Screening dyslexia for English using HCI measures and machine learning

More than 10% of the population has dyslexia, and most are diagnosed only after they fail in school. This work seeks to change this through early detection via machine learning models that predict dyslexia by observing how people interact with a linguistic computer-based game. We designed items of the game taking into account (i) the empirical linguistic analysis of the errors that people with dyslexia make, and (ii) specific cognitive skills related to dyslexia: Language Skills, Working Memory, Executive Functions, and Perceptual Processes. . Using measures derived from the game, we conducted an experiment with 267 children and adults in order to train a statistical model that predicts readers with and without dyslexia using measures derived from the game. The model was trained and evaluated in a 10-fold cross experiment, reaching 84.62% accuracy using the most informative features.Peer ReviewedPostprint (author's final draft

How are AI assistants changing higher education?

ContextHigher education is changing at an accelerating pace due to the widespread use of digital teaching and emerging technologies. In particular, AI assistants such as ChatGPT pose significant challenges for higher education institutions because they bring change to several areas, such as learning assessments or learning experiences.ObjectiveOur objective is to discuss the impact of AI assistants in the context of higher education, outline possible changes to the context, and present recommendations for adapting to change.MethodWe review related work and develop a conceptual structure that visualizes the role of AI assistants in higher education.ResultsThe conceptual structure distinguishes between humans, learning, organization, and disruptor, which guides our discussion regarding the implications of AI assistant usage in higher education. The discussion is based on evidence from related literature.ConclusionAI assistants will change the context of higher education in a disruptive manner, and the tipping point for this transformation has already been reached. It is in our hands to shape this transformation

Inhibition of the Mitochondrial Enzyme ABAD Restores the Amyloid-β-Mediated Deregulation of Estradiol

Alzheimer's disease (AD) is a conformational disease that is characterized by amyloid-β (Aβ) deposition in the brain. Aβ exerts its toxicity in part by receptor-mediated interactions that cause down-stream protein misfolding and aggregation, as well as mitochondrial dysfunction. Recent reports indicate that Aβ may also interact directly with intracellular proteins such as the mitochondrial enzyme ABAD (Aβ binding alcohol dehydrogenase) in executing its toxic effects. Mitochondrial dysfunction occurs early in AD, and Aβ's toxicity is in part mediated by inhibition of ABAD as shown previously with an ABAD decoy peptide. Here, we employed AG18051, a novel small ABAD-specific compound inhibitor, to investigate the role of ABAD in Aβ toxicity. Using SH-SY5Y neuroblastoma cells, we found that AG18051 partially blocked the Aβ-ABAD interaction in a pull-down assay while it also prevented the Aβ42-induced down-regulation of ABAD activity, as measured by levels of estradiol, a known hormone and product of ABAD activity. Furthermore, AG18051 is protective against Aβ42 toxicity, as measured by LDH release and MTT absorbance. Specifically, AG18051 reduced Aβ42-induced impairment of mitochondrial respiration and oxidative stress as shown by reduced ROS (reactive oxygen species) levels. Guided by our previous finding of shared aspects of the toxicity of Aβ and human amylin (HA), with the latter forming aggregates in Type 2 diabetes mellitus (T2DM) pancreas, we determined whether AG18051 would also confer protection from HA toxicity. We found that the inhibitor conferred only partial protection from HA toxicity indicating distinct pathomechanisms of the two amyloidogenic agents. Taken together, our results present the inhibition of ABAD by compounds such as AG18051 as a promising therapeutic strategy for the prevention and treatment of AD, and suggest levels of estradiol as a suitable read-out

DysMusic: detecting dyslexia by web-based games with music elements

Comunicació presentada a: the 13th Web for All Conference (W4A'16). Doctoral Consortium, celebrat de l'11 al 13 d'abril a Montreal, Canadà.The aim of this research is to show that a playful approach combined with music can detect children with dyslexia. Early detection will prevent children from suffering in school until they are detected due to bad grades. Our envisioned web application will contribute to 10% of the population by giving them a chance to succeed in life and find their skills to impress the world

Early screening of dyslexia using a language-independent content game and machine learning

