Multimedia technology for speech and language diagnosis and therapy

Published research establishes that multimedia technology is increasingly being used to create learning environments in education and clinical settings. The aim of this study is to investigate the use of multimedia technology by speech and language therapists in developing their own digital material on diagnostic and therapeutic procedures. Undergraduate fourth-year students were motivated to use various multimedia editing and authoring tools for diagnosis and therapy procedures in building social skills deficits. The research questions concern students’ accomplishments on intergrading multimedia technology in speech and language diagnosis and therapy, and the effectiveness of the digital environment they created. The results revealed that students used text, graphic, audio and video materials and animation to facilitate procedures in speech and language diagnosis and therapy and accomplished good outcomes while designing multimedia activities, meeting technical and pedagogical/clinical criteria. Finally, students’ multimedia technology development approaches were discussed and concluded in the demands of modern information society.Moksliniai tyrimų rezultatai rodo, jog pasaulyje efektyvioms mokymosi aplinkoms kurti vis plačiau naudojamos multimedijos technologijos. Straipsnio tikslas – ištirti, kaip multimedijos technologijos taikomos diagnozuojant kalbos ir kalbėjimo sutrikimus irjpanaudojamos ugdymo procese. Epyro edukacinių technologijų instituto ketvirto kurso bakalauro studijų specialiosios pedagogikos studentai buvo mokomi naudoti įvairias multimedijos technologijas kuriant priemones ir jas naudojant socialinių įgūdžių nustatymo ir ugdymo procese. Tyrimo klausimai siejami su studentų gebėjimais naudoti multimedijos technologijas kalbos ir kalbėjimo sutrikimų diagnostikos ir terapijos metu ir jų pagalba kalbos klaida sukurtos skaitmeninės mokymo medžiagos panaudojimu edukaciniame procese ą neaiški mintis. Tyrimo duomenimis, studentai, naudodami tekstą, grafiką, garso, vaizdo medžiagą ir animaciją, siekdami palengvinti kalbos ir kalbėjimo sutrikimų diagnostiką ir terapiją, pasiekė gerų rezultatų: tyrimo dalyviai pademonstravo multimedijos žinias ir įgūdžius, būtinus skaitmeninei visuomenei palaikyti; gebėjo modeliuoti ir efektyviai panaudoti multimedijos technologijas diagnostikos ir intervencijos procedūromis ugdant socialinius įgūdžius; išsakė nuomonę, kad multimedijos technologijų naudojimas yra teigiama patirtis, skatinanti mokymąsi ir kūrybiškumą; gebėjo dinamiškai pritaikyti ir personalizuoti skaitmeninę mokymosi aplinką atsižvelgdami į įvairius mokymosi stilius. Naudodamiesi multimedijomis, studentai sukūrė priemones, atitinkančias edukacinių / klinikinių technologijų kriterijus. Studentų praktikuojami multimedijos technologijų plėtojimo būdai straipsnyje nagrinėjami ir apibendrinami šiuolaikinės informacinės visuomenės poreikių kontekste

ICT USE IN EARLY CHILDHOOD EDUCATION: STORYTELLING

The aim of this study is to investigate the ICT use by pre-service preschool teachers and pre-service speech and language therapists in developing their digital case of a storytelling. Students were stimulated to use various multimedia editing and authoring tools. The research questions concern the students’ accomplishments on integrating technology in digital storytelling and the effectiveness of that learning environment they created. The results revealed a difference by implying that not only the learning theory and the teaching practices but also the content, the structure and the nature of the course together with the social interactions play an important role on how people learn and develop their skills.The results revealed that students of both departments overall accomplished very good project outcomes in digital storytelling meeting technical and pedagogical criteria. Finally similarities and differences of the students’ approaches in the digital storytelling development are discussed and concluded in the demands of modern information society.KEYWORDS: ICT, digital storytelling, childhood, preschool.DOI: http://dx.doi.org/10.15181/tbb.v66i1.78

Evaluating New Approaches of Intervention in Reading Difficulties in Students with Dyslexia: The ilearnRW Software Application

The aim of this paper is to increase knowledge and understanding on how the implementation of language content through specialized software, such as the “Integrated Intelligent Learning Environment for Reading and Writing-iLearnRW”, can enhance learning during intervention procedures to enhance reading skills for children with dyslexia.The iLearnRW software is a newly designed tool that makes use of innovative technology and provides individualized intervention through games that incorporate learning activities, addressing those language areas that are most challenging for children with dyslexia in a highly entertaining and motivating way. Individualized intervention is provided through an underlying user profile, which incorporates these language features and is constantly updated as the child uses the software playing games, presenting language material selected based on his difficulties and recording his progress. A group of 78 students (52 male, 26 female) diagnosed with dyslexia, aged between 9 and 11 years old, was assessed for phonological, morphological and vocabulary skills. The students logged in the iLearnRW software on a mean of 14.18 days over a six-month intervention. After the 6-month intervention, the students were assessed once again on the same skills so as to establish the tool’s effectiveness.The results’ analysis revealed the following: (i) there was a strong constructional linkage between the profile entries of the sample, the language content of the tasks of the screening test as well of the games and its effectiveness in the students’ performance; (ii) the students who received specific guidance by their teachers, obtained higher success rates in most of the games than the students without any guidance, and (iii) the quantity of the language content and the time playing were not correlated with the students’ performance in the software’s games. Keywords: Digital technology, assistive computer software, dyslexia, learning environmen

