# CoEd, an interactive website for the stakeholders of school inclusion of children with ASD: an iterative design including user testing

Isabeau Saint-Supery, Hélène Sauzéon, Eric Meyer, Cécile Mazon

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Isabeau Saint-Supery, Hélène Sauzéon, Eric Meyer, Cécile Mazon. CoEd, an interactive website for the stakeholders of school inclusion of children with ASD: an iterative design including user testing. Technology, Pedagogy and Education, 2025, pp.1-23. 10.1080/1475939X.2025.2554881. hal-05283659

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# CoEd, an interactive website for the stakeholders of school inclusion of children with ASD: an iterative design including user testing

## Authors

I. Saint-Supery<sup>a, b</sup>; H. Sauzéon<sup>a, b</sup>; E. Meyer<sup>b</sup>; C. Mazon<sup>a, b</sup>

## Affiliations:

<sup>a</sup> Team Ageing, Chronic diseases, Technology, Disability, and Environment (ACTIVE), Université de Bordeaux, Inserm, BPH, U1219. Site Carreire, zone Nord, bat. 1B. 146 rue Léo Saignat, 33076, Bordeaux, Gironde, Nouvelle-Aquitaine, France

<sup>b</sup> Project team Flowers, Inria Bordeaux Sud-Ouest, 200 Avenue de la Vieille Tour, 33405, Talence Cedex, Gironde, Nouvelle-Aquitaine, France

**E-mail:** isabeau.saint-supery@u-bordeaux.fr; cecile.mazon@u-bordeaux.fr; eric.meyer@inshea.fr; helene.sauzeon@inria.fr

## Orcid:

- Isabeau Saint-Supery: 0000-0002-8709-6396

- Hélène Sauzéon: 0000-0001-5781-9891

- Eric Meyer: 0000-0003-2658-7121

- Cécile Mazon: 0000-0003-3959-2712

## Corresponding authors

Cécile Mazon, Assistant Professor

Équipe-projet Flowers, Centre Inria de l'Université de Bordeaux, 200 Avenue de la Vieille Tour, 33405 Talence cedex, France

**Email:** cecile.mazon@u-bordeaux.fr
**Abstract:**

School inclusion of children with Autism Spectrum Disorder represents a challenge for all parties involved (family, school and out-of-school actors such as health care professionals). Existing tools focus either on the pupil, or the relationships between two parties, and notably parent-professional relationships. Considering a systemic approach, we designed the web-based app *CoEd* to enhance and facilitate communication and information sharing among them, using participatory methods. We used (fictitious) realistic cases to create scenarios that contain tasks to do on the web application, adapted to each participant role (parent, teacher and external actors). We used objective (interaction annotations, website navigation inspection) and subjective (questionnaires) measures to assess the usability, user experience, cognitive load and elicited self-determination. Results indicated that *CoEd* was very usable, with an excellent user experience, and a relatively low cognitive demand. Some design mistakes have been identified and corrected for the enhanced final release.

**Keywords:** Stakeholders relationships; Technology evaluation; Co-education process; Autism spectrum disorder; Participatory design.
## Introduction

Autism Spectrum Disorder (ASD) is an early-onset neurodevelopmental condition characterized by persistent difficulties in social communication and interaction (e.g., social reciprocity, verbal and non-verbal communication, social awareness), as well as a set of restricted, repetitive, and inflexible patterns of behavior, interests or activities (e.g., adherence to routine and rules, ritualized behaviors, hyper/hypo-sensitivity, intolerance to change) (American Psychiatric Association, 2022; World Health Organization, 2019). There is a large inter-individual variability in the nature and intensity of ASD appealing to hugely personalize care and support, often running counter to the “one size fits all” functioning of mainstream schools (Fage et al., 2016). Despite the tangible benefits of early school inclusion for children with ASD (Ferraioli & Harris, 2011; Salceanu, 2020), it remains an inclusion gap from primary to secondary education (Hodges et al., 2020; Stephenson et al., 2021). Technology-based education for special learning needs has a growing development for addressing challenges related to personal factors, i.e., traditional person-technology match approach (Mazon et al., 2019; Odom et al., 2015; Suhaila & Nordin, 2022). In contrast, there is a paucity of research addressing the socio-environmental factors regarding the collaborative issues of proximal social environments of children including family, school and health care (Vlcek et al., 2020). As a result, transitioning from a model where disability is solely an individual matter to one where it concerns everyone is challenging. Additionally, there is complexity in addressing the needs of multiple end-users for a specific technology. From a previous analysis of co-education needs of stakeholders (teachers, parents and health care professionals) of schooling children with ASD leading to the co-designing of a web app, i.e., CoEd (Mazon et al., 2022), the present study aims to first, draw up an original method based on realistic students cases for a three-parties-user assessment (parents, teachers and health care professionals) with both objective (interaction data) and subjective metrics (standardized questionnaires) to extract relevant and to-be-improved features of CoEd app.

