
Autora: Tracey Tokuhama-Espinosa, Ph.D., docente en la Universidad de Harvard, Extension School
Traductora: Cynthia Borja, Ph.D
Algunos de ustedes han preguntado recientemente si hay algún estudio que establezca la calidad del aprendizaje en línea en comparación con el presencial. Existe, y puede encontrar varios artículos revisados por pares para que los lea usted mismo en esta colección (mini-biblioteca) sobre Aprendizaje en línea.
En resumen, la evidencia sugiere seis grandes ideas que se deben tener en cuenta antes de pasar sus clases a modalidad en línea : (a) los factores de profesores; (b) factores estudiantiles; (c) uso de materiales; (d) comunicación; (e) aprendizaje en línea más allá de los MOOC; y (f) gestión del cambio. Doy un breve resumen de la literatura a continuación y luego resumo con algunas recomendaciones.
Factores de profesores
Primero, en línea es una modalidad. Esto no convierte mágicamente a un maestro «malo» en uno «bueno» o viceversa. La calidad del maestro sigue siendo el factor determinante más importante en los resultados positivos de aprendizaje ( Darling-Hammond et al., 2017 ). Es decir, no es la herramienta o modalidad lo que importa, es la competencia del instructor la que determina si los estudiantes informan que aprenden bien o no. El centro de la enseñanza de calidad es la autoeficacia del profesor, y aún más importante es la autoeficacia grupal (Hattie & Clark, 2018 ; McCarty, 2018 ). Cuando varios miembros de la facultad envían el mismo mensaje al alumno sobre su potencial para aprender, la propia percepción del estudiante como alumno cambia y los resultados se ven afectados, para bien o para mal.
Hay una curva inversa en los niveles educativos y la adaptación a lo que sabemos sobre el aprendizaje moderno; los educadores de la primera infancia generalmente parecen aceptar la novedad, mientras que los profesores universitarios son a menudo los más adversos al cambio (Tagg, 2012). En general, existe un alto nivel de autocrítica y monitoreo que ocurre en el aula de la primera infancia y una relativa apertura a la novedad educativa que parece disminuir por parte de los maestros a medida que los estudiantes avanzan a niveles superiores de educación. La educación terciaria ha sido la más lenta para adaptarse a las demandas del estudiante moderno, a menudo ignorando el progreso en la tecnología educativa y los descubrimientos sobre cómo aprende el cerebro, a pesar de que estos mismos descubrimientos a menudo se realizan dentro de las propias universidades (Keeling y Hersh, 2016). La mayoría de los profesores universitarios nunca han tenido una clase sobre cómo enseñar. Aunque eruditos en campos especializados de conocimiento, pocos profesores universitarios conocen los beneficios de una herramienta o estrategia de enseñanza por sobre otra y priorizan su experiencia de vida como guía. Esto puede tener resultados tanto positivos como negativos, aunque generalmente negativos, ya que los maestros no actualizan su base de conocimiento pedagógico.
Factores estudiantiles
En segundo lugar, existe evidencia de que las actitudes de los estudiantes (individual y colectivamente), así como sus autopercepciones como aprendices exitosos (o no) tiene uno de los mayores impactos del tamaño del efecto en resultados de aprendizaje (Hattie, 2012). Si bien no es el único factor, si un estudiante piensa que él/ella no va a tener éxito, esto lleva a la profecía autocumplida del fracaso (Zimmerman et al., 2017). Por otro lado, si un estudiante tiene una mentalidad de crecimiento (Dweck, 2006), una actitud abierta hacia una nueva experiencia, y cree que puede aprender y aprenderá con el esfuerzo apropiado, entonces ese estudiante generalmente lo hace bien (Tang et al., 2019), algo que también se ha mostrado en la modalidad en línea (McClendon et al., 2017). Esto sugiere que la actitud, incluso más que la aptitud, juega un importante papel en los resultados de aprendizaje (Artlet, et al., 2003; Côté, 2000). En una investigación relacionada, existe una fuerte evidencia de que la autorregulación y otras habilidades de funciones ejecutivas representan casi el doble que el impacto de la inteligencia nativa en relación con los resultados de aprendizaje (Duckworth, 2011 ; Moffitt, 2011). Esto significa que poder concentrarse en una tarea y cumplirla es más importante que simplemente nacer inteligente.
Materiales de calidad
En tercer lugar, el aprendizaje en línea (incluidas las aulas virtuales) plantea muchos desafíos tanto para los profesores como para los estudiantes en términos de materiales debido a la elección. La “parálisis de análisis” puede ocurrir cuando una persona ingresa al aula virtual, ya que hay muchas más herramientas disponibles para mejorar el intercambio de enseñanza-aprendizaje en una plataforma de aprendizaje en línea que en un aula presencial (Crawford-Ferre, 2012). Esto se debe en parte a las miles de aplicaciones educativas y plug-ins disponibles en la actualidad que trabajan, en gran parte, para facilitar la corrección y el ensayo de los aspectos más técnicos o mecánicos de aprendizaje (por ejemplo, la comprobación de comas), dejando más tiempo para que los maestros utilicen para el aspecto humano del proceso de enseñanza- aprendizaje, como tomar el pulso emocional de los alumnos y motivarlos a alcanzar su propio potencial en la materia.
