Current and future multimodal learning analytics data challenges

Multimodal Learning Analytics (MMLA) captures, integrates and analyzes learning traces from different sources in order to obtain a more holistic understanding of the learning process, wherever it happens. MMLA leverages the increasingly widespread availability of diverse sensors, highfrequency data collection technologies and sophisticated machine learning and artificial intelligence techniques. The aim of this workshop is twofold: first, to expose participants to, and develop, different multimodal datasets that reflect how MMLA can bring new insights and opportunities to investigate complex learning processes and environments; second, to collaboratively identify a set of grand challenges for further MMLA research, built upon the foundations of previous workshops on the topic

2nd Crossmmla: Multimodal learning analytics across physical and digital spaces

© 2018 CEUR-WS. All Rights Reserved. Students’ learning is ubiquitous. It happens wherever the learner is rather than being constrained to a specific physical or digital learning space (e.g. the classroom or the institutional LMS respectively). A critical question is: how to integrate and coordinate learning analytics to provide continued support to learning across physical and digital spaces? CrossMMLA is the successor to the Learning Analytics Across Spaces (CrossLAK) and MultiModal Learning Analytics (MMLA) series of workshops that were merged in 2017 after successful cross-pollination between the two communities. Although it may be said that CrossLAK and MMLA perspectives follow different philosophical and practical approaches, they both share a common aim. This aim is: deploying learning analytics innovations that can be used across diverse authentic learning environments whilst learners feature various modalities of interaction or behaviour

Learning Sciences Beyond Cognition: Exploring Student Interactions in Collaborative Problem Solving

Composed of insightful essays from top figures in their respective fields, the book also shows how a thorough understanding of this critical discipline all but ensures better decision making when it comes to education

Gesture Assessment of Teachers in an Immersive Rehearsal Environment

Interactive training environments typically include feedback mechanisms designed to help trainees improve their performance through either guided- or self-reflection. When the training system deals with human-to-human communications, as one would find in a teacher, counselor, enterprise culture or cross-cultural trainer, such feedback needs to focus on all aspects of human communication. This means that, in addition to verbal communication, nonverbal messages must be captured and analyzed for semantic meaning. The goal of this dissertation is to employ machine-learning algorithms that semi-automate and, where supported, automate event tagging in training systems developed to improve human-to-human interaction. The specific context in which we prototype and validate these models is the TeachLivE teacher rehearsal environment developed at the University of Central Florida. The choice of this environment was governed by its availability, large user population, extensibility and existing reflection tools found within the AMITIES framework underlying the TeachLivE system. Our contribution includes accuracy improvement of the existing data-driven gesture recognition utility from Microsoft; called Visual Gesture Builder. Using this proposed methodology and tracking sensors, we created a gesture database and used it for the implementation of our proposed online gesture recognition and feedback application. We also investigated multiple methods of feedback provision, including visual and haptics. The results from the conducted user studies indicate the positive impact of the proposed feedback applications and informed body language in teaching competency. In this dissertation, we describe the context in which the algorithms have been developed, the importance of recognizing nonverbal communication in this context, the means of providing semi- and fully-automated feedback associated with nonverbal messaging, and a series of preliminary studies developed to inform the research. Furthermore, we outline future research directions on new case studies, and multimodal annotation and analysis, in order to understand the synchrony of acoustic features and gestures in teaching context

Measuring Mobile Collaborative Learning and academic achievement : whatsapp and students in South Africa

