Modelling collaborative problem-solving competence with transparent learning analytics: is video data enough?

In this study, we describe the results of our research to model collaborative problem-solving (CPS) competence based on analytics generated from video data. We have collected ~500 mins video data from 15 groups of 3 students working to solve design problems collaboratively. Initially, with the help of OpenPose, we automatically generated frequency metrics such as the number of the face-in-the-screen; and distance metrics such as the distance between bodies. Based on these metrics, we built decision trees to predict students' listening, watching, making, and speaking behaviours as well as predicting the students' CPS competence. Our results provide useful decision rules mined from analytics of video data which can be used to inform teacher dashboards. Although, the accuracy and recall values of the models built are inferior to previous machine learning work that utilizes multimodal data, the transparent nature of the decision trees provides opportunities for explainable analytics for teachers and learners. This can lead to more agency of teachers and learners, therefore can lead to easier adoption. We conclude the paper with a discussion on the value and limitations of our approach

Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features

Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates high-fidelity synchronised multimodal recordings of small groups of learners interacting from diverse sensors that include computer vision, user generated content, and data from the learning objects (like physical computing components or laboratory equipment). We processed and extracted different aspects of the students' interactions to answer the following question: which features of student group work are good predictors of team success in open-ended tasks with physical computing? The answer to the question provides ways to automatically identify the students' performance during the learning activities

Quantifying Collaboration Quality in Face-to-Face Classroom Settings Using MMLA

Producción CientíficaThe estimation of collaboration quality using manual observation and coding is a tedious and difficult task. Researchers have proposed the automation of this process by estimation into few categories (e.g., high vs. low collaboration). However, such categorical estimation lacks in depth and actionability, which can be critical for practitioners. We present a case study that evaluates the feasibility of quantifying collaboration quality and its multiple sub-dimensions (e.g., collaboration flow) in an authentic classroom setting. We collected multimodal data (audio and logs) from two groups collaborating face-to-face and in a collaborative writing task. The paper describes our exploration of different machine learning models and compares their performance with that of human coders, in the task of estimating collaboration quality along a continuum. Our results show that it is feasible to quantitatively estimate collaboration quality and its sub-dimensions, even from simple features of audio and log data, using machine learning. These findings open possibilities for in-depth automated quantification of collaboration quality, and the use of more advanced features and algorithms to get their performance closer to that of human coders.European Union via the European Regional Development Fund and in the context of CEITER and Next-Lab (Horizon 2020 Research and Innovation Programme, grant agreements no. 669074 and 731685)Junta de Castilla y León (Project VA257P18)Ministerio de Ciencia, Innovación y Universidades (Project TIN2017-85179-C3-2-R

Facilitating self-regulated learning with personalized scaffolds on student's own regulation activities

The focus of education is increasingly set on students' ability to regulate their own learning within technology-enhanced learning environments. Scaffolds have been used to foster self-regulated learning, but scaffolds often are standardized and do not do not adapt to the individual learning process. Learning analytics and machine learning offer an approach to better understand SRL-processes during learning. Yet, current approaches lack validity or require extensive analysis after the learning process. The FLORA project aims to investigate how to advance support given to students by i) improving unobtrusive data collection and machine learning techniques to gain better measurement and understanding of SRL-processes and ii) using these new insights to facilitate student’s SRL by providing personalized scaffolds. We will reach this goal by investigating and improving trace data in exploratory studies (exploratory study 1 and study 2) and using the insight gained from these studies to develop and test personalized scaffolds based on individual learning processes in laboratory (experimental study 3 and study 4) and a subsequent field study (field study 5). At the moment study 2 is ongoing. The setup consists of a learning environment presented on a computer with a screen-based eye-tracker. Other data sources are log files and audio of students’ think aloud. The analysis will focus on detecting sequences that are indicative of micro-level self-regulated learning processes and aligning them between the different data sources

Reconsolidation of traumatic memories protocol compared to trauma-focussed cognitive behaviour therapy for post-traumatic stress disorder in UK military veterans: a randomised controlled feasibility trial

Background Post-traumatic stress disorder (PTSD) occurs more commonly in military veterans than the general population. Whilst current therapies are efective, up to half of veterans commencing treatment do not complete it. Reconsolidation of Traumatic Memories (RTM) protocol is a novel, easy to train, talking therapy with promising findings. We examine the feasibility of undertaking an efcacy trial of RTM in veterans. Methods A parallel group, single-centre randomised controlled feasibility trial with a post-completion qualitative interview study. Sixty military veterans were randomised 2:1 to RTM (n=35) or Trauma Focussed Cognitive Behaviour Therapy (CBT) (n=25). We aimed to determine the rate of recruitment and retention, understand reasons for attrition, determine data quality and size of efcacy signal. We explored veterans’ perceptions of experiences of joining the trial, the research procedures and therapy, and design improvements for future veteran studies. Military veterans with a diagnosis of PTSD or complex PTSD, and clinically signifcant symptoms, were recruited between January 2020 and June 2021. Primary outcome was feasibility using pre-determined progression criteria alongside PTSD symptoms, with depression, recovery, and rehabilitation as secondary outcomes. Data were collected at baseline, 6, 12, and 20 weeks. Interviews (n=15) were conducted after 20 weeks. Both therapies were delivered by trained charity sector provider therapists. Results Participants’ mean age was 53 years, the mean baseline PTSD symptoms score assessed by the Post-trau?matic Stress Checklist (PCL-5) was 57 (range 0–80). Fifty had complex PTSD and 39 had experienced≥4 traumas. Data were analysed at 20 weeks for feasibility outcomes (n=60) and mental health outcomes (n=45). Seven of eight progression criteria were met. The RTM group experienced a mean 18-point reduction on the PCL-5. TFCBT group participants experienced a mean reduction of eight points. Forty-eight percent of the RTM group no longer met diagnostic criteria for PTSD compared to 16% in the TFCBT group. All veterans reported largely positive experiences of the therapy and research procedures and ways to improve them. Conclusion RTM therapy remains a promising psychological intervention for the treatment of PTSD, including complex PTSD, in military veterans. With specifc strengthening, the research protocol is fit for purpose in delivering an efficacy trial.</p

EduBrowser : a multimodal automated monitoring system for co-located collaborative learning

Majority of learning analytics systems are designed to monitor and analyze students’ online interactions during collaborative learning. In the case of co-located collaborative learning, student interactions take place in the physical space as well as online. While existing learning management systems provide specific logs and snapshots of students’ online responses that are automatically captured, the potential of insights that can be derived from students’ non-digital face-to-face interactions during collaborative discourse remains untapped. In this paper, we propose an architecture for data acquisition and processing from co-located face-to-face collaborative learning, designed to be scalable beyond dyadic and triadic collaborative learning and across different curricula. We outline the system design, current experience of deployment across 4 sessions of co-located collaborative learning sessions, as well as brief examples of acquired data