Por otra parte, las herramientas típicas como pruebas, foros de discusión y trabajo en grupos pequeños pueden ser utilizados para objetivos de aprendizaje adicionales. Por ejemplo, los cuestionarios autocorregidos se pueden usar para aumentar la cantidad de pruebas de bajo riesgo que mejoran la memoria (Grisson et al., 2011). Como la memoria y la atención son vitales para el aprendizaje, el aprendizaje en sí mismo aumenta. Esta herramienta automatizada le permite al profesor pasar más tiempo en estrategias de enseñanza y aprendizaje dependientes de los humanos como ayudar a los estudiantes a comprender por qué se equivocaron en sus procesos de pensamiento.
Del mismo modo, las discusiones en línea sobre un tema pueden tener lugar a lo largo del tiempo, de forma asíncrona, lo cual permite un desarrollo de un diálogo entre estudiantes en lugar de ser la reflexión simple y solitaria de un solo alumno. Esto significa que además de escuchar las respuestas individuales, se construye una comunidad de estudiantes a través del intercambio lo cual beneficia la toma de perspectiva, la investigación, el debate y las habilidades de comunicación en general.
El trabajo en grupos pequeños puede beneficiar el aprendizaje (Aldrich, 2016). La creación de estos grupos tarda aproximadamente 10 segundos en la estructura de un videoconferencia en línea como lo son las salas pequeñas de Zoom, mientras que el reorganizar a los estudiantes en de un aula física puede tardar varios minutos. El ahorro de tiempo en la organización crea tiempo adicional para una discusión más profunda y un mejor uso del tiempo de contacto con los estudiantes.
Comunicación
Cuarto, existe un fuerte argumento de que nada reemplaza la experiencia presencial debido a la necesidad humana de contacto social (Hodas y Lerman, 2014). Existe una gran cantidad de evidencia internacional que muestra que las relaciones profesor-estudiante tienen una gran influencia en el apoyo a los resultados de aprendizaje de los alumnos (Hattie y Anderman, 2013). Esto sugeriría que la capacidad de «conectarse» juega un papel muy importante en la motivación del alumno para aprender y, en consecuencia, en los resultados. A pesar de la creencia popular de que estar presencialmente es necesario para sentir los beneficios de la socialización, hay investigaciones que demuestran que el contagio social – la capacidad de un individuo de «infectar» a otros a nivel emocional y propagarse definir el entorno de aprendizaje – es igual de impactante en contextos en línea como lo es en contextos presenciales (Johnson, et al., 2011).
Otros van tan lejos como para sugerir que el contagio social en realidad aumenta en la configuración en línea debido a la visibilidad de los miembros (Bowers y Kumar, 2015). En las reuniones en línea sincrónicas, la gente ve los rostros de todos los demás todo el tiempo, mientras que en una clase cara a cara a menudo tienen sus espaldas el uno al otro. La capacidad de ver las caras de los demás acelera la tasa de contagio social que experimenta el grupo. Esto significa que un grupo emocionado y motivado se vuelve aún más emocionado y motivado, pero lo contrario también puede ser cierto. Esta es la razón por la cual la gestión de grupos en contextos virtuales adquiere un papel magnificado. La buena gestión del aula en línea es la clave para aprovechar el contagio social a favor del buen ambiente de aprendizaje. Además, en los entornos de videoconferencia, los alumnos también se ven a sí mismos, lo que los hace aún más conscientes de su lenguaje corporal, expresiones faciales y comunicación general hacia el grupo. Esta autoconciencia mejorada cambia la dinámica del grupo y, si el instructor la aprovecha bien, puede avanzar significativamente en el aprendizaje grupal.
Hay un fenómeno adicional en el aprendizaje en línea, que está empezando a ser explorado, que es el efecto de desinhibición, o la forma en que el aprendizaje en línea crea un nivel de “protección” y, por tanto, un mayor nivel de compromiso social (Pytash y Ferdig, 2017). Los autores sugieren que los alumnos están más dispuestos a compartir ideas e interactuar entre ellos en línea porque se sienten protegidos al no estar en un espacio físicamente compartido (Salter, et al., 2017).
Aprendizaje en línea: más allá de los «MOOC»
Quinto, el aprendizaje «en línea» es un concepto global que abarca distintas experiencias de aprendizaje, que van desde MOOCs, seminarios web, laboratorios de realidad virtual, clases suplementarias K-12 y a aulas universitarias 100% en línea. Esta amplia gama de subtipos de aprendizaje en línea hace que sea difícil calificar lo que se entiende por «trasladar su clase a modalidad en línea», ya que esto podría significar simplemente ofrecer un depósito de documentos o un lugar para recibir las tareas cargadas en la plataforma, a una experiencia de aprendizaje»altamente directa» asistida por la tecnología y que altera la vida.
En la actualidad existe una gran cantidad de evidencia sobre la ineficacia de los Cursos Online Masivos y Abiertos (Massive Online Open Courses o MOOCS por sus siglas en inglés) (Mayer et al., 2020). Los MOOC llegaron a la escena del aprendizaje en vigor a principios de 2000 y prometieron no solo democratizar la información a un costo bajo, pero también capturaron la imaginación de muchos líderes educativos que rápidamente idearon esquemas de certificación a través del cual se podía derivar ingresos. La realidad de los MOOC, y su eficacia, sin embargo, es que tienen una tasa de terminación extremadamente baja, requieren un alto nivel de auto- control, y todavía no tienen acuerdos sobre un esquema de evaluación con el que se pueda medir los resultados del aprendizaje (Weinhardt y Sitzmann, 2019). Si bien tienen éxito en la democratización del acceso abierto al conocimiento, los MOOC tienen mucho menos éxito en garantizar un aprendizaje profundo, tienen poco o ningún contacto de alumno a alumno o de maestro a alumno, y no tienen una forma sólida o universalmente acordada de provisión de evidencia del aprendizaje.
