Introducing the CASS Guided Reading Project (Part 1)

In collaboration with the Department of Psychology, CASS is investigating the critical features of guided reading that can benefit the language and literacy skills of typically developing children.

What is guided reading?

Guided reading is a technique used by teachers to support literacy development. The teacher works with a small group of children, typically not more than 6, who are grouped according to ability and who work together on the same text. This ability-grouping enables the teacher to focus on the specific needs of those children, and to provide opportunities for them to develop their understanding of what they read through discussion, as well as their reading fluency. In this project we are investigating the features of effective guided reading, with a particular emphasis on reading comprehension.

Features of guided reading

Teachers aim to bridge the gap between children’s current and potential ability. Research indicates that this is best achieved by using methods that facilitate interaction, rather than by providing explicit instruction alone (e.g., Pianta et al., 2007).

The strategies that teachers can use to support and develop understanding of the text are best described as lying on a continuum, from low challenge strategies – for example, asking children simple yes/no or closed-answer questions – to high challenge strategies, that might require children to explain a character’s motivation and evaluate the text. Low challenge strategies pose more limited constraints on possible answers: they may simply require children to repeat back part of the text or provide a one word response, such as a character’s name. High challenge strategies provide greater opportunity for children to express their own interpretation of the text.

Low challenge questions can be used by the teacher to assess children’s basic level of understanding and are also a good way to encourage children to participate in the session. High challenge questions assess a deeper understanding and more sophisticated comprehension skills. Skilled teachers will adapt questions and their challenge depending on the group and individual children’s level of understanding and responsiveness, with the intent of gradually increasing the responsibility for the children to take turns in leading the discussion. This technique is used to scaffold the discussion.

Our investigation: How is guided reading effective?

Previous studies observing guided reading highlight substantial variability in what teachers do and, therefore, in our understanding of how guided reading can be used to best foster language and literacy skills. A more fine-grained and detailed examination of teacher input and its relation to children’s responses is needed to determine the teacher strategies that are most effective in achieving specific positive outcomes (see Burkins & Croft, 2009; Ford, 2015).

Previous research on this topic has typically taken the form of observational studies, in which researchers have had to laboriously parse and hand-code transcriptions of the teacher-children interactions (a corpus) to identify teacher strategies of interest. Because this is a long and painstaking process, it limits the size of the corpus to one that can be coded within a realistic time window. In this project, we aim to maximise interpretation of these naturalistic classroom interactions using powerful corpus search tools. These enable precise computer-searches for a wide range of language features, and are much faster and more reliable compared to hand-coding. This enables us to create and explore a much larger corpus of guided reading sessions than in previous studies, making a fine-grained analysis possible. For an introduction to corpus search methods, check out this CASS document.

Future blogs will provide more detail about the specific corpus search measurements that CASS are using to identify what makes for effective guided reading. The next (upcoming) blog, however, will explain the motivation for using corpus methods to investigate the effective outcomes of guided reading.

Meet the Author of this blog: Liam Blything

Since July 2016, I have been working as a Senior Research Associate on the CASS guided reading project. My Psychology PhD focused on language acquisition and has been awarded by Lancaster University. It is a great privilege to be working on such an exciting project that answers psychological questions with all these exciting and advanced corpus linguistics methods. I look forward to providing future updates!



Burkins, J. & Croft, M. M. (2010). Preventing misguided reading: new strategies for guided reading teachers. Thousand Oaks CA: Corwin.

Pianta, R. C., Belsky, J., Houts, R., Morrison, F., & the National Institute of Child Health and Human Development Early Child Care Research Network. (2007). Opportunities to learn in America’s elementary classrooms. Science, 315, 1795–1796.

Ford, M. P. (2015). Guided Reading: What’s New, and What’s Next? North Mankato, MN : Capstone.


Controlling the scale and pace of immigration: changes in UK press coverage about migration

The issue of immigration prominently featured in debates leading up to the June 2016 EU Referendum vote. It was argued that too many people were entering the UK, largely from other EU member states. Politicians and media also talked about ‘taking back control’—notably in the contexts of deciding who can enter Britain and enforcing borders. But, as our new Migration Observatory report ‘A Decade of Immigration in the British Press’ reveals through corpus linguistic methods, such language wasn’t necessarily new: in fact, under the coalition government from 2010-2015, the press was increasingly casting migration in terms of its scale or pace. And, the relative importance of ‘limiting’ or ‘controlling’ migration rose over this period, too.

Our report aimed to understand how British press coverage of immigration had changed in the decade leading up to the May 2015 General Election. We built upon previous research done at Lancaster University (headed by CASS Deputy Director Paul Baker) into portrayals of migrant groups. Our corpus of 171,401 items comes from all 19 national UK newspapers (including Sunday versions) that continuously published between January 2006 and May 2015. Using the Sketch Engine, we identified the kinds of modifiers (adjectives) and actions (verbs) associated with the terms ‘immigration’ and ‘migration’.

