Análisis de redes sociales para la inclusión entre iguales en discusiones en línea con estudiantes de universidad

Píxel-Bit. Revista de Medios y Educación. 2023 - ISSN: 1133-8482. e-ISSN: 2171-7966. Píxel-Bit. Revista de Medios y Educación. 2023 - ISSN: 1133-8482. e-ISSN: 2171-7966. PIXEL-BIT REVISTA DE MEDIOS Y EDUCACIÓN Nº 66 - ENERO - 2023 https://revistapixelbit.com Píxel-Bit. Revista de Medios y Educación. 2023 - ISSN: 1133-8482. e-ISSN: 2171-7966. PIXEL-BIT REVISTA DE MEDIOS Y EDUCACIÓN Nº 66 ENERO 2023 EQUIPO EDITORIAL (EDITORIAL BOARD) EDITOR JEFE (EDITOR IN CHIEF) Dr. Julio Cabero Almenara, Departamento de Didáctica y Organización Educativa, Facultad de CC de la Educación, Director del Grupo de Investigación Didáctica. Universidad de Sevilla (España) EDITOR ADJUNTO (ASSISTANT EDITOR) Dr. Juan Jesús Gutiérrez Castillo, Departamento de Didáctica y Organización Educativa. Facultad de CC de la Educación, Universidad de Sevilla (España) Dr. Óscar M. Gallego Pérez, Grupo de Investigación Didáctica, Universidad de Sevilla (España) EDITORES ASOCIADOS Dra. Urtza Garay Ruiz, Universidad del País Vasco. (España) Dra. Ivanovnna Milqueya Cruz Pichardo, Pontificia Universidad Católica Madre y Maestra. (República Dominicana) CONSEJO METODOLÓGICO Dr. José González Such, Universidad de Valencia (España) Dr. Antonio Matas Terrón, Universidad de Málaga (España) Dra. Cynthia Martínez-Garrido, Universidad Autónoma de Madrid (España) Dr. Clemente Rodríguez Sabiote, Universidad de Granada (España) Dr. Luis Carro Sancristóbal, Universidad de Valladolid (España) Dra. Nina Hidalgo Farran, Universidad Autónoma de Madrid (España) Dr. Francisco David Guillén Gámez, Universidad de Córdoba (España) CONSEJO DE REDACCIÓN Dra. María Puig Gutiérrez, Universidad de Sevilla. (España) Dra. Sandra Martínez Pérez, Universidad de Barcelona (España) Dr. Selín Carrasco, Universidad de La Punta (Argentina) Dr. Jackson Collares, Universidades Federal do Amazonas (Brasil) Dra. Kitty Gaona, Universidad Autónoma de Asunción (Paraguay) Dr. Vito José de Jesús Carioca. Instituto Politécnico de Beja Ciencias da Educación (Portugal) Dra. Elvira Esther Navas, Universidad Metropolitana de Venezuela (Venezuela) Dr. Angel Puentes Puente, Pontificia Universidad Católica Madre y Maestra. Santo Domingo (República Dominicana) Dr. Fabrizio Manuel Sirignano, Università degli Studi Suor Orsola Benincasa (Italia) Dra. Sonia Aguilar Gavira. Universidad de Cádiz (España) Dra. Eloisa Reche Urbano. Universidad de Córdoba (España) CONSEJO TÉCNICO Dra. Raquel Barragán Sánchez, Grupo de Investigación Didáctica, Universidad de Sevilla (España) D. Antonio Palacios Rodríguez, Grupo de Investigación Didáctica, Universidad de Sevilla (España) D. Manuel Serrano Hidalgo, Grupo de Investigación Didáctica, Universidad de Sevilla (España) Diseño de portada: Dña. Lucía Terrones García, Universidad de Sevilla (España) Revisor/corrector de textos en inglés: Dra. Rubicelia Valencia Ortiz, MacMillan Education (México) Revisores metodológicos: evaluadores asignados a cada artículo CONSEJO CIENTÍFICO Jordi Adell Segura, Universidad Jaume I Castellón (España) Ignacio Aguaded Gómez, Universidad de Huelva (España) María Victoria Aguiar Perera, Universidad de Las Palmas de Gran Canaria (España) Olga María Alegre de la Rosa, Universidad de la Laguna Tenerife (España) Manuel Área Moreira, Universidad de la Laguna Tenerife (España) Patricia Ávila Muñoz, Instituto Latinoamericano de Comunicación Educativa (México) Antonio Bartolomé Pina, Universidad de Barcelona (España) Píxel-Bit. Revista de Medios y Educación. 2023 - ISSN: 1133-8482. e-ISSN: 2171-7966. Angel Manuel Bautista Valencia, Universidad Central de Panamá (Panamá) Jos Beishuizen, Vrije Universiteit Amsterdam (Holanda) Florentino Blázquez Entonado, Universidad de Extremadura (España) Silvana Calaprice, Università degli studi di Bari (Italia) Selín Carrasco, Universidad de La Punta (Argentina) Raimundo Carrasco Soto, Universidad de Durango (México) Rafael Castañeda Barrena, Universidad de Sevilla (España) Zulma Cataldi, Universidad de Buenos Aires (Argentina) Manuel Cebrián de la Serna, Universidad de Málaga (España) Luciano Cecconi, Università degli Studi di Modena (Italia) Jean-François Cerisier, Université de Poitiers, Francia Jordi Lluí s Coiduras Rodrí guez, Universidad de Lleida (España) Jackson Collares, Universidades Federal do Amazonas (Brasil) Enricomaria Corbi, Università degli Studi Suor Orsola Benincasa (Italia) Marialaura Cunzio, Università degli Studi Suor Orsola Benincasa (Italia) Brigitte Denis, Université de Liège (Bélgica) Floriana Falcinelli, Università degli Studi di Perugia (Italia) Maria Cecilia Fonseca Sardi, Universidad Metropolitana de Venezuela (Venezuela) Maribel Santos Miranda Pinto, Universidade do Minho (Portugal) Kitty Gaona, Universidad Autónoma de Asunción (Paraguay) María-Jesús Gallego-Arrufat, Universidad de Granada (España) Lorenzo García Aretio, UNED (España) Ana García-Valcarcel Muñoz-Repiso, Universidad de Salamanca (España) Antonio Bautista García-Vera, Universidad Complutense de Madrid (España) José Manuel Gómez y Méndez, Universidad de Sevilla (España) Mercedes González Sanmamed, Universidad de La Coruña (España) Manuel González-Sicilia Llamas, Universidad Católica San Antonio-Murcia (España) Francisco David Guillén Gámez (España) António José Meneses Osório, Universidade do Minho (Portugal) Carol Halal Orfali, Universidad Tecnológica de Chile INACAP (Chile) Mauricio Hernández Ramí rez, Universidad Autónoma de Tamaulipas (México) Ana Landeta Etxeberrí a, Universidad a Distancia de Madrid (UDIMA) Linda Lavelle, Plymouth Institute of Education (Inglaterra) Fernando Leal Ríos, Universidad Autónoma de Tamaulipas (México) Paul Lefrere, Cca (UK) Carlos Marcelo Garcí a, Universidad de Sevilla (España) Francois Marchessou, Universidad de Poittiers, Parí s (Francia) Francesca Marone, Università degli Studi di Napoli Federico II (Italia) Francisco Martí nez Sánchez, Universidad de Murcia (España) Ivory de Lourdes Mogollón de Lugo, Universidad Central de Venezuela (Venezuela) Angela