No todo el aprendizaje en línea consiste de MOOC, a pesar de ser el formato de aprendizaje en línea más estudiado. Muchas personas llegan a la conclusión de que «el aprendizaje en línea es ineficaz» y señalan estadísticas de las tasas de finalización de los MOOC y/o la naturaleza solitaria y los resultados de estudiar solo (Ejreaw & Drus, 2017). Si bien los MOOC pueden no ser un reemplazo efectivo para un curso, otras experiencias en línea bien diseñadas sugieren la naturaleza igual o incluso superior del aprendizaje en línea (Bazylak & Weiss, 2017).
Muchas universidades de todo el mundo han invertido cuantiosamente en la última década para estructurar experiencias de aprendizaje de alta calidad en la modalidad en línea aprovechando la tecnología más actualizada (He, et al., 2019). Un buen diseño en linea, similar al diseño de cualquier experiencia de aprendizaje presencial, requiere una amplia planificación para aprovechar las herramientas y estrategias adecuadas en el momento adecuado para el público adecuado.
Gestión del cambio
Sexto, es natural que los humanos teman al cambio por dos razones principales. Desde una perspectiva neurocientífica, esto se debe a que pasar de heurísticas comunes y nuestros prejuicios innatos hacia un nuevo aprendizaje requiere energía (Tokuhama-Espinosa y Nelson, 2019). Como el mayor consumidor de energía en el cuerpo a nivel de órganos, el cerebro trabaja duro para conservar todo lo que puede. Se necesita mucha menos energía para quejarse del cambio que imaginar las posibilidades de cambio, por ejemplo.
Segundo, el miedo es un fuerte motivador. Las personas toman decisiones basadas en el miedo más rápido que en cualquier otra emoción (Bechara, et al., 2000). En términos evolutivos, esto se explica fácilmente por la autoconservación: es mejor tener miedo y posiblemente salvarse a uno mismo que sentirse tranquilo y ser emboscado. Es mucho más difícil, pero rinde mucho más en términos de crecimiento personal y colectivo, tomarse el tiempo para ejercer una apertura cautelosamente optimista. Las personas que llegan a situaciones de cambio bien preparados y han estudiado un tema a profundidad, toman mejores decisiones que las personas que son reaccionarios y que no tienen más que la evidencia anecdótica (es decir, “Me fue mal en un MOOC, por lo tanto rechazo el aprendizaje en línea”.
Resumen
Existe una gran cantidad de literatura que apunta a los potenciales, las promesas y los peligros del aprendizaje en línea. Es fácil demostrar que el aprendizaje en línea tiene varias ventajas importantes observadas en estudios comparativos rigurosos en los que se encontró que “los estudiantes en los cursos en línea reportaron una mejor comprensión de la estructura del curso, una mejor comunicación con el personal del curso, revisaron las lecciones de video más y [tuvieron] mayor compromiso y satisfacción”(Soffer & Nachmias, 2018, p.534) que los mismos estudiantes en clases presenciales.
Las respuestas a los retos educativos nunca son fáciles, ya que hay múltiples variables de confusión que deben ser tomadas en consideración, siendo entre las mas importantes aquellas que tienen que ver con la calidad del profesor, las actitudes de los estudiantes, el uso del diseño de instrucción y herramientas disponibles, la personalización del esfuerzo, la base de conocimiento de los actores y la forma en que la institución gestiona el cambio.
Las respuestas a nuestras preguntas más frecuentes sobre el aprendizaje en línea son naturalmente complejas, ya que el aprendizaje humano y el cerebro son complejos. Todos los animales aprenden, pero pocos, incluidos los humanos, tienen una ciencia de la enseñanza (Blakemore y Frith, 2005), y solo los humanos enseñan en otras modalidades además de estar presencialmente. Aprender a enseñar en línea no es fácil porque requiere no solo (a) saber cómo enseñar y (b) comprender las herramientas y estrategias disponibles, sino que también supone que los profesores tengan (c) conocimiento del contenido o área de dominio (es decir, el profesor de historia sabe historia); y que el maestro tiene una (d) comprensión del cerebro y de cómo los humanos aprenden mejor. Ninguno de estos factores puede darse por sentado. Esto significa que para «trasladar su curso a modalidad en línea», los educadores y aquellos que soliciten este cambio deben ofrecer la capacitación adecuada para cubrir cualquier vacío potencial en las habilidades generales de los profesores.
En línea es una modalidad con un potencial maravilloso para ahorrar tiempo (es decir, no más movilización hacia la clase), llegar a más estudiantes (es decir, las clases ya no se limitan a los físicamente presentes, sino que pueden estar compuestas por centros escolares globales y la capacidad de incluir a aquellos tradicionalmente excluidos de las aulas regulares), diferenciar (es decir, ofrecer múltiples niveles de puntos de entrada a temas con una variedad más amplia de recursos) y personalizar el aprendizaje. Sin embargo, todo esto depende de garantizar que tanto los profesores como los estudiantes estén listos para el desafío y que estén bien equipados con premisas basadas en evidencia antes de iniciar sesión.
