School Archives - EduTec Science

Oct72026 by Prof. Dr. Hendrik DrachslerNo Comments

Keynote: 5 years of Highly Informative Learning Analytics

Artificial Intelligence, Conference, Feedback, Higher Education, Keynote, Learning Analytics, Learning Design, Research Methods, School

Five years in forty minutes On 7 October 2026, Hendrik Drachsler gave a keynote at the CBIE 2026 conference in Goiana, Brazil, that tried something unusual: time travel. He compressed five years of field research, seven projects and roughly 5,750 students into one forty-minute talk. The question behind the keynote was simple: after five years of developing and testing Highly Informative Learning Analytics (HILA) in real lecture halls, what do we actually know? Not what we hoped HILA might achieve back in 2020, but what five years of our own research and data now tell us. What HILA set out to do HILA starts with a modest observation: most lecture rooms still offer very little personalisation. Learning analytics promised to change that, but its rollout in Europe stalled on four…

Oct72026 by Prof. Dr. Hendrik DrachslerNo Comments

PhD defense: Congratulations, Dr. Manuel Valle Torre!

Artificial Intelligence, Higher Education, Learning Analytics, PhD defense, School

Today, 07.10.2026 at 15:00, Manuel Valle Torre successfully defended his PhD thesis "Sequences in Data to Represent Learning Processes: What have you tried until now?" at Delft University of Technology. Hendrik Drachsler was honoured to take part as an independent member of the doctoral committee. [pdf-embedder url="https://edutec.science/wp-content/uploads/2026/10/Manuel-defense-slides.pdf"] Manuel's thesis starts with a question every programming tutor knows: "What have you tried until now?" Digital learning environments usually assess only the final product, so this question is hard for them to answer. In three phases, Manuel shows how sequences of learner actions can be used to understand learning processes and support them while they happen. He developed ELAT for analysing edX log data, reviewed the field of learning sequence analysis, and studied problem-solving in data science notebooks. He then built JELAI,…

Jul312026 by Longwei CongNo Comments

[New Pub] Estimating LLM Grading Ability and Response Difficulty in Automatic Short Answer Grading via Item Response Theory

Artificial Intelligence, New Pub, School

On July 4, 2026, Longwei Cong virtually presented the paper “Estimating LLM Grading Ability and Response Difficulty in Automatic Short Answer Grading via Item Response Theory” at the ACL SIGEDU 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026). The work investigates automatic short answer grading with large language models from a psychometric perspective. Instead of relying only on aggregate evaluation metrics such as accuracy or macro-F1, the study applies Item Response Theory to jointly estimate the grading ability of individual LLMs and the difficulty of student responses. The results show that LLM graders differ not only in their overall performance, but also in their robustness when evaluating more difficult responses. In addition, highly difficult responses were found to induce systematic grading errors, including a tendency…

Jul312026 by Longwei CongNo Comments

[New Pub] Confidence Estimation in Automatic Short Answer Grading with LLMs

Artificial Intelligence, New Pub, School

On June 30, Longwei Cong presented the work “Confidence Estimation in Automatic Short Answer Grading with LLMs” at the 27th International Conference on Artificial Intelligence in Education (AIED 2026) in Seoul. The work investigates how confidence estimates can make automatic short-answer grading with large language models more reliable in practice. In addition to commonly used model-based confidence signals, it also considers uncertainty originating from the data itself. In particular, aleatoric uncertainty is estimated from the semantic heterogeneity of student responses and combined with several model-side confidence signals. The study evaluates these uncertainty estimates with a focus on practical use: whether they can support selective grading and help identify responses for which automated grading is less reliable and human review would be preferable. The paper is available via the following link:…

May122026 by Prof. Dr. Hendrik DrachslerNo Comments

How AI Systems Impact Mathematics Achievement in Rural Areas

Artificial Intelligence, PhD defense, School

On Friday, May 8, 2026, Rashmi Khazanchi successfully defended her doctoral dissertation, "Artificial Intelligence in Education: Impact of AI-Based Systems on Mathematics Achievement," at the Open Universiteit in Heerlen. The defense was supervised by Prof. Dr. Hendrik Drachsler, who holds a guest professorship at the Open Universiteit, alongside co-promotor Prof. Dr. Daniele Di Mitri (German University of Digital Sciences). The Research Question Khazanchi's work addresses a pressing challenge in education: whether AI-based learning systems can help close the mathematics achievement gap for students from socioeconomically disadvantaged backgrounds. Her research focused on students in a rural school district in South Georgia, USA — a setting often characterized by limited resources and a shortage of qualified teachers. What Was Studied The school implemented two AI-based systems to support struggling learners: ALEKS, an…

