Learning assessments in search-as-learning: A survey of prior work and opportunities for future research     

《Information processing & management》2022,59(2):102821

People often search for information in order to learn something new. In recent years, the “search-as-learning” movement has argued that search systems should be better designed to support learning. Current search systems (especially Web search engines) are largely designed and optimized to fulfill simple look-up tasks (e.g., navigational or fact-finding search tasks). However, they provide less support for searchers working on complex tasks that involve learning. Search-as-learning studies have investigated a wide range of research questions. For example, studies have aimed to better understand how characteristics of the individual searcher, the type of search task, and interactive features provided by the system can influence learning outcomes. Learning assessment is a key component in search-as-learning studies. Assessment materials are used to both gauge prior knowledge and measure learning during or after one or more search sessions. In this paper, we provide a systematic review of different types of assessments used in search-as-learning studies to date. The paper makes the following three contributions. First, we review different types of assessments used and discuss their potential benefits and drawbacks. Second, we review assessments used outside of search-as-learning, which may provide insights and opportunities for future research. Third, we provide recommendations for future research. Importantly, we argue that future studies should clearly define learning objectives and develop assessment materials that reliably capture the intended type of learning. For example, assessment materials should test a participant’s ability to engage with specific cognitive processes, which may range from simple (e.g., memorization) to more complex (e.g., critical and creative thinking). Additionally, we argue that future studies should consider two dimensions that are understudied in search-as-learning: long-term retention (i.e., being able to use what was learned in the long term) and transfer of learning (i.e., being able to use what was learned in a novel context).  相似文献   

搜索引擎的智能化与网络信息资源的检索研究   总被引：14，自引：1，他引：13  

赵红 《现代图书情报技术》2003,(5):51-54

在网络信息资源检索的研究中，搜索引擎的研究具有非常重要的意义。本文在基于对现有搜索引擎的分析的基础上，阐述了搜索引擎的智能化发展，以及将会从根本上改变现有信息资源检索方式和信息服务模式。  相似文献   

如何利用搜索引擎检索网络信息   总被引：23，自引：1，他引：22  

王启云 《现代图书情报技术》2001,17(4):40-43

从搜索引擎的工作机理, 评价标准, 分类, 使用方法, 使用技巧等五个方面介绍如何利用搜索引擎检索网络信息。  相似文献   

博弈树搜索算法的分析与实现     

危春波  王海瑞  文乔农 《科技广场》2007,6(5):15-17

α-β剪枝算法是博弈树搜索算法中成熟而重要的一枝。本文给出其各种改进策略及实现技术,并给出实验结果分析。B*算法是α-β剪枝算法的挑战者,它给我们以新的视角看待博弈树搜索,并能克服α-β剪枝算法的一些缺陷。  相似文献   

Comparing rankings of search results on the Web   总被引：1，自引：0，他引：1  

Judit Bar-Ilan   《Information processing & management》2005,41(6):1511-1519

The Web has become an information source for professional data gathering. Because of the vast amounts of information on almost all topics, one cannot systematically go over the whole set of results, and therefore must rely on the ordering of the results by the search engine. It is well known that search engines on the Web have low overlap in terms of coverage. In this study we measure how similar are the rankings of search engines on the overlapping results.We compare rankings of results for identical queries retrieved from several search engines. The method is based only on the set of URLs that appear in the answer sets of the engines being compared. For comparing the similarity of rankings of two search engines, the Spearman correlation coefficient is computed. When comparing more than two sets Kendall’s W is used. These are well-known measures and the statistical significance of the results can be computed. The methods are demonstrated on a set of 15 queries that were submitted to four large Web search engines. The findings indicate that the large public search engines on the Web employ considerably different ranking algorithms.  相似文献   

搜索引擎的选择与检索策略   总被引：8，自引：0，他引：8  

卢晓勤 《情报科学》2002,20(4):418-420

本文根据目前国内外搜索引擎研究状况，针对搜索引擎中出现的问题，提出了搜索引擎的选择方法，及利用技巧和策略。  相似文献   

利用网络日志分析提高搜索引擎的检准率   总被引：2，自引：0，他引：2  

葛蓉 《情报科学》2004,22(10):1250-1253

本文详细介绍了搜索引擎的发展现状和网络日志分析技术。在此基础上。探讨了如何利用网络日志分析提高搜索引擎的检准率。  相似文献   

新一代中文智能搜索引擎尤里卡研究   总被引：3，自引：1，他引：3  

李村合 《情报科学》2003,21(1):86-88

本文介绍了Internet新一代中文智能搜索引擎尤里卡的基本情况，它能对自然语言查询进行语义理解，克服了传播搜索方法与语义无关的缺点。研究了该搜索引擎的使用方法与技巧，同时客观地评价了它的优劣得失，指出需进一步完善之处。  相似文献   

搜索引擎搜索结果的评价技术   总被引：6，自引：0，他引：6  

陶跃华  孙茂松 《情报科学》2001,19(8):861-863,873

搜索引擎根据用户查询在自己的索引数据库中进行查找，并根据相关性分析将查询结果返回给用户。本文就传统信息检索系统的性能效率评价技术,针对Internet的特点，对搜索引擎搜索结果的评价进行了讨论。  相似文献   

A study of results overlap and uniqueness among major Web search engines     

Amanda Spink  Bernard J. Jansen  Chris Blakely  Sherry Koshman 《Information processing & management》2006

The performance and capabilities of Web search engines is an important and significant area of research. Millions of people world wide use Web search engines very day. This paper reports the results of a major study examining the overlap among results retrieved by multiple Web search engines for a large set of more than 10,000 queries. Previous smaller studies have discussed a lack of overlap in results returned by Web search engines for the same queries. The goal of the current study was to conduct a large-scale study to measure the overlap of search results on the first result page (both non-sponsored and sponsored) across the four most popular Web search engines, at specific points in time using a large number of queries. The Web search engines included in the study were MSN Search, Google, Yahoo! and Ask Jeeves. Our study then compares these results with the first page results retrieved for the same queries by the metasearch engine Dogpile.com. Two sets of randomly selected user-entered queries, one set was 10,316 queries and the other 12,570 queries, from Infospace’s Dogpile.com search engine (the first set was from Dogpile, the second was from across the Infospace Network of search properties were submitted to the four single Web search engines). Findings show that the percent of total results unique to only one of the four Web search engines was 84.9%, shared by two of the three Web search engines was 11.4%, shared by three of the Web search engines was 2.6%, and shared by all four Web search engines was 1.1%. This small degree of overlap shows the significant difference in the way major Web search engines retrieve and rank results in response to given queries. Results point to the value of metasearch engines in Web retrieval to overcome the biases of individual search engines.  相似文献