Recurrent routines: Analyzing and supporting orchestration in technology-enhanced primary classrooms

Prieto, Luis P.; Sobrino, Sara L. Villagrá; Abellán, Iván M. Jorrín; Martínez‐Monés, Alejandra; Dimitriadis, Yannis

doi:10.1016/j.compedu.2011.01.001

Cited by 38 publications

(20 citation statements)

References 13 publications

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“…Over the recent years, many scholars started to also pay attention to extrinsic constraints, i.e. constraints that do not relate to any learning theory, but nonetheless shape classroom practices, such as the time budget and time segmentation, the physical constraints of the classroom space, and the need to maintain a reasonable level of discipline, to minimize the teachers' workload, or to cope with the heterogeneity of the class (Dillenbourg & Jermann, 2010;Moraveji, Morris, Morris, Czerwinski & Riche, 2011;Roschelle, Rafanan, Estrella, Nussbaum & Claro, 2009;Prieto, Villagra-Sobrino, Jorrin-Abellan, Martinez-Mones & Dimitriadis, 2011). By referring to them as the 'logistics' of classrooms, Nussbaum and Diaz (2011) stress that these constraints do not correspond to a grand learning theory but to practicalities that, if they are neglected, may spoil the most effective instructional design.…”

Section: Usability At the Classroom Levelmentioning

confidence: 99%

Designing augmented reality for the classroom

Cuendet

Bonnard

Do-Lenh

et al. 2013

Computers & Education

249

120

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Augmented reality (AR) has recently received a lot of attention in education. Multiple AR systems for learning have been developed and tested through empirical studies often conducted in lab settings. While lab studies can be insightful, they leave out the complexity of a classroom environment. We developed three AR learning environments that have been used in genuine classroom contexts, some of them being now part of classroom regular practices. These systems and the learning activities they provide have been co-designed with teachers, for their own classrooms, through multiple cycles of prototyping and testing. We present here the features that emerged from these co-design cycles and abstract them into design principles.

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Section: Usability At the Classroom Levelmentioning

confidence: 99%

Designing augmented reality for the classroom

Cuendet

Bonnard

Do-Lenh

et al. 2013

Computers & Education

249

120

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“…con otros sistemas dentro del ciclo de vida CSCL. En este sentido, una de las investigaciones en curso promueve la utilización de una arquitectura denominada GLUE!-PS (GLUE!-Pedagogical Scripting -GLUE!-Guiado Pedagógico) [Mun12b,Pri11] para el despliegue de diseños de aprendizaje generados con múltiples herramientas de autoría y lenguajes de modelado en múltiples VLE. Estos diseños pueden particularizarse también utilizando herramientas externas integradas mediante la arquitectura GLUE!.…”

Section: Trabajo Futurounclassified

“…core, called GLUE!-PS (GLUE!-Pedagogical Scripting) [Mun12b,Pri11], to enable the deployment of abstract learning designs generated with multiple authoring tools and learning design languages into multiple VLEs.…”

Section: Future Workmentioning

confidence: 99%

GLUE!: An architecture for the integration of external tools in Virtual Learning Environments

Alario-Hoyos

Bote‐Lorenzo

Sánchez

et al. 2013

Computers & Education

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A mis padres. AcknowledgementsThe opening lines of this document serve to close this dissertation. In them, I want to sincerely thank all those who have made this work possible, and without whom this dissertation could not have been accomplished. First, I would like to thank my supervisors Miguel and Eduardo, with whom I share the credit of this work, and who have supported me throughout all these years in which we have worked together very closely. They believed in me from the very rst moment I ran into them, and they spent much time and e ort to make this dissertation succeed.Yannis is also one of the great minds that has most in uenced me over these years, and so I owe him much of this work. He insisted in showing me the right way, and he never hesitated to give me on-the-y advice or answers to my questions, despite his busy schedule. Of course, I have to specially thank Asen, Guillermo and Adolfo, who have very closely followed my dissertation, and with whom I had deep and intense discussions.Regarding the development tasks, I have been very lucky to be supported by David and Javier; without their work, this dissertation would have needed much more time, and the de- This fellowship gave me economic stability for almost three years and allowed me to make several short research stays abroad.Despite this ideal working environment, I could never have nished this dissertation without the support of my family and friends. Among them, special words of thanks are to Martín, iii Eduardo, Natalia, Miguel Ángel, Dani, Quini and many others who stood by my side to a greater or lesser extend throughout these years. Here, I would also like to remember all my college friends which led me to the front door of this PhD after a ve-year degree in which we all shared great times and memories.My parents, Julián and Tere, deserve my best words. They raised me in the values of sacri ce and knowledge. They always backed me up in every choice, and helped me overcome the di culties, and what is more important, they still keep doing it day by day. They are the ones that encourage me to get the best out of myself.Finally, my last words of gratitude must be for Bea, because she su ered this dissertation as I did. She was always by my side and never gave up on me. She gave me the strength to face this challenge, being even able to follow me to the end of the world. This dissertation proposes a middleware integration architecture called GLUE! (Group Learning Uniform Environment) that enables the lightweight integration of multiple existing external tools in multiple existing VLEs, overcoming these limitations. GLUE! fosters this integration by imposing few restrictions on VLE and tool providers, as well as by expecting an attainable e ort from developers. Besides, GLUE! facilitates the instantiation and enactment of collaborative learning situations within VLEs, leveraging the VLEs distinctive features for the management of users and groups. By means of GLUE!, practitioners may use external tools as if they were VLE built-in tools, and w...

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“…This kind of graph, representing time horizontally and social plane (individual, group, or whole-class) vertically, can be used to model the student learner activities, but also the teachers' supporting actions (such as explanation, monitoring, repairs, etc.) [34], and have been used extensively in computer-supported collaborative learning (CSCL), both to express the teacher plan (or 'script') [9] and the improvised actions during enactment [34].…”

Section: Operationalizing 'Teacher Prac-tice': Orchestrationmentioning

confidence: 99%

Teaching analytics

Prieto

Sharma

Dillenbourg

et al. 2016

Proceedings of the Sixth International Conference on Learning Analytics &Amp; Knowledge - LAK '16

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ABSTRACT'Teaching analytics' is the application of learning analytics techniques to understand teaching and learning processes, and eventually enable supportive interventions. However, in the case of (often, half-improvised) teaching in face-to-face classrooms, such interventions would require first an understanding of what the teacher actually did, as the starting point for teacher reflection and inquiry. Currently, such teacher enactment characterization requires costly manual coding by researchers. This paper presents a case study exploring the potential of machine learning techniques to automatically extract teaching actions during classroom enactment, from five data sources collected using wearable sensors (eye-tracking, EEG, accelerometer, audio and video). Our results highlight the feasibility of this approach, with high levels of accuracy in determining the social plane of interaction (90%, κ=0.8). The reliable detection of concrete teaching activity (e.g., explanation vs. questioning) accurately still remains challenging (67%, κ=0.56), a fact that will prompt further research on multimodal features and models for teaching activity extraction, as well as the collection of a larger multimodal dataset to improve the accuracy and generalizability of these methods.

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Recurrent routines: Analyzing and supporting orchestration in technology-enhanced primary classrooms

Cited by 38 publications

References 13 publications

Designing augmented reality for the classroom

Designing augmented reality for the classroom

GLUE!: An architecture for the integration of external tools in Virtual Learning Environments

Teaching analytics

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