Teaching Analytics: Towards Automatic Extraction of Orchestration Graphs Using Wearable Sensors

"Teaching analytics" is the application of learning analytics techniques to understand teaching and learning processes, and eventually enable supportive interventions. However, in the case of (often, half-improvised) teaching in face-to-face classrooms, such interventions would require first an understanding of what the teacher actually did, as the starting point for teacher reflection and inquiry. Currently, such teacher enactment characterization requires costly manual coding by researchers. This paper presents a case study exploring the potential of machine learning techniques to automatically extract teaching actions during classroom enactment, from five data sources collected using wearable sensors (eye-tracking, EEG, accelerometer, audio and video). Our results highlight the feasibility of this approach, with high levels of accuracy in determining the social plane of interaction (90%, k=0.8). The reliable detection of concrete teaching activity (e.g., explanation vs. questioning) accurately still remains challenging (67%, k=0.56), a fact that will prompt further research on multimodal features and models for teaching activity extraction, as well as the collection of a larger multimodal dataset to improve the accuracy and generalizability of these methods

A Functional Taxonomy of Music Generation Systems

Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.Comment: survey, music generation, taxonomy, functional survey, survey, automatic composition, algorithmic compositio

The impact of study load on the dynamics of longitudinal email communications among students

With the advent of information technology, emails have gained wide acceptability among students as an asynchronous communication tool. According to the current pedagogy literature the overall trend of the use of email communication by university students has been increasing significantly since its inception, despite the rapid growth of the popularity and acceptability of other social mediums (e.g. Mobile phone and Facebook). In this study, we explore a longitudinal email communication network, which evolved under an increasing study load among 38 students throughout a university semester, using measures of social network analysis (SNA) and exponential random graph (ERG) models. This longitudinal network was divided into three waves, where each wave represents the portion of the complete longitudinal network that evolves between two consecutive observations. An increased study load was imposed through the assessment components of the course. SNA measures of degree centrality (i.e. the activity of an actor or actor popularity), betweenness centrality (i.e. the capacity to control the flow of information in a network), closeness centrality (i.e. reachable to other nodes) and reciprocity (i.e. tendency to make reciprocal links) are considered to explore this longitudinal network. ERG models are probabilistic models that are presented by locally determined explanatory variables and can effectively identify structural properties of networks. From the analysis of this email communication network, we notice that students’ network positions and behaviours change with the changes in their study load. In particular, we find that (i) students make an increased number of email communications with the in-crease of study load; (ii) the email communication network become sparse with the increase of study load; and (iii) the 2-star parameter (a subset of three nodes in which one node is connected to each of the other two nodes) and the triangle parameter (a subset of three nodes in which each node is connected to the other two nodes) can effectively explain the formation of network in wave3; whereas, the 3-star parameter (a subset of four nodes in which one node is connected to each of other three nodes) can effectively explain the formation of network in wave1 and wave2. Interpretations of these findings for the monitoring of student behaviour in online learning environments, as well as the implications for the design of assessment and the use of asynchronous tools are discussed in this paper

Pervasive learning analytics for fostering learners' self-regulation

Today's tertiary STEM (Science, Technology, Engineering and Mathematics) education in Europe poses problems to both teachers and students. With growing enrolment numbers, and numbers of teaching staff that are outmatched by this growth, student-teacher contact becomes more and more difficult to provide. Therefore, students are required to quickly adopt self-regulated and autonomous learning styles when entering European universities. Furthermore, teachers are required to divide their attention between large numbers of students. As a consequence, classical teaching formats of STEM education which often encompass experimentation or active exploration, become harder to implement. Educational software holds the promise of easing these problems, or, if not fully solving, at least of making them less acute: Learning Analytics generated by such software can foster self-regulation by providing students with both formative feedback and assessments. Educational software, in form of collaborative social media, makes it easier for teachers to collaborate, allows to reduce their workload and enables learning and teaching formats otherwise infeasible in large classes. The contribution of this thesis is threefold: Firstly, it reports on a social medium for tertiary STEM education called "Backstage2 / Projects" aimed specifically at these points: Improving learners' self-regulation by providing pervasive Learning Analytics, fostering teacher collaboration so as to reduce their workload, and providing means to deploy a variety of classical and novel learning and teaching formats in large classes. Secondly, it reports on several case studies conducted with that medium which point at the effectiveness of the medium and its provided Learning Analytics to increase learners' self-regulation, reduce teachers' workload, and improve how students learn. Thirdly, this thesis reports on findings from Learning Analytics which could be used in the future in designing further teaching and learning formats or case studies, yielding a rich perspective for future research and indications for improving tertiary STEM education

