A Learning Analytics Dashboard to Analyse Learning Activities in Interpreter Training Courses

Taibi, Davide; Bianchi, Francesca; Kemkes, Philipp; Marenzi, Ivana

doi:10.1007/978-3-030-21151-6_14

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…The paper's intention is thus to describe and define the pedagogic models that such projects incorporate by collating descriptions of their nature and functions obtained from publications and discussions with teachers about their experiences Cambria, this volume;Coccetta 2022). OpenMWS also contributes to learning analytics in these specialised pedagogical contexts (Bianchi et al, 2022;Taibi, 2021;Taibi et al, 2018;Fulantelli & Taibi, 2014;Taibi & Dietze, 2012) by storing data relating to computer-user interactions and file management, all of which helps reconstruct University students' experiences of remote-learning projects. What follows describes the circumstances that have shaped remote learning models in both expected and unexpected ways, together with reflections on their pedagogical implications that may assist further research into remote learning.…”

Section: Introductionmentioning

confidence: 99%

Remote Learning Models in Video Corpus Construction: Simulation, Creativity and Remodelling as Pedagogic Tools

Taibi

2023

REI

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Within online learning environments research, this paper identifies four remote-learning models that have characterised the use of the OpenMWS platform within the processes of selection, transcription, annotation and concordance-like searches characterising video corpus construction and analysis. They relate to student tasks such as the creation of video corpora ex novo when completing dissertations, traineeships, group project work and with remodelling existing video corpora to meet the needs of new audiences such as primary and secondary schoolchildren. The paper makes reference to how analytics records student interactions with the platform, an approach inviting students to reflect on their own learning trajectories.

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Section: Introductionmentioning

confidence: 99%

Remote Learning Models in Video Corpus Construction: Simulation, Creativity and Remodelling as Pedagogic Tools

Taibi

2023

REI

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“…In the ROLE project [4], the goal was to use web technologies in order to provide personalized and adaptive learning environment. In another study, a dashboard was integrated into an online learning system [5]. Furthermore, a tool was developed that guides students in their work, traces their search for reference materials on the web and tracks their interaction with the system.…”

Section: Introductionmentioning

confidence: 99%

Adaptation of Electrical Engineering Education to the COVID-19 Situation: Method and Results

Evstatiev

Hristova

2020

2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME)

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In this paper are presented a new extended methodology and tools for providing electrical engineering education in a distant form. It was used by two Bulgarian universities, in order to adapt their Electrical engineering classes to the COVID-19 situation. The methodology includes several phases: needs analysis, preparation of educational materials, selection of teaching methods, increasing competencies and selection of assessment methods. In the results of the paper is presented the implementation of the methodology based on the EVEEE environment for electrical engineering equipment. At the end of the semester a questionnaire was conducted among the students. The results about the students' opinion clearly indicate that engineering education should be implemented in a distant form only during emergency situations.

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A Learning Analytics Dashboard to Analyse Learning Activities in Interpreter Training Courses

Cited by 2 publications

References 12 publications

Remote Learning Models in Video Corpus Construction: Simulation, Creativity and Remodelling as Pedagogic Tools

Remote Learning Models in Video Corpus Construction: Simulation, Creativity and Remodelling as Pedagogic Tools

Adaptation of Electrical Engineering Education to the COVID-19 Situation: Method and Results

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