## Related work

Various educative adaptations (e.g., pedagogical adaptations, compensatory tools, specific teaching) are required for meeting the pupils' specific needs for their school inclusion. In France, these adaptations are gathered in the Individual Education Plan (IEP)<fnref n="1" /> paper document, which is also a monitoring tool throughout the schooling process. The IEP is critical by gathering all the educational levers for ensuring as best as possible a successful achievement of the child's educational project. It should therefore be as accessible and understandable as possible to teachers and all those involved in school inclusion, and should be updated as and when the student's progress, life events, or preferences and choices in terms of life plans are made. The entire IEP process – development, implementation, follow-up, and updates – relies on collaboration and close coordination between the family, the school, and the student's health care providers. Then, the IEP process could be seen as a by-product of a co-education process, as it requires a structured collaboration, joint decision-making, and coordinated support to the student, whether the student is in mainstream or specialized setting. Despite being a commonly advocated approach, co-education faces significant challenges in real-world implementation. Without listing them exhaustively, most of the barriers are related to the structural

<footnote n="1">¹ The French term "Plan Personnalisé de Scolarisation" is best translated as "Personalized Education Plan." However, we use "Individual Education Plan" (IEP), as it is the most widely recognized term for documents outlining support and accommodations for the education of students with disabilities. This terminology appears in several English-speaking countries (e.g., United States, Australia, New Zealand) and has equivalents in Brazil, Mexico, Japan, India, and Italy for example.</footnote>
design of schools (tailored services and logistical issues) and school-specific social environment (Coster et al., 2013), which together negatively influence the child's feeling of school connectedness (Saggers et al., 2016), i.e., "*the extent to which students feel personally accepted, respected, included, and supported by others in the school social environment*" (Goodenow, 1993, p. 80).

First, a cultural information gap on ASD is widely documented in studies of the school's social environment (Kinnear et al., 2016; Mitter et al., 2019). Whether it is the teacher or the peer students, a lack of knowledge about ASD is noticeable (Fage et al., 2017; Gómez-Marí et al., 2021). For dealing with ASD-related stigma, several studies leveraged from technologies as training tool for informed representations on ASD and for enhanced empathy and positive attitudes toward ASD (e.g., *Auti-Sim Intervention*, Sarge et al., 2020). Although such anti-stigma interventions are useful, a direct communication between school's actors and child's parents or ASD-trained mediator is reported as impacting more positively (Azad et al., 2018; Stephenson et al., 2021).

Second, *the time availability of parents, teachers or health/social workers* is also an issue in co-education. Aware of the critical role of education for the child's future, parents often take on the role of coordinating their child's schooling, which is costly, in terms of time, energy and health pools when combined with daily parental activities, e.g., work, household chores, mental workload (Abolkheirian et al., 2022; Behnia et al., 2017; Budi Santoso, 2021; ten Hoopen et al., 2022). Similarly, teachers face pressure and lack of time for training and gathering pedagogical resources (Azad et al., 2018; Minke et al., 2014). Finally, each stakeholder in the educational pathway faces time managing difficulties of their own. This may lead to tensions between the three parties due to a lack of time or place for communication, often having a direct impact on the child's progress and even success at school (Crowell et al., 2019; Li et al., 2022). Yet, a relationship of trust between parents and other stakeholders facilitates the achievement of the child's educational goals (Bateman et al., 2022; Garcia-Melgar et al., 2022; Rispoli et al., 2019). Therefore, the paucity of continuous school-parent communication opportunities is an impediment of co-education process of children with ASD. Surprisingly, very few technology-based studies have addressed the parent-teacher relationship in order to foster co-education process (Azad et al., 2018; Mazon et al., 2022). The mainstream tools of communication between the family and the school remain fairly traditional (Olmstead, 2013), such as the home liaison diary, social networks or software packages often tailored for a passive curriculum follow-up of students without IEP. Industrial schooling software packages cover some of the requirements regarding the child's educational pathway, but do not, for example, make it possible to observe changes in social skills, since they are usually acquired at primary level (Casartelli et al., 2020). As a result, there is a lack of a real communication tool for the family-school relationship with regard to the IEP (Mazon et al., 2022).

Third, the *multi-sectorial information gap* is also a hindrance to co-education. In numerous countries of OECD, health care professionals involved in the rehabilitation of ASD (e.g., social skills, language, writing) do not have direct access to the school and parents remain their representatives to explain the disorders and educational needs of the child with ASD (Baker et al., 2020; Shahidullah et al., 2020). Once again, several studies revealed the technology interests for promoting the communication between health care professionals and parents of children with ASD such as tools to help announce ASD diagnosis and understand its consequences (Gibbons, 2015; Sarge et al., 2020) or even to favor care follow-up (Mazon et al., 2019, 2022; Pontikas et al., 2022; Shahidullah et al., 2020; Vlcek et al., 2020), etc. Care-school relationships would gain in efficiency from sharing a genuine communication place to serve the child schooling while respecting of privacy rules of medical information. Hence, an efficient co-education requires not only parent-teacher dialogue but a three-
way (parent, school, health care) channel of communication to foster at best the support to the child. Additionally, coordination and cooperation from all persons involved in a process (health care or education) is beneficial regarding the time it takes (Gomes & McVilly, 2019) but also regarding stakeholder's well-being (Lefebvre et al., 2005), thus makes it a key to successful inclusive schooling of a child with ASD.

Four, *the evolutivity of child's educational needs* is hugely challenging for co-education process, especially for children with ASD (Mintz et al., 2021; Stepney, 2008). Saggers et al. (2016) stressed the crucial role of a three-way, continuous and responsive collaboration and communication about educational needs changes and curriculum adjustments and support. Importantly, the stakeholders themselves perceived this role as critical to support the school connectedness of child and thus called for a whole-school approach as success keys. At the best of our knowledge, no technology-based intervention has addressed the collaborative follow-up of educational needs changes of children with ASD.

