关特征,以便优化搜索系统能够在用户搜索过程的信息源获取、选择或使用阶段所存在的问题提供精准高效的帮助,实现学习效果的提升。

With the volume of information increasing and online quality in question, it becomes more difficult for users to acquire appropriate information that satisfies their needs and enriches their knowledge by using search engines. When searching, users’ information selection and subsequent use depend largely on information sources. A user experiment was conducted in order to design two kinds of tasks, one related to receptive learning and the other to critical learning. This study focused on the differences in the distribution of information source types during the users’ process of source selection and use. It also explored the relationship between users’ information source strategies and their learning outcomes. The results showed that users were more dependent on the information sources they ultimately selected rather than those recommended by search engines. According to the degree of dependence, four types of information source strategies were identified: total dependence, non-dependence, selective dependence, and use-dependence. We also found that users’ information source strategies had significant impact on learning outcomes. Users adopting non-dependent strategies for receptive learning- related tasks, as well as those adopting total dependence strategies for critical learning-related tasks, could achieve better learning outcomes. This study contributes by clarifying the characteristics of users’ information sources during selection and subsequent use in order to optimize search engine systems to improve users’ learning outcomes by providing accurate and efficient guidance.