Muschitiello, Università degli studi di Bari (Italia) Margherita Musello, Università degli Studi Suor Orsola Benincasa (Italia) Elvira Esther Navas, Universidad Metropolitana de Venezuela (Venezuela) Trinidad Núñez Domínguez, Universidad de Sevilla (España) James O’Higgins, de la Universidad de Dublín (UK) José Antonio Ortega Carrillo, Universidad de Granada (España) Gabriela Padilla, Universidad Autónoma de Tumalipas (México) Ramón Pérez Pérez, Universidad de Oviedo (España) Angel Puentes Puente, Pontificia Universidad Católica Madre y Maestra. Santo Domingo (República Dominicana) Julio Manuel Barroso Osuna, Universidad de Sevilla (España) Rosalía Romero Tena. Universidad de Sevilla (España) Hommy Rosario, Universidad de Carabobo (Venezuela) Pier Giuseppe Rossi, Università di Macerata (Italia) Jesús Salinas Ibáñez, Universidad Islas Baleares (España) Yamile Sandoval Romero, Universidad de Santiago de Cali (Colombia) Albert Sangrá Morer, Universidad Oberta de Catalunya (España) Ángel Sanmartí n Alonso, Universidad de Valencia (España) Horacio Santángelo, Universidad Tecnológica Nacional (Argentina) Francisco Solá Cabrera, Universidad de Sevilla (España) Jan Frick, Stavanger University (Noruega) Karl Steffens, Universidad de Colonia (Alemania) Seppo Tella, Helsinki University (Finlandia) Hanne Wacher Kjaergaard, Aarhus University (Dinamarca) Píxel-Bit. Revista de Medios y Educación. 2023 - ISSN: 1133-8482. e-ISSN: 2171-7966. FACTOR DE IMPACTO (IMPACT FACTOR) SCOPUS Q1 Education: Posición 236 de 1406 (83% Percentil). 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PIXEL-BIT REVISTA DE MEDIOS Y EDUCACIÓN Nº 66 ENERO 2023 índice 1.- Análisis de redes sociales para la inclusión entre iguales en discusiones en línea con estudiantes de universidad // Social network analysis for peer inclusion in undergraduate online discussions Fran J. Garcia-Garcia, Inmaculada López-Francés, Cristian Molla-Esparza 2.- Revisión de la literatura sobre anotaciones de vídeo en la formación docente // Literature review on video annotations in teacher education Violeta Cebrián Robles, Ana-Belén Pérez-Torregrosa, Manuel Cebrián de la Serna 3.- Diseño, validación y usabilidad de un aplicativo móvil para la enseñanza de electrocardiografía // Design, validation and usability of a mobile application for teaching electrocardiography Judy Ximena Ramos Garzón 4.- Evaluación de una APP de realidad aumentada en niños/as con dislexia: estudio piloto // Evaluation of an augmented reality APP for children with dyslexia: a pilot study Vanesa Ausín Villaverde, Sonia Rodríguez Cano, Vanesa Delgado Benito, Radu Bogdan Toma 5.- Variables asociadas al uso de pantallas al término de la primera infancia // Variables associated with the use of screens at the end of early childhood Carla Ortiz-de-Villate, Javier Gil-Flores, Javier Rodríguez-Santero 6.- ¿Crea contenidos digitales el profesorado universitario? Un diseño mixto de investigación // Do university teacher create digital content? Mixed research design María de Lourdes Ferrando-Rodríguez, Vicente Gabarda Méndez, Diana Marín- Suelves, Jesús Ramón-Llin Más 7.- Instantáneas culturales y Flipped Classroom: percepciones de futuros docentes // Cultural snapshots and Flipped Classroom: prospective teachers’ perceptions Ernesto Colomo-Magaña, Andrea Cívico-Ariza, Enrique Sánchez-Rivas, Teresa Linde-Valenzuela 8.- Imbricación del Metaverso en la complejidad de la educación 4.0: Aproximación desde un análisis de la literatura // Imbrication of the Metaverse in the complexity of education 4.0: Approach from an analysis of the literature Carlos Enrique George-Reyes, María Soledad Ramírez-Montoya, Edgar Omar López-Caudana 9.- Redes sociales y smartphones como recursos para la enseñanza: percepción del profesorado en España // Social media and smartphones as teaching resources: Spanish teacher’s perceptions Francisco-Javier Lena-Acebo, Ana Pérez-Escoda, Rosa García-Ruiz, Manuel Fandos-Igado 10.- El robot M Bot para el aprendizaje de coordenadas cartesianas en Educación Secundaria // The M Bot robot for learning Cartesian coordinates in Secondary Education José-Manuel Sáez-López, Rogelio Buceta-Otero 7 31 59 87 113 137 173 199 239 271 Pixel-Bit. Revista de Medios y Educación, 66, 7-29 | 2023 | https://doi.org/10.12795/pixelbit.95555 Análisis de redes sociales para la inclusión entre iguales en discusiones en línea con estudiantes de universidad Social network analysis for peer inclusion in undergraduate online discussions Dr. Fran J. Garcia-Garcia Profesor ayudante Doctor. Universidad de Valencia. España Dra. Inmaculada López-Francés Profesora Contratada Doctora. Universidad de Valencia. España Dr. Cristian Molla-Esparza Profesor ayudante doctor. Universidad de Valencia. España Recibido: 2022/07/13; Revisado: 2022/07/15; Aceptado: 2022/10/21; Preprint: 2022/12/01; Publicado: 2023/01/07 RESUMEN Las discusiones asíncronas en línea (DAL) sirven para aprender en la universidad, pero pierden eficacia conforme disminuye la participación de los estudiantes. Este estudio aporta un método basado en el Análisis de Redes Sociales (ARS) que aborda este problema, identificando estudiantes con un alto potencial de inclusión entre iguales durante los debates asíncronos. Para probar el método, configuramos foros de discusión en Moodle y examinamos las interacciones de 93 estudiantes de grado en el área de Ciencias de la Educación. Analizamos las redes sociales que surgieron de los debates, incluyendo 1818 conexiones. Los resultados mostraron que algunos estudiantes tenían más centralidad de cercanía y eran más accesibles que el resto. Una vez identificados, el profesorado pudo animar a estos estudiantes a incluir a quienes participaban menos en los debates. La inclusión de los compañeros tenía sentido cuando participaban en el debate sin obtener una respuesta de sus comentarios fácilmente. Este estudio abre la puerta a más investigación sobre la eficacia de las estrategias docentes basadas en el ARS, concretamente sobre la eliminación de las barreras al aprendizaje y la participación en una DAL con estudiantes