Utilizing Constructed Neural Networks for Autism Screening

Autism Spectrum Disorder is known to cause difficulties in social interaction and communication, as well as repetitive patterns of behavior, interests, or hobbies. These challenges can significantly affect the individual’s daily life. Therefore, it is crucial to identify and assess children with Autism Spectrum Disorder early to significantly benefit the long-term health of children. Unfortunately, many children are not diagnosed or are misdiagnosed, which means they miss out on the necessary interventions. Clinicians and other experts face various challenges during the diagnostic process. Digital tools can facilitate early diagnosis effectively. This study aimed to explore the use of machine learning techniques on a dataset collected from a serious game designed for children with autism to investigate how these techniques can assist in classification and make the clinical process more efficient. The responses were gathered from children who participated in interactive games deployed on mobile devices, and the data were analyzed using various types of neural networks, such as multilayer perceptrons and constructed neural networks. The performance metrics of these models, including error rate, precision, and recall, were reported, and the comparative experiments revealed that the constructed neural network using the integer rule-based neural networks approach was superior. Based on the evaluation metrics, this method showed the lowest error rate of 11.77%, a high accuracy of 0.75, and a good recall of 0.66. Thus, it can be an effective way to classify both typically developed children and children with Autism Spectrum Disorder. Additionally, it can be used for automatic screening procedures in an intelligent system. The results indicate that clinicians could use these techniques to enhance conventional screening methods and contribute to providing better care for individuals with autism

Applying Neural Networks on Biometric Datasets for Screening Speech and Language Deficiencies in Child Communication

Screening and evaluation of developmental disorders include complex and challenging procedures, exhibit uncertainties in the diagnostic fit, and require high clinical expertise. Although typically, clinicians’ evaluations rely on diagnostic instrumentation, child observations, and parents’ reports, these may occasionally result in subjective evaluation outcomes. Current advances in artificial intelligence offer new opportunities for decision making, classification, and clinical assessment. This study explores the performance of different neural network optimizers in biometric datasets for screening typically and non-typically developed children for speech and language communication deficiencies. The primary motivation was to give clinicians a robust tool to help them identify speech disorders automatically using artificial intelligence methodologies. For this reason, in this study, we use a new dataset from an innovative, recently developed serious game collecting various data on children’s speech and language responses. Specifically, we employed different neural network approaches such as Artificial Neural Networks (ANNs), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), along with state-of-the-art Optimizers, namely the Adam, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), Genetic algorithm (GAs), and Particle Swarm Optimization algorithm (PSO). The results were promising, while Integer-bounded Neural Network proved to be the best competitor, opening new inquiries for future work towards automated classification supporting clinicians’ decisions on neurodevelopmental disorders

Employing Classification Techniques on SmartSpeech Biometric Data towards Identification of Neurodevelopmental Disorders

Early detection and evaluation of children at risk of neurodevelopmental disorders and/or communication deficits is critical. While the current literature indicates a high prevalence of neurodevelopmental disorders, many children remain undiagnosed, resulting in missed opportunities for effective interventions that could have had a greater impact if administered earlier. Clinicians face a variety of complications during neurodevelopmental disorders’ evaluation procedures and must elevate their use of digital tools to aid in early detection efficiently. Artificial intelligence enables novelty in taking decisions, classification, and diagnosis. The current research investigates the efficacy of various machine learning approaches on the biometric SmartSpeech datasets. These datasets come from a new innovative system that includes a serious game which gathers children’s responses to specifically designed speech and language activities and their manifestations, intending to assist during the clinical evaluation of neurodevelopmental disorders. The machine learning approaches were used by utilizing the algorithms Radial Basis Function, Neural Network, Deep Learning Neural Networks, and a variation of Grammatical Evolution (GenClass). The most significant results show improved accuracy (%) when using the eye tracking dataset; more specifically: (i) for the class Disorder with GenClass (92.83%), (ii) for the class Autism Spectrum Disorders with Deep Learning Neural Networks layer 4 (86.33%), (iii) for the class Attention Deficit Hyperactivity Disorder with Deep Learning Neural Networks layer 4 (87.44%), (iv) for the class Intellectual Disability with GenClass (86.93%), (v) for the class Specific Learning Disorder with GenClass (88.88%), and (vi) for the class Communication Disorders with GenClass (88.70%). Overall, the results indicated GenClass to be nearly the top competitor, opening up additional probes for future studies toward automatically classifying and assisting clinical assessments for children with neurodevelopmental disorders