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How Human-Centered Design and Technology can be used for good? Presenting at Human technology interaction course at Milano Bicocca

May 2022

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How Human-Centered Design and

Technology can be used for good?

Prof. Dr. Maria Rauschenberger

Faculty of Technology

University of Applied Sciences Emden/Leer, Germany

maria.rauschenberger@hs-emden-leer.de

23.05.2022

Human technology interaction course at Milano Bicocca

Prof. Dr. Maria Rauschenberger

Academic

•Professor Digital Media (since 2020)

–Research Group: Research Group for Agile Software Development

and User Experience

•2020 Post-Doc at Max-Planck Institute in Saarbrucken

•PhD from Universitat Pompeu Fabra (2016—2019)

supervised by Ricardo Baeza-Yates and L uz Rello

–Internship, ChangeDyslexia, Spain (2017)

–Research Visit, Carnegie Mellon University, USA (2017)

•Research Associate, OFFIS, Germany (2015)

Selection Research Network

•Maria Jose Escalona University of Sevilla

•Thies Pfeiffer, University of Applied Science

Industrial

•Internship, maximago GmBh (2009)

•MSP Medien-Systempartner (2010-2014)

maria.rauschenberger@hs-emden-leer.de

mariarauschenberger.net

Google scholar Index Mai 2022

Team Members

Andreas M. Klein, M.Eng.

PhD candidate University of

Seville with Maria José

Escalona

focus on human computer

interaction of Voice User

Interfaces

Kristina Kölln, M.Sc.

prospective PhD candidate

focus on human computer

interaction, especially with

accessibility in combination

with gamification and e-

learning.

Anna Weigand, M.Sc.

cooperative PhD

focus on human computer

interaction and machine

learning (small data)

For example

Weigand, A. C., Lange, D. and Rauschenberger, M.

(2021) ‘How can Small Data Sets be Clustered ?’,

in Mensch und Computer 2021}{Workshopband}

(UCAI ’21). Association for Computing Machinery.

doi: 10.18420/muc2021-mci-ws02-284.

For example

Klein, A. M. et al. (2021) ‘Comparing Voice Assistant Risks and

Potential with Technology-Based Users: A Study from Germany

and Spain’, p. 23. doi: 10.13140/RG.2.2.25678.18243/1.

Part of the Research Group in Emden and Collaborators

(alphabetical order)

•Dr. Andreas Hinderks,

•Hanna Looks, PhD Candidate,

University of Seville

•Prof. Dr. Maria Rauschenberger, HSEL

•Prof. Dr. Jörg Thomaschewski, HSEL

•Prof. Dr. Eva-Maria Schön, HSEL

•Dr. Martin Schrepp, SAP

•Dominique Winter, PhD Candidate,

University of Siegen

•there are more…

How to solve social issues with

computer science techniques

My Goal

Such as Human Computer Interaction or

Machine Learning

Early Prevention of Dyslexia

•Screening and prevention of Dyslexia at small cost

and available for pre-readers in different languages

–DytectiveU, ChangeDyslexia (non-profit organization)

–Dyseggxia, Cookie Cloud (non-profit organization)

–Research Applications

•MusVis - Rauschenberger, M., Baeza-Yates, R. A. and Rello, L. (2022) ‘A Universal Screening Tool

for Dyslexia by a Web-Game and Machine Learning’, Frontiers in Computer Science, 3, p. 111.

doi: 10.3389/fcomp.2021.628634.

•Dgames - Rauschenberger, M., Rello, L. and Baeza-Yates, R. (2018) ‘A tablet game to target

dyslexia screening in pre-readers’, in Proceedings of the 20th International Conference on

Human-Computer Interaction with Mobile Devices and Services Adjunct -MobileHCI ’18.

Barcelona: ACM Press, pp. 306–312. doi: 10.1145/3236112.3236156.

•PuzzleApp - Rauschenberger, M. et al. (2019) ‘Designing a new puzzle app to target dyslexia

screening in pre-readers’, in ACM International Conference Proceeding Series. doi:

10.1145/3342428.3342679.

https://www.changedyslexia.org/

Future Skills Applied

"Digital networking of teaching/learning

locations" focuses on the development,

implementation and testing of courses

whose content is shifted completely or

partially into the sphere of the students.

The aim is to break down the traditional

separation of teaching/learning locations

with the university as a place of classroom

teaching and application-oriented learning

and the workplace at home as a pure place

of self-study.

