New Pub: Superpowers in the Classroom: Hyperchalk is an Online Whiteboard for Learning Analytics Data Collection

New Pub: Superpowers in the Classroom: Hyperchalk is an Online Whiteboard for Learning Analytics Data Collection

Computer-supported collaborative learning, Conference, Conference, Event, Further Education, General education, Higher Education, Learning Analytics, Lifelong Learning, Publication, School, Technical paper
A new system demonstration paper authored by Lukas Menzel, Sebastian Gombert, Daniele Di Mitri and Henrik Drachsler has been released as part of the ECTEL 2022 proceedings. In this paper, we present Hyperchalk, a self-hosted collaborative online whiteboard software. Similar to commercial solutions like Miro or Flinga, this software provides users with collaborative boards which they can use to draw, write or sketch together. However, unlike commercial solutions, Hyperchalk allows for collecting rich log data, which can be used to study the behaviour of its users and to allow Learning Analytics and studies on computer-supported collaborative learning. Moreover, Hyperchalk comes with a built-in replay mode which allows watching how users behave in its spaces. It supports the LTI1.3 standard, which enables seamless integration with learning management systems such as Moodle,…
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New Pub: FoLA2 — A Method for Co-creating Learning Analytics–Supported Learning Design

New Pub: FoLA2 — A Method for Co-creating Learning Analytics–Supported Learning Design

Further Education, General education, Higher Education, Journal, Learning Analytics, Learning Design, Open access, Publication, School
Finally, we have published an article about the FOLA2 method that we have used for over two years frequently to design Learning Analytics supported Learning Designs with teachers of various backgrounds. Learning activities are at the core of every educational design effort. Designing learning activities is a process that benefits from reflecting on previous runs of those activities. One way to measure the behaviour and effects of design choices is to use learning analytics (LA). The challenge, however, lies in the unavailability of an easy-to-use, LA-supported learning design (LD) method. We established a method—the Fellowship of Learning Activities and Analytics (FoLA2)—reinforced by a gameboard and cards to provide structure and inspiration. The method enables several participants with different roles to interact with a set of card decks to create an…
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New Pub: Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence-centred design

New Pub: Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence-centred design

Computational Psychometrics, Journal, Learning Analytics, Learning Design, Open access, Project, Publication, School
[caption id="attachment_4319" align="alignright" width="450"] A procedure for developing digital learning environments that allow for the automated assessment of learning.[/caption] National educational standards stress the importance of science and mathematics learning for today’s students. However, across disciplines, students frequently struggle to meet learning goals about core concepts like energy. Digital learning environments enhanced with artificial intelligence hold the promise to address this issue by providing individualized instruction and support for students at scale. Scaffolding and feedback, for example, are both most effective when tailored to students’ needs. Providing individualized instruction requires continuous assessment of students’ individual knowledge, abilities, and skills in a way that is meaningful for providing tailored support and planning further instruction. While continuously assessing individual students’ science and mathematics learning is challenging, intelligent tutoring systems show that it…
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Transfer Workshop @ Digitaltag 2022 on highly informative and data-driven feedback with AI in higher education

Transfer Workshop @ Digitaltag 2022 on highly informative and data-driven feedback with AI in higher education

Artificial Intelligence, Conference, Event, Higher Education, Learning Analytics, Open access, School, Transfer Activity, Webinar, Workshop
The third Digitaltag will take place on June 24, 2022. The initiative "Digital for All" is behind the Digitaltag. The alliance of more than 25 organizations from the fields of civil society, culture, science, business, welfare and the public sector unites a common goal: to promote digital participation in Germany. Prof. Dr. Hendrik Drachsler contributes a talk on highly informative and data-driven feedback with AI in higher education. Feedback is a powerful element of all learning processes. However, it is difficult to offer highly informative feedback to large groups of learners. The Educational Technology Collective at Goethe University and the DIPF Leibniz Institute Frankfurt am Main are conducting research on this challenge in a variety of projects. In this talk, Professor Dr. Drachsler will present on designing highly informative feedback…
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New Pub: Effects of Intelligent Tutoring Systems on Mathematics Achievement of Underachieving Students

New Pub: Effects of Intelligent Tutoring Systems on Mathematics Achievement of Underachieving Students

