Teachers, inclusive education specialists, parents, and students with autistic spectrum disorder (autism) are the participants of the educational process, which is aimed not only to teach such a student. The correction of communication skills is also the aim of such education, and the education process is complicated with no-doubt need to fit it to both individual needs and possibilities of every unique student. Supporting such education process with an information technology allows to simplify the decision making while choosing an application, that fits most needs of all the education process participants.
Autism Spectrum Disorder (ASD) is usually diagnosed at the age of 2-4 years, when specialists can detect abnormalities in communication with other people and the outside world. The difficulties with social communication, interaction and specific imagination can be diagnosed. ASD is a general term used to group brain dysfunction, including autistic disorders, Asperger's syndrome, disintegration disorder in children, and other common developmental disorders.
A child with ASD has special needs in the organization of the educational process. Therefore, the education of such a child demands inclusive environment. An important factor of such education is the use of actual information technology to improve the educational process.
In recent years, new correctional and developmental and training programs were designed all over the world, and such applications are adjusted to be used on mobile devices such as smartphones and tablets, as well as laptops and PCs. Therefore, the issue of choosing the optimal software for a person with ASD is relevant, considering his personalized needs, as well as the results of psychophysical diagnostics.
The most effective technology for improving traditional learning is computer learning, an approach
that is widely used to support students with ASD. Such technologies are especially popular with mobile
devices like iPad [
A catalog of useful software applications based on iPad mobile technologies for people with ASD
was formed by a researcher from the University of Edinburgh [
There is an urgent need to develop a unique learning trajectory for students with ASD, which will not only cover the learning needs of such students, but try to address the social, emotional and communication needs of children and youth with ASD, support their independence and well-being. Inclusive classroom staff should be highly educated and highly motivated. Cooperation with health professionals, in particular language therapists, medical specialists and mental health professionals, as well as specialists in health care, social care, should be ensured.
That is why, the implementation of actual information technology for education to support people with ASD, the use of innovative and personalized methods of curriculum adaptation, the use of strengths and interests of autistic students, etc. are crucial. Therefore, the creation of an information system for the selection of available software applications to support the learning of students with ASD is an urgent task.
To support a decision making in education of students with autism, authors suggest a concept of an appropriate information system. Such system is to help in choosing the best software to be used in classes. Such software should meet the needs of all the participants of educational process, i.e., not only students with ASD and their teachers, but parents and other professionals, as well. To present such concept we used the context diagram. The following graphical elements were used: data streams, data drives, processes, and external entities (Fig. 2-3).
External entities in this diagram are Teachers, teacher assistants, teachers of inclusive educational institution, Student with ASD, Parents of students with ASD, inclusive education specialists (IES), and Software developers. The following data streams are submitted to the system input: Request for training and development software, Request for supplemented training, Evaluation of software effectiveness, Demo version of training and development software, Learning test results. The information flows are: Recommended education and development software, Recommendations-wishes from teachers, parents, inclusive education specialists, and test results.
The processes here are Process software requests for students with ASD, Search for educational software for students with ASD, Recommend educational software for teachers, Conduct a survey of the effectiveness of software use and Generate a request to develop educational and developmental Software.
The tree structure with clearly defined hierarchical levels allows to present the structure of the developed system in the form of a hierarchy of tasks on a different levels. This technique is designed to optimize the interaction between tasks; the hierarchy of processes of the developed information support system for learning for children with ASD is shown in Fig. 4.
The main goal is to use the information support system for education of children with ASD, and the processes are Create a set of recommended software for teaching children with ASD, Conduct a stream online, and Process the survey results.
In the presented above concept of a system for choosing the best software application for teaching children with ASD, there are several-steps testing of relevant software with a questionnaire survey of participants of the process, namely parents and teachers. Results
For further investigation, the specialized software was chosen. It is a software that supports the education of children with ASD and has positive feedback on the Internet (Table 1).
The main participants are parents and students with ASD, they get online acquainted with some software – guided by the IT specialist. This way of presenting information is quite convenient, as it does not require the presence of participants in one room. Also, it seems convenient to create a separate page for such broadcasts on the official websites of relevant institutions that support the education of children with ASD. The description of the stream is accompanied by relevant documentation on the recommended software, their short description, download links, preliminary rating compiled by IT specialists. After the the stream and testing of the proposed software, parents, teachers and others are invited to undergo a survey. The results of the analysis can be used in following decision-making.
