The role of learning theory in multimodal learning analytics

This study presents the outcomes of a semi-systematic literature review on the role of learning theory in multimodal learning analytics (MMLA) research. Based on previous systematic literature reviews in MMLA and an additional new search, 35MMLA works were identified that use theory. The results show that MMLA studies do not always discuss their findings within an established theoretical framework. Most of the theory-driven MMLA studies are positioned in the cognitive and affective domains, and the three most frequently used theories are embodied cognition, cognitive load theory and control–value theory of achievement emotions. Often, the theories are only used to inform the study design, but there is a relationship between the most frequently used theories and the data modalities used to operationalize those theories. Although studies such as these are rare, the findings indicate that MMLA affordances can, indeed, lead to theoretical contributions to learning sciences. In this work, we discuss methods of accelerating theory-driven MMLA research and how this acceleration can extend or even create new theoretical knowledge

CNN-based classifier as an offline Trigger for the CREDO experiment

Marcin Piekarczyk, Olaf Bar, Łukasz Bibrzycki, Michał Niedźwiecki, Krzysztof Rzecki, Sławomir Stuglik, Thomas Andersen, Nikolay M. Budnev, David E. Alvarez-Castillo, Kévin Almeida Cheminant, Dariusz Góra, Alok C. Gupta, Bohdan Hnatyk, Piotr Homola, Robert Kamiński, Marcin Kasztelan, Marek Knap, Péter Kovács, Matías Rosas, Oleksandr Sushchov, Katarzyna Smelcerz, Karel Smolek, Jarosław Stasielak, Tadeusz Wibig, Krzysztof W. Woźniak, Jilberto Zamora-SaaGamification is known to enhance users’ participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process

How do preschoolers interact with peers? Characterising child and group behaviour in games with tangible interfaces in school

Learning social skills is an important part of the socialisation process of children, which should occur at school, at home and in any place where children live. There are very few studies on social interaction and collaboration roles with 3–4 year old. In this paper, we aim to understand collaboration in young children to help them develop their social skills and improve their overall development. To get this, we have designed an observational experiment to monitor and characterise group activity and roles, mediated by technology and using data mining techniques. First, we have designed a game as a free-play situation where the conditions require interplay of three children with toys and among interaction among peers. Children interacts with game through tangible toys. The environment collects accurate data on children’s actions automatically and non-intrusively. We also consider other data from direct observation by psychologists and educators. Then, we have organised a study for groups(triads) of 3 to 4-year-old children playing with this game. We analyse data from 81 children (51.9% boys and 48.1% girls) in groups of three randomly selected. The work proposes a set of actions in the game and from them a set of indicators, which are used as intermediate measures of observation to analyse the playing process. Social interaction is characterised in 5 levels: Coordination, Cooperation, Collaboration, Troubled and Unproductive; and five roles: Saboteur, Missing, Explorer, Actor, Collaborator and Director. We found that children interact socially, engage in play, help each other and mostly reach the level of collaboration. There are minority cases of non-cooperation (Troubled or Unproductive), with conflict situations or trial and error solving processes, which cause the task to last a long time before it is finally finished. We have also found that children can adopt different roles in the group. (...)Funding for open access charge: Universidad de Málaga / CBUA