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What the Research Says

Digital technology, the panacea to solving literacy difficulties?

With so much technology around to support literacy, you would be forgiven for thinking that computers are the panacea to solve reading and writing difficulties. Hamish’s story, where he talks about using an iPad, coupled with Siri and some apps, is indicative of how technology can help to overcome barriers to learning.

Digital tools such as text-to-speech, colour highlighting, screen masking, speech recognition, reading pens, eBooks and a plethora of apps offer a variety of potential solutions to individuals who experience a range of difficulties such as decoding, fluency, comprehension, responding etc.

Moreover, the Scottish Government, in partnership with CALL Scotland, provides guidance in the form of checklists Appendix D and Appendix E on planning and implementing appropriate ‘assistive technology’(AT) in schools to ensure learners with additional support needs (ASN) can access the curriculum, who may otherwise be excluded from learning.

Additionally, figures from the SQA in regards to digital exam papers show their popularity has increased over the years with the number of requests increasing year on year (although not all schools have embraced them) decreasing the reliance on scribes and readers - which can only be a good thing.

Is there any evidence?

Despite these innovative moves we are still very much in the dark as to the how and why of technology as an intervention to support learners with literacy difficulties? What does technology help with? To what extent does it help pupils to decode, to become fluent readers, to improve comprehension? Become competent and confident writers? Can technology help all these areas or is it limited to one or two? What evidence is there to support the case of technology?

Therein lies the problem. It is easy to embrace technology, to recommend one device or one program over another as an intervention in the hope it will alleviate barriers and pave the way to inclusive education.

As Kennedy and Deshler (2010) argue, recent research has confirmed that many practitioners working with pupils with ASN do not use evidenced-based strategies to help raise literacy achievement.

There are many questions but relatively few answers. The consensus among researchers suggests that despite the intuitive appeal of technology and its increase in popularity as an accommodation, the “research base on the impact on student performance is limited.” (Fitzgerald, Koury, & Mitchem, 2008; Toppo, 2012).

For example, research results on the impact of text-to-speech by Strangman & Dalton (2006); Farmer, Klein, and Bryson (1992) are mixed and conflicting, particularly in relation to fluency and comprehension.

Research carried out in the US by Stodden et al. (2012) on the impact of text-to-speech and reading comprehension reported positive results. They found that 69 students significantly improved comprehension after reading content materials with text-to-speech at least 40 minutes a week for one semester.

But in another study by Stodden et al. (2012) with 35 high school pupils with literacy difficulties, who used text-to-speech to read classroom material and assignments for at least 30 hours a week for one semester, no significant gains were made in comprehension although there was a slight improvement in vocabulary.

In contrast, Lundberg and Olofosson (1993) found secondary students with a reading difficulty who used text-to-speech for 25 sessions across 10 weeks improved in comprehension, more than pupils who did not use text-to-speech.

A confusing picture

What are we to make of what appears to be inconsistent research findings? Research by, Parr (2013) in relation to text-to-speech highlights the difficulties of information processing particularly for those pupils who struggle with decoding,

by the time they have successfully decoded the word, they have little to no energy or capacity to solve the word, let alone make sense of it, and then do something with it (i.e., comprehend, respond).

Others such as Labbo & Reinking (1999) argue that bypassing decoding issues with text-to-speech may prevent the cycle of withdrawal, low levels of motivation and reduce the reliance on ‘human’ supports and enhance independence. Text-to-speech simply “reads the decoding way”, text-to-speech supports decoding which frees the listener to focus on the meaning of the text (Wise, Ring, & Olson, 2000).

Listening to the learner

In contrast to the varying impact results of text-to-speech, is the consistent positive feedback from pupils. For example, in a study by Meyer & Bouck (2014) which resulted in no major improvements in oral reading, fluency, comprehension or task completion, students who participated believed they read more fluently, comprehended more, and spent less time on the reading task with text-to-speech than when tTechnology is only part of the solutionhey read without text-to-speech.