Jun122025 by Heather WehnerNo Comments

New Pub: Tracking students’ progression in developing understanding of energy using AI technologies

Artificial Intelligence, Journal, Publication, School

[caption id="attachment_7553" align="alignright" width="400"] Instructional unit, with pre- and post-test, as well as lesson-set-level assessment[/caption] In physics education, some students fail to have the foundational knowledge of energy concepts needed to engage in societal debates on climate change and energy transformation. A newly published study highlights the potential of AI to identify students with different learning trajectories and to help bridge the knowledge gaps. The researchers used a digital workbook designed to teach energy concepts to collect detailed interaction data from over 500 students. After applying exclusion criteria, data from 172 students were analyzed to identify their productive and unproductive learning curves. [caption id="attachment_7554" align="alignleft" width="400"] Example single choice pretest item[/caption] By using machine learning, specifically random forest models, and natural language processing (NLP), the researchers were able to classify…

Feb52025 by Heather WehnerNo Comments

New Pub: Memorizing Plans With an App

Journal, Learning Analytics, Publication, School, Self-Regulation

Mobile phones and devices are an integral part of the daily lives of students, and educators are increasingly trying to take advantage of this day-to-day usage for educational purposes. But how can mobile technologies, like app-based learning activities, be designed to effectively help students in the learning process? [caption id="attachment_7331" align="alignright" width="500"] Screenshots of the reading (A), puzzle (B), and emoji (C) activity[/caption] A newly published study from Daniel Biedermann, Jasmin Breitwieser, Lea Nobbe, Hendrik Drachsler and Garvin Brod tries to answer this question. Using the PROMT app, the team compared three types of learning activities used by children aged 9 to 14 years to memorize one learning plan per day over the course of 27 days. The activities varied in their planned level of cognitive engagement based on the…

Dec192024 by Prof. Dr. Hendrik DrachslerNo Comments

Its a Wonderful (EduTec) Life: Reflecting 2024.

Alumni, Higher Education, School, Team

Near Christmas time, many people traditionally watch the movie classic “It’s a Wonderful Life” about a man named George, who loses his courage to face life on Christmas night due to a series of difficulties and ends up being saved by an angel who shows him how life would be for the people around him if he had never been born. Poor George has let himself get so caught up in the troubles of everyday life that he has lost sight of all the positive things that he has achieved and the impact he has on those around him. This movie reminds us that we are like George sometimes: we tend to focus on the negative things in daily life and sometimes lose our view of the bigger picture and…

Dec22024 by Prof. Dr. Hendrik DrachslerNo Comments

PhD Defense: A Bridge between Learning Analytics and Learning Design

Book, Event, Higher Education, Learning Analytics, Learning Design, Multimodal Learning Analytics, PhD defense, Publication, School

We warmly congratulate our esteemed associate partner, DR. Marcel Schmitz, on successfully defending his PhD thesis on November 29, 2024. Marcel is a senior lecturer and researcher in Data Intelligence and the Applied Data Science & Artificial Intelligence program at Zuyd University of Applied Sciences. In his dissertation, titled “Towards Learning Analytics-Supported Learning Design”, he focused on how education can be better personalized by incorporating learning analytics already in the design and course planning. His dissertation was supervised by Prof. Dr. Hendrik Drachsler (DIPF | Leibniz Institute & Goethe University Frankfurt), with co-supervisors Prof. Dr. Maren Scheffel (Ruhr University Bochum) and Dr. Roger Bemelmans (Zuyd University of Applied Sciences). Marcel's dissertation provides actionable strategies not only for higher education but also for other educational sectors. His work envisions a future…

Oct232024 by Heather WehnerNo Comments

New Pub: How Can Learning Analytics Dashboards Help Improve Students’ Self-Regulated Learning?

Conference, Empirical Study, Learning Analytics, Publication, School, Self-Regulation

Learning Analytics Dashboards (LADs) are important and widely-used tools used to give feedback to students and to aid them in their self-regulating learning process. Much has been done to investigate the design of LADs, but there is still a lack of knowledge regarding how students interpret the information shown on LADs and how they actually use these tools while learning. In a newly published study, we try to fill this gap. In an experimental study, we compared two groups of students. One group was given personalized self-regulared learning (SRL) feedback on their interactions and learning advances. The control group was only given minimal feedback calculated from the average class scores. After reviewing their feedback, students submitted written reflections on how they would adjust their study behavior. The researchers then analyzed…