universitarios. ABSTRACT Asynchronous Online Discussion (AOD) is useful for learning at university, but it loses effectiveness as student participation decreases. This study provides a method based on Social Network Analysis (SNA) that addresses this issue, by identifying students with a high potential for peer-to-peer inclusion during asynchronous debates. To test this method, we created discussion forums in Moodle and examined the interactions of 93 undergraduate students in the field of Educational Sciences. We analysed the social networks that emerged from the discussions, including 1818 connections. The results showed that some students had more closeness centrality and were more accessible than the others. Once this group was identified, the teacher was then able to encourage those students to include others with less active participation in the discussions. Peer-to- peer inclusion made sense when there were participants who engaged in the debate but did not easily get a response from their classmates. This study leads the way for more research on the effectiveness of teaching strategies based on SNA, specifically on removing barriers to learning and participation in AOD with university students. PALABRAS CLAVES · KEYWORDS Discusión (método pedagógico); Método de aprendizaje; Informática educativa; Tecnología educacional; Análisis de redes; Enseñanza superior Discussions (teaching method); Learning methods; Computer uses in education; Educational technology; Network analysis; Higher Education Pixel-Bit. Revista de Medios y Educación, 66, 7-29 | 2023 | https://doi.org/10.12795/pixelbit.95555 PÁGINA | 8 1. Introducción Las universidades están incorporando cada vez más la tecnología educativa para mejorar las experiencias de aprendizaje (Bond et al., 2020; Müller & Wulf, 2020). En este contexto la Discusión Asíncrona en Línea (DAL) es una de las técnicas más exploradas de las últimas décadas (Andresen, 2009; Fehrman & Watson, 2020; Gao et al., 2013; Thomas, 2013), junto con las clases invertidas y los cursos masivos. En este estudio aportamos un método nuevo para solucionar un problema de la DAL identificado en estudios anteriores. Hacer preguntas y compartir ideas sobre un mismo tema estimula la interacción entre los estudiantes en entornos virtuales. El debate favorece la sensación de pertenencia a una comunidad en línea y eso acaba mejorando el aprendizaje (Waltonen-Moore et al., 2006; Yang et al., 2010). Cuando la discusión es asíncrona, permite a los estudiantes interactuar y debatir en línea a su propio ritmo y en el momento y lugar que prefieran. Los hilos de discusión quedan registrados para revisarlos tanto como sea necesario y reflexionar sobre los comentarios antes de incluirlos en el debate. La DAL promueve el aprendizaje en la universidad (Fehrman & Watson, 2020), sobre todo cuando se trata de desarrollar el pensamiento crítico o las habilidades de razonamiento de orden superior (Jeong & Chiu, 2020; Thomas, 2013). También aumenta la interacción entre estudiantes (Almatrafi & Johri, 2019; An et al., 2009) y ayuda a generar un clima óptimo para la construcción social del conocimiento (Al-Dheleai et al., 2020). Los últimos estudios sobre DAL en la universidad han incorporado el Análisis de Redes Sociales (ARS) para entender la complejidad de las interacciones durante el debate (Jan & Vlachopoulos, 2019; Saqr et al., 2020), abriendo la puerta a nuevos diseños de enseñanza y evaluación. 1.1. Por qué es tan importante la inclusión en el debate Aunque la DAL favorece el aprendizaje colaborativo, sólo tiene éxito si los estudiantes se involucran en el debate (Hew & Cheung, 2008). Eso ha despertado el interés de la comunidad académica por examinar la interacción entre estudiantes a través del ARS. En un estudio encontraron que la interacción era mayor en el primer semestre e iba disminuyendo conforme pasaba el tiempo (da Silva et al., 2019). En otro se analizó la relación entre la manera de interactuar y el prestigio que se adquiría en la red (Zou et al., 2021). Las últimas investigaciones con ARS todavía no han explorado si las barreras de participación explican en cierto modo una actividad menor en la discusión y, sin embargo, esta es una posibilidad real (Amor et al., 2019; Booth & Ainscow, 2011). Es importante incluir a los individuos situados en la periferia de la red, ya que estos son los que menos interactúan con sus compañeros de clase y los beneficios de la DAL no tienen tanto efecto para ellos. Además, su aparente apatía podría contagiar a los demás, haciéndoles sentir que reducir la participación en el debate no supone ningún problema. Hasta ahora no hay evidencias sobre cómo el ARS ayuda a incluir a los estudiantes más desconectados en la DAL. Algunos estudios han analizado la estructura de las redes (Garcia-Garcia et al., 2021; Lee et al., 2021), donde parece evidente que ciertos estudiantes tenían posiciones más accesibles que otros y podían interpelar con más facilidad a quienes menos interactuaban. En este sentido, medir la cercanía ayudaría al profesor a detectar individuos con facilidad para conectar a quienes están situados en la periferia de la red con las personas cuyos comentarios son más centrales en la discusión. Pixel-Bit. Revista de Medios y Educación, 66, 7-29 | 2023 | https://doi.org/10.12795/pixelbit.95555 PÁGINA | 9 1.2. Cómo medir el potencial de inclusión entre iguales El ARS permite medir la centralidad o la importancia de un estudiante en una determinada red. Una forma de entender la centralidad es la cercanía, que se expresa como la distancia desde cada individuo de la red hacia todas las demás personas (Freeman, 1978). En el caso de la DAL, unos estudiantes comentan acerca de lo que dijeron otros anteriormente, construyendo un hilo de discusión. Unos citan las ideas de otros y, de esta forma, interactúan y establecen conexiones. Los estudiantes con más centralidad como cercanía son aquellos que se conectan directamente a más compañeros de lo habitual. Cuanta más cercanía tienen los estudiantes, más accesibles son para sus compañeros, y su situación en la red