Towards searching as a learning process : A review of current perspectives and future directions <s> Estimating motivation to learn while searching <s> A potentially useful feature of information retrieval systems for students is the ability to identify documents that not only are relevant to the query but also match the student's reading level. Manually obtaining an estimate of reading difficulty for each document is not feasible for very large collections, so we require an automated technique. Traditional readability measures, such as the widely used Flesch-Kincaid measure, are simple to apply but perform poorly on Web pages and other non-traditional documents. This work focuses on building a broadly applicable statistical model of text for different reading levels that works for a wide range of documents. To do this, we recast the well-studied problem of readability in terms of text categorization and use straightforward techniques from statistical language modeling. We show that with a modified form of text categorization, it is possible to build generally applicable classifiers with relatively little training data. We apply this method to the problem of classifying Web pages according to their reading difficulty level and show that by using a mixture model to interpolate evidence of a word's frequency across grades, it is possible to build a classifier that achieves an average root mean squared error of between one and two grade levels for 9 of 12 grades. Such classifiers have very efficient implementations and can be applied in many different scenarios. The models can be varied to focus on smaller or larger grade ranges or easily retrained for a variety of tasks or populations. <s> BIB001 </s> Towards searching as a learning process : A review of current perspectives and future directions <s> Estimating motivation to learn while searching <s> A user's expertise or ability to understand a document on a given topic is an important aspect of that document's relevance. However, this aspect has not been well-explored in information retrieval systems, especially those at Web scale where the great diversity of content, users, and tasks presents an especially challenging search problem. To help improve our modeling and understanding of this diversity, we apply automatic text classifiers, based on reading difficulty and topic prediction, to estimate a novel type of profile for important entities in Web search -- users, websites, and queries. These profiles capture topic and reading level distributions, which we then use in conjunction with search log data to characterize and compare different entities. We find that reading level and topic distributions provide an important new representation of Web content and user interests, and that using both together is more effective than using either one separately. In particular we find that: 1) the reading level of Web content and the diversity of visitors to a website can vary greatly by topic; 2) the degree to which a user's profile matches with a site's profile is closely correlated with the user's preference of the website in search results, and 3) site or URL profiles can be used to predict 'expertness' whether a given site or URL is oriented toward expert vs. non-expert users. Our findings provide strong evidence in favor of jointly incorporating reading level and topic distribution metadata into a variety of critical tasks in Web information systems. <s> BIB002
Given a particular web search-based learning task, other recent work has attempted to assess the motivation that users exhibit when attempting to complete information-seeking tasks. In a study by Kim et al. BIB002 , the authors attempted to capture a user's motivation to find important information by estimating the effort they undertook to get it, via their reading stretch behaviour, whereby users engage in short-term search activity that involves searching and browsing documents far above their 'typical' reading level. To detect reading stretch behaviour, Kim et al. first computed a text readability score for every web page accessed in the search log, using the robust statistical approach given by CollinsThompson and Callan BIB001 . With these readability scores, they were able to estimate a user's typical reading level profile over a 2 month period by computing the distribution of readability scores over past clicked documents. Next, they identified search sessions in which the average reading level of clicked documents was at least four US grade levels higher than the average reading level of that same user's reading profile. By comparing normalized word histograms from titles of clicked pages in 'stretch' sessions with titles in all search sessions, they elicited the topics and tasks that were highly associated with stretch reading behaviour. These tasks corresponded to high-motivation scenarios in which users were trying to learn about areas such as medical tests and procedures, college entrance tests and financial aid options, filling out government forms, finding technical solutions to computer crashes and virus infections, and looking for a new job.