GlyphReader App: a support game for the application of the Orton- Gillingham method with DataMining Techniques
Attention Deficit Hyperactivity Disorder, known by its Spanish acronym as ADHD, is a topic of great interest to professionals in psychology, psychiatry, and neuroscience in general, due to the negative impact it has on social functioning, personal, work, academic and family of those who suffer from...
- Autores:
-
Ariza Colpas, Paola Patricia
Leon Jacobus, Alexandra
Oñate-Bowen, Alvaro Agustín
Suarez-Brieva, Eydy del Carmen
Piñeres Melo, Marlon Alberto
Butt Shariq, Aziz
COLLAZOS MORALES, CARLOS ANDRES
Ramayo González, Ramón Enrique
MARTÍNEZ PALMERA, OLGA
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8695
- Acceso en línea:
- https://hdl.handle.net/11323/8695
https://doi.org/10.1016/j.procs.2021.07.071
https://repositorio.cuc.edu.co/
- Palabra clave:
- Teaching
Narrative genre
Story
Fable
Primary school
Learning software
- Rights
- openAccess
- License
- CC0 1.0 Universal
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GlyphReader App: a support game for the application of the Orton- Gillingham method with DataMining Techniques |
| title |
GlyphReader App: a support game for the application of the Orton- Gillingham method with DataMining Techniques |
| spellingShingle |
GlyphReader App: a support game for the application of the Orton- Gillingham method with DataMining Techniques Teaching Narrative genre Story Fable Primary school Learning software |
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GlyphReader App: a support game for the application of the Orton- Gillingham method with DataMining Techniques |
| title_full |
GlyphReader App: a support game for the application of the Orton- Gillingham method with DataMining Techniques |
| title_fullStr |
GlyphReader App: a support game for the application of the Orton- Gillingham method with DataMining Techniques |
| title_full_unstemmed |
GlyphReader App: a support game for the application of the Orton- Gillingham method with DataMining Techniques |
| title_sort |
GlyphReader App: a support game for the application of the Orton- Gillingham method with DataMining Techniques |
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Ariza Colpas, Paola Patricia Leon Jacobus, Alexandra Oñate-Bowen, Alvaro Agustín Suarez-Brieva, Eydy del Carmen Piñeres Melo, Marlon Alberto Butt Shariq, Aziz COLLAZOS MORALES, CARLOS ANDRES Ramayo González, Ramón Enrique MARTÍNEZ PALMERA, OLGA |
| dc.contributor.author.spa.fl_str_mv |
Ariza Colpas, Paola Patricia Leon Jacobus, Alexandra Oñate-Bowen, Alvaro Agustín Suarez-Brieva, Eydy del Carmen Piñeres Melo, Marlon Alberto Butt Shariq, Aziz COLLAZOS MORALES, CARLOS ANDRES Ramayo González, Ramón Enrique MARTÍNEZ PALMERA, OLGA |
| dc.subject.spa.fl_str_mv |
Teaching Narrative genre Story Fable Primary school Learning software |
| topic |
Teaching Narrative genre Story Fable Primary school Learning software |
| description |
Attention Deficit Hyperactivity Disorder, known by its Spanish acronym as ADHD, is a topic of great interest to professionals in psychology, psychiatry, and neuroscience in general, due to the negative impact it has on social functioning, personal, work, academic and family of those who suffer from it. This research aims to become a new contribution to the problems that ADHD represents in the world. Regarding its structure, this study starts from a detailed review of the existing theory about ADHD (from a historical perspective, neurobiology, neuropsychology, etiology, epidemiology, treatment, prognosis, among others) and reviews about cognitive development in the stage of childhood, to move to a methodological approach that allows obtaining data on the prevalence of ADHD symptoms in a sample of children, as well as analysis of results that establish the usefulness and reliability of different scales and / or diagnostic criteria in exploring symptoms adjusted to current definitions of this disorder. |
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2021 |
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2021-09-15T14:45:43Z |
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2021-09-15T14:45:43Z |
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2021 |
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https://hdl.handle.net/11323/8695 |