## A Technology based on the Whole-school approach: the CoEd app

Moving the educational technology field for children with ASD forward into a whole-school approach, Mazon et al. (2022) have codesigned a web-based application promoting stakeholders' collaboration for co-education: *CoEd* app. Inspired by the ecosystemic model for pupils with ASD (Cappe & Boujut, 2016), this app places the child with ASD at the center of all proximal social environments systems (family, school, health care). *CoEd* offers both the child follow-up and the three-way collaboration of the stakeholders through a suite of interactive features (pupil's profile, assessment and follow-up on targeted skills, etc. – see Fig. 1) for which content always may be updated.

<figure><img src="image_2.png" /><figcaption>**Figure 1. Dashboard and Profile interfaces of CoEd mock-up.** A: Dashboard interface with a list of student files and a list of the meetings to come for the authenticated user. B: Profile interface, the first tab of a student's file</figcaption></figure>

*CoEd* app is a breakthrough for co-education because it is supported by an upstream analysis of the multiple end-users' needs and its functionality and features are driven by a participatory design method (e.g., Rahimi & Ibarra, 2014), but this app requires to be investigated by a user study for demonstrating its added value. However, standardized user assessments methods with simple web browsing scenarios (e.g., Allison et al., 2019) would not be very informative on the ergonomics value of *CoEd* in terms of usability and user experience while raising some methodological and ethical concerns. First, it is ethically questionable to deploy a technology in a real situation with a fragile public and social environment already under strong tension. Second, *CoEd* app involves multiple end-users, who are motivated by the same co-educational purpose but who take on different functions (parents, teachers, health care professionals) and therefore make qualitatively and quantitatively
different contributions to co-education depending on the student's educational needs. Additionally, an important property that was unanimously praised by all three parties is a low cognitive load to the use of *CoEd* (Mazon et al., 2022) to achieve the co-education process embedded into the app. For all these reasons, a new assessment method based on usage scenarios has been drawn up from the creation of three realistic cases of children with ASD varying by their educational needs (**Appendix B**). We collected various *objective and subjective measures* of *CoEd* use in terms of usability, user experience and cognitive load.

## Objectives of the study

This study aims to conduct a user study of the *CoEd app*, a web-based application designed through participatory methods to promote three-way collaboration and communication among parents, teachers, healthcare professionals for supporting the inclusive education of children with ASD. More precisely, the research objective of this study is to evaluate usability and user experience through multiple end-user feedback using objective (effectiveness and efficiency based on video-based extracted interaction data) and subjective measurements (questionnaires on perceived usability, user experience, cognitive load, and elicited self-determination) to provide insights on the ergonomic quality of the app and identify areas for improvement. To this end, we develop an assessment method for multi-stakeholders’ evaluation using realistic student cases to provide scenarios for using the *CoEd app* in a setup similar to real-world use.

## Method

This user study consisted in a multiple end-user assessment through usage scenario method to collect both objective and subjective measures of usability, user experience and cognitive load elicited by the use of the *CoEd app*. We recruited end-users representing the different roles of future users of the *CoEd* app (between-group factor: parent, teacher, external school actors (e.g., healthcare, psycho-educative)), and asked them to use an interactive mock-up according to three typical usage scenarios (within-group factor: first, second, and third scenario) for covering the exploration of the different features it offers. The consent form as well as the whole of research purpose and design, have been approved by the French national ethics committee of Inria research center, i.e. COERLE (N°2022-08) and the CNIL (French national committee securing the individual rights in terms of issues related to person privacy and technology). The study was remotely conducted for having a real-life setting (home, work places; own computers and web browsers) between April and June 2020.

## Participants

According to Nielsen & Landauer (1993), 5 to 10 subjects are the minimum required to catch 80% of ergonomics design errors. Then, 21 adults (3M; 18F) have participated to this study: 7 parents of pupils with ASD enrolled in middle or high school, 7 specialized teachers, and 7 external school actors involved in the educational inclusion of students with ASD (i.e., specialized educator, psychologist, etc.). Participants were aged between 27 and 60 years-old (meanAge= 44.81; SD = 6.95). There were recruited through an email campaign into France national education organizations, researchers' networks and partners, and parent associations. When people were willing to participate, eligibility criteria were checked, that is being parent of a middle-school-aged child with ASD (10-16 years old) or being a professional (school or external) implied in the education of a middle-school-aged child with ASD.
## The CoEd mock-up

In respect of GDPR (European general data protection regulation) during the user testing study, an interactive mock-up of the CoEd app has been created with the Axure RP 9 software, in order to have an app analog shared online with the participants, without cloud processing of interaction data while enabling their local collecting and storing via a video-based recording. Once authenticated on the CoEd mock-up (for details on CoEd mock-up, see **Appendix A**), the user faces a dashboard divided in two blocks; the right one contains a list of student files accessible by the user; and on the left is a list of next meetings regarding those pupils. Each student's file contains the following sections:

(1) **Profile**: information on the pupil and their particularities on various themes (e.g., health, previous schools, center of interest);

(2) **Skills**: classified in two categories that are school-related and socio-adaptive, skills evolution is displayed in a graph;

(3) **Meetings**: skills assessment, evaluation matrix, generating meeting report, history of the previous meetings;

(4) **Compendium of tips**: effective aides, strategies for the child;

(5) **News**: communication channel for any member of the support team who wants to share a past, present or future event concerning the pupil.