Els nens amb dislèxia tenen dificultats per aprendre a llegir i escriure. Sovint se'ls diagnostica després de fallar a l'escola, encara que la dislèxia no estigui relacionada amb la intel·ligència general. En aquesta tesi, presentem un enfocament per a la selecció prèvia de la dislèxia mitjançant un joc independent del llenguatge en combinació amb models d’aprenentatge automàtic formats amb les dades d’interacció. Abans volem dir abans que els nens aprenguin a llegir i escriure. Per assolir aquest objectiu, vam dissenyar el contingut del joc amb el coneixement de l'anàlisi de paraules d'error de persones amb dislèxia en diferents idiomes i els paràmetres relacionats amb la dislèxia com la percepció auditiva i la percepció visual. Amb els nostres dos jocs dissenyats (MusVis i DGames) vam recollir conjunts de dades (313 i 137 participants) en diferents idiomes (principalment espanyols i alemanys) i els vam avaluar amb classificadors d'aprenentatge automàtic. Per a MusVis utilitzem principalment contingut que fa referència a un únic indicador acústic o visual, mentre que el contingut de DGames fa referència a diversos indicadors (també contingut genèric). El nostre mètode proporciona una precisió de 0,74 per a l'alemany i 0,69 per a espanyol i una puntuació de F1 de 0,75 per a alemany i de 0,75 per a espanyol a MusVis quan s'utilitzen arbres extraestats. DGames es va avaluar principalment amb alemany i obté la màxima precisió de 0,67 i la màxima puntuació de F1 de 0,74. Els nostres resultats obren la possibilitat de la dislèxia de detecció precoç a baixos costos ia través del web.Children with dyslexia have difficulties in learning how to read and write. They are often diagnosed after they fail in school, even though dyslexia is not related to general intelligence. In this thesis, we present an approach for earlier screening of dyslexia using a language-independent game in combination with machine learning models trained with the interaction data. By earlier we mean before children learn how to read and write. To reach this goal, we designed the game content with knowledge of the analysis of word errors from people with dyslexia in different languages and the parameters reported to be related to dyslexia, such as auditory and visual perception. With our two designed games (MusVis and DGames) we collected data sets (313 and 137 participants) in different languages (mainly Spanish and German) and evaluated them with machine learning classifiers. For MusVis we mainly use content that refers to one single acoustic or visual indicator, while DGames content refers to generic content related to various indicators. Our method provides an accuracy of 0.74 for German and 0.69 for Spanish and F1-scores of 0.75 for German and 0.75 for Spanish in MusVis when Random Forest and Extra Trees are used in . DGames was mainly evaluated with German and reached a peak accuracy of 0.67 and a peak F1-score of 0.74. Our results open the possibility of low-cost and early screening of dyslexia through the Web.Los niños con dislexia tienen dificultades para aprender a leer y escribir. A menudo se les diagnostica después de fracasar en la escuela, incluso aunque la dislexia no está relacionada con la inteligencia general. En esta tesis, presentamos un enfoque para la detección temprana de la dislexia utilizando un juego independiente del idioma en combinación con modelos de aprendizaje automático entrenados con los datos de la interacción. Temprana aquí significa antes que los niños aprenden a leer y escribir. Para alcanzar este objetivo, diseñamos el contenido del juego con el conocimiento del análisis de las palabras de error de las personas con dislexia en diferentes idiomas y los parámetros reportados relacionados con la dislexia, tales como la percepción auditiva y la percepción visual. Con nuestros dos juegos diseñados (MusVis y DGames) recogimos conjuntos de datos (313 y 137 participantes) en diferentes idiomas (principalmente español y alemán) y los evaluamos con clasificadores de aprendizaje automático. Para MusVis utilizamos principalmente contenido que se refiere a un único indicador acústico o visual, mientras que el contenido de DGames se refiere a varios indicadores (también contenido genérico). Nuestro método proporciona una exactitud de 0,74 para alemán y 0,69 para español y una puntuación F1 de 0,75 para alemán y 0,75 para español en MusVis cuando se utilizan Random Forest y Extra Trees. DGames fue evaluado principalmente con alemán y obtiene una exactitud de 0,67 y una puntuación F1 de 0,74. Nuestros resultados abren la posibilidad de una detección precoz y de bajo coste de la dislexia a través de la We

DysMusic: detecting dyslexia by web-based games with music elements

Comunicació presentada a: the 13th Web for All Conference (W4A'16). Doctoral Consortium, celebrat de l'11 al 13 d'abril a Montreal, Canadà.The aim of this research is to show that a playful approach combined with music can detect children with dyslexia. Early detection will prevent children from suffering in school until they are detected due to bad grades. Our envisioned web application will contribute to 10% of the population by giving them a chance to succeed in life and find their skills to impress the world

How to handle health-related small imbalanced data in machine learning?

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

A universal screening tool for dyslexia by a web-game and machine learning

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 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

“We Need To Talk About ChatGPT”: The Future of AI and Higher Education

On November 30th, 2022, OpenAI released the large language model ChatGPT, an extension of GPT-3. The AI chatbot provides real-time communication in response to users’ requests. The quality of ChatGPT’s natural speaking answers marks a major shift in how we will use AI-generated information in our day-to-day lives. For a software engineering student, the use cases for ChatGPT are manifold: assessment preparation, translation, and creation of specified source code, to name a few. It can even handle more complex aspects of scientific writing, such as summarizing literature and paraphrasing text. Hence, this position paper addresses the need for discussion of potential approaches for integrating ChatGPT into higher education. Therefore, we focus on articles that address the effects of ChatGPT on higher education in the areas of software engineering and scientific writing. As ChatGPT was only recently released, there have been no peer-reviewed articles on the subject. Thus, we performed a structured grey literature review using Google Scholar to identify preprints of primary studies. In total, five out of 55 preprints are used for our analysis. Furthermore, we held informal discussions and talks with other lecturers and researchers and took into account the authors’ test results from using ChatGPT. We present five challenges and three opportunities for the higher education context that emerge from the release of ChatGPT. The main contribution of this paper is a proposal for how to integrate ChatGPT into higher education in four main areas

A language resource of German errors written by children with dyslexia

Comunicació presentada a la LREC 2016, Tenth International Conference on Language Resources and Evaluation, celebrada del 23 al 28 de maig de 2016 a Portorož, Eslovènia.In this paper we present a language resource for German, composed of a list of 1,021 unique errors extracted from a collection of texts written by people with dyslexia. The errors were annotated with a set of linguistic characteristics as well as visual and phonetic features. We present the compilation and the annotation criteria for the different types of dyslexic errors. This language resource has many potential uses since errors written by people with dyslexia reflect their difficulties. For instance, it has already been used to design language exercises to treat dyslexia in German. To the best of our knowledge, this is first resource of this kind in German