Parr (2013) highlights numerous case studies where pupils offer positive feedback on text-to-speech, e.g.,

Yin (1984) defines the case study research method as:

an empirical inquiry that investigates a contemporary phenomenon within its real-life context.

Additionally, this case study from Denny High School provides useful insights on the benefits of providing text-to-speech across the school.

From the pupils’ perspective, digital technology tools are about independence, control, confidence, engagement, motivation, being included and achievement.

A universal learning approach?

Much of the research and evidence offered above is far from current (something most researchers do agree on!) and a great deal has changed in recent years. Computer voices have improved and text-to-speech programs are more ubiquitous, e.g., WordTalk and Speak Screen on the iPad. Portable devices provide easy access to a range of tools to support reading and writing in an unobtrusive and ‘cool’ way that was previously unavailable.

The range of built-in tools available in modern devices allows learners to create their own accommodations, personalise their devices and learning materials to better suit their needs. For example,

Other integral tools such as cameras and microphones offer a means for learners to capture notes, record lessons, create engaging stories in a multimodal way that was unimaginable a decade ago.

In essence, these devices allow learners to "learn in different ways through multiple means of engagement, representation, and expression" (Hall, Meyer, Rose, & Gordon, 2014), otherwise known as Universal Design for Learning (UDL). Universal Design for Learning is a framework first defined in the 1990s by David Rose of the Harvard School of Education. It addresses the primary understanding that individuals learn in different ways through multiple means of engagement, representation and expression.

According to Rose, UDL is a set of principles for curriculum development that gives all individuals equal opportunities to learn and provides a blueprint for creating instructional goals, methods, materials, and assessments that work for everyone - not a single, one-size-fits-all solution but rather flexible approaches that can be customised and adjusted to individual needs. Many apps now integrate speech enabled homophone and spell checkers. Others offer word prediction, where a pupil types a letter and the app offers a list of contextualised words allowing the learner to ‘see the word’, ‘hear the word’ and even see a matching picture of the word before choosing.

Are digital technologies and UDL compatible?

Some educators mistakenly assume UDL will replace digital technologies [assistive technology] since all needs will be anticipated and addressed.
Rose, Hasselbring, Stahl, and Zabala (2005) address these concerns by noting that AT and UDL can be thought of as two interventions on a continuum that involves reducing barriers. At one end of the continuum, UDL seeks to reduce barriers for everyone. At the other end of the continuum, AT is used to reduce barriers for learners with additional support needs.

Hamish provides a good example of a young person embracing technology and UDL. Using his iPad with text-to-speech, Siri speech recognition and a few apps he is able to:

learn in different ways through multiple means of engagement, representation and expression.
Hamish is creating his own accommodations and personalising his learning environment. These constructs provide an opportunity for all learners to find success. As a result, Hamish is an independent learner, and definitely sounds like a successful learner, a confident individual, a responsible citizen and an effective contributor!

 

Where now?

Despite the uptake and benefits of technology, research into the how and why is lacking. Does it make a difference? Yes, it almost certainly would. Having evidence-based research would give us greater insights into what works best and what doesn’t work for individual learners. If practitioners working with pupils with ASN use evidenced-based strategies, it will undoubtedly help raise literacy achievement (Kennedy and Deshler, 2010).

This article this has focussed on text to speech but other inclusive digital technologies offer support for learners with literacy difficulties, either as a stand-alone technology or combined with others, e.g., the Livescribe pen can be connected to an iPad or Android for taking audio notes which are synchronised to the device’s app. Mind maps can be exported to word processing programs or presentation programs such as PowerPoint.

Technology is only part of the solution

It is important to remember that technology is only part of the solution. Without the right training, choice of tool for the required task, proper implementation, continued maintenance (such as software updates), consultation and collaboration with teachers, parents and other professionals, technology can quickly have an adverse effect and become a potential barrier to learning. 

If you are interested in further research on this topic, (see the reading list below) with a view to looking at other digital technologies to support literacy please see the following:

Reading list

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