Measuring the Written Language Disorder among Students with Attention Deficit Hyperactivity Disorder

Diane Mitchnick, Clayton Clemens, Jim Kagereki, Vivekanandan Kumar, Dr. Kinshuk, Shawn Fraser

Abstract


  • Background: Attention Deficit Hyperactivity Disorder (ADHD) is a mental health disorder. People diagnosed with ADHD are often inattentive (have difficulty focusing on a task for a considerable period), overly impulsive (make rash decisions), and are hyperactive (move excessively, often at inappropriate times). ADHD is often diagnosed through psychiatric assessments with additional input from physical/neurological evaluations. Written Language Disorder (WLD) is a learning disorder. People diagnosed with WLD often make multiple spelling, grammar, and punctuation mistakes, have sentences that lack cohesion and topic flow, and have trouble completing written assignments. Typically, WLD is also diagnosed through psychological educational assessments with additional input from physical/neurological evaluation.

  • Literature Review: Previous research has shown a link between ADHD and writing difficulties. Students with ADHD have an increased likelihood of having writing difficulties, and rarely is there a presence of writing difficulties without ADHD or another mental health disorder. However, the presence of writing difficulties does not necessarily indicate the presence of a WLD. There are other physical and behavioral factors of ADHD that can contribute to a student having a WLD as well. Therefore, a statistical association between these factors (in conjunction with written performance) and WLD must first be established.

  • Research Question: To determine the statistical association between WLD and physical and behavioral aspects of ADHD that indicate writing difficulties, this research reviewed methodologies from the literature pertaining to contemporary diagnoses of writing difficulties in ADHD students, and reveal diagnostic methods that explicitly associate the presence of WLD with these writing difficulties among students with ADHD. The results demonstrate the association between writing difficulties and WLD as it pertains to ADHD students using an integrated computational model employed on data from a systematic review. These results will be validated in a future study that will employ the integrated computational model to measure WLD among students with ADHD.

  • Methodology: To measure the association of WLD among students with ADHD, the authors created a novel computational model that integrates the outcomes of common screening methods for WLD (physical questionnaire, behavioral questionnaire, and written performance tasks) with common screening methods for ADHD (physical questionnaire, behavioral questionnaire, adult self-reporting scales, and reaction-based continuous performance tasks (CPTs)). The outcomes of these screening methods were fed into an artificial neural network (ANN ) first, to ‘artificially learn’ about measuring the prevalence of WLD among ADHD students and second, to adjust the prevalence value based on information from different screening methods. This can be considered as the priming of the ANN. The ANN model was then tested with data from previous studies about ADHD students who had writing difficulties. The ANN model was also tested with data from students without ADHD or WLD, to serve as control.

  • Results: The results show that physical, behavioral, and written performance attributes of ADHD students have a high correlation with WLD (r = 0.72 to 0.80) in comparison to control students (r = 0.30 to 0.20), substantiating the link between WLD and ADHD. It should be noted that due to lack of female participation, most studies in the literature only employed and reported on the relationship between WLD and ADHD for male participants.

  • Discussion and Conclusion: By testing ADHD students and control students against the WLD criteria, the study shows a strong correlation between WLD and ADHD. There are limitations to the results’ accuracy in terms of a) sample size (average n=88, mean age = 19, 8 studies used for a meta-analysis), b) analysis (original study reviewing ADHD factors first, WLD factors second), and c) causation (the study only reviews prevalence of WLD in ADHD students, not causation). A clinical trial will validate the data and address some of these limitations in a future phase of the research. A computational causal model will be introduced in the discussion portion to illustrate how causation between writing metrics and WLD as it pertains to ADHD can be achieved. These results open the door to advancing pedagogical techniques in education, where students afflicted with ADHD and/or WLD could not only receive assistance for the behavioral aspects of their disorder, but also expect assistance for the learning aspects of their disorder, empowering them to succeed in their studies.