The modifiers that were most frequently associated with either of these terms included ‘mass’ (making up 15.7% of all modifiers appearing with either word), ‘net’ (15.6%), and ‘illegal’ (11.9%). Closer examination of the top 50 modifiers revealed a group of words related to the scale or pace of migration: in addition to ‘mass’ and ‘net’, these included terms such as ‘uncontrolled’, ‘large-scale’, ‘high’, and ‘unlimited’. Grouping these terms together, and tracking their proportion of all modifiers compared to those related to illegality—which is another prominent way of referring to immigrants—reveals how these terms made up an increasingly larger share of modifiers under both the Labour and coalition governments since 2006. Figure 1 shows how these words made up nearly 40% of all modifiers in 2006, but over 60% in the five months of 2015. Meanwhile, the share of modifiers referring to legal aspects of immigration (‘illegal’, ‘legal’, ‘unlawful’, or ‘irregular’) declined from 22% in 2006 to less than 10% in January-May 2015.

Figure 1.









Another way of examining this dimension of ‘scale’ or ‘pace’ is to look at the kinds of actions (verbs) done to ‘immigration’ or ‘migration’. For example, in the sentences ‘the government is reducing migration’ and ‘we should encourage more highly-skilled immigration’, the verbs ‘reduce’ and ‘encourage’ signal some kind of action being done to ‘immigration’ and ‘migration’. In a similar way to Figure 1, we looked at the most frequent verbs associated with either term. A category of words expressing efforts to limit or control movement—what we call ‘limit’ verbs in the report—emerged from the top 50 verbs. These included examples such as ‘control’, ‘tackle’, ‘reduce’, and ‘cap’.

Figure 2 shows how the overall frequency of these limit verbs, indicated by the solid line, rose by about five times between 2006 and the high point in 2014—most notably from 2013. But, as a share of all verbs expressing some action towards ‘immigration’ or ‘migration’, this category was consistently making up 30-40% from 2010 onwards. This suggests that, although these kinds of words weren’t that frequent in absolute terms until 2014, the press had already started moving towards using them from 2010.

Figure 2.









These results show how the kind of language around immigration has changed since 2006. Corpus methods allow us to look at a large amount of text—in this case, over a significant period of time in British politics—in order to put recent rhetoric in its longer context. By doing so, researchers contribute concrete evidence about how the British press has actually talked about migrants and migration. Such evidence opens timely and important debates about the role of the press in public discussion (how does information presented through media impact public opinion?) and the extent to which press outputs should be scrutinised.

About the author: William Allen is a Research Officer with The Migration Observatory and the Centre on Migration, Policy, and Society (COMPAS), both based at the University of Oxford. His research focuses on the ways that media, public opinion, and policymaking on migration interact. He also is interested in the ways that migration statistics and research evidence is used in non-academic settings, especially through data visualisations.

Corpus Linguistics, and why you might want to use it, despite what (you think) you know about it

As part of the Spatial Humanities project at Lancaster University, and in collaboration with the Centre for Corpus Approaches to Social Sciences, the central aim of my PhD research project is to investigate the potential of corpus linguistics to allow for the exploration of spatial patterns in large amounts of digitised historical texts. Since I come from a Sociology/Linguistics background, my personal aim since the start of my PhD journey has been to try and understand what historical scholarship practices look like, what kinds of questions historians are interested in (whether they are presently being asked or not), how historians may benefit from using corpus linguistics, and also what challenges historians might encounter when trying to take advantage of corpus linguistics’ affordances. I don’t think I can over-stress how helpful coming to the RSVP conferences has been in this respect, and how grateful I am to the welcoming and helpful community of scholars I have encountered there.

I have chosen to write this post as an introduction to corpus linguistics for several reasons. First, on many counts RSVP members have asked me to explain to them what corpus linguistics consists of; I hope this post begins to answer that question. Second, I have sometimes encountered a reluctance to consider computerised text analysis methods. This reluctance is understandable and should be taken seriously. There are indeed very real challenges to working with computers in the Humanities and it is worth considering them. Ultimately, I hope to help bring corpus linguistics to the attention of those scholars who may find it useful.

The Humanities unavoidably involve messy data and the messy, fluid, categories which we try to apply to them. Computers on the other hand are all about known quantities and a lack of ambiguity. So why use computers in the Humanities? Computers are bad at what humans are good at: understanding. But they are also good at what humans are bad at: performing large and accurate calculations at remarkable speed. Generating historical insight cannot be done at the press of a button, but computers can assist us in manipulating the large amounts of data which are relevant to the questions we care about.

The most fundamental tool in corpus linguistics – the area of linguistics devoted to developing tools and methods to facilitate the quantitative and qualitative analysis of large amounts of text – is the concordance: a method which has been in use for centuries. A concordance is simply a list of occurrences of a word (or expression) of interest, accompanied by some limited context (see figure 1). Drawing up such a concordance manually is a very lengthy process, which can occupy years of an individual’s life. In contrast, once the data has been prepared in certain ways, specialised computer-based corpus linguistic tools can draw up such a concordance within a few seconds, even for tens of thousands of lines of text drawn from a database containing millions of words. For just this simple feat, computers are invaluable for the historian. But why use corpus linguistics tools? After all, all historical digital collections come with interfaces which offer searchability through queries of some sort.

Figure 1: Search results for ‘police’ presented as a concordancefigure1-png

Read the rest from the RSVP website.