Data-driven detection and characterization of communities of accounts collaborating in MOOCs

Ruipérez‐Valiente, José A.; Jaramillo‐Morillo, Daniel; Joksimovíc, Srécko; Kovanović, Vitomir; Muñoz‐Merino, Pedro J.; Gašević, Dragan

doi:10.1016/j.future.2021.07.003

Cited by 19 publications

(7 citation statements)

References 30 publications

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“…Dentro de los aspectos que se consideran para mejorar el índice de deserción, se consideró principalmente la motivación, la generación de responsabilidades, pero también facilitar el ritmo de estudio adaptado a los equipos de estudiantes y la resolución permanente de dudas por parte del docente. Otro aspecto relevante fue la mejora en la colaboración de los estudiantes, comparado con la mayoría de los cursos masivas cuya colaboración se limita al uso de los foros (Ruipérez-Valiente et al, 2021), en el curso presentado además del uso de foro para solucionar inquietudes los estudiantes desarrollaron habilidades de colaboración y tuvieron dependencia de los compañeros para desarrollar los proyectos en equipos, creando una alternativa para aplicar aprendizaje colaborativo en cursos masivos.…”

Section: Discussionunclassified

“…Cuando se desea llevar a cabo un curso de desarrollo de software con esta metodología en un contexto de masividad, se presentan dos problemas principales. El primero es la alta deserción de estudiantes en estos cursos (Escanés et al, 2014), mientras que el segundo se relaciona con las dificultades para la implementación del aprendizaje colaborativo soportado por computador debido a 1) la naturaleza de los cursos, 2) la distribución de los estudiantes, y 3) generalmente la colaboración se limita principalmente al uso de foros o interacciones sociales (Ruipérez-Valiente et al, 2021). Como respuesta a estos problemas, Sun et al (2020) y Castellanos-Reyes (2021) mencionan que para aumentar la posibilidad de retención y finalización exitosa de cursos masivos se debe fortalecer la interacción de los estudiantes con la plataforma y sus compañeros, además de fomentar un ambiente de colaboración.…”

Section: Introductionunclassified

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Adaptación de herramientas web para la implementación de un curso masivo colaborativo de desarrollo de software

Jaramillo¹,

Cadavid

Zapata-Jaramillo

2022

Inf. tecnol.

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En este estudio se presenta el diseño de un curso masivo colaborativo de desarrollo de software mediante la adaptación de herramientas web. Se integra la enseñanza de desarrollo de software y de metodologías ágiles con base en el aprendizaje basado en proyectos, lo que permite a los estudiantes relacionar la teoría y la práctica. Se realiza un caso de estudio con la participación de 475 estudiantes en un curso de Node.JS y metodologías agiles. Los resultados del curso y el uso de las diferentes herramientas son satisfactorios tanto en los productos finales de los estudiantes como en la percepción de aprendizaje y la tasa de finalización efectiva del curso masivo. Se concluye que el curso masivo diseñado aquí posibilita la enseñanza de desarrollo web en equipos de trabajos colaborativos, emulando un entorno real mediante plataformas de gestión de proyectos y de repositorios con mediación de un sistema de gestión de aprendizaje.

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Section: Discussionunclassified

Section: Introductionunclassified

Adaptación de herramientas web para la implementación de un curso masivo colaborativo de desarrollo de software

Jaramillo¹,

Cadavid

Zapata-Jaramillo

2022

Inf. tecnol.

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“…In recent years, with the construction of intelligent campuses in critical universities across the country, the smart campus combining teaching, scientific research, management, and the Internet of things (IoT) has been created in the wireless Internet environment, improving the efficiency of campus management and teaching. It is an important topic to change the thinking of Japanese teachers and continuously improve teaching methods through artificial intelligence (AI) empowerment in the AI era [ 1 ]. As a powerful auxiliary means of educational informationization, computer vision technology such as object detection is helpful to the improvement of classroom teaching evaluation and teaching management.…”

Section: Introductionmentioning

confidence: 99%

The Cognitive Transformation of Japanese Language Education by Artificial Intelligence Technology in the Wireless Network Environment

Zhang

2022

Computational Intelligence and Neuroscience

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This study aims to solve the multiscale problems faced by the current classroom student behavior target detection based on the convolutional neural network (CNN) in the wireless network environment. Firstly, the recent reform of Japanese language education is introduced. Secondly, the multiscale problem research of classroom student behavior target detection is discussed. A CNN-based new extraction network is designed based on dilated convolution and pyramid features. An anchor reconstruction algorithm based on improved K-means clustering is presented for the self-made student behavior dataset. Finally, the performance of the designed algorithm is tested. The anchor reconstruction algorithm’s mean average precision is 83.2%, and the average intersection over union is 73.7%. The experimental results of this scheme outperform the original single-shot multibox detector and K-means algorithms. Compared with other algorithms, the designed multiscale detection algorithm of classroom student behavior has the best detection effect on Pascal visual object classes (VOC) dataset. The detection accuracy of the entire dataset is 79.8%. Overall, the multiscale detection algorithm for classroom student behavior has a better detection effect on the Pascal VOC dataset and has good generalization ability and robustness. This research can guide students to recognize their class status and make corresponding adjustments to improve their learning efficiency, which has essential research significance and application value.

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“…However, academic dishonesty has accentuated worryingly during the COVID-19 pandemic due to virtual education [14][15][16][17][18][19][20][21][22][23]. Many efforts have been made attempting to translate the "non-virtual" control mechanisms to a virtual environment [19,17,[24][25][26][27] using platforms and technologies addressed in this direction [19].…”

Section: Introductionmentioning

confidence: 99%

Education in the Time of COVID-19: The Improvised Experiment of Virtual Assessments

Hidalgo

2022

Preprint

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One of the aspects in which the COVID-19 pandemic impacted the most was education. Teachers and students had to face a new reality for which they were not prepared adapting in an improvised way new methods and strategies to teach and to learn. Within virtual education, exams reduced in some cases to multiple choice tests while others tried to mimic traditional (pen and paper) exams. In this paper, these two kind of evaluations are compared. Although the results appear to be similar, a deeper look shows that their structure is completely different and some groups of students are unfairly harmed or benefited depending on the assessment applied. Beyond analyzing the reasons of this discrepancy, it is determined that for some type of evaluation, at least 21.1% of students maybe passing a course irregularly meanwhile, at least 5.5% could be failing a course despite their actual capabilities.

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Data-driven detection and characterization of communities of accounts collaborating in MOOCs

Cited by 19 publications

References 30 publications

Adaptación de herramientas web para la implementación de un curso masivo colaborativo de desarrollo de software

Adaptación de herramientas web para la implementación de un curso masivo colaborativo de desarrollo de software

The Cognitive Transformation of Japanese Language Education by Artificial Intelligence Technology in the Wireless Network Environment

Education in the Time of COVID-19: The Improvised Experiment of Virtual Assessments

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