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Larmuseau, C., Desmet, P., & Depaepe, F. (2019). Perceptions of instructional quality: impact on acceptance and use of an online learning environment. Interactive Learning Environments, 27(7), 953-964.
Law, K. M., Geng, S., & Li, T. (2019). Student enrollment, motivation and learning performance in a blended learning environment: The mediating effects of social, teaching, and cognitive presence. Computers & Education, 136, 1-12.
Lin, M. H., Chen, H. C., & Liu, K. S. (2017). A study of the effects of digital learning on learning motivation and learning outcome. Eurasia Journal of Mathematics, Science and Technology Education, 13(7), 3553-3564.
Lodge, J. M., & Horvath, J. C. (2016). Science of learning and digital learning environments. In J.C. Horvath, .M. Lodge and J. Hattie’s From the laboratory to the classroom: Translating science of learning for teachers.
Lodge, J. M., Kennedy, G., & Lockyer, L. (2016). Brain, mind and educational technology. Australasian Journal of Educational Technology, 32(6).
Ludvigsen, S., & Steier, R. (2019). Reflections and looking ahead for CSCL: digital infrastructures, digital tools, and collaborative learning. International Journal of Computer-Supported Collaborative Learning, 14(4), 415-423.
Macgilchrist, F. (2019). Cruel optimism in edtech: when the digital data practices of educational technology providers inadvertently hinder educational equity. Learning, Media and Technology, 44(1), 77-86.
Mao, J., Ifenthaler, D., Fujimoto, T., Garavaglia, A., & Rossi, P. G. (2019). National policies and educational technology: a synopsis of trends and perspectives from five countries. TechTrends, 63(3), 284-293.
Martin, F., Ritzhaupt, A., Kumar, S., & Budhrani, K. (2019). Award-winning faculty online teaching practices: Course design, assessment and evaluation, and facilitation. The Internet and Higher Education, 42, 34-43.
Mirriahi, N., Liaqat, D., Dawson, S., & Gašević, D. (2016). Uncovering student learning profiles with a video annotation tool: reflective learning with and without instructional norms. Educational Technology Research and Development, 64(6), 1083-1106.
Muir, T., Milthorpe, N., Stone, C., Dyment, J., Freeman, E., & Hopwood, B. (2019). Chronicling engagement: students’ experience of online learning over time. Distance Education, 40(2), 262-277.
Muljana, P. S., & Luo, T. (2019). Factors contributing to student retention in online learning and recommended strategies for improvement: A systematic literature review. Journal of Information Technology Education: Research, 18.
O’Brien, J. (2017). Back to the future of edtech: A meditation. EDUCAUSE. [blog] https://www.educause.edu/interactive/2017/4/back-to-the-future-of-edtech/
Otterborn, A., Schönborn, K., & Hultén, M. (2019). Surveying preschool teachers’ use of digital tablets: general and technology education related findings. International journal of technology and design education, 29(4), 717-737.
Ozernov‐Palchik, O., Norton, E. S., Sideridis, G., Beach, S. D., Wolf, M., Gabrieli, J. D., & Gaab, N. (2017). Longitudinal stability of pre‐reading skill profiles of kindergarten children: implications for early screening and theories of reading. Developmental science, 20(5), e12471.
Pacheco, B. (2018). The rise of the human digital brain: How multidirectional thinking is changing the way we learn. IAP.
Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., & Mirriahi, N. (2019). Using learning analytics to scale the provision of personalised feedback. British Journal of Educational Technology, 50(1), 128-138.
Parsons, D., Inkila, M., & Lynch, J. (2019). Navigating learning worlds: Using digital tools to learn in physical and virtual spaces. Australasian Journal of Educational Technology, 35(4).
Parsons, S. A., Hutchison, A. C., Hall, L. A., Parsons, A. W., Ives, S. T., & Leggett, A. B. (2019). US teachers’ perceptions of online professional development. Teaching and Teacher Education: An International Journal of Research and Studies, 82(1), 33-42.
Parsons, T. D., Lin, L., & Cockerham, D. (Eds.). (2018). Mind, brain and technology: Learning in the age of emerging technologies. Springer.
Pechenkina, E., & Aeschliman, C. (2017). What do students want? Making sense of student preferences in technology-enhanced learning. Contemporary Educational Technology, 8(1), 26-39.
Sandanayake, T. C. (2019). Promoting open educational resources-based blended learning. International Journal of Educational Technology in Higher Education, 16(1), 3.
Soffer, T., & Nachmias, R. (2018). Effectiveness of learning in online academic courses compared with face‐to‐face courses in higher education. Journal of Computer Assisted Learning, 34(5), 534-543.
Study.com (n.d.) Educational technology trends: What teachers should know. Available on https://study.com/academy/lesson/educational-technology-trends-what-teachers-should-know.html
Surabhi, S. (2019). 3 ways machine learning in EdTech is changing the educational industry. Net Solutions. [webpage] https://www.netsolutions.com/insights/machine-learning-in-edtech/
Technology for Every Student? (2:43 minutes) Interview with researcher Todd Rose about digital technology and Universal Design at the Center for Applied Special Technology (CAST).
Uncapher, M. R. (2018). Design considerations for conducting large‐scale learning research using innovative technologies in schools. Mind, Brain, and Education. https://doi.org/10.1111/mbe.12185
Universal Design for Learning (UDL) UDL is a set of principles for curriculum development that give all individuals equal opportunities to learn. UDL provides a blueprint for creating instructional goals, methods, materials, and assessments that work for everyone–not a single, one-size-fits-all solution but rather flexible approaches that can be customized and adjusted for individual needs.