(6) **Contacts**: list of stakeholders and their contact details

Access to these sections can only be guaranteed for authenticated users, which was simulated with a log-in page before accessing any other content. Each user is identified according to their role (i.e., teacher, parent, external) and a referent is designated to regularly monitor the pupil's record and coordinate the stakeholders. Though, the whole support team can add or update any information regarding the child, to place them in equal terms.

## Usage scenarios based on realistic cases of children with ASD

Three fictitious student profiles, derived from real cases of children who participated in previous studies related to educational technologies (Fage et al., 2016). This method is inspired from the persona method (Miaskiewicz & Kozar, 2011). Personas represent, hypothetically, behavior synthesis, main characteristics, motivations and expectations of real users (e.g., Melo et al., 2020). Here, personas are used not to create empathy between designers and future app end-users but to support a realistic use projection for future end-users. The three personas of child with ASD referred to three kinds of educational needs depending on child's functioning and ASD severity (for details of realistic cases, see **Appendix B**). Then, four usage scenarios have been designed for each persona, for covering every feature of the app, with each of the different roles (parents, teachers, health professionals and referent) (Fig. 2). Participants were given tasks to do on each persona. Personas' order was randomized between participants of the same role. Tasks were attributed depending on the participant's role(s) – teachers and health professionals also had the referent role – and each task was adapted using perspective-taking in order to match what they would have to do or share on a pupil's file based on their role (Wetzels et al., 2011).

Often, one of the tasks necessitated actions from them such as clicking and eventually typing, and the other was simply visually finding and reading information on the displayed page. The different app's features were distributed across the three personas (i.e., finding information, adding information, editing the profile) and the user role (i.e., parent, teacher, external actor). As the role of referent can be ascribed to either a specialized teacher or an external actor, their scenarios contained referent-related
features (i.e., deleting, planning meeting). To prevent any order effect, scenarios' order has been randomized between participants (see **Appendix B** for details on the actions of each scenario depending on the user role).

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Persona-based Scenarios according to participant's role and Use Measures. The figure summarizes the methodology used with the 3 personas which were used to create scenarios allowing interactions with the mock-up, according to different perspectives (Family – School – Care). From this we collected subjective and objective measures</figcaption></figure>

## Procedure

Once the eligibility criteria had been checked, the researchers informed the participants of the purpose and course of the study. The consent form was sent by e-mail, and participants were given a week to consider and return it signed to take part in the study. Participants and researchers met using videoconference, and the participants shared their screen in order to monitor/record their use of the mock-up. After the researcher reminded the participant of the purpose and process of the study, the participant was invited to connect to the mock-up through the provided URL and begin the first usage scenario. Each scenario began by a description of the fictious pupil, followed by the list of tasks that the participants had to conduct. Once the participants read the case description, they can begin to use the mock-up following the usage scenario. To avoid back and forth between the scenario file and the mock-up, participants were given oral instructions while they were on the mock-up, with 2 to 3 tasks to do per tab. After the participant finished the three scenarios, they were invited to answer to the questionnaires through an online form. Once completed, researchers asked questions to gather participants' feedback on the mock-up in a non-directive interview. In average, the full experimental session lasted about 1.5 hours.
The experimenter supervised the participants' performance, and intervened only if the participant was stuck, or when a glitch occurs in the mock-up. Otherwise, the experimenter let the participant perform the tasks by themselves, even if the participant performed wrong actions. When participants asked questions, the experimenter answered by eventually giving clues, but was careful not to give the solution. The screen and audio were recorded on the experimenter's computer during the whole experiment, using the *Monosnap* software (participants were invited to turn off their camera to anonymize the recording). The video recordings analysis was carried out by one researcher, but two other researchers performed the video analysis on a randomly picked video to ensure inter-rater reliability. Inter-rater agreement was calculated using intra-class correlation, based on the ratings of the three researchers. The intra-class correlation is commonly used to assess the interrater agreement on continuous measures, and is easy to interpret as this coefficient is a correlation (Gisev et al., 2013). The inter-rater agreement on the annotation grid was excellent (r = .968), which means that the annotation grid was clear and valid.

## Measurement

Video recordings provided different objective use measures through a video analysis performed by a researcher according to a specific grid to collect the measures of interest, based on the cursor path (Kirsh & Joy, 2020) and participants oral responses. Videos were annotated using ELAN software version 6.1 to extract the time spent per task from the scenario. We recorded participants' response time as from the moment they started to move their mouse until the end of their answer, whether it was correct or not. When the cursor remained steady, we started recording response time when they began to speak or make sound (e.g., onomatopoeia) (Sears & Jacko, 2009). In parallel, we filled out a spreadsheet to collect data regarding each participants' response (i.e., correctness, number of active interactions [clicking and typing], cursor path and potential comments).

## Objective measurement:

- **Usability measures:** *Effectiveness* - assessed using the proportion of correct responses (based on oral responses and active interactions) and the completion time across the different scenarios. *Efficiency* - assessed using the number of active interactions per minute; and an optimality score based on the ratio between participants' speed and a theoretical optimal speed (computed in a within-subject fashion by dividing their maximal number of active interactions by their minimal time to complete a scenario).

- **Website navigation performance:** the cursor path was annotated as where the participant was looking, in order to get an insight of the website's usability: if the information to look for and the mouse were in the same area (e.g., heading menu) from the beginning, we could assume that content was properly organized on the page (Kirsh & Joy, 2020). When participants wandered on the website or gave wrong answers, we noted hovered elements and items clicked in the wrong place, while they were interacting with the website. As participants were doing the asked tasks on the mock-up, the experimenter was writing down participant's comments and/or reactions to gather qualitative data.
**Subjective measurement:**

- **Scenario-based Subjective usability:** for each task, the participant was invited to self-assess their interaction difficulty to perform the task with a 10-points Likert scale (from 0 "very easy" to 10 "very difficult").

