Multimodal Learning Analytics Archives - Page 2 of 3

Mar232022 by Prof. Dr. Hendrik DrachslerNo Comments

New Pub: Towards Collaborative Convergence: Quantifying Collaboration Quality with Automated Co-located Collaboration Analytics

Conference, Conference, Event, Higher Education, Learning Design, Multimodal Learning Analytics, Publication

Collaboration is one of the four important 21st-century skills. With the pervasive use of sensors, interest on co-located collaboration (CC) has increased lately. Most related literature used the audio modality to detect indicators of collaboration (such as total speaking time and turn taking). CC takes place in physical spaces where group members share their social (i.e., non-verbal audio indicators like speaking time, gestures) and epistemic space (i.e., verbal audio indicators like the content of the conversation). Past literature has mostly focused on the social space to detect the quality of collaboration. In this study, we focus on both social and epistemic space with an emphasis on the epistemic space to understand different evolving collaboration patterns and collaborative convergence and quantify collaboration quality. We conduct field trials by collecting audio recordings…

Mar112022 by Prof. Dr. Hendrik DrachslerNo Comments

PhD defense: Measuring the Unmeasurable? Towards Automatic Co-located Collaboration Analytics

Artificial Intelligence, Book, Higher Education, Learning Analytics, Multimodal Learning Analytics, PhD defense, Press

Collaboration is one of the most important skills in the 21st century. Education, therefore, focuses on learning to cooperate, both in online and face-to-face settings. Learning Analytics is increasingly being used to analyse collaborations. Can face-to-face collaborations be analysed automatically by means of sensor technology? And what is the quality of the analyses generated by this? Sambit Praharaj developed a technical prototype to achieve automated collaboration analytics. On Friday 11 March 2022 at 1.30 pm Sambit defended his thesis 'Measuring the Unmeasurable? Towards Automatic Co-located Collaboration Analytics' at the Open University in Heerlen. Collaboration analytics using sensor technology Sambit Praharaj investigated the possibilities of automating collaboration analytics in face-to-face settings. He developed a prototype that measures the quality of collaboration. Praharaj also developed a dashboard that visualises the data and…

Nov112021 by Daniele Di MitriNo Comments

Milky-Psy first face-to-face meeting

Artificial Intelligence, General education, Multimodal Learning Analytics, Project meeting

On November 9, 2021, the first face-to-face meeting of the BMBF funded project “Multimodal Immersive Learning with Artificial Intelligence for Psychomotor Skills” (Milki-Psy) consortium took place in the city of Cologne where the DIPF, as an active member, was represented by Dr Daniele Di Mitri, Dr Jan Schneider, Gianluca Romano and Fernando P. Cardenas-Hernandez. The purpose of this meeting was to present the progress of each project partner as well as to propose and discuss possible solutions for the two case studies of this project, which are running case and robot case. (more…)

Oct192021 by George-Petru Ciordas-HertelNo Comments

New Pub: Mobile Sensing with Smart Wearables of the Physical Context of Distance Learning Students to Consider Its Effects on Learning

General education, Journal, Learning Analytics, Lifelong Learning, Multimodal Learning Analytics, Open access, Project, Publication, Research topic, Target group, Technical paper

Research shows that various contextual factors can have an impact on learning. Some of these factors can originate from the physical learning environment (PLE) in this regard. When learning from home, learners have to organize their PLE by themselves. This paper is concerned with identifying, measuring, and collecting factors from the PLE that may affect learning using mobile sensing. More specifically, this paper first investigates which factors from the PLE can affect distance learning. The results identify nine types of factors from the PLE associated with cognitive, physiological, and affective effects on learning. Subsequently, this paper examines which instruments can be used to measure the investigated factors. The results highlight several methods involving smart wearables (SWs) to measure these factors from PLEs successfully. Third, this paper explores how software infrastructure…

Oct122021 by Jan SchneiderNo Comments

New Pub: Analysis of the “D’oh!” moments.