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https://doi.org/10.1016/j.procs.2021.07.071 |
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eng |
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eng |
| dc.relation.references.spa.fl_str_mv |
[1] Miranda, A., Berenguer, C., Colomer, C., & Roselló, R. (2014). Influence of the symptoms of Attention Deficit Hyperactivity Disorder (ADHD) and comorbid disorders on functioning in adulthood. Psicothema, 26(4), 471-476. [2] KJoffre-Velázquez, V. M., García-Maldonado, G., & Joffre-Mora, L. (2007). Trastorno por déficit de la atención e hiperactividad de la infancia a la vida adulta. Archivos en Medicina Familiar, 9(4), 176-181. [3] PJC, J., Ramos-Quiroga, J. A., Chalita, P. J., Vidal, R., Bosch, R., Palomar, G., & Prats, L. (2012). Diagnóstico y tratamiento del trastorno por déficit de atención/hiperactividad en adultos. Rev neurol, 54(Supl 1), S105-15. [4] Truffino, J. C., & Villamisar, D. G. (2006). Trastorno por déficit de atención e hiperactividad en el adulto: perspectivas actuales. Psiquiatría Biológica, 13(3), 86- 94. [5] Rodríguez Molinero, L., López Villalobos, J. A., Garrido Redondo, M., Sacristán Martín, A. M., Martínez Rivera, M. T., & Ruiz Sanz, F. (2009). Estudio psicométrico-clínico de prevalencia y comorbilidad del trastorno por déficit de atención con hiperactividad en Castilla y León (España). Pediatría Atención Primaria, 11(42), 251-270. [6] Mannuzza, S., Gittleman, R., Konig, P. H., & Giampino, T. (1989). Hyperactive boys almost grown-up: VI. Criminality and its relationship to psychiatric status. Archives of General Psychiatry, 46, 1073–1079 [7] Young, S., Morris, R., Toone, B., & Tyson, C. (2007). Planning ability in adults with attention-deficit/hyperactivity disorder. Neuropsychology, 21(5), 581. [8] García-Villamisar, D., Del Pozo Armentia, A., & Cabanyes, J. (2006). Educación de personas adultas con autismo. Madrid: Comunidad de Madrid.. [9] CreAPPcuentos- Available online: https://apps.apple.com/es/app/creappcuentos/id777978879 [10] Storyjumper. Available online: https://www.storyjumper.com/ [11] My Story School eBook Maker. Avaliable online: https://apps.apple.com/us/app/my-story-school-ebook-maker/id449232368 [12] TeCuento App. Available on: https://play.google.com/store/apps/details?id=tresandroides.tecuento&hl=es_CO [13] Scribble: Kids Book Maker App: https://play.google.com/store/apps/details?id=com.fingerprintplay.scribble2paid&hl=es_CO [14] Sonja O’Neill, Sarah ason, Guido Parente, ark P Donnelly, Christopher D Nugent, Sally McClean, Bryan Scotney, and David Craig. Video reminders as cognitive prosthetics for people with dementia. Ageing International, 36(2):267–282, 2011. [15] Ariza Colpas, P. P., Leon Jacobus, A., De-laHoz, S., Piñeres-Melo, M., Guerrero-Cuentas, H., Consuegra-Bernal, M., ... & Collazos Morales, C. A. (2020). Glyph reader app: multisensory stimulation through ICT to intervene literacy disorders in theclassroom. [16] Troussas, C., Krouska, A., & Virvou, M. (2020). Using a multi module model for learning analytics to predict learners’ cognitive states and provide tailored learning pathways and assessment. In Machine Learning Paradigms (pp. 9-22). Springer.Cham [17] Lara, J. A., Aljawarneh, S., & Pamplona, S. (2020). Special issue on the current trends in E-learning Assessment. Journal of Computing in Higher Education, 32(1), 1-8. [18] Zagorskis, V., Gorbunovs, A., & Kapenieks, A. (2020). TELECI ARCHITECTURE FOR MACHINE LEARNING ALGORITHMS INTEGRATION IN AN EXISTING LMS. Emerging Extended Reality Technologies for Industry 4.0: Early Experiences with Conception, Design, Implementation, Evaluation and Deployment, 121. [19] Cerezo, R., Bogarín, A., Esteban, M., & Romero, C. (2020). Process mining for self-regulated learning assessment in e-learning. Journal of Computing in Higher Education, 32(1), 74-88 [20] Nilashi, M., Ahmadi, N., Samad, S., Shahmoradi, L., Ahmadi, H., Ibrahim, O., ... & Yadegaridehkordi, E. (2020). Disease Diagnosis Using Machine Learning Techniques: A Review and Classification. Journal of Soft Computing and Decision Support Systems, 7(1), 19-30. [21] Alihodzic, A., Tuba, E., & Tuba, M. (2020). An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm. In Nature- Inspired Computation in Data Mining and Machine Learning (pp. 95-112). Springer, Cham [22] Itani, S., Rossignol, M., Lecron, F., & Fortemps, P. (2019). Towards