- **System Usability Scale (SUS, Brooke, 1996; Gronier & Baudet, 2021):** it consists in 10 items rated using a 5-points Likert scale (max score is 100: the higher the score is, the better the usability is).

- **User Experience Questionnaire (UEQ, Laugwitz et al., 2008)** with 26 items probes 6 users' experience dimensions from which *Pragmatic Quality*. (Efficiency, Perspicuity and Dependability) and *Hedonic Quality* (Stimulation and Novelty) are assessed.

- **NASA-TLX - Task Load Index (6-items, Cegarra & Morgado, 2009)** assesses 6 workload factors (i.e., mental demand, physical demand, temporal demand, performance, effort, frustration) rated on a linear scale from 0 to 100. The higher the score is, the higher the cognitive load is.

- **Elicited self-determination for a collaborative work:** it was an adapted questionnaire of (Vallerand et al., 1992) entailing 9-items on a 5-points scale (from *strongly disagree* (0) to *strongly agree* (4)). The items were distributed into the three main concepts of Self-Determination Theory (Ryan & Deci, 2020): *Autonomy* (i.e., "I would be more effective in following-up the student/my child by using this tool"), *Competence* (i.e., "I would have the ability to organize the support of the student/my child with this tool") and *Relatedness* (i.e., "With this tool, I could better participate in the follow-up of the student/my child").

**Data Analysis**

Statistical analyses were conducted using R software version 4.2.1. For objective and subjective measures, average scores for each measure were screened in the whole user group to conclude on global usability of the web application. We also explored the differences between each users' role, that is, between parents, teachers and medico-social practitioners. As objective measures and subjective difficulty questions were distributed on the three scenarios, we carried out two-way mixed ANOVA, setting the Role as a between-group variable (i.e., parent, teacher, external) and the Time as a within-group variable (i.e., T1, T2, T3) for determining whether usability differs across time (training effects) and depending on the specific needs of each category of user targeted by the *CoEd* app. In addition, a separate ANOVA was carried out to examine the effect of the Referent's role, as a between-group variable, and the Time as a within-group variable (T1 vs. T3 only). For the result of the questionnaires, we carried out ANOVA with the Role as a between-group variable. When appropriate, the Dimension was added as a within group variable (only for questionnaire composed with several dimensions). For each effect, we computed eta-squared (η²) to examine the effect size, according to the following classes (Cohen, 1988): small (η²<.01), medium (.01 < η² < .06), and large (η² > .14).

Details of the statistical results are provided in **Appendix C**.
# Results

## Objective Use measures

### Effectiveness

*Completion time.* Participants took in average 10 min to complete a scenario (range: 5.13min-19.33min). There was an improvement across the 3 scenarios [Time: $F(2,36) = 20.842; p < .001; \eta^2 = .340$], but there was no difference for the global completion time between users' category [*p* > .200; $\eta^2$ = .079]. The interaction effect was not significant. Pairwise t-test revealed significant differences between T1 – T2 [*t*(20) = 5.896; $p_{adj} < .01$] and T1 – T3 [*t*(20) = 4.942; $p_{adj} < .001$]. As depicted in Figure 3, the completion time decreased over time, in compliance with a large training effect, irrespective of the participant's role. Like the 3 main roles (parent, teacher, external actor), there is no effect for the Referent role [*p* > .800; $\eta^2$ = .002]. Training effects between T1 and T3 are found when the teacher's or external actor's role was combined with being Referent [*F*(1,18) = 33.404; $p < .001$; $\eta^2$ = .346].

<figure><img src="image_4.png" /><figcaption>**Figure 3. Completion time (ss.ms) across time.** A: Line chart for the whole group with a significant performance drop between T1 and T2, and a slow decrease between T2 and T3. B: Line chart by role, completion time significantly decreases for each role between T1 and T2. Between T2 and T3, it slowly decreases for Parents and Teachers but increases a little bit for External</figcaption></figure>

*Proportion of correct responses.* Participants reached 92% of correct responses (range: 77%-100%). There was an improvement across time [*F*(2,36) = 19.467; $p < .001; \eta^2$ = .324], irrespective of the participant's role [*p* > .700; $\eta^2$ = .017] (Fig. 4). The interaction effect was not significant. Pairwise t-test revealed significant differences between T1 – T2 [*t*(20) = -3.389; $p_{adj} < .001$] and T1 – T3 [*t*(20) = -5.786; $p_{adj} < .001$]. Like the 3 main roles (parent, teacher, external actor), there is no effect for the Referent role [*p* > .600; $\eta^2$ = .006]. Training effects between T1 and T3 are found when the teacher's or external actor's role was combined with being Referent [*F*(1,18) = 25.248; $p < .001$; $\eta^2$ = .327].
<figure><img src="image_5.png" /><figcaption>**Figure 4.** *Proportion of correct responses across time.* A: Histogram for the whole group where the performance increases at each scenario. B: Histograms by role. Teachers' and parents' performance had a huge increase between T1 and T2, improvements slowed down for T3. This noticeable increase happened between T2 and T3 for external actors, as their progression was low between T1 and T2</figcaption></figure>

In summary, effectiveness measures showed that participants successfully managed - in terms of correct responses and time per scenario - the scenarios, and improved their performance after completing the first one.