Analysing, visualising and supporting collaborative learning using interactive tabletops

The key contribution of this thesis is a novel approach to design, implement and evaluate the conceptual and technological infrastructure that captures student’s activity at interactive tabletops and analyses these data through Interaction Data Analytics techniques to provide support to teachers by enhancing their awareness of student’s collaboration. To achieve the above, this thesis presents a series of carefully designed user studies to understand how to capture, analyse and distil indicators of collaborative learning. We perform this in three steps: the exploration of the feasibility of the approach, the construction of a novel solution and the execution of the conceptual proposal, both under controlled conditions and in the wild. A total of eight datasets were analysed for the studies that are described in this thesis. This work pioneered in a number of areas including the application of data mining techniques to study collaboration at the tabletop, a plug-in solution to add user-identification to a regular tabletop using a depth sensor and the first multi-tabletop classroom used to run authentic collaborative activities associated with the curricula. In summary, while the mechanisms, interfaces and studies presented in this thesis were mostly explored in the context of interactive tabletops, the findings are likely to be relevant to other forms of groupware and learning scenarios that can be implemented in real classrooms. Through the mechanisms, the studies conducted and our conceptual framework this thesis provides an important research foundation for the ways in which interactive tabletops, along with data mining and visualisation techniques, can be used to provide support to improve teacher’s understanding about student’s collaboration and learning in small groups

Enhancing Situational Awareness for Tutors of Cybersecurity Capture the Flag Games

Supervised Capture the Flag games represent a popular method of practical hands-on training in cybersecurity education. However, as cybersecurity training sessions are process-oriented, tutors have only a limited insight into what trainees are doing and how they deal with the tasks. From their perspective, it is necessary to have situational awareness, enabling them to identify and react to any issues during a training session as soon as they emerge. We propose a tool designed in collaboration with cybersecurity educators. Based on user requirements, we developed the Progress Visualization Tool, which provides educators with timely feedback through the session. More specifically, the tool informs educators of the training progression, helps identify the students who might struggle with their tasks, and reveals overall deviation from the schedule. We validated the tool through formative and summative qualitative in-lab evaluations. The participants appraised the impact on the training workflow and gave further insights regarding the tool. We discuss the insights and recommendations that arose from the evaluations as they could aid the design of future tools for supporting educators, not only of CTFs but also in other domains.Supervised Capture the Flag games represent a popular method of practical hands-on training in cybersecurity education. However, as cybersecurity training sessions are process-oriented, tutors have only a limited insight into what trainees are doing and how they deal with the tasks. From their perspective, it is necessary to have situational awareness, enabling them to identify and react to any issues during a training session as soon as they emerge. We propose a tool designed in collaboration with cybersecurity educators. Based on user requirements, we developed the Progress Visualization Tool, which provides educators with timely feedback through the session. More specifically, the tool informs educators of the training progression, helps identify the students who might struggle with their tasks, and reveals overall deviation from the schedule. We validated the tool through formative and summative qualitative in-lab evaluations. The participants appraised the impact on the training workflow and gave further insights regarding the tool. We discuss the insights and recommendations that arose from the evaluations as they could aid the design of future tools for supporting educators, not only of CTFs but also in other domains

Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data

© 2019, Copyright © 2017 Taylor & Francis Group, LLC. Learning to collaborate effectively requires practice, awareness of group dynamics, and reflection; often it benefits from coaching by an expert facilitator. However, in physical spaces it is not always easy to provide teams with evidence to support collaboration. Emerging technology provides a promising opportunity to make collocated collaboration visible by harnessing data about interactions and then mining and visualizing it. These collocated collaboration analytics can help researchers, designers, and users to understand the complexity of collaboration and to find ways they can support collaboration. This article introduces and motivates a set of principles for mining collocated collaboration data and draws attention to trade-offs that may need to be negotiated en route. We integrate Data Science principles and techniques with the advances in interactive surface devices and sensing technologies. We draw on a 7-year research program that has involved the analysis of six group situations in collocated settings with more than 500 users and a variety of surface technologies, tasks, grouping structures, and domains. The contribution of the article includes the key insights and themes that we have identified and summarized in a set of principles and dilemmas that can inform design of future collocated collaboration analytics innovations