Artificial Intelligence, Conference, Conference, Further Education, Higher Education, Lifelong Learning, Multimodal Learning Analytics, Publication

“Soul and body, I suggest reacting sympathetically upon each other. A change in the state of the soul produces a change in the shape of the body and conversely, a change in the shape of the body produces a change in the state of the soul.” To test this hypothesis proposed by Aristotle, our bachelor student Tetiana Buraha investigated the physiology of students performing task-switching exercises. The physiological data were collected using an Empatica E4 band. The performances of the students were compared against the physiological data using descriptive statistics and machine learning techniques. The analysis of Tetiana enabled the identification of interesting correlations between galvanic skin response and performance, and models to predict performance based on the physiological data. Results of her excellent thesis were published and presented at…

Oct122021 by Jan SchneiderNo Comments

New Pub: Get your Back Straight!

Conference, Conference, Game, Lifelong Learning, Multimodal Learning Analytics, Publication

“Mens sana in corpore sano” is a phrase that we all have heard multiple times in our life. We know all the positive effects of Sports and Exercise. However, let’s face it! Physical activity is not always fun and when doing it incorrectly without any guidance or feedback it is difficult to see improvements or even worse, we can face the risk of injury. To address these issues Anna Meik for her bachelor thesis developed the Pilates correction App. An application designed to support the practice of the “Kneeling Arm and Leg Reach” Pilates exercise in a gamified way, where the user helps a virtual rocket to reach high scores through the stability of their lower backs. Meik, A., Schneider, J., & Schiffner, D. (2021). Get your back straight! Learn…

Sep82021 by Prof. Dr. Hendrik DrachslerNo Comments

New MMLA Pub: Get your back straight! Learn Pilates with the Pilates Correction App

Conference, General education, Multimodal Learning Analytics, Open access, Publication

Currently, a vast number of the population faces several barriers like the lack of motivation and guidance that impede them from practicing physical activities. Thus, we developed the Pilates Correction Game (PCG), a gamified application designed to support learners with the practice of Pilates. The PCG is composed of two applications: a smartphone application that tracks the learner’s back posture and a PC game that steers a rocket and calculates a score based on the smartphone's information. In this paper, we present a user experience evaluation on the PCG. Our results show that PCG was positively perceived by participants and in most cases helped them to improve their posture while doing the Pilates exercise. Furthermore, it is also motivating them to continue with the training. Reference: Meik, A., Schneider, J.,…

Jul282021 by Gianluca RomanoNo Comments

Gianluca Romano joins the team

Higher Education, Multimodal Learning Analytics, Team

Starting July 2021, Gianluca Romano joins the team as a doctoral researcher. He holds a Master's as well as a Bachelor's degree in Computer Science from the Goethe University Frankfurt. He finished his study with a thesis on an Intelligent Tutoring System for dancing which was also published in an online journal. Further, he has experience as an AI Engineer and currently is also employed as a Machine Learning Engineer.

Jul282021 by Fernando P. Cardenas-HernandezNo Comments

Fernando P. Cardenas-Hernandez joins the team

Multimodal Learning Analytics, Project, Team

Starting 1st July 2021, Fernando P. Cardenas-Hernandez joins the team as a doctoral researcher. He earned his Master’s degree in Microsystems from the University of Freiburg. After his graduation, he worked as a software engineer in different companies. Some of his previous projects made use of microcontrollers, SBCs and thermal & industrial cameras. He is currently involved in the MILKI-PSY project.

Jul212021 by Prof. Dr. Hendrik DrachslerNo Comments

New Pub: Literature Review on Co-Located Collaboration Modeling Using Multimodal Learning Analytics—Can We Go the Whole Nine Yards?

General education, Journal, Literature review, Multimodal Learning Analytics, Open access, Publication

Collaboration is one of the important 21st-century skills. It can take place in remote or co-located settings. Co-located collaboration (CC) is a very complex process that involves subtle human interactions that can be described with indicators like eye gaze, speaking time, pitch, and social skills from different modalities. With the advent of sensors, multimodal learning analytics has gained momentum to detect CC quality. Indicators (or low-level events) can be used to detect CC quality with the help of measurable markers (i.e., indexes composed of one or more indicators) which give the high-level collaboration process deﬁnition. However, this understanding is incomplete without considering the scenarios (such as problem solving or meetings) of CC. The scenario of CC affects the set of indicators considered: for instance, in collaborative programming, grabbing the mouse…