Wilkerson, M. H. (2017). Teachers, students, and after-school professionals as designers of digital tools for learning. In Participatory Design for Learning (pp. 125-138). Routledge.
Williamson, B., Pykett, J., & Nemorin, S. (2018). Biosocial spaces and neurocomputational governance: brain-based and brain-targeted technologies in education. Discourse: Studies in the Cultural Politics of Education, 39(2), 258-275. [Request access from HOLLIS]
Witton, G. (2017). The value of capture: Taking an alternative approach to using lecture capture technologies for increased impact on student learning and engagement. British Journal of Educational Technology, 48(4), 1010-1019.
Xu, H., Liu, S., & Liu, M. (2019). Analysis of the application of modern educational technology in middle school mathematics teaching. International Journal of Innovation and Research in Educational Sciences, 6(3), 2349-5219.
Yen, S. C., Lo, Y., Lee, A., & Enriquez, J. (2018). Learning online, offline, and in-between: comparing student academic outcomes and course satisfaction in face-to-face, online, and blended teaching modalities. Education and Information Technologies, 23(5), 2141-2153.
Zhao, Y., Frey, B., Rice, S., Rury, J. L., & Isaacson, R. (2019). Investigating the Relationship between faculty perception of educational technology and the level of technology integration into teaching and learning (Doctoral dissertation, University of Kansas).
Instructional Design (Creation of Learning Environments)
Al Mamun, M. A., Lawrie, G., & Wright, T. (2020). Instructional design of scaffolded online learning modules for self-directed and inquiry-based learning environments. Computers & Education, 144, 103695.
Alomyan, H., & Green, D. (2019, August). Learning theories: Implications for online learning design. In Proceedings of the 2019 3rd International Conference on E-Society, E-Education and E-Technology (pp. 126-130).
Baldwin, S. J., & Ching, Y. H. (2019). An online course design checklist: development and users’ perceptions. Journal of Computing in Higher Education, 31(1), 156-172.
Holland, A. A. (2019). Effective principles of informal online learning design: A theory-building metasynthesis of qualitative research. Computers & Education, 128, 214-226.
Ou, C., Joyner, D. A., & Goel, A. K. (2019). Designing and developing video lessons for online learning: A seven-principle model. Online Learning, 23(2), 82-104.
Powell, C. G., & Bodur, Y. (2019). Teachers’ perceptions of an online professional development experience: Implications for a design and implementation framework. Teaching and Teacher Education, 77, 19-30.
Suartama, I. K., Setyosari, P., Sulthoni, S., & Ulfa, S. (2019). Development of an instructional design model for mobile blended learning in higher education. International Journal of Emerging Technologies in Learning (iJET), 14(16), 4-22.
Educational Technology (Creation of stand-alone and complementary tools in formal and informal learning)
Angeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data mining in educational technology classroom research: Can it make a contribution?. Computers & Education, 113, 226-242.
Bartolomé, A., Castañeda, L., & Adell, J. (2018). Personalisation in educational technology: the absence of underlying pedagogies. International Journal of Educational Technology in Higher Education, 15(1), 14. [Request access from HOLLIS]
Bateman, B. L. (2019). Internet resources: Educational technology: A guide to resources on the Web. College & Research Libraries News, 64(1), 9-13.
Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: a systematic evidence map. International Journal of Educational Technology in Higher Education, 17(1), 2.
Bond, M., Zawacki‐Richter, O., & Nichols, M. (2019). Revisiting five decades of educational technology research: A content and authorship analysis of the British Journal of Educational Technology. British Journal of Educational Technology, 50(1), 12-63.
Brookings Institution. (2013). Education technology: The next generation. [ video ] (1:23:44 minutes). Available on Full Event – Education Technology: The Next Generation
Department of Education, U.S. (2014). Devices. Office of Educational Technology. [ video ] (2:34 minutes). Available on: Department of Education: Devices
Freina, L., & Ott, M. (2015, January). A literature review on immersive virtual reality in education: state of the art and perspectives. In The International Scientific Conference eLearning and Software for Education (Vol. 1, p. 133). Carol I National Defense University.
Gottschalk, F. (2019). Impacts of technology use on children: Exploring literature on the brain, cognition and well-being. Paris: OECD.
Hew, K. F., Lan, M., Tang, Y., Jia, C., & Lo, C. K. (2019). Where is the “theory” within the field of educational technology research?. British Journal of Educational Technology, 50(3), 956-971.
Hollands, F., & Escueta, M. (2019). How research informs educational technology decision-making in higher education: the role of external research versus internal research. Educational Technology Research and Development, 1-18.
Howard, M. C., & Gutworth, M. B. (2020). A meta-analysis of virtual reality training programs for social skill development. Computers & Education, 144, 103707.
Huang, R., Spector, J. M., & Yang, J. (2019). Educational Technology: A primer for the 21st centuary. Springer.
Ifenthaler, D., & Tracey, M. W. (2016). Exploring the relationship of ethics and privacy in learning analytics and design: implications for the field of educational technology. Educational Technology Research and Development, 64(5), 877-880.
Jacobs, K., Leopold, A., Hendricks, D. J., Sampson, E., Nardone, A., Lopez, K. B., … & Dembe, J. (2017). Project career: Perceived benefits of iPad apps among college students with Traumatic Brain Injury (TBI). Work, 58(1), 45-50.
