New Pub: How Virtual Reality Mental Training Impacts Race Preparation in Recreational Runners

New Pub: How Virtual Reality Mental Training Impacts Race Preparation in Recreational Runners

Augmented Reality, Computational Psychometrics, General education, Journal, New Pub, Publication
Can Virtual Reality Mental Training Help Recreational Runners Race Smarter? We’re glad to announce that our paper has just been published! 🎉 In this post, we share the key ideas and early findings from our newly published study exploring how virtual reality (VR) mental training—grounded in cognitive-behavioral (CB) techniques—may support long-distance recreational runners in adopting race strategies and strengthening motivation within a coaching context. What happens when cognitive-behavioral (CB) techniques like imagery and self-talk meet virtual reality (VR) in a coaching context? An exploratory study of recreational long-distance runners provides intriguing early signals. Why this study matters VR has been used in sports settings to support skill learning and performance, but it’s still relatively uncommon to see VR paired directly with cognitive-behavioral mental training—especially practical tools like imagery and self-talk…
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New Pub: GRIPF at TSAR 2025 Shared Task Towards controlled CEFR level simplification with the help of inter-model interactions

New Pub: GRIPF at TSAR 2025 Shared Task Towards controlled CEFR level simplification with the help of inter-model interactions

New Pub
Language learners make the fastest progress when reading texts that match their proficiency level. But most real-world texts are too hard—and manually adapting them is time-consuming. So the big question is: Can AI automatically simplify texts to a specific CEFR level without losing meaning? We explored exactly this in the TSAR 2025 Shared Task, where systems had to rewrite advanced English texts (B2+) to easier levels like A2 or B1. Our team submitted two different approaches: EZ-SCALAR and SAGA. EZ-SCALAR works like an expert panel of AI models. Two large language models (GPT-5 and Claude) each produce their own simplification, critique each other, refine their versions, and then a final “judge” model picks the best result. An extended version, EZ-SCALAR Lex, adds something extra: a vocabulary check using EFLLex, a…
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New Pub: Characterizing students’ energy learning trajectories

New Pub: Characterizing students’ energy learning trajectories

New Pub
Helping students apply energy ideas to everyday situations is a core goal in physics education. But not all students get there—and it’s not just about who knows the most content. In a 10-week classroom study with 165 students, we tracked both their energy understanding and their affective and metacognitive factors (like emotions, cognitive load, and self-regulation). Using k-means clustering on their learning trajectories, we identified three distinct student groups that differed in the coherence of their energy knowledge development. The key insight: Students who learned the most also felt more positive, experienced lower cognitive load, and used stronger metacognitive strategies. Those who struggled often felt overwhelmed or disengaged. The takeaway is clear: supporting emotions and self-regulation is just as important as teaching physics content. Instruction that addresses these factors can…
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New Pub: Optimizing Formative Assessment with Learning Analytics

New Pub: Optimizing Formative Assessment with Learning Analytics

Assessment, Learning Analytics, Literature review, New Pub
The teaching and learning processes in education need to be effective. This is something that all parents, teachers and educational scientists can agree on. To help us track the learners’ achievements and educational progress and ultimately show whether the teaching and learning processes are effective, we rely on formative assessment. Learning analytics has the potential to assist in formative assessment. So far there has not been enough evidence collected to prove this potential support. Thus, many have reservations about the connection between the results of learning analytics and formative assessment models. If the results from learning analytics don’t match well with formative assessment approaches, teachers may be reluctant to trust, understand or use those insights to guide their teaching. This issue is addressed in a recently published study which introduces…
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New Pub: ChatGPT in Education

