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

  • Diane Mitchnick Athabasca University
  • Clayton Clemens Athabasca University
  • Jim Kagereki Athabasca University
  • Vivekanandan Kumar Athabasca University
  • Dr. Kinshuk University of North Texas
  • Shawn Fraser Athabasca University
Keywords: ADHD, attention deficit hyperactivity disorder, data analytics, neural networks, WLD, written language disorder

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.

Author Biographies

Diane Mitchnick, Athabasca University
Diane Mitchnick holds a Bachelor of Science degree in Computing and Information Systems from Athabasca University, Canada and is currently working on her graduate thesis. Her research areas encompass neural networks and artificial intelligence, with the goal to leverage from these areas a healthcare analytics package that will provide insight into learning the diagnostic behaviour of written language disorder (WLD) in adults with attention deficit hyperactivity disorder (ADHD). To learn more about her research, visit http://learninganalytics.ca/research/mhads/.
Clayton Clemens, Athabasca University
Clayton Clemens holds a Master of Science in Information Systems, with a specialization in natural language processing, learning analytics, and causal inference. Clayton works full-time as a business analyst for the City of Edmonton, Alberta, where he leverages his knowledge of computational techniques to support continuously-improving recreation opportunities for citizens.
Jim Kagereki, Athabasca University
Jim Kagereki holds a Bachelor of Science degree in Computing and Information Systems from Athabasca University, Canada. His interest in managing relational datasets contributed to the database architecture of the MHADS (Mental Health Analysis and Diagnostic Service) tool used in this research.
Vivekanandan Kumar, Athabasca University
Dr. Vivekanandan Kumar is a Professor in the School of Computing and Information Systems at Athabasca University, Canada. He holds the Natural Sciences and Engineering Research Council of Canada’s (NSERC) Discovery Grant on Anthropomorphic Pedagogical Agents, funded by the Government of Canada. His research focuses on developing anthropomorphic agents, which mimic and perfect human-like traits to better assist learners in their regulatory tasks. His research includes investigating technology-enhanced erudition methods that employ big data learning analytics, self-regulated learning, co-regulated learning, causal modeling, and machine learning to facilitate deep learning and open research. For more information, visit http://vivek.athabascau.ca.
Dr. Kinshuk, University of North Texas
Dr. Kinshuk is the Dean of the College of Information at the University of North Texas, USA. Prior to that, he held the NSERC/CNRL/Xerox/McGraw Hill Research Chair for Adaptivity and Personalization in Informatics, funded by the Federal government of Canada, Provincial government of Alberta, and by national and international industries. His work has been dedicated to advancing research on the innovative paradigms, architectures and implementations of online and distance learning systems for individualized and adaptive learning in increasingly global environments. Areas of his research interests include learning analytics; learning technologies; mobile, ubiquitous and location aware learning systems; cognitive profiling; and, interactive technologies. For more information, visit http://www.kinshuk.info/.
Shawn Fraser, Athabasca University
Dr. Shawn Fraser is an Associate Professor and incoming Associate Dean of Teaching and Learning in the Faculty of Health Disciplines at Athabasca University, Canada. He is interested in interdisciplinary approaches to research design and data analysis.

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Published
2017-09-07
Section
Articles