Keywords


ADHD; attention deficit hyperactivity disorder; data analytics; neural networks; WLD; written language disorder

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References


Abu-Hamour, B., Al Hmouz, H., Mattar, J., & Muhaidat, M. (2012). The use of Woodcock-Johnson tests for identifying students with special needs-a comprehensive literature review. Procedia Social and Behavioral Sciences, 47, 665-673. doi: 10.1016/j.sbspro.2012.06.714

Bálint, S., Bitter, I., Czobor, P., Mészáros, A., & Simon, V. (2009). Prevalence and correlates of adult attention-deficit hyperactivity disorder: Meta-analysis. The British Journal of Psychiatry, 194(3), 204-211. doi:10.1192/bjp.bp.107.048827

Barbaresi, W. J., Colligan, R. C., Katusic, S. K., & Weaver, A. L. (2009). The forgotten learning disability: Epidemiology of Written-Language Disorder in a population-based birth cohort (1976–1982), Rochester, Minnesota. Pediatrics, 123(5), 1306-1313. doi:10.1542/peds.2008-2098

Bishop, D. (2004). Expression, Reception, and Recall of Narrative Instrument (ERRNI). Retrieved from http://www.pearsonassess.ca/en/programs/00/51/69/p005169.html

Bitsko, R. H., Danielson, L., Holbrook, J. R., Visser, S. N., & Zablotsky, B. (2015). Diagnostic experiences of children with attention-deficit/hyperactivity disorder. Hyattsville: National Health Statistics Reports.

Boulanger, D., Seanosky, J., Clemens, C., Kumar, V.S., Kinshuk. (2016). A smart competence analytics solution for English writing, International Conference on Advanced Learning Technologies, pp. 468-472, Austin, TX, USA, July 25-28.

Breaux, K. C., & Frey, F. E. (2017, 04 15). Assessing writing skills using correct–incorrect word sequences: A national study . Retrieved from http://images.pearsonclinical.com/images/products/wiat-iii/WIAT-III_NASP_Poster.pdf

CADDRA. (2014, November). CADDRA ADHD Assessment Toolkit (CAAT) forms. Retrieved from http://www.caddra.ca/pdfs/caddraGuidelines2011_Toolkit.pdf

Casas, A. M., Ferrer, M. S., & Fortea, I. B. (2013). Written composition performance of students with attention-deficit/hyperactivity disorder. Applied Psycholinguistics, 34(3), 443-460. doi:10.1017/S0142716411000828

Clemens, C. (2014). MI-Writer Demo. Retrieved from http://learninganalytics.ca/research/mi-writer/

Clemens, C., Kumar, V.S., Boulanger, D., Seanoksy, J., Kinshuk. (2017, accepted) Learning Traces, competence, and causal inference for English composition. In A. Essa, R. Koper, V. Mao (Eds.) Big Data in Education, Springer.

Clemens, C., Chang, M., Wen, D., Kumar, V., Lin, O., & Kinshuk. (2011). Traces of writing competency – Surfing the classroom, social, and virtual worlds. In proceedings of the 2011 11th IEEE International Conference on Advanced Learning Technologies, Athens, Georgia, USA, 6–8 July (pp. 625–626). doi: 10.1109/ICALT.2011.193 [Acceptance rate: 24.6%] [IEEE]

Clemens, C. (2017). A causal model of writing competence (Master's thesis). Reetrieved from https://dt.athabascau.ca/jspui/handle/10791/233

Conners, C. K. (2013). Conners CATA report: Grant sample. Toronto.

Cortese, S., Kelly, C., Chabernaud, C., Proal, E., Di Martino, A., Milham, M., & Castellanos, F. (2012). Toward systems neuroscience of ADHD: A meta-analysis of 55 fMRI studies. Am J Psychiatry, 169(10), 1038-1055. doi:10.1176/appi.ajp.2012.11101521.