We combine competence-oriented,

practical vocational learning with virtual

reality and gamification. Instead of

working through linear-deterministic

experiment regulations, students decide

independently in the virtual laboratory in

VR goggles. Gamification increases

intrinsic motivation and enhances learning

success.

01.08.2021 — 31.07.2024, Project: Stiftung Innovation

in der Hochschule

Prof. Dr. T. Pfeiffer, Prof. Dr. M. Sohn, Prof. Dr. M.

Rauschenberger

Measuring the User Experience

•User Experience Questionnaire, SAP & DATEV

https://www.ueq-online.org/

http://ueqplus.ueq-research.org/

University of Applied Sciences Emden/Leer

§Housing Assistance

§Buddy Program

§Orientation Weeks

§Free German and English Language Courses

§Get together: Excursions and International

Evenings (Due to COVID-19, no excursions and international

evening are currently offered)

§Key Competences (My Campus)

§Cooperation with Chamber of Commerce

and Industry: find a work placement

in our region

Support Service

for International Students

FOR INTERNATIONAL EXCHANGE STUDENTS

from partner universities

COSTS

•No tuition fees

•Semester fee Spring Term 2021: Campus Emden 233,20 €, Campus Leer

155,20 € per semester; including

ØCampus Card: Student ID, Semester Ticket: free rides by train in certain areas in

the Lower Saxony, Bremen and Hamburg, Library Card, Cafeteria & Canteen Card,

Account for Computer Centre

•Orientation Weeks including German intensive language hours and the

“Bridging Course English” before lecture start in September and in

February (Orientation Weeks costs approx. 40€; if Online: no costs)

• Average accommodation cost per month 250-350€

• Average cost of living per month excluding accommodation 400-500€

QUESTIONS?

HUMAN-CENTERED DESIGN

Human Centred Design EN ISO 9241-210:2019

Process 9241-210:2019

Example Projects

•Designing a Web-Game to measure Dyslexia

•Measuring the User Experience

–UEQ Spanish

Recently we work on …

•Selecting the best Gamification Process for

your context

•Small Data Analysis HCD and Machine

Learning

•Cooperative PhD with University of Sevilla

–Voice User Interfaces, Andreas M. Klein

•Andreas –Please take it away!

References (1/3)

Rauschenberger, M., Hinderks, A. and Thomaschewski, J. (2011) ‘Benutzererlebnis bei Unternehmenssoftware: Ein Praxisbericht über die Umsetzung attraktiver Unternehmenssoftware. (Enterprise Software User

Experience: A real-world report on how enterprise software can be made attractive.)’, in Usability Professionals Konferenz 2011. Stuttgart: UPA, pp. 154--158. Available at: https://issuu.co m/germanupa/docs/ usability-

professionals-2011.

Rauschenberger, M. et al. (2012) ‘Measurement of user experience: A Spanish language version of the User Experience Questionnaire (UEQ)’, in Iberian Conference on Information Systems and

Technologies, CISTI.

Hinderks, A. et al. (2012) ‘Konstruktion eines Fragebogens für jugendliche Personen zur Messung der User Experience. (Construction of a questionnaire for young people to measure user

experience.)’, in Usability Professionals Konferenz 2012, pp. 78–83.

Rauschenberger, M. et al. (2012) ‘Measurement of user experience: A Spanish Language Version of the User Experience Questionnaire (UEQ)’, in Rocha, A. et al. (eds) Sistemas Y Tecnologias De

Informacion. Madrid,Spain: IEEE, pp. 471–476. doi: 10.13140/2.1.1783.9045.

Rauschenberger, M. et al. (2013) ‘Efficient Measurement of the User Experience of Interactive Products. How to use the User Experience Questionnaire (UEQ). Example: Spanish Language’,

International Journal of Artificial Intelligence and Interactive Multimedia (IJIMAI), 2(1), pp. 39–45. Available at:

http://www.ijimai.org/journal/sites/default/files/files/2013/03/ijimai20132_15_pdf_35685.pdf.

Rauschenberger, M., Sinning, H. and Thomaschewski, J. (2013) ‘Die Benutzung des Styleguides für Software-Entwickler optimieren’, in Usability Professionals Konferenz 2013, pp. 214–219.

Rauschenberger, M., Schrepp, M. and Thomaschewski, J. (2013) ‘User Experience mit Fragebögen messen--Durchführung und Auswertung am Beispiel des UEQ (Measuring User Experience with

Questionnaires--Execution and Evaluation using the Example of the UEQ)’, in In Usability Professionals Konferenz 2013. German UPA eV, pp. 72–76.

