WordDriver is a web app developed by Toni & Rob Seiler from ELR Software for Toni's PhD research entitled "The effectiveness of a computer-supported intervention targeting phonological recoding and orthographic processing for children with word reading impairment" (Curtin University thesis, 2015) . Toni's PhD supervisors and co-authors on papers published from this project are Assoc Prof Suze Leitao (School of Occupational Therapy, Social Work & Speech Pathology) and Dr Mara Blosfelds (School of Psychology), Curtin University, Perth, WA, Australia.

WordDriver is based on the evidence supporting the dual-route model of skilled reading (Coltheart, 2006), Ehri's phase model of reading development (Ehri, 2005), and Share's phonological recoding theory (Share, 1995). Research examining these theories has shown that:

Our preliminary research suggests that this intervention has the potential to be an efficient evidence-based component of reading interventions for children with severe and persistent word reading delay (Seiler et al, 2019 ). Using single subject research designs, we found that all participants (aged 7-8 years who had not responded to previous reading interventions) made significant gains in decoding skills within 15 x 20 minutes sessions.

WordDriver displays graphics on the screen that use an analogy of learning to drive a car. A functional version is being made available without charge for clinicians and other researchers to examine and perhaps integrate into their own projects. We would appreciate feedback and discussion about any aspects of WordDriver and this project - please email info@elr.com.au

WordDriver has two stages:

  1. WordDriver-1 provides training in accurate decoding. It is designed for children who have mastered grapheme-phoneme (letter-sound) knowledge of short vowels and single consonants, but are unable to use this knowledge to accurately decode short words (3-letter, 4-letter words). It presents items with 1:1 letter-sound correspondence (starting at 2- and progressing to 6-letter items).
  2. WordDriver-2 follows on from WordDriver-1. It aims to expand orthographic knowledge by delivering items with consonant and vowel digraphs.

WordDriver modules

Within the intervention modules (L-Plate, P-Plate, D-Plate):

Within the testing modules (T-Plate):

Storage of results:

From the loader module

Intervention: L-Plate, P-Plate, D-Plate

All modules are delivered as described in the above overview, except the L-Plate. The L-Plate occurs in WordDriver-1 to familiarise the student with the software, and to teach the decoding process at each level (i.e., 3-letter, 4-letter, and so on). As a teaching module, it differs from the P-Plate and D-Plate in two ways. First, the instructor does all actions: touching the Go button, demonstrating decoding, and putting the items in the Book or Bin. Second, the Help button is used for all items as this allows increased opportunity for the instructor to teach the decoding process - sounding out and blending.

Following pre-intervention assessment, the instructor determines an appropriate starting level (2-letter, 3-letter etc.). Progression to the next level (eg from 3- to 4-letter items) occurs once the student achieves 90% accuracy at the current level. For example, if a student is on 3-letter words, a score of 90% on the P-Plate allows progression to the D-Plate; and a score of 90% suggests the student can move to the 4-letter level.

Testing: T-Plate

There are 41 nonword lists, each comprising items with 1:1 letter-sound correspondence. To deliver the T-Plate, the instructor selects the list and the length (70 or 35 items), and the T-Plate is administered as described above. Complete nonword lists/response forms for these T-Plates are available here.

From the loader module

Intervention: P-Plate and D-Plate

On selection of a P-Plate and D-Plate, you are presented with a choice of targets. This allows you to provide specific intervention for one or a group of digraphs.

Following intervention of the selected target digraphs, you may wish to then include "Foil" words (words and nonwords which contain only short vowels), as this develops discrimination skills. For example, to accurately decode "rat" versus "rate", or "hut" and "hurt".

Testing: T-Plate

Each T-Plate currently allows assessment of a group of digraphs. This allows the instructor to measure change in the targeted compared to untreated digraphs. Delivery is as described above. Complete nonword lists/response forms for these T-Plates are available here.