interpretable machine learning models for diagnosis aid: a case study on attention deficit/hyperactivity disorder. PloS one, 14(4), e0215720. [23] Heller, M. D., Roots, K., Srivastava, S., Schumann, J., Srivastava, J., & Hale, T. S. (2013). A machine learning-based analysis of game data for attention deficit hyperactivity disorder assessment. GAMES FOR HEALTH: Research, Development, and Clinical Applications, 2(5), 291-298. [24] Morrow, A. S., Campos Vega, A. D., Zhao, X., & Liriano, M. M. (2020). Leveraging machine learning to identify predictors of receiving psychosocial treatment for Attention Deficit/Hyperactivity Disorder. Administration and Policy in Mental Health and Mental Health Services Research, 47, 680-692. [25] Zhang-James, Y., Helminen, E. C., Liu, J., Franke, B., Hoogman, M., Faraone, S. V., & ENIGMA-ADHD working group. (2019). Machine learning classification of attention-deficit/hyperactivity disorder using structural MRI data. bioRxiv, 546671. [26] Chen, H., Chen, W., Song, Y., Sun, L., & Li, X. (2019). EEG characteristics of children with attention-deficit/hyperactivity disorder. Neuroscience, 406, 444-456 |
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Ariza Colpas, Paola PatriciaLeon Jacobus, AlexandraOñate-Bowen, Alvaro AgustínSuarez-Brieva, Eydy del CarmenPiñeres Melo, Marlon AlbertoButt Shariq, AzizCOLLAZOS MORALES, CARLOS ANDRESRamayo González, Ramón EnriqueMARTÍNEZ PALMERA, OLGA2021-09-15T14:45:43Z2021-09-15T14:45:43Z20211877-0509https://hdl.handle.net/11323/8695https://doi.org/10.1016/j.procs.2021.07.071Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Attention Deficit Hyperactivity Disorder, known by its Spanish acronym as ADHD, is a topic of great interest to professionals in psychology, psychiatry, and neuroscience in general, due to the negative impact it has on social functioning, personal, work, academic and family of those who suffer from it. This research aims to become a new contribution to the problems that ADHD represents in the world. Regarding its structure, this study starts from a detailed review of the existing theory about ADHD (from a historical perspective, neurobiology, neuropsychology, etiology, epidemiology, treatment, prognosis, among others) and reviews about cognitive development in the stage of childhood, to move to a methodological approach that allows obtaining data on the prevalence of ADHD symptoms in a sample of children, as well as analysis of results that establish the usefulness and reliability of different scales and / or diagnostic criteria in exploring symptoms adjusted to current definitions of this disorder.Ariza Colpas, Paola Patricia-will be generated-orcid-0000-0003-4503-5461-600Leon Jacobus, Alexandra-will be generated-orcid-0000-0003-3128-1274-600Oñate-Bowen, Alvaro AgustínSuarez-Brieva, Eydy del CarmenPiñeres Melo, Marlon Alberto-will be generated-orcid-0000-0002-1858-2083-600Butt Shariq, AzizCOLLAZOS MORALES, CARLOS ANDRES-will be generated-orcid-0000-0002-1996-1384-600Ramayo González, Ramón Enrique-will be generated-orcid-0000-0001-6137-6181-600MARTÍNEZ PALMERA, OLGA-will be generated-orcid-0000-0002-7930-7624-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050921014745TeachingNarrative genreStoryFablePrimary schoolLearning softwareGlyphReader App: a support game for the application of the Orton- Gillingham method with DataMining TechniquesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Miranda, A., Berenguer, C., Colomer, C., & Roselló, R. (2014). Influence of the symptoms of Attention Deficit Hyperactivity Disorder (ADHD) and comorbid disorders on functioning in adulthood. Psicothema, 26(4), 471-476.[2] KJoffre-Velázquez, V. M., García-Maldonado, G., & Joffre-Mora, L. (2007). Trastorno por déficit de la atención e hiperactividad de la infancia a la vida adulta. Archivos en Medicina Familiar, 9(4), 176-181.[3] PJC, J., Ramos-Quiroga, J. A., Chalita, P. J., Vidal, R., Bosch, R., Palomar, G., & Prats, L. (2012). Diagnóstico y tratamiento del trastorno por déficit de atención/hiperactividad en adultos. Rev neurol, 54(Supl 1), S105-15.[4] Truffino, J. C., & Villamisar, D. G. (2006). Trastorno por déficit de atención e hiperactividad en el adulto: perspectivas actuales. Psiquiatría Biológica, 13(3), 86- 94.