les permite desempeñar un rol estratégico para lograr una DAL más inclusiva. Si el profesor pide a uno de ellos que pregunte a los que aportan las ideas más citadas qué les parece lo que dijo uno de los compañeros más desconectados, este último accedería directamente al centro del debate. Cuando la clase empezase a razonar sobre sus ideas, tendría la oportunidad de responderles y entablar una conversación. Este tipo de estrategias podrían contribuir a mejorar la implicación de los estudiantes más rezagados, ayudando a cohesionar la red social, lo cual beneficiaría a todo el grupo. Una red más cohesionada refleja que los miembros de la clase se implican en el debate y que la DAL puede ser efectiva para aprender. Sin embargo, antes de diseñar estrategias basadas en la cercanía, es necesario saber qué sentido tiene hacerlo en un debate asíncrono. Por tanto, en este estudio abordamos la evaluación de la cercanía y las condiciones en las que tiene sentido diseñar estrategias docentes basadas en ARS para la inclusión entre iguales. 1.3. Preguntas de investigación (PI) El propósito de este estudio fue demostrar que el ARS permite detectar qué estudiantes se encuentran en una situación más favorable para incluir a los compañeros menos implicados en la DAL. Respondimos a las siguientes preguntas para mostrar qué condiciones permiten diseñar estrategias basadas en la cercanía para lograr una DAL más inclusiva. • PI1. ¿Hay diferentes niveles de cercanía cuando concluye el tiempo de formación? • PI2. ¿Hay modificaciones en la cercanía de los estudiantes a lo largo del debate? Si no logramos una respuesta positiva a ninguna de las dos preguntas, seguramente no tendrá tanto sentido diseñar estrategias basadas en la cercanía para la inclusión entre iguales en una DAL con estudiantes universitarios. 2. Método Se llevó a cabo un estudio cuasiexperimental con dos grupos de estudiantes de grado seleccionados de manera no probabilística. El estudio fue exploratorio, ya que el propósito era demostrar cómo el ARS permite detectar situaciones estratégicas para la inclusión entre iguales durante una DAL. Pixel-Bit. Revista de Medios y Educación, 66, 7-29 | 2023 | https://doi.org/10.12795/pixelbit.95555 PÁGINA | 16 inactivos, mientras anima a estos últimos a comentar algo más en el foro. De esta forma, los estudiantes influyentes difundirán las ideas que aportaron los estudiantes desconectados y éstas se comentarán más a menudo. Si los estudiantes debatían menos porque estaban encontrando barreras de participación derivadas de la interacción, entonces deberían empezar a participar activamente en el debate después de poner en marcha la estrategia. 4.4. Limitaciones e investigación emergente En este estudio evitamos explicar la detección de estudiantes que se implican en el debate y, al no recibir respuesta de sus compañeros, acaban desconectándose. Lo hicimos con el fin de economizar el contenido del artículo y centrarnos en la detección de estudiantes con alto potencial para la inclusión entre iguales. En resumen, un estudiante tiene barreras de participación cuando comenta con una frecuencia regular en el foro, su centralidad en la red es escasa desde hace tiempo, y en las últimas semanas ha reducido su actividad. Tampoco se mostró cómo detectar estudiantes con influencia en la discusión porque hay estudios recientes dedicados a este tema (p. ej., Garcia-Garcia et al., 2021). El método que proponemos para la inclusión entre iguales está pensado específicamente para incluir a los estudiantes con barreras de participación derivadas de las interacciones durante el debate. Si el método se utiliza para resolver otros problemas de participación, quizás no funcione. Con todo, el método permite la detección de estudiantes con alto potencial para incluir a sus compañeros y el estudio muestra que es útil para ello, pero las estrategias docentes que se diseñen a partir de la detección deben ser probadas en futuros estudios para obtener evidencias empíricas sobre su efectividad. La muestra fue seleccionada de forma no probabilística y sería interesante realizar estudios con muestreo aleatorio para confirmar los resultados. Aun así, las redes sociales son sistemas complejos (Thurner et al., 2018) y su análisis sirve para comprender fenómenos estructurales y no tanto para predecir variables. Por lo demás, la DAL es efectiva para aprender en la universidad, pero todavía está en fase de exploración (Fehrman & Watson, 2020; Thomas, 2013), de modo que este estudio da paso a resolver problemas de una forma alternativa, encontrar soluciones nuevas y buscar la confirmación de su efectividad. 5. Financiación Ministario de Ciencia e Innovación (Código PID2021-123523NB-I00); Universidad de Valencia (UV-SFPIE_PID- 2074926). Pixel-Bit. Revista de Medios y Educación, 66, 7-29 | 2023 | https://doi.org/10.12795/pixelbit.95555 PÁGINA | 17 Social network analysis for peer inclusion in undergraduate online discussions 1. Introduction Universities are increasingly incorporating educational technology to enhance the learning experience for the students (Bond et al., 2020; Müller & Wulf, 2020). In line with this initiative, Asynchronous Online Discussion (AOD) is one of the techniques that has most attracted attention in the last decades (Andresen, 2009; Fehrman & Watson, 2020; Gao et al., 2013; Thomas, 2013), along with other pieces of research such as flipped classrooms or Massive Open Online Courses This study offers a novel method to solve an AOD problem that has been identified in previous studies. Asking questions and sharing arguments on specific topics stimulates interaction among students in virtual environments. Discussions foster a sense of belonging to an online community, which in turn enhances learning (Waltonen-Moore et al., 2006; Yang et al., 2010). Asynchronous discussions allow learners to interact and discuss issues online at their own pace and at the time and place of their choice. Threads relating to these discussions are recorded and available for students whenever they have time to read them –and as many times they need to review them– to be able to form their own arguments. In this way, they can reach deeper thinking before they submit their comments in the discussion. AOD promotes learning at the university level (Fehrman & Watson, 2020), especially when it is geared toward promoting critical thinking or higher-order reasoning skills (Jeong & Chiu, 2020; Thomas, 2013). It also increases interaction among students (Almatrafi & Johri, 2019; An et al., 2009) and helps generate an optimal climate for the social construction of knowledge (Al-Dheleai et al., 2020). Recent studies on undergraduate AODs that have incorporated Social Network Analysis (SNA) were directed at understanding the complexity of interactions during discussions (Jan & Vlachopoulos, 2019; Saqr et al., 2020), thus opening the door to new teaching and assessment designs. 