WordDriver can be shared in teletherapy sessions as a "shared screen" in Zoom, and as the "Add-On" WordDriver for Coviu. On either platform, both presenter and the client can interactively share the mouse/touch controls.

Two, free public access modes are available

Anyone may use the "Unregistered" mode which has only 2 predefined "number plates" ("ABC123" and "XYZ789"). The "Registered" mode is more suitable if you wish to use WordDriver for your research or teaching, and requires that you have arranged with the authors for your own (free) set of "number plates".

Usage and privacy notes

  1. PDF Preliminary Study (2013) Journal of Clinical Practice in Speech-Language Pathology
  2. Link to Download Thesis (2015) by Dr. Toni Seiler from the Curtin University institutional repository
  3. PDF Summary of Thesis (2017) by Toni Seiler describing the WordDriver research project
  4. PDF Unpublished Paper (2017) describing WordDriver-1 design and development
  5. Link to Research Report (2019) International Journal of Language and Communication Disorders
  6. PDF Literacy and WordDriver 1 (2020) Learning Difficulties Australia: Developing accuracy and fluency in word reading skills
  7. PDF Literacy and WordDriver 2 (2023) Australian Journal of Learning Difficulties: Targeting phonological recoding to support orthographic learning: effectiveness of WordDriver delivered via telehealth
  8. PDF Nonword Assessment Response Forms (2020-23) As used in WordDriver 1 & 2
  9. Video Short clips showing WordDriver in use

  1. Apel, K., Thomas-Tate, S., Wilson-Fowler, E. B., & Brimo, D. (2012). Acquisition of initial mental graphemic representations by children at risk for literacy development. Applied Psycholinguistics, 33(2), 365-391. doi:10.1017/s0142716411000403
  2. Coltheart, M. (2006). Dual route and connectionist models of reading: An overview. London Review of Education, 4(1), 5-17. doi:10.1080/13603110600574322
  3. Gough, P. B., & Tunmer, W. E. (1986). Decoding, reading, and reading disability. Remedial and Special Education, 7, 6 - 10.
  4. Lervag, A., Hulme, C., & Melby-Lervag, M. (2018). Unpicking the developmental relationship between oral language skills and reading comprehension: It's simple, but complex. Child Development, 89(5), 1821-1838. doi:doi:10.1111/cdev.12861
  5. Martin-Chang, S., Ouellette, G., & Bond, L. (2017). Differential effects of context and feedback on orthographic learning: How good Is good enough? Scientific Studies of Reading, 21(1), 17-30. doi:10.1080/10888438.2016.1263993
  6. Seiler, A., Leitao, S., & Blosfelds, M. (2018). WordDriver-1: Evaluating the efficacy of an app-supported decoding intervention for children with reading impairment. International Journal of Language & Communication Disorders, http://dx.doi.org/10.1111/1460-6984.12388(0). doi:doi:10.1111/1460-6984.12388
  7. Seiler, A. & Leitao, S. (2023) Targeting phonological recoding to support orthographic learning: effectiveness of WordDriver delivered via telehealth, Australian Journal of Learning Difficulties, 28:1, 1-26, DOI: 10.1080/19404158.2023.2208135
  8. Share, D. L. (1995). Phonological recoding and self-teaching: sine qua non of reading acquisition. Cognition, 55(2), 151-218. doi:10.1016/0010-0277(94)00645-2
  9. Snowling, M. J., & Hulme, C. (2012). Annual Research Review: The nature and classification of reading disorders - a commentary on proposals for DSM-5. Journal of Child Psychology and Psychiatry, 53(5), 593-607. doi:10.1111/j.1469-7610.2011.02495.x
  10. Rinderknecht, M.D., Ranzani, R., Popp, W.L. et al. Algorithm for improving psychophysical threshold estimates by detecting sustained inattention in experiments using PEST. Atten Percept Psychophys 80, 1629-1645 (2018). https://doi.org/10.3758/s13414-018-1521-z

Last updated: Jun 08, 2023

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