Search engines clearly predate web 2.0; for a history of search engines from 1994-2010, see PPC Blog’s History of Search infographic. However, search tools have been moving steadily in a web 2.0 direction as they have become more user-centred, interactive and multimodal. They have also been moving in a web 3.0 direction with the arrival of personalised search. Search is of course available on mobile devices, though the trend towards the use of dedicated apps has in some ways rendered search less important on the mobile web.
Search engines are essential to help users find materials in the vast ocean of online content. Search on both desktop/laptop computers and mobile devices is dominated by Google, with other major search engines including Yahoo!, Microsoft’s Bing, and Baidu (百度) However, there are also specialised search services which are worth knowing about, as these may sometimes return more appropriate results than the major search engines. Much more customisation of search has become possible across all search services, from the major to the specialised ones, giving users more control over search parameters and more options for storing and sharing search results. Much more automated personalisation of search is now also becoming evident, as the major search engines in particular work towards ensuring that relevant, time-sensitive, context-sensitive information flows automatically to users without the need for specific searches to find it; this may, ironically, render the activity of searching less common in the future. Nevertheless, there are issues with an approach where search engines, rather than users, decide a priori what is relevant and useful to individuals.
While there is no doubt that students need to search for information online for a variety of purposes, it has become evident that many students lack the requisite digital literacy skills in this area. This has resulted in programmes at all levels of the education system to help students acquire various sets of literacy skills:
- the search literacy to conduct effective keyword searches for the content needed
- the information literacy and critical literacy to evaluate information and differentiate reliable from unreliable content, especially in the context of the proliferation of false stories and information across the internet in recent years
- the tagging literacy to keep track of, categorise and organise the content found online
Key innovations in search customisation and personalisation are outlined below, along with examples. Firstly, there is customisation of search content. Most major search engines offer their own customisation tools (see for example Google Advanced Search), which has led to the disappearance of many specific search services over recent years. However, there are still a number that remain active and allow very focused searches, as seen below.
|CC Search, CC Search [images]||Creative Commons content|
|Dogpile||metasearch (compiles results from other search engines)|
|Google Scholar||academic content|
|Trove||Australian historical content|
For more options, see Wikipedia’s List of Search Engines. To create your own fully customised web search, try Google Custom Search. To compare global search trends for different terms, check out Google Trends.
Secondly, there is customisation of search presentation. It is possible to choose among different presentation formats of search interfaces or search results, some of which are very different from traditional search boxes or linear lists of results, as seen in the examples below.
|Human Body Systems||visual search interface for the human body|
|Liveplasma||mind map of film & music preferences|
|Tag Galaxy||solar system map of Flickr search results|
Thirdly, there is customisation of search context, or personalisation of search. It is increasingly the case that the search context is defined in relation to the individual doing the searching, and the other people to whom that individual is connected. It is typically an automated function of the major search services, notably Google, which tailors search results to individuals on the basis of information tracked or inferred about them as a result of past searches, online connections, and online activities. The result is that two individuals searching for exactly the same topic using exactly the same search terms in Google may find that they receive very different results. Indeed, the ultimate aim of social search is to obviate the need for searching at all, with search engines becoming more like prediction engines which anticipate users’ needs.
A similar approach is used to determine what news items a Facebook user sees in his or her newsfeed. Of course, once information is filtered through users’ social networks on Facebook, Twitter and other similar services, it flows automatically to them through their newsfeeds – which partly eliminates the need for searching, because relevant information is already immediately available.
The key issue for educators is that when search engines or social media services tailor news and results to individual users, the range of information received becomes ever narrower. To the extent that education is about broadening rather than narrowing students’ horizons, this is a concern. For a statement about the dangers of ‘filter bubbles’, see Eli Pariser’s TED Talk Beware Online ‘Filter Bubbles’, which dates from 2011 but has become even more relevant in the intervening years.
Latest on TwitterMy Tweets
- Smart language learning July 3, 2019PPTELL Conference Taipei, Taiwan 3-5 July 2019 The second Pan-Pacific Technology-Enhanced Language Learning Conference took place over three days in midsummer in Taipei, with a focus on language learning within smart learning environments. In his keynote, In a SMART world, why do we need language learning?, Robert Godwin-Jones spoke of visions of a world with universal […]