1.1. Why is inclusion so important in a debate? Although AOD encourages collaborative learning, it is only successful if students participate (Hew & Cheung, 2008). This qualification has sparked the academic community’s interest in examining student interaction through SNA. One study focusing on interactions concluded that discussions were highest in the first semester and decreased as time went on (da Silva et al., 2019). In another study, the relationship between the way of interacting and the attention these interactions generated in the network, were analysed and conclusions were drawn (Zou et al., 2021). The latest research with SNA has not yet explored whether barriers to participation is because of other lower-level activities outside of these discussions, but this is a real possibility (Amor et al., 2019; Booth & Ainscow, 2011). It is important to include those students who seem to be marginalised, or who intentionally isolate themselves. They are the ones who interact the least with their classmates, and so the benefits of AOD do not have as much effect on them. Moreover, their apparent apathy could rub off on others, making more of the group feel that reducing participation in the discussion is not a serious issue. Pixel-Bit. Revista de Medios y Educación, 66, 7-29 | 2023 | https://doi.org/10.12795/pixelbit.95555 PÁGINA | 18 So far, there is no evidence on how SNA helps to include the most disengaged students in AOD. Some studies have analysed the structure of networks (Garcia-Garcia et al., 2021; Lee et al., 2021), where it seems evident that certain students had more access than others and could more easily challenge the comments of those who interacted less often. In this sense, being able to measure the influence of accessibility would help the teacher to identify the differences between the comments of students who have more or less ease in connecting to the discussions. 1.2. How to measure the potential for peer-to-peer inclusion? SNA makes it possible to measure the centrality or importance of a student in a given network. One way of understanding centrality is closeness, which is expressed as the distance from each individual in the network to all others (Freeman, 1978). In the case of AOD, some students comment on what others said earlier, building discussion threads. Some cite the arguments of others and, in this way, establish more intricate connections. Students with lesser closeness centrality are those who connect directly to more classmates than others. The closer the students are to each other, the more accessible they are to their peers, and their status in the network allows them to play a strategic role in making AOD more inclusive. If the teacher asks one of the closer students to ask those who contribute the most quoted ideas what they think of what one of the more disconnected peers said, the latter will have direct access to the centre of the discussion. When the class begins reasoning about their ideas, the disconnected classmates will have the opportunity to give an answer and engage in conversation. Strategies such as these could help improve the engagement of students who are less active, helping to make the social network more unified, thus benefiting the whole group. A more cohesive network reflects a group that is engaged in discussion and so AOD becomes a more effective learning technique. However, before designing strategies based on closeness centrality, it is necessary to know what sense it makes to do so in an asynchronous discussion. Therefore, we addressed the assessment of closeness centrality and the conditions under which it makes sense to design SNA-based teaching strategies for peer inclusion. 1.3. Research questions (RQ) The purpose of this study was to reveal how SNA makes it possible to detect which students are in a more favourable situation to include the peers less involved in an AOD. The following questions were asked to show which conditions make it possible to design strategies based on closeness centrality to achieve a more inclusive discussion. • RQ1. Are there different levels of closeness when the training period is over? • RQ2. Are there changes in the students' closeness throughout the debate before it ends? If the answers were not positive to either question, the thinking was that it would not make as much sense to design strategies based on closeness centrality for peer inclusion in a AOD with undergraduates. Pixel-Bit. Revista de Medios y Educación, 66, 7-29 | 2023 | https://doi.org/10.12795/pixelbit.95555 PÁGINA | 19 2. Methods We carried out a quasi-experimental study with two groups of undergraduate students who were selected in a non-probabilistic manner. The study was exploratory, since the purpose was to demonstrate how SNA allows detecting strategic situations for peer inclusion during an AOD. 2.1. Participants Social networks are complex systems, and their analysis makes it difficult to generalize results since a single element of the system could modify the pattern of the entire structure at any time (Mason, 2008; Morin, 1992). In AODs a single controversial post could increase the frequency with which comments are posted throughout the discussion. With this idea in mind, two groups were selected to compare