An xAPI application profile to monitor self-regulated learning strategies

Self-regulated learning (SRL) is being promoted and adopted increasingly due to the needs of current education, student centered and focused on competence development. One of the main components of SRL is learners' self-monitoring, which eventually contributes to a better performance. Monitoring is also important for teachers, as it enables them to know to what extent their learners are doing well and progressing properly. At the same time, the use of technology for learning is now common and facilitates monitoring. Nevertheless, the available software still offers poor support from the SRL point of view, especially, for SRL monitoring. This clashes with the growth of learning analytics and educational data mining. The main issue is the wide variety of SRL actions that need to be captured, commonly performed in different tools, and the need to integrate them to support the development of analytics and data mining developments, making imperative the search of interoperable solutions. This paper focuses on the standardization of SRL traces to enable data collection from multiple sources and data analysis with the goal of easing the monitoring process for teachers and learners. First, the paper analyzes current monitoring software and its limitations for SRL. Then, after a brief analysis of available standards on this area, an application profile for the eXperience API specification is proposed to enable the interoperable recording of the SRL traces. The paper describes the process followed to create the profile, from the analysis to the final implementation, including the selection of the interactions that represent relevant SRL actions, the selection of vocabularies to record them and a case study.Xunta de Galicia | Ref. ED431B 2017/67Xunta de Galicia | Ref. ED431D 2017/1

Variability Handling in Educational Context

Today there are many different forms of educational activities present, e.g., traditional lecturing, e-learning, blended learning and living labs. Also, the audience becomes more and more international and heterogeneous in terms of background knowledge of students, their educational purposes, capabilities and expectations. This introduces a high level of variability in educational settings and requires new methods and tools for managing this variability. Customized application of feature models, known in software product line management, is one possible solution applicable for variability handling in educational context. This paper proposes the development of a feature model as the method for variability handling

Proceedings of the 4th Workshop on Awareness and Reflection in Technology Enhanced Learning. In conjunction with the 9th European Conference on Technology Enhanced Learning: Open Learning and Teaching in Educational Communities

Awareness and reflection can be viewed from the differing perspectives of the disciplines informing Technology-Enhanced Learning, such as Psychology, Educational and Learning Sciences, or Computer Science. A common denominator can be identified, though, and enhancing ’awareness’ of learners and other participants involved in learning processes by technology means augmenting formal or informal learning experiences, typically in real-time, with information on progress, presence, outcomes, workspace, and the like. Supporting ’reflection’ then means enabling learners to capture, adapt, re-evaluate, and share experience in anticipation of future situations it will prove relevant to. Reflection supported digitally is a creative act, adding sense and meaning to experiences made. Combining support for 'awareness' and 'reflection' bears huge potential for improving the learning and training with respect to utility, self-regulation, usability, and user experience. The ARTEL workshop series brings - for the 4th time in 2014 - together researchers and professionals from different backgrounds to provide a forum for discussing the multi-faceted area of awareness and reflection. For this year 2014, the workshop organizes discussion and meta-reflection amongst researchers around the application of awareness and reflection in practice, its impact on learners and questions of feasibility, and sustainability for awareness and reflection in education and work. This year's workshop theme is: How does computer-support for awareness and reflection need to be embedded into practical (working or learning) contexts in order for learners to benefit from such computer support

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

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks

Instructional Designers Conducting Professional Learning Using Social Media: A Phenomenological Study of Their Experiences Through a Self-regulated Learning Lens

Because the instructional design and technology field is dynamic (Sharif & Cho, 2015; Wang et al., 2021), instructional designers need to pursue continuous, just-in-time professional learning (Carliner, 2018) to improve knowledge, skills, and abilities (Sharif & Cho, 2015; Ritzhaupt & Martin, 2015), without being constrained by location, budget, and time (Muljana et al., 2020; Muljana et al., 2021). On the one hand, the omnipresent social media technologies offer affordances for facilitating this type of professional learning. Such technologies allow instructional designers to reach out to colleagues, search for ready-to-implement strategies, and find relevant, timely information. On the other hand, conducting continuous learning requires proactive and strategic planning, in which self-regulated learning (SRL) plays a role. Unfortunately, not all working professionals are aware of the strategies to develop SRL skills. In addition, using social media may be perceived as a learning distraction. A call for an in-depth exploration of intersecting instructional designers’ continuous professional learning, social media, and SRL emerges to address such challenges. This qualitative study is aimed to explore instructional designers’ SRL experiences conducting professional learning using social media. Three research questions guide this study: (1) How were instructional designers’ SRL experiences conducting professional learning in a social media environment? (2) How did instructional designers support their SRL by using social media? (3) What challenges did instructional designers experience when conducting professional learning using social media? These questions are addressed through a phenomenological study that employs semi-structured interviews and thematic analysis using multiple coding approaches. The findings suggest that an application of SRL seems to occur while instructional designers use social media for professional learning (e.g., through determining the sources of motivation, setting proximal goals and strategic plans, seeking help, trying the strategies offered by colleagues, an adaptation of strategies, and open-minded attitudes during self-reflection activities). Additionally, there appears to be a gradual development of SRL skills while instructional designers interact in social media environments. They also encounter challenges, but some challenges can potentially be overcome by applying SRL strategies. Discussion and implications inform (a) instructional designers who pursue continuous professional development, (b) educational programs and instructors who educate prospective instructional designers regarding ways to promote relevant skills by scaffolding SRL skills and considering social media-supported learning, and (c) employers and those with supervisory roles who support employee’s just-in-time learning

Integration of a recommender system into an online video streaming platform

The ultimate goal of this project is to develop a recommender system for the SmartVideo platform. The platform streams different content of local channels for the Grand Est Region of France to a large public. So, we aim to propose a solution to alleviate the data representation and data collection issue of recommender systems by adopting and adjusting the xAPI standard to fit our case of study and to be able to represent our usage data in a formal and consistent format. Then, we will propose and implement a bunch of recommendation algorithms that we are going to test in order to evaluate our developed recommender system.Le but ultime de ce projet est de développer un système de recommandation dédié à la plateforme SmartVideo de diffusion de vidéo en ligne. En effet, la plateforme met à disposition diverses contenus des chaînes locales de la région Grand Est du France. Alors, nous allons présenter une solution pour alléger le problème de représentation et de collecte de données d’usages par adopter et ajuster le standard xAPI pour représenter et collecter les données de façon simple et formelle. Ensuite, nous allons proposer et implanter des algorithmes de recommandation que nous allons les tester pour évaluer notre système de recommandation