Johnson, P., Anderson, A., & Cammidge, T. (2019). EdTech+ EdTeach: Exploring the Integration of Educational Technology Through Teacher Education. Edited by: Wafa Zoghbor, Suhair Al Alami, & Thomaï Alexiou, 71.
Kickmeier-Rust, M. D., Göbel, S., & Albert, D. (2008, September). 80Days: Melding adaptive educational technology and adaptive and interactive storytelling in digital educational games. In Proceedings of the First International Workshop on Story-Telling and Educational Games (STEG’08).
Lampert, B., Pongracz, A., Sipos, J., Vehrer, A., & Horvath, I. (2018). MaxWhere VR-learning improves effectiveness over clasiccal tools of e-learning. Acta Polytechnica Hungarica, 15(3), 125-147.
Liou, H. H., Yang, S. J., Chen, S. Y., & Tarng, W. (2017). The influences of the 2D image-based augmented reality and virtual reality on student learning. Journal of Educational Technology & Society, 20(3), 110-121.
London School of Economics. (2016). Edtech: The student view on educational technology. [ video ] (2:19 minutes). Available on: Edtech – The student view on educational technology
Maastricht University. (2018). Trend in educational technology. [webpage and videos]. https://library.maastrichtuniversity.nl/trends-in-educational-technology/
Olmos-Raya, E., Ferreira-Cavalcanti, J., Contero, M., Castellanos-Baena, M. C., Chicci-Giglioli, I. A., & Alcañiz, M. (2018). Mobile virtual reality as an educational platform: A pilot study on the impact of immersion and positive emotion induction in the learning process. In Eurasia Journal of Mathematics Science and Technology Education (Vol. 14, No. 6, pp. 2045-2057). Eurasia Publishing House.
Olmos, E., Cavalcanti, J. F., Soler, J. L., Contero, M., & Alcañiz, M. (2018). Mobile virtual reality: A promising technology to change the way we learn and teach. In Mobile and ubiquitous learning (pp. 95-106). Springer, Singapore. [Request access from HOLLIS]
Rankin, J. (2018). Teaching with educational technology. MIT OpenCourseWare. [ video ] (1:07:10 minutes). Available on: 8. Teaching with Educational Technology
Riva, G., Wiederhold, B. K., & Mantovani, F. (2019). Neuroscience of virtual reality: From virtual exposure to embodied medicine. Cyberpsychology, Behavior, and Social Networking, 22(1), 82-96.
Roberts-Mahoney, H., Means, A. J., & Garrison, M. J. (2016). Netflixing human capital development: Personalized learning technology and the corporatization of K-12 education. Journal of Education Policy, 31(4), 405-420.
Sahin, N. T., Abdus-Sabur, R., Keshav, N. U., Liu, R., Salisbury, J. P., & Vahabzadeh, A. (2018). Augmented Reality intervention for social communication in autism in a school classroom: Rated by teachers and parents as effective and usable in a controlled, longitudinal pilot study. https://doi.org/10.31234/osf.io/h2eu8
Sousa, M. J., Cruz, R., & Martins, J. M. (2017). Digital learning methodologies and tools–a literature review. Edulearn17 Proceedings, 5185-5192.
Spector, J. M., Ifenthaler, D., Sampson, D., Yang, J. L., Mukama, E., Warusavitarana, A., … & Bridges, S. (2016). Technology enhanced formative assessment for 21st century learning. Journal of Educational Technology & Society, 19(3), 58-71.
Spencer, K. (2017). The psychology of educational technology and instructional media. Routledge.
Gaming (Use of human thinking algorithms to reinforce learning)
Alstad, Z., Dahlstrom-Hakki, I., Asbell-Clarke, J., Rowe, E., & Altman, M. (2016). The use of multidimensional biopsychological markers to detect learning in educational gaming environments. Working Paper.
Bavelier, D. (2012). Your brain on video games. Ted Talk. [ video ] (17:15 minutes). Available on: https://www.ted.com/talks/daphne_bavelier_your_brain_on_video_games?language=en
Bediou, B., Adams, D. M., Mayer, R. E., Tipton, E., Green, C. S., & Bavelier, D. (2018). Meta-analysis of action video game impact on perceptual, attentional, and cognitive skills. Psychological Bulletin, 144(1), 77.
Burgers, C., Eden, A., van Engelenburg, M. D., & Buningh, S. (2015). How feedback boosts motivation and play in a brain-training game. Computers in Human Behavior, 48, 94-103.
Chalki, P., Tsiara, A., & Mikropoulos, T. A. (2019). An educational neuroscience approach in the design of digital educational games. Themes in eLearning, 12, 17-34.
Charland, P., Allaire-Duquette, G., & Léger, P. M. (2018). Collecting neurophysiological data to investigate users’ cognitive states during game play. GSTF Journal on Computing (JoC), 2(3).
Churchill Club. (2012). Technology in education: How will it change the game? [ video ] (1:30:05 minutes). Available on: 5.7.12 Technology in education: How will it change the game?
Cohen, A. M. (2011). The gamification of education. The Futurist, 45(5), 16.
Cowley, B., Fantato, M., Jennett, C., Ruskov, M., & Ravaja, N. (2014). Learning when serious: Psychophysiological evaluation of a technology-enhanced learning game. Educational Technology & Society, 17(1), 3-16.
Deterding, S. (2012). Gamification: designing for motivation. Interactions, 19(4), 14-17.