changes in closeness centrality during their training and in this way, better understand peer inclusion strategies. Group A was composed of 48 undergraduate students in Social Education between the ages of 18 and 31 (Mean = 19.48, SD = 2.22), in which 79.17% were female and 20.83% were male. They participated in an AOD for 10 weeks. Group B consisted of 45 students in Pedagogy between the ages of 18 and 25 years (Mean = 20.14, SD = 2.08). Eighty-six percent were female and 13.3% were male and all participated in an AOD for 14 weeks. At the university where we conducted the study, more than 85% of the students enrolled in either of these two degrees during the last three academic years were female. Therefore, it was like the original population. Each group generated a social network from interaction in the AOD. A total of 542 comments were analysed in Group A and 1276 in Group B. The teacher moderated the discussions by communicating with the students via email and only participated in the posts to establish discussion topics. 2.2. Learning environment A semi-structured format discussion forum was created for the study (Dommett, 2019) for each group, using the Moodle platform. We used more guidelines than are usually present in such forums (Hammond, 2019) to encourage participation during the debate. In normal circumstances, forums with prompts or recommendations help students to find and select each other’s comments more easily, and this increases the frequency with which they read and reply to their peers (Wang & Yang, 2012). However, the researchers of this study made a decision not to use forum prompts because students end up relying too much on them and this takes attention away from the main threads and in the end, this affects the cognitive load needed to perform the main task (Sachdeva & Gilbert, 2020). Instead, they were asked to use the Moodle search engine. The goal of the AOD was not to focus on frequently asked questions (FAQ), but rather the question answering forum model (Sindhgatta et al., 2017) in which questions encourage interaction among students and the social construction of knowledge (G. Chen et al., 2020; Gargallo López, 2017). Therefore, they were directed to formulate their posts as questions, or answers related to content that had been contributed by their peers in other previous comments. The configuration of the forum was such that modifications to posts were not allowed to be able to evaluate the development of each student from the beginning to the end of their participation in the discussion. We also utilised the Urkund text recognition system for Pixel-Bit. Revista de Medios y Educación, 66, 7-29 | 2023 | https://doi.org/10.12795/pixelbit.95555 PÁGINA | 20 plagiarism detection, to ensure, as much as possible, that the posts were original. The authors used a standard of less than 15% detection of plagiarism in each post. 2.3. Data collection The data were downloaded directly from Moodle. Two researchers from the team were in charge of organising them into categories to reduce the probability of making systematic errors, thus avoiding compromising the validity of the results (Rabinovich, 2006). The researchers had received training in working in teams –specifically in pairs– as that has been proven to optimize the data organization procedure (Escalante Ferrer et al., 2020). These teams recorded the interactions between students, identifying the authors of the posts and with whom they interacted. 2.4. Data analysis The connections between students were analysed by studying the social networks that emerged from the AODs. During the training period, we analysed the evolution of closeness centrality by means of a nonparametric analysis. 2.4.1. Social Network Analysis We also analysed the network structures to confirm that they were similar, and they could be compared without additional adjustments. Modularity was calculated using the randomized algorithm of Blondel et al. (2008) with a resolution of 1.0 (Lambiotte et al., 2014). We obtained the diameter, the average path length normalized in the range 0-1, and the density based on n (n-1) potential connections for directed networks. Once the structures of each network were analysed, we found the closeness centrality of students through the average of the distances from a given individual to all other individuals in the network (Freeman, 1978). We normalized closeness centrality to the range 0-1, and maps were plotted of both networks after the last week of training, highlighting in deeper shades of blue those students with lesser closeness score. 2.4.2. Nonparametric analysis The Shapiro-Wilk test was calculated to assess the assumption of normality for the closeness scores, which showed that the distributions were not always normal. Therefore, we conducted a nonparametric analysis. Table 1 shows the result of the normality contrasts, along with the average closeness and its dispersion for each week. The Friedman test served as an alternative to a repeated-measures ANOVA to rule out that closeness centrality was a stable factor. Kendall’s W coefficient of concordance was taken to measure effect size in the Friedman test, as recommended in recent methodology textbooks (King et al., 2018). Next, Wilcoxon’s W test was calculated to analyse differences in closeness scores between pairs of weeks, including metrics for effect size (dz) (Cohen, 1988) and for statistical power (1-β) (Faul et al., 2009). Statistical power was only analysed for the Wilcoxon W test, keeping in mind that the Friedman test was initially used to reject that closeness centrality was stable. Along these lines, we were interested in ruling out false negatives by rejecting changes in closeness scores as training weeks elapsed. Pixel-Bit. Revista de Medios y Educación, 66, 7-29 | 2023 | https://doi.org/10.12795/pixelbit.95555 PÁGINA | 21 3. Results The two social networks acquired similar structures, thus facilitating comparison. Group A achieved a density of 22.80% and the average path length was 2.015 (diameter = 5). Although five communities were