Matviienko, A. et al. (2015) ‘Towards new ambient light systems: A close look at existing encodings of ambient light systems’, Interaction Design and Architecture(s), 26(1).

Rauschenberger, M. et al. (2015) Exercises for German-speaking children with dyslexia, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture

Notes in Bioinformatics). doi: 10.1007/978-3-319-22701-6_33.

Matviienko, A. et al. (2015) ‘Deriving design guidelines for ambient light systems’, in Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia - MUM ’15. New York,

New York, USA: ACM Press, pp. 267–277. doi: 10.1145/2836041.2836069.

Rauschenberger, M. et al. (2015) Exercises for German-speaking children with dyslexia, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture

Notes in Bioinformatics). doi: 10.1007/978-3-319-22701-6_33.

Rauschenberger, M. et al. (2015) ‘Exercises for German-Speaking Children with Dyslexia’, Human-Computer Interaction--INTERACT 2015, 9296, pp. 445–452. doi: 10.1007/978-3-319-22701-6.

Matviienko, A. et al. (2015) ‘Towards New Ambient Light Systems: a Close Look at Existing Encodings of Ambient Light Systems’, IxD{&}amp;A Journal Special issue on: Designing for Peripheral

Interaction: seamlessly integrating interactive technology in everyday life, 26(26), pp. 10–24.

Matviienko, A. et al. (2015) ‘Deriving design guidelines for ambient light systems’, in Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia. New York, NY, USA:

ACM, pp. 267–277. doi: 10.1145/2836041.2836069.

Rauschenberger, M. et al. (2015) ‘A Game to Target the Spelling of German Children with Dyslexia’, in Proceedings of the 17th international ACM SIGACCESS conference on Computers {\&}

accessibility - ASSETS ’15. Lisbon, pp. 445–446. doi: 10.1145/2700648.2811345.

Rauschenberger, M. et al. (2015) ‘Lumicons: Mapping Light Patterns to Information Classes’, in Mensch und Computer 2015. De Gruyter Oldenbourg. Available at: http://dl.mensch-und-

computer.de/handle/123456789/4643 (Accessed: 11 September 2015).

Gottwald, L. et al. (2016) ‘Der mobile Nutzungskontext – Einflussfaktoren verstehen und nutzen’, in Usability Professionals Konferenz 2016. Hess, S. & Fischer, H. (Hrsg.), pp. 1–6. doi:

10.18420/muc2016-up-0131.

References (2/3)

Rauschenberger, M. et al. (2016) ‘A Language Resource of German Errors Written by Children with Dyslexia.’, in The International Conference on Language Resources and Evaluation —LREC 2016.

Portorož, Slovenia: European Language Resources Association (ELRA), pp. 83–87. Available at: http://www.lrec-conf.org/proceedings/lrec2016/summaries/136.html.

Rauschenberger, M. (2016) ‘[DC] DysMusic: Detecting Dyslexia by Web-based Games with Music Elements’, in The Web for All conference Addressing information barriers -- W4A’16.

Montreal,Canada: ACM Press. doi: 10.1145/2899475.2899503.

Rauschenberger, M. et al. (2017) ‘Towards the Prediction of Dyslexia by a Web-based Game with Musical Elements’, in Proceedings of the 14th Web for All Conference on The Future of Accessible

Work - W4A ’17. Perth, Western Australia: ACM Press, pp. 1–4. doi: 10.1145/3058555.3058565.

Rauschenberger, M. et al. (2018) ‘Towards Language Independent Detection of Dyslexia with a Web-based Game’, in Proceedings of the Internet of Accessible Things on - W4A ’18. New York, New

York, USA: ACM Press, pp. 1–10. doi: 10.1145/3192714.3192816.

Rello, L. et al. (2018) ‘Screening Dyslexia for English Using HCI Measures and Machine Learning’, in Proceedings of the 2018 International Conference on Digital Health - DH ’18. New York, New

York, USA: ACM Press, pp. 80–84. doi: 10.1145/3194658.3194675.

Rauschenberger, M., Rello, L. and Baeza-Yates, R. (2018) ‘A tablet game to target dyslexia screening in pre-readers’, in Proceedings of the 20th International Conference on Human-Computer

Interaction with Mobile Devices and Services Adjunct -MobileHCI ’18. Barcelona: ACM Press, pp. 306–312. doi: 10.1145/3236112.3236156.