Deterding, S., Sicart, M., Nacke, L., O’Hara, K., & Dixon, D. (2011, May). Gamification. using game-design elements in non-gaming contexts. In CHI’11 Extended Abstracts on Human Factors in Computing Systems (pp. 2425-2428). ACM.
Devonshire, I. M., Davis, J., Fairweather, S., Highfield, L., Thaker, C., Walsh, A., … & Hathway, G. J. (2014). Risk-based learning games improve long-term retention of information among school pupils. PloS One, 9(7), e103640.
Domínguez, A., Saenz-De-Navarrete, J., De-Marcos, L., FernáNdez-Sanz, L., PagéS, C., & MartíNez-HerráIz, J. J. (2013). Gamifying learning experiences: Practical implications and outcomes. Computers & Education, 63, 380-392.
Dondlinger, M. J. (2007). Educational video game design: A review of the literature. Journal of Applied Educational Technology, 4(1), 21-31.
Erhel, S., & Jamet, E. (2019). Improving instructions in educational computer games: Exploring the relations between goal specificity, flow experience and learning outcomes. Computers in Human Behavior, 91, 106-114.
Gentry, S. V., Gauthier, A., Ehrstrom, B. L. E., Wortley, D., Lilienthal, A., Car, L. T., … & Car, J. (2019). Serious gaming and gamification education in health professions: systematic review. Journal of medical Internet research, 21(3), e12994.
Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., & Edwards, T. (2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in human behavior, 54, 170-179.
Hebert, S. (2018). The power of gamification education. [ video ] (18:48 minutes). TedTalk. Available on: The Power of Gamification in Education | Scott Hebert | TEDxUAlberta
Howard-Jones, P. (2013). Minds, brains and learning games at #LEGup. [ video ] (34:57 minutes). Available on: Dr Paul Howard-Jones on Minds, Brains and Learning Games at #LEGup
Howard-Jones, P. (2013). Plenary 3-Minds, brains and learning games.
Howard-Jones, P. A., Jay, T., Mason, A., & Jones, H. (2015). Gamification of learning deactivates the Default Mode Network Frontiers in Psychology, 6.
Kapp, K. M. (2012). The gamification of learning and instruction: game-based methods and strategies for training and education. Hoboken, NJ: John Wiley & Sons.
Kasemsap, K. (2016). Mastering educational computer games, educational video games, and serious games in the digital age. Gamification-Based E-Learning Strategies for Computer Programming Education, 30.
Kim, J. T., & Lee, W. H. (2015). Dynamical model for gamification of learning (DMGL). Multimedia Tools and Applications, 74(19), 8483-8493. [Request access from HOLLIS]
Koivisto, J., & Hamari, J. (2019). The rise of motivational information systems: A review of gamification research. International Journal of Information Management, 45, 191-210
Landers, R. N. (2014). Developing a theory of gamified learning linking serious games and gamification of learning. Simulation & Gaming, 45(6), 752-768.
Nelson, N. J., Fien, H., Doabler, C. T., & Clarke, B. (2016). Considerations for realizing the promise of educational gaming technology. Teaching Exceptional Children, 48(6), 293-300.
Ozcelik, E., Cagiltay, N. E., & Ozcelik, N. S. (2013). The effect of uncertainty on learning in game-like environments. Computers & Education, 67, 12-20.
Pozzi, F., Asensio-Perez, J. I., Ceregini, A., Dagnino, F. M., Dimitriadis, Y., & Earp, J. (2020). Supporting and representing learning design with digital tools: in between guidance and flexibility. Technology, Pedagogy and Education, 1-20.
Pynn, I. L. (2017). School has a bad storyline: Gamification in educational environments. Electronic Theses and Dissertations. 5652. University of Central Florida.
Ronimus, M., Kujala, J., Tolvanen, A., & Lyytinen, H. (2014). Children’s engagement during digital game-based learning of reading: The effects of time, rewards, and challenge. Computers & Education, 71, 237-246.
Sanger, J., Wilson, J., Davies, B., & Whittaker, R. (2019). Young children, videos and computer games: Issues for teachers and parents. Routledge.
Serrano-Laguna, Á., Manero, B., Freire, M., & Fernández-Manjón, B. (2018). A methodology for assessing the effectiveness of serious games and for inferring player learning outcomes. Multimedia Tools and applications, 77(2), 2849-2871.
Shi, L., Cristea, A. I., Hadzidedic, S., & Dervishalidovic, N. (2014, August). Contextual gamification of social interaction–towards increasing motivation in social e-learning. In International Conference on Web-Based Learning (pp. 116-122). Springer International Publishing.
Strickland, H. P., & Kaylor, S. K. (2016). Bringing your a-game: Educational gaming for student success. Nurse Education Today, 40, 101-103.
Thomas, A. (2018). The effective use of game-based education. TedTalk. [ video ] (17:08 minutes). Available on: The Effective Use of Game-Based Learning in Education | Andre Thomas | TEDxTAMU
Trajkovik, V., Malinovski, T., Vasileva-Stojanovska, T., & Vasileva, M. (2018). Traditional games in elementary school: Relationships of student’s personality traits, motivation and experience with learning outcomes. PloS one, 13(8).
Maddison, R., Simons, M., Straker, L., Witherspoon, L., Palmeira, A., & Thin, A. G. (2013). Active video games: An opportunity for enhanced learning and positive health effects?. Cognitive Technology, 18(1), 6-13.