*Efficiency*

*Number of active interactions per minute.* Participants reached in average a speed of 4.754 active interactions per minute (range: 2.010-9.547). There was an improvement across time [Time: $F(2,36) = 15.545; p < .001; \eta^2 = .252$], irrespective of the participant's role [$p > .100; \eta^2 = .140$]. The interaction effect was not significant. Pairwise t-test revealed significant differences between T1 – T3 [$t(20) = -4.536; p_{adj} < .001$] and T2 – T3 [$t(20) = -3.858; p_{adj} < .005$]. The results depicted in Figure 5 showed that the number of active interactions per minute increased over time, in compliance with a large training effect, despite the participant's role. Like the 3 main roles (parent, teacher, external actor), there is no effect for the Referent role [$p > .100; \eta^2 = .078$]. Training effects between T1 and T3 are found when the teacher's or external actor's role was combined with being Referent [$F(1,18) = 23.800; p < .001; \eta^2 = .266$].
<figure><img src="image_6.png" /><figcaption>**Figure 5.** *Number of active interactions per minute across scenarios.* **A:** Line chart for the whole group. In average, participants' number of active interactions per minute improved over time with a slow increase between T1 and T2, and a noticeable increase between T2 and T3. **B:** Line chart differentiated by role. Participants' number of active interactions per minute increased over time for parents. Teachers' and external actors' number of active interactions per minutes slowly decreased between T1 and T2 and increased for T3</figcaption></figure>

*Optimality score.* Overall, participants reached in average 63.9% of their optimal speed (range: 43.8%-100%). There was an improvement across the 3 scenarios [Time: *F*(2,36) = 13.880; *p* < .001; *η*² = .315], irrespective of the participant's role [*p* > .400; *η*² = .032]. The interaction effect was not significant. Pairwise t-test revealed significant differences between T1 – T3 [*t*(20) = -4.276; *p*adj < .002] and T2 – T3 [*t*(20) = -3.662; *p*adj < .006]. As depicted in Figure 6, the optimality increased over time, in compliance with a large training effect, despite the participant's role. Like the 3 main roles (parent, teacher, external actor), there is no effect for the Referent role [*p* > .300; *η*² = .024]. Training effects between T1 and T3 are found when the teacher's or external actor's role was combined with being Referent [*F*(1,18) = 23.800; *p* < .001; *η*² = .388].

<figure><img src="image_7.png" /><figcaption>**Figure 6.** *Optimal score (participants' speed / theoretical optimal speed) across scenarios.* **A:** Histogram for the whole group revealing a slight performance increase between T1 and T2, and a noticeable increase between T2 and T3. **B:** Histogram by role. Teachers' and externals' optimal score</figcaption></figure>
decreased between T1 and T2 and significantly improved at T3. Parents' optimal score improved over time

In summary, as participants were progressing in the experiments, they were more efficient in the use of the interface. Their speed has increased from the 2nd scenario, and there were approaching their maximum efficiency.

## Subjective Use measures

### Scenario-based subjective usability

The average scenario-based subjective usability score for the whole group was 0.73/10 (0.83), meaning that user perceived interactions as easy (Figure 7). For the subsequent analysis, three independent variables have been considered for this score using a mixed three-way ANOVA (3 Time × 3 Role × 7 Tab) with the Role factor as between-group factor (See Appendix C).

The Role effect was not significant [$F(2,18) = 0.479; p > .600; \eta^2 = .011$], meaning that globally, the perceived interaction difficulty did not differ depending on the user role. The ANOVA yields a significant Tab effect [$F(5,90) = 11.537; p < .001; \eta^2 = .208$]: interactions in the “skills” tab and the “profile” tab were perceived slightly more difficult than the four other tabs ($p_{adj} < .01$). The Time effect [$F(2,36) = 3.697; p < .01; \eta^2 = .024$] was significant too, with a diminished score between the first and the second instances [$t(125) = 3.304, p_{adj} > .01$], and a tendency between the first and the third instance [$t(125) = 2.407, p_{adj} = .053$]. Finally, we found a significant Tab x Time interaction effect [$F(10,180) = 2.353; p < .01; \eta^2 = .035$]: the higher difficulty perceived in “profile” and “skills” tabs compared to others was only present during the first scenario ($p_{adj} < .05$). None of the other interaction effects were significant.

<figure><img src="image_8.png" /></figure>

**Figure 7.** Perceived difficulty answers' distribution depending on the role, the assessed tab and the test instance on a scale from 0 (very easy) to 10 (very difficult). Parents' and teachers' perceived difficulty was relatively higher for T1 than for T2 and T3. Level of perceived difficulty for external actors remained quite similar across time
## System Usability Scale

The average SUS score for the whole group was 92.02 (8.04), revealing an excellent usability of the CoEd. Between-group comparisons did not reveal significant differences [$p$ > .600; $\eta^2$ = .047], meaning that the usability is equally assessed depending on the user role.