New Pub: ChatGPT in Education

Journal, New Pub, Research Methods
Early studies on the usage of ChatGPT in educational settings have reported substantial learning gains from ChatGPT applications. But how valid are these studies? Is using ChatGPT in education really as effective as it seems? A newly published paper takes a deeper look at key findings from past debates about media and teaching methods to reveal frequent conceptual challenges that arise in studies about the effectiveness of ChatGPT. When researchers compare different types of media for learning, they sometimes mix up the effects of the teaching style with the features of the technology. If the instructional methods and the technological features are confused with one another, it makes it difficult to be able to interpret the actual effect of ChatGPT. To help pinpoint the conceptual difficulties of these efficacy studies,…
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New Pub: TBA at BEA 2025 Shared Task: Transfer-Learning from DARE-TIES Merged Models for the Pedagogical Ability Assessment of LLM-Powered Math Tutors

New Pub: TBA at BEA 2025 Shared Task: Transfer-Learning from DARE-TIES Merged Models for the Pedagogical Ability Assessment of LLM-Powered Math Tutors

New Pub
In the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-Powered Tutors, the goal was to evaluate how well LLM-based math tutors support students. The task focused on four aspects of feedback: spotting mistakes identifying where the mistake happens giving guidance providing actionable suggestions For our submission, we built on FLAN-T5 models with a multi-step training pipeline. In addition to standard fine-tuning, we used model merging (DARE-TIES) to leverage information across all four labels – and saw clear improvements over plain fine-tuning. Our models achieved F1 scores between 52 and 69 and accuracies between 62% and 87%, ranking 11th, 8th, 11th, and 9th across the four tracks. Link to the paper: Link
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New Pub: How Feedback Literacy Moderates Student Perceptions of Feedback

New Pub: How Feedback Literacy Moderates Student Perceptions of Feedback

Feedback, Higher Education, Journal, New Pub
Whether or not students receive effective feedback can have a big impact on their overall learning process. The more qualitative and personalized the feedback is, the more effective it is in supporting the students with their individual learning goals. The latest developments in learning analytics and artificial intelligence have made it possible to provide a large number of students with personalized feedback automatically and simultaneously. Despite these technical advances, there is still much to be learned about the ability of students to use this feedback for their scholastic benefit. So far, only a limited number of studies have examined the impact of feedback literacy on students' perceptions of feedback, which is particularly true for technology-enhanced learning environments. A newly published paper addresses this issue and focuses on how students interpret…
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New Pub: How Learners Interpret and Respond to Feedback in Learning Analytics Dashboards

New Pub: How Learners Interpret and Respond to Feedback in Learning Analytics Dashboards

Feedback, Journal, New Pub, Self-Regulation
Online learning platforms can generate a great amount of data about how students engage in the learning process. This data is used to develop learning analytics dashboards as feedback tools to assist students in self-regulating their learning. But how do students use these tools to self-reflect? And how can they use the information provided by learning analytics dashboards (LAD) in a meaningful way? In a new publication these questions are explored using the data from an experimental study with 417 students, which investigated how the students interpret and respond to feedback from LADs. In the study,  the students were divided into 2 groups: A treatment group was given personalized self-regulated learning (SRL) feedback from the LAD on the basis of interactions and progress; A control group was given minimal feedback…
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New Pub: Tracking students’ progression in developing understanding of energy using AI technologies

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…
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A Warm Welcome to Our Guest Researcher Yildiz Uzun

A Warm Welcome to Our Guest Researcher Yildiz Uzun

New Pub
We are delighted to welcome Yildiz Uzun as a guest researcher in our team! Yildiz is a PhD student at the Knowledge Lab, Institute of Education (IOE), University College London (UCL). Her research explores how students regulate their learning with the support of learning analytics feedback in the form of a dashboard, with a particular focus on how they engage with this feedback. She also examines how engagement with analytics feedback can be scaffolded and enhanced through the integration of an Artificial Intelligence (AI) assistant. During her visit to DIPF, she will collaborate with Prof. Hendrik Drachsler and the EduTec team to advance her current AI-driven feedback process by analysing student queries to the AI assistant, evaluating the pedagogical quality of AI responses and identifying ways to optimise conversational systems…
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