Majuri, J., Koivisto, J., & Hamari, J. (2018). Gamification of education and learning: A review of empirical literature. In Proceedings of the 2nd International GamiFIN Conference, GamiFIN 2018. CEUR-WS.
Mayer, R. E. (2019). Computer games in education. Annual review of psychology, 70, 531-549.
McGonigal, J. (2010). Gaming can make a better world. Ted Talk. [ video ] (19:56 minutes). Available on: https://www.ted.com/talks/jane_mcgonigal_gaming_can_make_a_better_world?language=en
Huizenga, J. C., Ten Dam, G. T. M., Voogt, J. M., & Admiraal, W. F. (2017). Teacher perceptions of the value of game-based learning in secondary education. Computers & Education, 110, 105-115.
Howard-Jones, P. (2012). Neuroscience, games & learning. [ video ] (29:26 minutes). Available on: Dr Paul Howard-Jones – Neuroscience, Games & Learning
Barata, G., Gama, S., Jorge, J., & Gonçalves, D. (2013, October). Improving participation and learning with gamification. In Proceedings of the First International Conference on gameful design, research, and applications (pp. 10-17). ACM.
Haskell, C. (2014). Blowing up the gradebook. [ video ] (17:44 minutes). Blowing up the gradebook – using video games for learning: Chris Haskell at TEDxAmmon
AI-assisted Learning
Ahlberg, S. (2018). N. Katherine Hayles, Unthought: The power of the cognitive nonconscious. Chicago and London: University of Chicago Press, 2017. 250 pages. ISBN-13: 978-0-226-44774-2 (cloth); 978-0-226-44788-9 (paper); 978-0-226-44791-9 (e-book). Studia Neophilologica, 90(2), 273-274.
Ahmadi, M., Borcea, C., & Jones, Q. (2019, March). Collaborative lifelogging through the integration of machine and human computation. In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion (pp. 23-24).
Araujo, T., Helberger, N., Kruikemeier, S., & De Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & SOCIETY, 1-13.
Arntz, M., Gregory, T., & Zierahn, U. (2017). Revisiting the risk of automation. Economics Letters, 159, 157-160.
Baker, M. J. (2000). The roles of models in Artificial Intelligence and Education research: a prospective view. Journal of Artificial Intelligence and Education, 11, 122-143..
Bakkar, N., Kovalik, T., Lorenzini, I., Spangler, S., Lacoste, A., Sponaugle, K., … & Bowser, R. (2018). Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathologica, 135(2), 227-247.
Bala, B. M. (2019). Artificial Intelligence and its Implications for Future. Artificial Intelligence.
Bellamy, R. K., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., … & Ramamurthy, K. N. (2019). Think your artificial intelligence software is fair? Think again. IEEE Software, 36(4), 76-80.
Bodily, R., Kay, J., Aleven, V., Jivet, I., Davis, D., Xhakaj, F., & Verbert, K. (2018, March). Open learner models and learning analytics dashboards: a systematic review. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 41-50). ACM.
Bundy, A. (2017). Preparing for the future of Artificial Intelligence. Edinburgh Research Explorer.The University of Ediburgh.
Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019). Artificial intelligence, bias and clinical safety. BMJ Qual Saf, 28(3), 231-237.
Chui, M. (2017). Artificial intelligence the next digital frontier?. McKinsey and Company Global Institute, 47, 3-6.
Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation (No. w24449). National bureau of economic research.
Conitzer, V. (2019). Designing preferences, beliefs, and identities for artificial intelligence. Durham, NC: Duke University. Association For the Advancement of Artificial Intelligence.
Conitzer, V., Sinnott-Armstrong, W., Borg, J. S., Deng, Y., & Kramer, M. (2017, February). Moral decision making frameworks for artificial intelligence. In Thirty-first aaai conference on artificial intelligence.
Crawford, K. (2016). Artificial intelligence’s white guy problem. The New York Times, 25.
D’Alfonso, S., Santesteban-Echarri, O., Rice, S., Wadley, G., Lederman, R., Miles, C., … & Alvarez-Jimenez, M. (2017). Artificial intelligence-assisted online social therapy for youth mental health. Frontiers in Psychology, 8, 796.
Das, A. K., Ashrafi, A., & Ahmmad, M. (2019, February). Joint cognition of both human and machine for predicting criminal punishment in judicial system. In 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) (pp. 36-40). IEEE.
Dean, J. (2017). How will artificial intelligence affect your life. [ video ] (15:55 minutes). Available on: How Will Artificial Intelligence Affect Your Life | Jeff Dean | TEDxLA
Deng, L. (2018). Artificial intelligence in the rising wave of deep learning: The historical path and future outlook [perspectives]. IEEE Signal Processing Magazine, 35(1), 180-177.
Deweerdt, S. (2019). Deep connections. Nature, 571(7766), S6-S8.
Dewey, D. (2013). The long-term future of AI (and what we can do about it). Ted Talk. (15:05 minutes)-[ video ]. Available on:The long-term future of AI(and what we can do about it): Daniel Dewey at TEDxVienna (Links to an external site.)Links to an external site.
Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018). From signals to knowledge: A conceptual model for multimodal learning analytics. Journal of Computer Assisted Learning, 34(4), 338-349.
Ding, R. X., Palomares, I., Wang, X., Yang, G. R., Liu, B., Dong, Y., … & Herrera, F. (2020). Large-scale decision-making: characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective. Information Fusion.
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