[5] Rodríguez Molinero, L., López Villalobos, J. A., Garrido Redondo, M., Sacristán Martín, A. M., Martínez Rivera, M. T., & Ruiz Sanz, F. (2009). Estudio psicométrico-clínico de prevalencia y comorbilidad del trastorno por déficit de atención con hiperactividad en Castilla y León (España). Pediatría Atención Primaria, 11(42), 251-270.[6] Mannuzza, S., Gittleman, R., Konig, P. H., & Giampino, T. (1989). Hyperactive boys almost grown-up: VI. Criminality and its relationship to psychiatric status. Archives of General Psychiatry, 46, 1073–1079[7] Young, S., Morris, R., Toone, B., & Tyson, C. (2007). Planning ability in adults with attention-deficit/hyperactivity disorder. Neuropsychology, 21(5), 581.[8] García-Villamisar, D., Del Pozo Armentia, A., & Cabanyes, J. (2006). Educación de personas adultas con autismo. Madrid: Comunidad de Madrid..[9] CreAPPcuentos- Available online: https://apps.apple.com/es/app/creappcuentos/id777978879[10] Storyjumper. Available online: https://www.storyjumper.com/[11] My Story School eBook Maker. Avaliable online: https://apps.apple.com/us/app/my-story-school-ebook-maker/id449232368[12] TeCuento App. Available on: https://play.google.com/store/apps/details?id=tresandroides.tecuento&hl=es_CO[13] Scribble: Kids Book Maker App: https://play.google.com/store/apps/details?id=com.fingerprintplay.scribble2paid&hl=es_CO[14] Sonja O’Neill, Sarah ason, Guido Parente, ark P Donnelly, Christopher D Nugent, Sally McClean, Bryan Scotney, and David Craig. Video reminders as cognitive prosthetics for people with dementia. Ageing International, 36(2):267–282, 2011.[15] Ariza Colpas, P. P., Leon Jacobus, A., De-laHoz, S., Piñeres-Melo, M., Guerrero-Cuentas, H., Consuegra-Bernal, M., ... & Collazos Morales, C. A. (2020). Glyph reader app: multisensory stimulation through ICT to intervene literacy disorders in theclassroom.[16] Troussas, C., Krouska, A., & Virvou, M. (2020). Using a multi module model for learning analytics to predict learners’ cognitive states and provide tailored learning pathways and assessment. In Machine Learning Paradigms (pp. 9-22). Springer.Cham[17] Lara, J. A., Aljawarneh, S., & Pamplona, S. (2020). Special issue on the current trends in E-learning Assessment. Journal of Computing in Higher Education, 32(1), 1-8.[18] Zagorskis, V., Gorbunovs, A., & Kapenieks, A. (2020). TELECI ARCHITECTURE FOR MACHINE LEARNING ALGORITHMS INTEGRATION IN AN EXISTING LMS. Emerging Extended Reality Technologies for Industry 4.0: Early Experiences with Conception, Design, Implementation, Evaluation and Deployment, 121.[19] Cerezo, R., Bogarín, A., Esteban, M., & Romero, C. (2020). Process mining for self-regulated learning assessment in e-learning. Journal of Computing in Higher Education, 32(1), 74-88[20] Nilashi, M., Ahmadi, N., Samad, S., Shahmoradi, L., Ahmadi, H., Ibrahim, O., ... & Yadegaridehkordi, E. (2020). Disease Diagnosis Using Machine Learning Techniques: A Review and Classification. Journal of Soft Computing and Decision Support Systems, 7(1), 19-30.[21] Alihodzic, A., Tuba, E., & Tuba, M. (2020). An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm. In Nature- Inspired Computation in Data Mining and Machine Learning (pp. 95-112). Springer, Cham[22] Itani, S., Rossignol, M., Lecron, F., & Fortemps, P. (2019). Towards interpretable machine learning models for diagnosis aid: a case study on attention deficit/hyperactivity disorder. PloS one, 14(4), e0215720.[23] Heller, M. D., Roots, K., Srivastava, S., Schumann, J., Srivastava, J., & Hale, T. S. (2013). A machine learning-based analysis of game data for attention deficit hyperactivity disorder assessment. GAMES FOR HEALTH: Research, Development, and Clinical Applications, 2(5), 291-298.[24] Morrow, A. S., Campos Vega, A. D., Zhao, X., & Liriano, M. M. (2020). Leveraging machine learning to identify predictors of receiving psychosocial treatment for Attention Deficit/Hyperactivity Disorder. Administration and Policy in Mental Health and Mental Health Services Research, 47, 680-692.[25] Zhang-James, Y., Helminen, E. C., Liu, J., Franke, B., Hoogman, M., Faraone, S. V., & ENIGMA-ADHD working group. (2019). Machine learning classification of attention-deficit/hyperactivity disorder using structural MRI data. bioRxiv, 546671.[26] Chen, H., Chen, W., Song, Y., Sun, L., & Li, X. (2019). EEG characteristics of children with attention-deficit/hyperactivity disorder. 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