It is important to have an opportunity to watch the broadcasts offline afterwards. It seems most expedient to take part in online streams, because in the process of getting acquainted with educational support content one can always ask questions, consult on the relevant technical aspect, etc. The participation of the child with ASD is also important while streaming, as his or hers reaction to visual stimuli must be taken into account when choosing the appropriate software.
10. How effective was the recommended software for your child?
After completing the survey, the results are processed. If the paper questionnaires were filled out, the results should be transferred into electronic form.
A prerequisite for the ability to watch the broadcast in real time is the presence of an announcement on the website of the appropriate institution (the site of an inclusive school) with a link. The description of the stream gives the characteristics of the recommended software applications. IT specialists start the broadcast by acquainting parents, teachers, and everyone with the optimal software to support training on the desired topics (Fig. 5).
The interaction of cartoon characters and children with ASD requires in-depth research because developers have to take into account the individual characteristics of each child and the behavior of such children. Color accents can also play a key role in the student's interaction with the software application. People with ASD often avoid sharp color accents, prefer harmonious additive color combinations, etc.
In software applications, one should also pay attention to the motor abilities of the child with ASD. For example, to move certain objects on a tablet or mobile device (Fig. 6), the child must have the appropriate skills.
After the stream, parents, teachers, and anyone interested (for example, school administration) are asked to download a specially designed questionnaire that uses the Likert psychometric scale, implemented through Google forms. In a performed research, 38 respondents were involved in the survey, and the results from Microsoft Excel were uploaded to SPSS Statistics for further analysis. To perform factor analysis and isolation of the main components, the command Analyze - Dimension Reduction Factor was performed (Fig. 7).
The factor analysis begins with the interpretation of the correlation matrix and compliance with the Bartlett sphericity criterion (p <0.05), which indicates the feasibility of factor analysis, and the value of CMO on the accepted adequacy of the sample (0.741> 0.5). The obtained results confirm the possibility of such an analysis. Correlation coefficients indicate the density of the relationship between the variables of this array (Fig. 8). In the resulting table, you can see high values of correlation between variables.
The isolation of a few factors is explained by the total variance, which works by the method of principal components (Fig. 9, a). Thus, the optimal number of factors is three. The graph of shedding (Fig. 9 b) shows its own values in the coordinate system, which confirms the number of factors. To obtain information that variables correlate with factors 1, 2, 3 allows the matrix to invert the component. For example, Benefits of Software Help correlates well with Factor 1, Software Download Speed with Factor 2, Software Installation Difficulty with Factor 3.
Visualization of the results of the survey based on Likert data is implemented with the means of Microsoft Excel spreadsheets in the form of a histogram with accumulation. Such visualization is possible after calculating the number of psychometric assessments of respondents, ie how many times was chosen Very dissatisfied, Rather dissatisfied, etc. The formula = COUNTIF (range: to; condition) was used for this sub-account (Fig. 10).
Visualization of the results of the survey of respondents to determine the effectiveness of the software application Math Kids is shown in Fig. 11.
The results show that parents experienced some difficulties with downloading process. As a negative feature was mentioned the lack of communication with other students because most of the applications are designed for the individual training. The teacher would appreciate the ability to combine the results of all his students.
The article described the concept of supporting the process of choosing an application for teaching children with ASD. A system concept has been developed. Testing of the recommended software to support the learning of children with ASD was conducted and the results were analyzed. In the choice and substantiation of methods and means of solving the problem, it is proposed to evaluate the effectiveness of educational and developmental software by a survey using the psychometric Likert scale. The choice and justification of the means of solving the problem was made, YouTube platform with a special program OBS Studio was used for streams, the questionnaires with Google Forms tools were used for surveys, Microsoft Excel for data processing, IBM SPSS Statistics for data mining.
The RStudio, a software environment for statistical processing and visualization of data that uses the R programming language, is effective for processing and displaying Likert scale data. In future research, we will use RStudio, where ggplot packages (developed by Hadley Wickham) and HH (proposed by Heiberger), as well as the Likert package by Jason Brier [32]. This package contains options for pregeneralized data, but this option has limited flexibility.