## User Experience Questionnaire (UEQ)

UEQ scores for the whole group was 2.385/3 (SD = 0.642) for pragmatic qualities, 1.750/3 (SD = 0.808) for hedonic qualities, and 2.373/3 (SD = 0.643) for the attractiveness (Figure 8B). These differences are significant [$F(2,18) = 14.538; p$ < .001; $\eta^2$ = .167], irrespective of the role factor [$p$ > .400; $\eta^2$ = .060]. Indeed, the hedonic score is significantly lower than both the pragmatic [$t(20) = -4.122; p_{adj}$ < .005] and the attractiveness scores [$t(20) = 4.514; p_{adj}$ < .001]. Yet, hedonic and pragmatic scores placed the CoEd is in the desired area (Figure 8A).

The analysis of the 6 dimensions with the Benchmark tool is displayed on the Figure 8B. We observed that Novelty is good and all the other dimensions are excellent. The effect of Dimension is significant [$F(5,90) = 11.496; p$ < .001; $\eta^2$ = .219]. Pairwise comparisons revealed that only the Novelty dimension is significantly different from the other dimensions [.05 > $p_{adj}$ > .001].

A

<figure><img src="image_9.png" /></figure>

B

<figure><img src="image_10.png" /></figure>

**Figure 8.** Visual representation of UEQ results using Benchmark tool. **A:** Mean UEQ results along hedonic and pragmatic dimension, it placed the web application CoEd in the “Desired” area. **B:** Each scale of the UEQ had “Excellent” scores except for the “Novelty” scale that was “Good”

From these overall data, CoEd elicited excellent user experience.

## NASA - Task Load Index

Average NASA-TLX score for the whole group was 12.98/100 (SD = 9.71), meaning that CoEd elicited few use-related cognitive load (Figure 9). The effect of user's role was not significant [$p$ > .200; $\eta^2$ = .066]. However, the effect of NASA-TLX Dimensions was significant [$F(6,108) = 7.588; p$ < .001; $\eta^2$ = .189]. The physical demand score is lower than the temporal demand score [$t(20) = -4.334; p_{adj}$ < .01], the mental demand score [$t(20) = 3.728; p_{adj}$ < .05], and the total raw score [$t(20) = 4.306; p_{adj}$ < .01]. Also, the temporal demand score is significantly higher than the frustration score [$t(20) = 4.002; p_{adj}$ < .02]. Indeed, the physical demand (M = 4.048) and the
frustration (M = 6.905) were the lowest scores observed, while the mental demand (M = 21.667) and the temporal demand (M = 21.905) were the highest scores (albeit fairly low compared to the max score).

<figure><img src="image_11.png" /><figcaption>**Figure 9. NASA-TLX score and subscales.** Total score indicates a higher cognitive load for teachers, a lower one for external actors and a lower one for parents. Temporal and Mental load show the highest (but still low, below 40/100) scores for each role. Physical load shows the lowest score for each role.</figcaption></figure>

### Elicited self-determination

Average self-determination score for the whole group was 3.84/4 (SD = 0.26), meaning that *CoEd* elicited a great level of self-determination feeling for collaborative activities. There are no significant differences depending on the user's role [*p* > .300; *η*² = .094], nor between autonomy, competence and relatedness [*p* > .100; *η*² = .019].

### Discussion

The objective of the present work was to conduct a user assessment study to ultimately achieve the participatory design of the *CoEd* app, designed to promote collaboration and improve the follow-up of the school inclusion of children with ASD, by all IEP stakeholders (i.e., family, school, and external actors). We explored both objective (effectiveness and efficiency based on video-based extracted interaction data) and subjective measurements (questionnaires on perceived usability, user experience, cognitive load, and elicited self-determination) to provide a complete assessment of the ergonomic quality of the *CoEd* app.

Overall, the results show high effectiveness and efficiency of use, according to our objective measures, across scenarios and user roles. Additionally, the user testing reveals an excellent user experience, as revealed by the scores on the SUS and the UEQ. As we expected, *CoEd* elicited more task-oriented, pragmatic values, than hedonic ones, meaning that it has been appreciated for its usefulness qualities. Moreover, 20 out of 21 participants (95%) said that they would recommend it to their friends and/or colleagues, and use it every day, or at least once a week. The relatively low NASA-TLX score indicated that the *CoEd*'s use requires low cognitive load, but this result is contrasted by the fact that mental and temporal exigence are the items with the higher scores. This result is confirmed by informal discussions at the end of the experiments, which revealed concerns from the participant
regarding data entry time. However, when a technology meets the needs of its users by proposing
features allowing them to perform a task – collaborate in this case -, it makes the effort acceptable
(Goodhue & Thompson, 1995; Spies et al., 2020).

The great level of elicited self-determination by *CoEd* underlines the need for a support tool to cooperate by gathering information regarding the pupil and any ongoing intervention in order to coordinate actions lead towards them. Such a tool also facilitates inclusion of every stakeholder in the follow-up team by empowering them as they are all invited to contribute to the pupil's file and thus giving them a voice to participate equally in every collective decision for the child. From the above results, user study of *CoEd* appears conclusive.

Despite of these positive results, we identified a particular difficulty with the “Profile” and the
“Skills” tabs, for which the perceived difficulty was slightly higher than for other tabs. Also, 6 out of
21 participants expressed this difficulty in the questionnaire, but this feeling was often expressed at
the first sight of the web application, and attenuated over the course of scenario-based testing, as
highlighted by significant Time effect across objective measures revealing an efficient learning of
*CoEd* handling. Hence, we can assume that *CoEd* is suitable and ergonomic, as difficulties disappeared
entirely or at least considerably reduced for all participants during the three scenarios. According to
Rogers' theory of diffusion (Rogers, 1995) which offers a comprehensive view regarding the processes
involved in accepting or discontinuing use of technology, the prior trialability is a key dimension for
continued use of technology, particularly for individuals with cognitive impairments (i.e., Parette &
VanBiervliet, 1992; Riemer-Reiss & Wacker, 2000). Trialability is the degree to which the user has
prior use experiences with the technology for its long-term adopting. From this key concept, it could
be assumed that observed first positive interaction experience with the features of *CoEd* are decisive
elements for expecting its long-term adoption amongst the different targeted end-users.