Rauschenberger, M., Rello, L. and Baeza-Yates, R. (2019) ‘Technologies for Dyslexia’, in Yesilada, Y. and Harper, S. (eds) Web Accessibility Book. 2nd edn. London: Springer-Verlag London, pp. 603–

627. doi: 10.1007/978-1-4471-7440-0.

Rauschenberger, M. et al. (2019) ‘Designing a New Puzzle App to Target Dyslexia Screening in Pre-Readers’, in Proceedings of the 5th EAI International Conference on Smart Objects and

Technologies for Social Good -GOODTECHS. Valencia: Association for Computing Machinery, pp. 155–159. Available at: https://dl.acm.org/event.cfm?id=RE1354.

Rauschenberger, M. et al. (2019) ‘Towards the use of gamification frameworks in learning environments’, Journal of Interactive Learning Research, 30(2), pp. 147–165. Available at:

https://www.aace.org/pubs/jilr/.

Rauschenberger, M. et al. (2019) ‘Designing a new puzzle app to target dyslexia screening in pre-readers’, in ACM International Conference Proceeding Series. doi: 10.1145/3342428.3342679.

Rauschenberger, M. and Baeza-Yates, R. (2020) ‘[Workshop] Recommendations to Handle Health-related Small Imbalanced Data in Machine Learning’, in Hansen, Christian AND Nürnberger,

Andreas AND Preim, B. (ed.) Mensch und Computer 2020 -Workshopband (Human and Computer 2020 -Workshop proceedings). Bonn: Gesellschaft für Informatik e.V., pp. 1–7. doi:

10.18420/muc2020-ws111-333.

Rauschenberger, M., Baeza-Yates, R. and Rello, L. (2020) ‘Screening Risk of Dyslexia through a Web-Game using Language-Independent Content and Machine Learning’, in W4a’2020. Taipei: ACM

Press, pp. 1–12. doi: 10.1145/3371300.3383342.

Klein, A. et al. (2020) ‘Exploring Voice Assistant Risks and Potential with Technology-based Users [DC]’, in Proceedings of the 16th International Conference on Web Information Systems and

Technologies. SCITEPRESS - Science and Technology Publications, pp. 147–154. doi: 10.5220/0010150101470154.

Klein, A. et al. (2020) ‘Exploring Voice Assistant Risks and Potential with Technology-based Users’, in Proceedings of the 16th International Conference on Web Information Systems and

Technologies (WEBIST). SCITEPRESS - Science and Technology Publications, pp. 147–154. doi: 10.5220/0010150101470154.

Rauschenberger, M. and Baeza-Yates, R. (2020) ‘How to Handle Health-Related Small Imbalanced Data in Machine Learning?’, i-com, 19(3), pp. 215–226. doi: https://doi.org/10.1515/icom-2020-

0018.

Weigand, A. C., Lange, D. and Rauschenberger, M. (2021) ‘How can Small Data Sets be Clustered ?’, in Mensch und Computer 2021}{Workshopband}{Workshop on User-Centered Artificial

Intelligence (UCAI ’21). Association for Computing Machinery. doi: 10.18420/muc2021-mci-ws02-284.

References (3/3)

Rauschenberger, M. (2021) ‘Acceptance by Design : Voice Assist ants’, in 1st AI-DEbate Workshop : workshop establishing An In terDisciplinary pErspective on speech-BAsed TEchnology, p. 27.09.2021. doi: 10.25673/38476.

Thomas, A. et al. (2021) ‘Integrating Gamification: The Human-Centered Gamification Process’, in Proceedings of the 17th International Conference on Web Information Systems and Technologies.

SCITEPRESS - Science and Technology Publications, pp. 430–435. doi: 10.5220/0010712500003058.

Klein, A. M. et al. (2021) ‘Comparing Voice Assistant Risks and Potential with Technology-Based Users: A Study from Germany and Spain’, p. 23. doi: 10.13140/RG.2.2.25678.18243/1.

Rauschenberger, M. and Baeza-Yates, R. (2021) ‘How to Handle Health-Related Small Imbalanced Data in Machine Learning?’, i-com, 19(3). doi: 10.1515/icom-2020-0018.

Rauschenberger, M., Baeza-Yates, R. A. and Rello, L. (2022) ‘A Universal Screening Tool for Dyslexia by a Web-Game and Machine Learning’, Frontiers in Computer Science, 3, p. 111. doi:

10.3389/fcomp.2021.628634.

