Student engagement with resources as observable signifiers of success in practice based learning

Practice-based learning activities with a focus on Science, Technology, Art, Math and Engineering (STEAM) are providing new opportunities for teaching these subjects. However, we lack widely accepted ways of assessing and monitoring these practices to inform educators and learners and enable the provision of effective support. Here, we report the results from a study with 15 teenage students taking part in a 2-day Hack. We present results from analysis of video data recording collaborative working between groups of students. The analysis of the video data is completed using the ERICAP analytical framework (Luckin et al., 2017) based on ecology of resources and interactive, constructive, active and passive engagement concepts. The results illustrate the differences between students' engagement with resources which might be utilized as signifiers of student success in similar learning environments.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Micro-Doppler Based Human-Robot Classification Using Ensemble and Deep Learning Approaches

Radar sensors can be used for analyzing the induced frequency shifts due to micro-motions in both range and velocity dimensions identified as micro-Doppler ($\boldsymbol{\mu}$-D) and micro-Range ($\boldsymbol{\mu}$-R), respectively. Different moving targets will have unique $\boldsymbol{\mu}$-D and $\boldsymbol{\mu}$-R signatures that can be used for target classification. Such classification can be used in numerous fields, such as gait recognition, safety and surveillance. In this paper, a 25 GHz FMCW Single-Input Single-Output (SISO) radar is used in industrial safety for real-time human-robot identification. Due to the real-time constraint, joint Range-Doppler (R-D) maps are directly analyzed for our classification problem. Furthermore, a comparison between the conventional classical learning approaches with handcrafted extracted features, ensemble classifiers and deep learning approaches is presented. For ensemble classifiers, restructured range and velocity profiles are passed directly to ensemble trees, such as gradient boosting and random forest without feature extraction. Finally, a Deep Convolutional Neural Network (DCNN) is used and raw R-D images are directly fed into the constructed network. DCNN shows a superior performance of 99\% accuracy in identifying humans from robots on a single R-D map.Comment: 6 pages, accepted in IEEE Radar Conference 201

Proceedings of the International Workshop on EuroPLOT Persuasive Technology for Learning, Education and Teaching (IWEPLET 2013)

"This book contains the proceedings of the International Workshop on EuroPLOT Persuasive Technology for Learning, Education and Teaching (IWEPLET) 2013 which was held on 16.-17.September 2013 in Paphos (Cyprus) in conjunction with the EC-TEL conference. The workshop and hence the proceedings are divided in two parts: on Day 1 the EuroPLOT project and its results are introduced, with papers about the specific case studies and their evaluation. On Day 2, peer-reviewed papers are presented which address specific topics and issues going beyond the EuroPLOT scope. This workshop is one of the deliverables (D 2.6) of the EuroPLOT project, which has been funded from November 2010 – October 2013 by the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission through the Lifelong Learning Programme (LLL) by grant #511633. The purpose of this project was to develop and evaluate Persuasive Learning Objects and Technologies (PLOTS), based on ideas of BJ Fogg. The purpose of this workshop is to summarize the findings obtained during this project and disseminate them to an interested audience. Furthermore, it shall foster discussions about the future of persuasive technology and design in the context of learning, education and teaching. The international community working in this area of research is relatively small. Nevertheless, we have received a number of high-quality submissions which went through a peer-review process before being selected for presentation and publication. We hope that the information found in this book is useful to the reader and that more interest in this novel approach of persuasive design for teaching/education/learning is stimulated. We are very grateful to the organisers of EC-TEL 2013 for allowing to host IWEPLET 2013 within their organisational facilities which helped us a lot in preparing this event. I am also very grateful to everyone in the EuroPLOT team for collaborating so effectively in these three years towards creating excellent outputs, and for being such a nice group with a very positive spirit also beyond work. And finally I would like to thank the EACEA for providing the financial resources for the EuroPLOT project and for being very helpful when needed. This funding made it possible to organise the IWEPLET workshop without charging a fee from the participants.

A taxonomy of video lecture styles

Many educational organizations are employing instructional video in their pedagogy, but there is limited understanding of the possible presentation styles. In practice, the presentation style of video lectures ranges from a direct recording of classroom teaching with a stationary camera and screencasts with voice-over, up to highly elaborate video post-production. Previous work evaluated the effectiveness of several presentation styles, but there has not been any consistent taxonomy, which would have made comparisons and meta-analyses possible. In this article, we surveyed the research literature and we examined contemporary video-based courses, which have been produced by diverse educational organizations and teachers across various academic disciplines. We organized video lectures in two dimensions according to the level of human presence and according to the type of instructional media. In addition to organizing existing video lectures in a comprehensive way, the proposed taxonomy offers a design space that facilitates the choice of a suitable presentation style, as well as the preparation of new ones.Comment: 14 pages, 5 figure

The Assistive Multi-Armed Bandit

Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are acting (noisily) optimally with respect to their preferences. Such approaches can fail when people are themselves learning about what they want. In this work, we introduce the assistive multi-armed bandit, where a robot assists a human playing a bandit task to maximize cumulative reward. In this problem, the human does not know the reward function but can learn it through the rewards received from arm pulls; the robot only observes which arms the human pulls but not the reward associated with each pull. We offer sufficient and necessary conditions for successfully assisting the human in this framework. Surprisingly, better human performance in isolation does not necessarily lead to better performance when assisted by the robot: a human policy can do better by effectively communicating its observed rewards to the robot. We conduct proof-of-concept experiments that support these results. We see this work as contributing towards a theory behind algorithms for human-robot interaction.Comment: Accepted to HRI 201

Revisión tecnológica del aprendizaje de idiomas asistido por ordenador: una perspectiva cronológica

El presente artículo aborda la evolución y el avance de las tecnologías del aprendizaje de lenguas asistido por ordenador (CALL por sus siglas en inglés, que corresponden a Computer- Assisted Language Learning) desde una perspectiva histórica. Esta revisión de la literatura sobre tecnologías del aprendizaje de lenguas asistido por ordenador comienza con la definición del concepto de CALL y otros términos relacionados, entre los que podemos destacar CAI, CAL, CALI, CALICO, CALT, CAT, CBT, CMC o CMI, para posteriormente analizar las primeras iniciativas de implementación del aprendizaje de lenguas asistido por ordenador en las décadas de 1950 y 1960, avanzando posteriormente a las décadas de las computadoras centrales y las microcomputadoras. En última instancia, se revisan las tecnologías emergentes en el siglo XXI, especialmente tras la irrupción de Internet, donde se presentan el impacto del e-learning, b-learning, las tecnologías de la Web 2.0, las redes sociales e incluso el aprendizaje de lenguas asistido por robots.The main focus of this paper is on the advancement of technologies in Computer-Assisted Language Learning (CALL) from a historical perspective. The review starts by defining CALL and its related terminology, highlighting the first CALL attempts in 1950s and 1960s, and then moving to other decades of mainframes and microcomputers. At the final step, emerging technologies in 21st century will be reviewed