Engel, R. R., & Schoechilin, C. (2005). Neuropsychological performance in adult attention-deficit hyperactivity disorder: Meta-analysis of empirical data. Archives of Clinical Neuropsychology, 20(6), 727-744. doi:10.1016/j.acn.2005.04.005

Jacobson, L. T., & Reid, R. (2012). Improving the writing performance of school students with attention deficit/hyperactivity disorder and writing difficulties. Exceptionality: A Special Education Journal, 20(4), 218-234. doi:10.1080/09362835.2012.724624

Kofler, M. J., Rapport, M. D., Sarver, D. E., Raiker, J. S., Orban, S. A., Friedman, L. M., & Kolomeyer, E. G. (2013). Reaction time variability in ADHD: A meta-analytic review of 319 studies. Clinical Psychology Review, 33(6), 795-811. doi:10.1016/j.cpr.2013.06.001

Kumar, V.S., Kinshuk, Clemens, C., & Harris, S. (2015). Causal models and big data learning analytics. In Kinshuk, & R. Huang (Eds.), Ubiquitous learning environments and technologies (pp. 31–53). Berlin, Germany: Springer Berlin Heidelberg. doi: 10.1007/978-3-662-44659-1_3

Lienemann, T. O., & Reid, R. (2006). Self-regulated strategy development for written expression with students with attention deficit/hyperactivity disorder. Exceptional Children, 73(1), 53-68. doi:10.1177/001440290607300103

McQuade, J. D., Tomb, M., Hoza, B., Waschbusch, D. A., Hurt, E. A., & Vaughn, A. J. (2011). Cognitive deficits and positively biased self-perceptions in children with ADHD. Journal of Abnormal Child Psychology, 39(2). doi:10.1007/s10802-010-9453-7

Miranda, A., Baixauli, I., & Colomer, C. (2013). Narrative writing competence and internal state terms of young adults clinically diagnosed with childhood attention deficit hyperactivity disorder. Research in Developmental Disabilities: A Multidisciplinary Journal, 34(6), 1938-1950.

Mitchnick, D., Kumar, V., Kinshuk, & Fraser, S. (2016). Using healthcare analytics to determine an effective diagnostic model for ADHD in students. 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), (pp. 1-4). Las Vegas. doi:10.1109/BHI.2016.7467133

Molitor, S. J., Langberg, J. M., & Evans, S. W. (2016). The written expression abilities of adolescents with Attention-Deficit/Hyperactivity Disorder. Research in Developmental Disabilities (VOLUME?No Volume for this one), 49-59. doi.org/10.1016/j.ridd.2016.01.005

Re, A. M., & Cornoldi, C. (2010). ADHD expressive writing difficulties of ADHD children: When good declarative knowledge is not sufficient. European Journal of Psychology of Education, 25(3), 315-323. doi:10.1007/s10212-010-0018-5

Re, A. M., Mirandola, C., Esposito, S. S., & Capodieci, A. (2014). Spelling errors among children with ADHD symptoms: The role of working memory. Research in Developmental Disabilities, 35(9), 2199-2204. doi:10.1016/j.ridd.2014.05.010

Robeva, R., Penberthy, J., Loboschefski, T., Cox, D., & Kovatchev, B. (2004). Combined psychophysiological assessment of ADHD: A pilot study of Bayesian probability approach illustrated by appraisal of ADHD in female college students. Applied Psychophysiology and Biofeedback, 29(1), 1-18. doi:10.1023/B:APBI.0000017860.60164.66

Rodríguez, C., González-Castro, P., Cerezo, R., & Álvarez, D. (2012). Attention deficit hyperactivity disorder (ADHD) and writing learning disabilities. In D. W. (Ed.), Learning disabilities. (Publisher Location?No publisher location, InTech is an OpenAccess journal) InTech. doi:10.5772/30985

Schrank, F. A. (2005). Using the Woodcock-Johnson III with individuals with ADHD. The ADHD Report, 13(5), 9-11. doi:10.1521/adhd.2005.13.5.9

World Health Organization. (2015, 30 5). Adult ADHD Self-Report Scale-V1.1 (ASRS-V1.1). Retrieved from http://www.hcp.med.harvard.edu/ncs/ftpdir/adhd/18Q_ASRS_English.pdf

Yoshimasu, K., Barbaresi, W. J., Colligan, R. C., Killian, J. M., Voigt, R. G., Weaver, A. L., & Katusic, S. K. (2011). Written-language disorder among children with and without ADHD in a population-based birth cohort. Pediatrics, 128(3), 605-612. doi:http://doi.org/10.1542/peds.2010-2581


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