identified in the network, the modularity was .136, so that students were not only strongly connected with members of their own community, but also with other classmates. The Group B network reached a density of 27.70% and the average path length was 1.839 (diameter = 4). There were also five communities, and the modularity was .068, reflecting connections between students from different communities in the network. At the end of the semester there were students who developed less closeness centrality than the rest of their peers in the two groups (RQ1), but the difference was even greater during the first few weeks, as shown in Table 1. Figure 1 shows the social networks that emerged after the entire discussion. Friedman’s test revealed that closeness scores changed throughout the training period (RQ2), as shown in Table 2. The passage of time had a great impact on the modifications of the scores, such that students found themselves in different situations with respect to closeness centrality during the discussion. Table 1 Descriptive statistics and normality tests Group A Group B Weeks Mean SD Shapiro-Wilk p Mean SD Shapiro-Wilk p Week 1 .728 .285 .821 < .001 .482 .443 .767 < .001 Week 2 .369 .186 .667 < .001 .446 .368 .851 < .001 Week 3 .399 .139 .598 < .001 .383 .112 .644 < .001 Week 4 .423 .061 .967 .201 .425 .081 .715 < .001 Week 5 .432 .055 .960 .097 .468 .056 .981 .646 Week 6 .439 .050 .967 .185 .491 .056 .975 .435 Week 7 .459 .043 .981 .626 .506 .057 .964 .176 Week 8 .493 .040 .971 .279 .521 .056 .974 .417 Week 9 .493 .033 .989 .935 .537 .059 .980 .637 Week 10 .498 .031 .990 .953 .540 .063 .987 .903 Week 11 .547 .062 .991 .975 Week 12 .556 .068 .990 .964 Week 13 .563 .073 .993 .994 Week 14 .567 .075 .992 .987 Pixel-Bit. Revista de Medios y Educación, 66, 7-29 | 2023 | https://doi.org/10.12795/pixelbit.95555 PÁGINA | 22 Figure 1 Social networks of groups A and B Table 2 Development of closeness centrality Sample Chi-squared df p Kendall’s W Group A 318.693 9 < .001 0.738 Group B 361.928 13 < .001 0.619 When significant changes were analysed, there was a different pattern of behaviour found in each group. While fluctuations in closeness scores were intermittent in Group A, in Group B there were significant and almost constant changes from the fourth week onwards. In some pairs of weeks, a significant development in closeness was detected, but it was not always assumed as such because sometimes a medium or large effect size was not obtained, or there was not enough statistical power to rule out an error in the findings. The results of the Wilcoxon W test are available in Table 3. Pixel-Bit. Revista de Medios y Educación, 66, 7-29 | 2023 | https://doi.org/10.12795/pixelbit.95555 PÁGINA | 23 Table 3 Differences in proximity between pairs of weeks Group A Group B Weeks (W) Wilcoxon W p Cohen’s dz 1-β Wilcoxon W p Cohen’s dz 1-β W1-2 913000 < .001 1.300 1.0 223000 .421 .091 .090 W2-3 113500 < .001 .239 .354 501000 .540 .177 .206 W3-4 95000 < .001 .152 .172 43000 < .001 .521 .916 W4-5 264500 .003 .458 .859 .000 < .001 .814 .999 W5-6 356500 .018 .371 .691 .000 < .001 1.157 1.0 W6-7 131000 < .001 1.056 .999 49000 < .001 .973 .999 W7-8 .000 < .001 2.122 1.0 .000 < .001 .987 .999 W8-9 620000 .388 .000 .050 .000 < .001 1.287 1.0 W9-10 213000 < .001 .444 .836 369500 .308 .223 .299 W10-11 161000 < .001 .605 .972 W11-12 .000 < .001 .850 .999 W12-13 .000 < .001 .761 .998 W13-14 .000 .001 .479 .866 The effect of the training period was larger in Group A, due to the large difference detected between the first and second week of training. However, a large effect size was not found until the seventh week. 4. Discussion Until now research showed that AOD is only successful when students are engaged in discussion (Hew & Cheung, 2008). This study provides an SNA-based method to detect students with high potential for peer inclusion in an AOD at university. From this method, new teaching techniques can be explored to address participation barriers that make it difficult to engage in AODs. The results showed that two groups of students come to experience changes in closeness centrality at different rates and it is necessary for the teacher to continuously assess when a student is in a leverageable position and can include a classmate. The relevance of this study is that it offers an innovative method that allows teachers to designing strategies to solve problems identified in previous studies on the effectiveness of AOD in Higher Education (Gao et al., 2013; Thomas, 2013). Generally speaking, modelling forms of interaction (Choi & Johnson, 2005; Smet et al., 2010) and adopting strategies to moderate discussion (N.-S. Chen et al., 2011) serve to increase student engagement. Directive teaching with clear instructions and expectations (Darabi et al., 2013) and learning analytics (Goh, 2020) are also valid. However, the method proposed in this study is oriented to solve the situations in which a student engages in the discussion but does not easily get a response from peers and thus ends up disengaging. These situations have already been studied and it has been shown that it is effective for students to formulate questions so that their peers feel engaged and so comment on the questions (Bradley et al., 2008; G. Chen et al., 2020). In this study, we have gone a step further and proposed a method for students’ questions as the impetus to include more disengaged participants. Pixel-Bit. Revista de Medios y Educación, 66, 7-29 | 2023 | https://doi.org/10.12795/pixelbit.95555 PÁGINA | 24 4.1. Participation in a debate presents challenges Although some authors have claimed that there is no precise method for valuing participation in AOD (Andresen, 2009), there are frameworks, such as Communities of Inquiry (Castellanos-Reyes, 2020; Garrison et al., 1999), that have established their own concept of participation as social, cognitive, and teaching presence. Other research has valued participation simply as interaction among students (Fehrman & Watson, 2020). In this study, we understood participation from the complexity paradigm and SNA (Jacobson et al., 2019; Morin, 1992). Participation was considered to be a structural factor in a network of students debating asynchronously, making connections with each other and organizing themselves autonomously (Holland, 2006). The students’ involvement in the debate is clearly more than just interaction. They also acquire status in the social network from their interactions with peers during these debates. 