[1] V. Juškienė, V. Voidogaitė, An Encounter between a Teacher and Child with ASD in a PreSchool Environment [Pedagogo ir ASD turinčio vaiko susitikimas ikimokyklinėje ugdymo įstaigoje] (2021) Acta Paedagogica Vilnensia, 47, pp. 143 – 155. [2] C.P. Santana, E.A. de Carvalho, I.D. Rodrigues, G.S. Bastos, A.D. de Souza, L.L. de Brito, rsfMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis (2022) Scientific Reports, 12 (1), art. no. 6030. [3] J. Melin, M. Jansson-Fröjmark, N.C. Olsson, Clinical practitioners’ experiences of psychological treatment for autistic children and adolescents with school attendance problems: a qualitative study (2022) BMC Psychiatry, 22 (1), art. no. 220. [23] X. Hu, H. Wang, Z.R. Han, Y. Zhao, L. Ke, The influence of visual supports and motivation on motor performance of the MABC-2 for Chinese school-aged children with autism spectrum disorder (2021) Scientific Reports, 11 (1), art. no. 15557. [24] Z. Zhao, H. Tang, X. Zhang, X. Qu, X. Hu, J. Lu, Classification of children with autism and typical development using eye-tracking data from face-to-face conversations: Machine learning model development and performance evaluation (2021) Journal of Medical Internet Research, 23 (8), art. no. e29328. [25] Harini Sampath, Bipin Indurkhya, Jayanthi Sivaswamy, A Communication System on Smart Phones and Tablets for Non-verbal Children with Autism. Computers Helping People with Special Needs: 13th International Conference, ICCHP 2012, Linz, Austria, July 11–13, 2012, Part II. Linz, 2012. pp.323–330. [26] N.E. Scheerer, E. Birmingham, T.Q. Boucher, G. Iarocci, Attention capture by trains and faces in children with and without autism spectrum disorder (2021) PLoS ONE, 16 (6 June), art. no. e0250763. [27] LAIKERT Rensis. Available at: https://persons-info.com/persons/LAIKERT_Rensis. [28] T. Kötter, S.I. Rose, K. Goetz, J. Steinhäuser, The predictive validity of admission criteria for the results of clinical competency assessment with an emphasis on family medicine in the fifth year of medical education: an observational study (2022) BMC Medical Education, 22 (1), art. no. 269. [29] A. Franz, S. Oberst, H. Peters, R. Berger, R. Behrend, How do medical students learn conceptual knowledge? High-, moderate- and low-utility learning techniques and perceived learning difficulties (2022) BMC Medical Education, 22 (1), art. no. 250. [30] E. Fisher, C. James, D. Mosca, B.J. Currie, A.P. Ralph, Evaluation of an ARF diagnosis calculator: a survey and content analysis (2022) BMC Medical Informatics and Decision Making, 22 (1), art. no. 77 [31] Y.-M. Huang, H.-Y. Chan, P.-I. Lee, Y.-W. Tang, T.-W. Chiou, K.C.S.C. Liu, Y.-F. Ho, Exploration of changes in pharmacy students’ perceptions of and attitudes towards professionalism: outcome of a community pharmacy experiential learning programme in Taiwan (2022) BMC Medical Education, 22 (1), art. no. 195 [32] Package ‘likert’. Available at https://cran.r-project.org/web/packages/likert/likert.pdf. [33] I. Tsmots, V. Teslyuk, V. Khavalko, Y. Lukashchuk, A. Poniszewska-Maranda, Use of augmented reality technology to develop an application for smart factory workers (2019) CEUR Workshop Proceedings, 2533, pp. 23 – 34. [34] Y. Kushnir, T. Sviridova, U. Marikutsa, Y. Sasenyuk, A. Zakaulov, Computer aided systems for target language teaching simplification (2007) The Experience of Designing and Application of CAD Systems in Microelectronics - Proceedings of the 9th International Conference, CADSM 2007, art. no. 4297660, pp. 577 - 579. [35] N. Melnykova, N. Shakhovska, V. Melnykov, M. Logoyda, Y. Peleshchak, The problem of analysing the relationships between individual characteristics of individuals with COVID`19 (2020) CEUR Workshop Proceedings, 2753, pp. 473 - 482.