ResearchGate has not been able to resolve any citations for this publication.

A Universal Screening Tool for Dyslexia by a Web-Game and Machine Learning

Article

Full-text available

Jan 2022

Children with dyslexia have difficulties learning how to read and write. They are often diagnosed after they fail school even if dyslexia is not related to general intelligence. Early screening of dyslexia can prevent the negative side effects of late detection and enables early intervention. In this context, we present an approach for universal screening of dyslexia using machine learning models with data gathered from a web-based language-independent game. We designed the game content taking into consideration the analysis of mistakes of people with dyslexia in different languages and other parameters related to dyslexia like auditory perception as well as visual perception. We did a user study with 313 children (116 with dyslexia) and train predictive machine learning models with the collected data. Our method yields an accuracy of 0.74 for German and 0.69 for Spanish as well as a F1-score of 0.75 for German and 0.75 for Spanish, using Random Forests and Extra Trees, respectively. We also present the collected user data, game content design, potential new auditory input, and knowledge about the design approach for future research to explore universal screening of dyslexia. Universal screening with language-independent content can be used for the screening of pre-readers who do not have any language skills, facilitating a potential early intervention.

Integrating Gamification: The Human-Centered Gamification Process

Conference Paper

Full-text available

Oct 2021

The dynamically growing research area of gamification is loaded with a lack of consensus on definitions, a variety of non-validated frameworks, and few practical insights. Hence, we conducted a literature review to explore current best practices in applying gamification for integration in a practical use case. Instead, we found a narrow focus on theoretical discussions. For a stronger representation of practical research, standards need to be established for transferring gamification concepts to practical application. To fill this research gap, we designed a process and tools for a practical, human-centered, and context-related gamification application. We derived the process and tools from insights of our literature review as well as the realization of a gamification use case on a German online comparison platform. In addition, we incorporated standards such as the Human-Centered Design Process to maintain the established quality level of the field of user experience. In this paper, we present the Human-Centered Gamification Process (HCGP) and provide tools as practical guidance to lower the barrier for researchers and professionals to conduct theoretical and practical gamification projects.

How can Small Data Sets be Clustered?

Conference Paper

Full-text available

Sep 2021

In many areas, only small data sets are available and big data does not play a significant role, e.g., in Human-Centered Design research. In the context of machine learning analysis, results of small data sets can be biased due to single variables or missing values. Nevertheless, reliable and interpretable results are essential for determining further actions, such as, e.g., treatments in a health-related use case. In this paper, we explore machine learning clustering algorithms on the basis of a small, health-related (variance) data set about early dyslexia screening. Therefore, we selected three different clustering algorithms from different clustering methods: K-Means, HAC and DBSCAN. In our case, K-Means and HAC showed promising results, while DBSCAN did not deliver distinct results. Based on our experiences, we provide first proposals on how to handle small data set clustering and describe situations in which using Human-Centered Design methods can increase interpretability of machine learning clustering results. Our work represents a starting point for discussing the topic of clustering small data sets.

Protocol for Comparing Voice Assistant Risks and Potential with Technology-Based Users: A Study from Germany and Spain

Data

Full-text available

Jul 2021

Protocol for Comparing Voice Assistant Risks and Potential with Technology-Based Users: A Study from Germany and Spain. Version 3.0 / 2021.

How to Handle Health-Related Small Imbalanced Data in Machine Learning?

Article

Full-text available

Dec 2020
com

When discussing interpretable machine learning results, researchers need to compare them and check for reliability, especially for health-related data. The reason is the negative impact of wrong results on a person, such as in wrong prediction of cancer, incorrect assessment of the COVID-19 pandemic situation, or missing early screening of dyslexia. Often only small data exists for these complex interdisciplinary research projects. Hence, it is essential that this type of research understands different methodologies and mindsets such as the Design Science Methodology, Human-Centered Design or Data Science approaches to ensure interpretable and reliable results. Therefore, we present various recommendations and design considerations for experiments that help to avoid over-fitting and biased interpretation of results when having small imbalanced data related to health. We also present two very different use cases: early screening of dyslexia and event prediction in multiple sclerosis.