4.2. When does Social Network Analysis help? Both research questions received positive answers, so the SNA-based method for peer inclusion made sense in the AODs conducted with university students. For this method to be useful, it is necessary for some students to be more approachable and accessible than others at a certain point in the discussion. Otherwise, from a network analysis perspective, all or none of them will be in a particularly favourable situation to include peers. The teacher could then select any student and guide him or her to include others without the need to apply any method. Even if there are individuals who have lesser closeness centrality than others, it is necessary that the closeness scores change significantly during the discussion. If it does not change, it would be reasonable to think that either the discussion topic has been exhausted and there is no reason to continue, or that only some students are debating, while others remain inactive throughout. In the second case it would not make sense to apply the method proposed in this study because the problem would be the willingness to participate and not the barriers to participation. In this scenario the teacher would have to find a way to motivate the inactive students, rather than solve their difficulties in interacting with peers so that they would consider their comments in the discussion. 4.3. Implications for teaching practice During the first weeks, students with more closeness centrality were easy to identify, because of the indications of the dispersion scores. In this case, the time of greatest dispersion coincided with the beginning of the discussion, but this does not exclude that other AODs develop differently, since there is no empirical evidence to claim otherwise. In addition, the study shows that the closeness can change significantly at any time during the discussion. Therefore, each week, the teacher should review the difference between the activity the students show in the network to be able to decide effectively who is in the best position to include their peers. Once teachers identify the students with highest closeness scores, they can design an SNA-based strategy for inclusion. The strategy is to identify the students who are most disconnected from the discussion and those who most influential and participatory. Teachers ask the closer students to ask the influential peers their opinion on what those who are more inactive have said, while encouraging the latter to comment further in the forum. In this way, the influential students will spread the ideas that the disengaged students contribute, and these will be commented on more often. If students were debating less because they were Pixel-Bit. Revista de Medios y Educación, 66, 7-29 | 2023 | https://doi.org/10.12795/pixelbit.95555 PÁGINA | 25 encountering barriers to participation arising from the interaction, then they should begin to actively participate in the discussion after implementing this strategy. 4.4. Limitations and emerging research The purpose of this study was not to detect students who engaged in discussion and, having not received a response from their peers, disengage. Instead, we wanted to economize the content of the article and focus on detecting students with a high potential for peer inclusion. In summary, students have barriers to participation when they comment regularly in the forum, their centrality in the network was low for some time, and in recent weeks they reduced their activity. The purpose was also not to identify students with influence in the discussion because there are recent studies dedicated to this topic (e.g., Garcia-Garcia et al., 2021). The method proposed in this study is related to peer inclusion, specifically intended to include students with participation barriers arising from interactions during discussion. If the method is used to solve other participation problems, it may not work. All in all, the method allows the detection of students with high potential for peer inclusion and the study shows that it is useful for this purpose, but the teaching strategies designed from the detection should be tested in future studies to obtain empirical evidence on their effectiveness. We selected the sample non-probabilistically, and it would be interesting to conduct studies with random sampling to confirm the results. Still, social networks are complex systems (Thurner et al., 2018) and their analysis serves to understand structural phenomena and not so much to predict variables. Otherwise, AOD is effective for learning at university, but it is in the exploration phase yet, so this study offers ways to solve problems in an alternative way to find new solutions and seek confirmation of its usefulness. 5. Funding details Ministry of Science and Innovation, Spain (Código PID2021-123523NB-I00); University of Valencia, Spain (UV-SFPIE_PID- 2074926). References Al-Dheleai, Y. M., Tasir, Z., & Jumaat, N. F. (2020). Depicting Students’ Social Presence on Social Networking Site in Course-Related Interaction. SAGE Open, 10(1), 1–8. https://doi.org/10.1177/2158244019899094 Almatrafi, O., & Johri, A. (2019). Systematic Review of Discussion Forums in Massive Open Online Courses (MOOCs). IEEE Transactions on Learning Technologies, 12(3), 413–428. https://doi.org/10.1109/TLT.2018.2859304 Amor, A. M., Hagiwara, M., Shogren, K. A., Thompson, J. R., Verdugo, M. Á., Burke, K. M., & Aguayo, V. (2019). International perspectives and trends in research on inclusive education: a systematic review. International Journal of Inclusive Education, 23(12), 1277–1295. https://doi.org/10.1080/13603116.2018.1445304