Conference, School
Rashmi Kazhanchi, an external PhD candidate supervised by prof Drachsler and Dr Di Mitri, has presented her latest publication Society for Information Technology & Teacher Education International Conference, Apr 11, 2022, in San Diego, CA, United States. The article titled: "Effects of Intelligent Tutoring Systems on Mathematics Achievement of Underachieving Students" Abstract The educational landscape has changed considerably in the past decade. The focus has shifted from using traditional teaching methods to integrating technology in today’s curricula. Much of the emphasis is on fostering 21st-century skills by using technology in new and innovative ways. Student engagement is a crucial component of learning. Intelligent Tutoring Systems (ITSs) are computer software that uses artificial intelligence to provide adaptive, customized learning based on students’ needs. This quasi-experimental research study explored the effect of…
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Keynote at LERN Jahrestagung 2022

Keynote at LERN Jahrestagung 2022

Computational Psychometrics, Higher Education, Invited talk, Keynote, Learning Analytics, Learning Design, School
On 30 March 2022, Hendrik Drachsler gave an invited keynote at the LERN conference 2022 where he reported on the latest research on data-driven high informative feedback in higher education and schools. The talk focused on the experience of edutec.science research collective that has been gained in the projects DIFA, HIKOF-DL, AFLEK and ALICE. The presentation provided an overview of actionable and supportive feedback to learners as well as the various technical products that the EduTec group has developed. It, therefore, applies web technology to support meta-cognitive and collaborative learning skills with high-informative feedback methods. Hendrik applies validated measurement instruments from the field of psychometrics and investigates to what extent Learning Analytics interventions can reproduce the findings of these instruments. He discussed the lessons learned from implementing TLA systems. He…
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AR4STEAM Meeting in Brussels: Successfull Ending of our Erasmus Plus Cooperation with Schools

AR4STEAM Meeting in Brussels: Successfull Ending of our Erasmus Plus Cooperation with Schools

Augmented Reality, Conference, Digitalisation, Event, Learning Analytics, Project, School
Today we gather in Brussels to celebrate the successful end of our 2,5 year collaboration on promoting Augmented Reality in STEM classes all over Europe. It has been our great pleasure to work on this EU project and to share a vision even in difficult times. #Schools #Science #STEM #Arts #Friendship #Europa #Europe #ErasmusPlus #Schools #AugmentedReality #Peace 
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Interview with Bildung.Table

Interview with Bildung.Table

Artificial Intelligence, Higher Education, Learning Analytics, Press, Project, School
On 23.02.2022 Hendrik Drachsler was interviewed by Niklas Prenzel from Table.Media about Artificial Intelligence in university teaching and the joint project IMPACT. In the interview, he outlines how AI can support teaching and help reduce the burden on lecturers as well as giving answers to the concerns from other disciplines.   The interview "KI darf nur unterstützen, niemals entscheiden" is available in German in the Bildung.Table issue #46 from the 23rd of February can be read here.
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Artificial intelligence for supporting disadvantaged learners

Artificial intelligence for supporting disadvantaged learners

Artificial Intelligence, Assessment, School
[caption id="attachment_3685" align="alignleft" width="300"] The Mitchell County High School[/caption] Can Artificial Intelligence support disadvantaged teenagers in improving their mathematics and reading skills? Doctoral candidate Rashmi Khazanchi is investigating this relevant research question in her PhD project conducted with the supervision of Prof Hendrik Drachsler at the Open University of The Netherlands and Dr Daniele Di Mitri at the DIPF. Mrs Khazanchi is a mathematic and science teacher at the Mitchell County High School, located in the rural area of Camilla, in the southwest of Georgia, United States. The school offers generalist education as preparation for college and vocation and professional training. The high school is a publicly funded school system, and the majority of its student population are African American people. Several of these young students live in modest socio-economic…
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Team of EduTec and TBA Members Wins Runners-Up Prize @ NAEP Reading Automated Scoring Challenge

Team of EduTec and TBA Members Wins Runners-Up Prize @ NAEP Reading Automated Scoring Challenge

Artificial Intelligence, Assessment, Award, Computational Psychometrics, School
The NAEP Reading Automated Scoring Challenge was a competition which was held in late 2021 by the National Center for Education (USA). The goal was to evaluate the applicability of natural language processing methodology to the task of scoring of large scale constructed response assessment data. In addition to this, participating models were also required to be interpretable and free of algorithmic bias against different student demographics. The data set which was used to evaluate the systems consisted of responses from 4th and 8th grade school student to 20 different reading comprehension tasks. More than a dozen different teams from universities and private assessment companies submitted contributions. Out of these, 12 contributions, including ours, were selected as eligible with respect to interpretability and algorithmic fairness. These contributions were then ranked…
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