Taken together, present results provided useful and extensive insights from the three kinds of end-users. Thanks to our new user-testing approach mixing personas and usage scenarios methods on an interactive mock-up, it was possible to collect objective and subjective measures while respecting GDPR. A such method could be fruitful for future user assessment studies.

Limitations and future works

Several limitations need to be addressed and they provide avenues for future work.

*Limit related to non-homogeneous interactive features across tabs:* depending on the tab of the website, more active interactions are solicited from the user (i.e., "skills" tab vs. "compendium of tips" tab) whose characteristic is to make the task more difficult and then error-prone. One way to address this limitation would be to avoid any free entry by the user, and propose a drop-down menu of possible entries, in accordance with criteria for error prevention in web navigation (Bastien & Scapin, 1993; Nielsen & Landauer, 1993). A specific study comparing a free-entry versus an entry selection system would allow to evaluate if it would enable to overcome this difficulty without frustrating the user who is stuck in a limited field of possible entries.

*Limit related to the collection of interaction data:* As a millisecond accuracy measurement does not provide an increased insight for the study purpose, we opted to track interactions with some interaction logs and video analysis of navigation. Despite of this, our method could be improved by extending the tracking of interactions in the HTML code to gain more accuracy (Atterer et al., 2006). It might be interesting to have a comparative study between our simple method and a method of exhaustive and accurate tracking of all interactions in order to evaluate whether our method is a good
proxy for what would be observed with a more exhaustive and accurate method of collecting the interaction logs.

*Limit of use scenario method:* Like any ergonomic study based on usage scenarios, an essential limit is that this method allows us to get closer to real life but for all that the conditions of real life are not met. It is therefore a simulation of use with its strengths and weaknesses. It is interesting to note that the 3 scenarios elaborated were judged by our participants as very heterogeneous (from easy to difficult), which was desired to be representative of the real conditions. In any case, this last limitation highlights the importance of a further field study to evaluate the long-term adoption and the usefulness of *CoEd* in order to have a full real-life assessment.

*Limit related to the participatory design.* A limitation of this study is the absence of direct participation from students with ASD in the design and evaluation of the *CoEd* app. While our primary focus was on fostering collaboration among parents, teachers, and healthcare professionals, concerns were raised by stakeholders about involving middle-school-aged students, which might be unsettled or might not fully understand the information exchanged about them between adults. However, including students’ voices could provide valuable insights into their own needs and enhance the app’s relevance. Future research will aim to involve students, especially older ones, in the co-design process to refine and expand the app’s features for more comprehensive support.

## Conclusions

*CoEd* design was based on user-centered methods involving Participatory Design (PD) process, beginning with an analysis of information needs of family and professional providers (teachers, healthcare) of student with ASD included in mainstream school and finishing with a conclusive user-testing in terms of both objective measures (usability effectiveness and efficiency) and subjective measures regarding usability, cognitive load, self-determination as well as user experience. PD process appears insightful for the field of technology-based interventions for children with ASD. By its highly field user need-driven nature, PD methods have enabled a step change in this field of research from an individual approach to ASD to a whole social approach where social environments are stressed as essential ingredients of an optimal school inclusion (Bateman et al., 2022; Bowman et al., 2021). By extension, this amounts to embedding in the technologies not an individual agentivity as already done, but a collective agentivity at the service of co-education. Future work will pursue the assessment of the *CoEd* app, with a field study including a large panel of users in order to cover the evaluation of its efficacy in supporting parents, teachers, and professional support providers, as well as the interactions between the three settings (i.e., family, school, medico-social). This future study may highlight whether the *CoEd* app may or not improve the quality of support provided to students with ASD included in mainstream schools.
**Declarations**

***Acknowledgments***

First of all, I would like to thank Caroline Simonpietri, Miliana Rahouadj and Kamélia Belassel, temporary study and research associate, design research intern and cognitive science research intern, for their help during experiments and video annotations.

Our gratitude is also expressed to Bénédicte Mendiboure, Austim & Inclusive Projects Director at the ARI association; the Academy of Bordeaux and especially Maïc Sagel from the ASH mission as well as Sébatien Gouleau from the digital mission, for their help in designing the web-app, recruiting participants and disseminating information on the project.

Thanks to parents, teachers and health and well-being professionals for their time and feedbacks on
the web app.

We thank also the support provided by our financial partners, the FIRAH, the Klésia, the CCAH and
the Nouvelle-Aquitaine region.

***Fundings***

This work was supported by the FIRAH<fnref n="2" /> and their partners Klésia and CCAH<fnref n="3" />, Paris, France; the Nouvelle-Aquitaine Region, France [convention n° AAPR2021-2020-11922210]; the French national PhD program into the EDSP2 - doctoral school of University of Bordeaux, Talence, France.

***Declaration of interests:***

The authors declare that they have no conflict of interest.

<footnote n="2">² International Foundation for Applied Research on Disability</footnote>

<footnote n="3">³ National Coordinating Committee on Disability Action</footnote>
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