Mobile learning has developed into an essential component within the education landscape and, with two billion users worldwide, the social media platform WhatsApp has become a prominent feature in this domain. Nevertheless, with ambiguity in the literature about the effects of WhatsApp on teaching and learning and especially a paucity of research measuring collaboration on WhatsApp in relation to students’ academic achievement. The purpose of the study was to explain and predict WhatsApp’s effect on academic achievement using a quantitative questionnaire. The results suggest that increased collaboration on WhatsApp may improve academic achievement. Additionally, improving other aspects, such as active learning, trust, support, formality, interaction and interdependence, may enhance collaboration and, in turn, improve academic achievement. The study has value by providing measurable scientific evidence about the effects of WhatsApp on learning that can be incorporated into the design of teaching and learning activities with WhatsApp to improve academic achievement.Uhlelo lokufunda uhamba (Mobile learning) selikhule ladlondlobala laba yisigaba esibalulekile ngaphansi komkhakha wemfundo kanti, lolu hlelo selunabasebenzisi abangamabhiliyoni amabili emhlabeni wonke jikelele, uhlelo lwenkundla yezokuxhumana komphakathi lwe-WhatsApp seluyinkanyezi egqamile kulesi sizinda. Yize-kunjalo, kukhona okungacaci kahle mayelana nombhalo wobuciko kwimiphumela yohlelo lwe-WhatsApp mayelana nokufundisa kanye nokufunda, kanti ikakhulu, uhlelo lwezocwaningo olulinganisa izinga lokusebenzisana kohlelo lwe-WhatsApp okumayelana nokuphumelela kwabafundi kwizifundo zabo. Inhloso yalolu cwaningo kwaye kuwukuchaza kanye nokuhlahla umphumela wohlelo lwe-WhatsApp kwezemfundo, ngokusebenzisa uhlelo locwaningo lwemibuzo egxile kumanani (quantitative questionnaire) . Imiphumela iphakamisa ukuthi izinga lokusbenzisana ohlelweni lwe-WhatsApp lungathuthukisa umphumela wezemfundo. Ngaphezu kwalokho, lungathuthukisa ezinye izinhlaka, ezinjengohlelo lokufunda olumatasa. Lungaletha ukwethembana, ukuxhasana, ukwenza izinto ngendlela esemthethweni, lungaletha ukuxoxisana kanye nokusebenzisana kwangaphakathi, lungaqinisa ukusebenzisana, kanti ngakolunye uhlangothi, lungaletha impumelelo kwezemfundo. Ucwaningo lubalulekile ngoba lunikeza ubufakazi bezesayensi obulinganisekayo mayelana nemithelela yohlelo lwe-WhatsApp ohlelweni lokufunda, okuwuhlelo olungafakwa ngaphansi kohlelo lokudizayina imisebenzi yohlelo lokufunda nokufundisa ku-WhatsApp ukuthuthukisa ukwenza ngcono imiphumela yezemfundo.Mobiele leer het in ’n noodsaaklike komponent van die onderwyslandskap ontwikkel en met twee miljard gebruikers wêreldwyd, het die sosiale mediaplatform WhatsApp ’n prominente kenmerk van hierdie domein geword. Nogtans bestaan daar dubbelsinnigheid in die letterkunde oor die uitwerking van WhatsApp op onderrig en leer, en is daar veral ’n gebrek aan navorsing wat die samewerking op WhatsApp in verhouding tot die studente se akademiese prestasies meet. Die doel van hierdie studie was om WhatsApp se uitwerking op akademiese prestasie aan die hand van ’n kwantitatiewe vraelys te verduidelik en te voorspel. Die resultate stel voor dat ’n groter mate van samewerking op WhatsApp akademiese prestasie kan verbeter. Dit kan ook ander aspekte soos aktiewe leer, vertroue, ondersteuning, formaliteit, interaksie en onderlinge afhanklikheid verbeter en kan samewerking verhoog, wat op sy beurt akademiese prestasie kan verbeter. Die studie is waardevol in die sin dat dit meetbare, wetenskaplike bewyse oor die uitwerking van WhatsApp op leer verskaf het, wat by die ontwerp van onderrig- en leeraktiwiteite geïnkorporeer kan word om akademiese prestasie te verbeter.School of ComputingM. Tech. (Information Technology

The Big Five:Addressing Recurrent Multimodal Learning Data Challenges

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data