Exploring Voice Assistant Risks and Potential with Technology-based Users

Conference Paper

Full-text available

Nov 2020

Voice user interfaces (VUIs) or voice assistants (VAs) such as Google Home or Google Assistant (Google), Cortana (Mircosoft), Siri (Apple) or Alexa (Amazon) are highly available in the consumer sector and present a smart home trend. Still, the acceptance seems to be culture-dependent, while the syntax of communication poses a challenge. So, there are some basic questions: 'Why do people buy VAs?' 'What do they use them for?' 'What could be improved in the future?'. We explore the opinion of a German technology-based user group to identify the challenges and opportunities of VAs. We focus on the interaction behaviour, frequency of use, concerns, and opinions of this target group as they show a higher variety of interaction as well as privacy concerns in representative population studies. Our preliminary findings confirm previous results (missing accuracy of commands and serious concerns about privacy issues) and show that technology-based users from Germany are intensive users, although with particular concerns about data collection. Probably, there is a correlation between privacy concerns and speech intelligibility as queries relating to VAs are problematic due to repetitions and refinement.

Screening Risk of Dyslexia through a Web-Game using Language-Independent Content and Machine Learning

Conference Paper

Full-text available

Apr 2020

Children with dyslexia are often diagnosed after they fail school even if dyslexia is not related to general intelligence. In this work, we present an approach for universal screening of dyslexia using machine learning models with data gathered from a web-based language-independent game. We designed the game content taking into consideration the analysis of mistakes of people with dyslexia in different languages and other parameters related to dyslexia like auditory perception as well as visual perception. We did a user study with 313 children (116 with dyslexia) and train predictive machine learning models with the collected data. Our method yields an accuracy of 0.74 for German and 0.69 for Spanish as well as an F1-score of 0.75 for German and 0.75 for Spanish, using Random Forests and Extra Trees, respectively. To the best of our knowledge, this is the first time that risk of dyslexia is screened using a language-independent content web-based game and machine-learning. Universal screening with language-independent content can be used for the screening of pre-readers who do not have any language skills, facilitating a potential early intervention.

A Tablet Game to Target Dyslexia Screening in Pre-readers

Conference Paper

Full-text available

Sep 2018

Using serious games to screen dyslexia has been a suc- cessful approach for English, German and Spanish. In a pilot study with a desktop game, we addressed pre-readers screening, that is, younger children who have not acquired reading or writing skills. Based on our results, we have redesigned the game content and new interactions with visual and musical cues. Hence, here we present a tablet game, DGames, which has the potential to predict dyslexia in pre-readers. This could contribute to around 10% of the population that is affected by dyslexia, as children will gain more time to learn to cope with the challenges of learning how to read and write.

Towards The Use Of Gamification Frameworks In Learning Environments

Article

Jan 2019

Gamification is an established concept to apply game elements in different contexts to engage and motivate users. Gamification has been successfully used in various use cases and applications as well as general frameworks have been established. To support the design of learning environments in order to improve students' engagement and motivation, applying the concept of gamification is beneficial to emphasize engagement and motivation. This article presents the results of a literature review performed to examine the systematic use of gamification in learning environments. Therefore, ten frameworks matching the search criteria of the systematic literature review are analyzed. The results show that game elements are used heterogeneously and game elements related to the emotions and progression are preferred in learning environments. Because of the diversity of game elements used in the applications analyzed, no reliable standard can be given to design gamified learning environments with game elements. Instead, we provide a short overview of how game elements have been applied in the different application context. As gamification is a relatively young field of research, future work is necessary to give a comprehensive assessment of the topic. 148 Rauschenberger, Willems, Ternieden, and Thomaschewski https://www.learntechlib.org/primary/p/181283/

Designing a New Puzzle App to Target Dyslexia Screening in Pre-Readers

Conference Paper

Sep 2019

Dyslexia is a widespread specific learning disorder, which can have a particularly negative influence on the learning success of children. Early detection of dyslexia is the foundation for early intervention, which is the key to reduce the adverse effects of dyslexia, e.g., bad school grades. In this paper, we present the prototype of a puzzle app, which we explicitly designed with the human-centred design (HCD) process for dyslexia screening in pre-reader using new indicators related to motor skills to ensure users needs for the data collection to apply machine learning prediction. The app records the telemetry of the gaming sequence in order to derive future statements about the prevalence of dyslexia based on the telemetry data. The high-fidelity prototype was evaluated with a five-user test usability study with five German-speaking child-parent pairs. The results show how young children and parents are interacting with new games, and how new applications (web and mobile technologies) which are used for online experiments, could be developed. The usability of the prototype is suitable for the target group with only minor limitations. CCS

How Human-Centered Design and Technology can be used for good? Presenting at Human technology interaction course at Milano Bicocca

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