Target group Archives - Page 16 of 17

Jul282021 by Prof. Dr. Hendrik DrachslerNo Comments

QE Webinar series moderated by DIPF

Further Education, General education, Higher Education, Lifelong Learning, School, Webinar

The monthly QE Webinar Series showcases emerging research and exciting new developments in Quantitative Ethnography. These one-hour events begin with a 30-minute presentation by scholars from the QE community, followed by 30 minutes of moderated discussion. Attendance is free and open to anyone who is interested! The DIPF is currently moderating a series of webinars of the Quantified Ethnography (QE) community as pre-events to the QE conference taking place in October. So far we have been discussing the following topics and the recording can be watched back: Putting the E in QE: How Quantitative Ethnography can Enrich Qualitative Analyses Yotam Hod University of Haifa QE Data ChallengeField Report: Reflecting on the benefits of multidisciplinary research teams Daniel Spikol University of Copenhagen, DK Barbara Wasson University of Bergen, Norway Stefano Schiavetto…

Jul282021 by GianlucaNo 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.

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…

Jul152021 by Sebastian WollnyNo Comments

New Pub: Are We There Yet? – A Systematic Literature Review on Chatbots in Education

Artificial Intelligence, General education, Journal, Learning Analytics, Literature review, Open access, Publication

Are We There Yet? - A Systematic Literature Review on Chatbots in Education Chatbots are a promising technology with the potential to enhance workplaces and everyday life. In terms of scalability and accessibility, they also offer unique possibilities as communication and information tools for digital learning. In this work, we present a systematic literature review investigating the areas of education where chatbots have already been applied, explore the pedagogical roles of chatbots, the use of chatbots for mentoring purposes, and their potential to personalize education. We conducted a preliminary analysis of 2,678 publications to perform this literature review, which allowed us to identify 74 relevant publications for chatbots’ application in education. Through this, we address five research questions that, together, allow us to explore the current state-of-the-art of this educational…

Jul62021 by Prof. Dr. Hendrik DrachslerNo Comments

SoLAR webinar on: Investigating persistent and new challenges of learning analytics

Artificial Intelligence, Computational Psychometrics, Higher Education, Keynote, Learning Analytics, Learning Design, Multimodal Learning Analytics, School, Workshop

On 29 June 2021, Hendrik Drachsler gave an invited webinar for the SoLAR webinar channel where he reflected the process of applying learning analytics solutions within higher education settings and schools, its implications, and the critical lessons learned in the Trusted Learning Research Program. The talk focused on the experience of edutec.science research collective consisting of researchers from the Netherlands and Germany that contribute to the Trusted Learning Analytics (TLA) research program. The TLA program aims to provide actionable and supportive feedback to students and stands in the tradition of human-centred learning analytics concepts. It, therefore, applies sensor technology to support psychomotor as well as web technology to support meta-cognitive and collaborative learning skills with high-informative feedback methods. Hendrik applies validated measurement instruments from the field of psychometric and investigates…

Jun292021 by Prof. Dr. Hendrik DrachslerNo Comments

New Pub: An exploratory latent class analysis of student expectations towards learning analytics services

Empirical Study, Higher Education, Open access, Publication

For service implementations to be widely adopted, it is necessary for the expectations of the key stakeholders to be considered. Failure to do so may lead to services reflecting ideological gaps, which will inadvertently create dissatisfaction among its users. Learning analytics research has begun to recognise the importance of understanding the student perspective towards the services that could be potentially offered; however, student engagement remains low. Furthermore, there has been no attempt to explore whether students can be segmented into different groups based on their expectations towards learning analytics services. In doing so, it allows for a greater understanding of what is and is not expected from learning analytics services within a sample of students. The current exploratory work addresses this limitation by using the three-step approach to latent class…

Jun152021 by Prof. Dr. Hendrik DrachslerNo Comments

Kickoff: HELA – Improving HEI maturity to implement learning analytics

Digitalisation, Learning Analytics, Project, School

In February 2021, the Croatian Science Foundation (HRZZ) approved funding of the research project "HELA - Improving HEI maturity to implement learning analytics" that will be implemented during the next 4 years at the Faculty of Organization and Informatics (FOI), University of Zagreb under the leadership of Full Prof. Nina Begičević Ređep, PhD The project, which started with its activities on March 1, 2021, will be financed by the HRZZ in the total amount of 776,230.00 HRK. The purpose of this research project is to contribute to a better understanding and optimization of the teaching and learning processes supported by LA through improving HEI maturity to implement LA. The expected impact of the research will be reflected in faster implementation of learning analytics in higher education, improved evidence-based strategic decision-making and…

Jun12021 by Prof. Dr. Hendrik DrachslerNo Comments

Kickoff Meeting of HICOF-DL – Highly Informative and Competence-based Feedback for Digital Learning

Computational Psychometrics, Higher Education, Learning Analytics, Project

Currently, almost all students learn only digitally at home, because in the wake of the COVID 19 pandemic, universities have largely converted their teaching to online courses. But the virtual lectures and seminars often still lack individual feedback for learners. A new project conducted by the DIPF | Leibniz Institute for Human Development and Educational Information and the Goethe University Frankfurt aims to change that. The researchers want to develop a software tool that uses modern, automated evaluation methods to help provide accurate feedback. Derzeit lernen fast alle Studierenden nur digital zu Hause, denn im Zuge der COVID-19-Pandemie haben die Hochschulen ihren Lehrbetrieb größtenteils auf Online-Veranstaltungen umgestellt. Doch den virtuellen Vorlesungen und Seminaren mangelt es häufig noch an individuellen Rückmeldungen für die Lernenden. Ein neues, vom DIPF | Leibniz-Institut für…

May32021 by Prof. Dr. Hendrik DrachslerNo Comments

New Pub: Towards Automatic Collaboration Analytics for Group Speech Data Using Multimodal Learning Analytics

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

Collaboration is an important 21st Century skill. Co-located (or face-to-face) collaboration (CC) analytics gained momentum with the advent of sensor technology. Most of these works have used the audio modality to detect the quality of CC. The CC quality can be detected from simple indicators of collaboration such as total speaking time or complex indicators like synchrony in the rise and fall of the average pitch. Most studies in the past focused on “how group members talk” (i.e., spectral, temporal features of audio like pitch) and not “what they talk”. The “what” of the conversations is more overt contrary to the “how” of the conversations. Very few studies studied “what” group members talk about, and these studies were lab based showing a representative overview of specific words as topic clusters…

Apr162021 by Prof. Dr. Hendrik DrachslerNo Comments

Best Paper award @LAK21 – Quantum of Choice

Award, Conference, Empirical Study, Higher Education, Learning Analytics, Open access, Publication

Learning analytics dashboards (LADs) are designed as feedback tools for learners, but until recently, learners rarely have had a say in how LADs are designed and what information they receive through LADs. To overcome this shortcoming, we have developed a customisable LAD for Coursera MOOCs on which learners can set goals and choose indicators to monitor. Following a mixed-methods approach, we analyse 401 learners’ indicator selection behaviour in order to understand the decisions they make on the LAD and whether learner goals and self-regulated learning skills influence these decisions. We found that learners overwhelmingly chose indicators about completed activities. Goals are not associated with indicator selection behaviour, while help-seeking skills predict learners’ choice of monitoring their engagement in discussions and time management skills predict learners’ interest in procrastination indicators. The…