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Hello world, this is my :
I am a doctoral candidate at the Research Group for Applied Software Engineering (Prof. Bruegge) at the Technical University of Munich (TUM). My research focuses on computer science education and automated assessments of textual exercises. In addition, I am teaching the lecture courses Introduction to Software Engineering (~2,200 students) and Patterns in Software Engineering (~700 students) at TUM.

@oneabstractaday

Evaluating 3D Human Motion Capture on Mobile Devices

by Lara Marie Reimer, Maximilian Kapsecker, Takashi Fukushima, and Stephan M. Jonas

"[...] In this study, we performed a laboratory experiment with ten subjects, comparing the joint angles in eight different body-weight exercises tracked by Apple ARKit, a mobile 3D motion capture framework, against a gold-standard system for motion capture: [...]"

mdpi.com/2076-3417/12/10/4806#

Wir müssen die #Chatkontrolle Stoppen!
„Die Chatkontrolle ist als fundamental fehlgeleitete Technologie grundsätzlich abzulehnen“ so der CCC
Mehr zu den Hintergründe findet ihr hier:
ccc.de/de/updates/2022/eu-komm

#Chatkontrolle verhindern!

@digitalcourage @digiges

"Schutz digitaler Rechte und Freiheiten bei der Gesetzgebung zur wirksamen Bekämpfung von Kindesmissbrauch"

digitalcourage.de/blog/2022/of

Crossposted from Twitter (@jpbernius@twitter.com) 

EU Commissioner @YlvaJohansson is preparing to launch a new law to force the mass surveillance of private online communications but has refused to meet with privacy experts like @edri.

Stop and now! Protect our !

We implemented this approach in a reference implementation called Athene and integrated it into Artemis. We used Athene to review 17 textual exercises in two large courses at the Technical University of Munich with 2,300 registered students and 53 teachers. On average, Athene suggested feedback for 26% of the submissions. Accordingly, 85% of these suggestions were accepted by the teachers, 5% were extended with a comment and then accepted, and 10% were changed.

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This paper presents CoFee, a machine learning approach designed to suggest computer-aided feedback in open-ended textual exercises. The approach uses topic modeling to split student answers into text segments and language embeddings to transform these segments. It then applies clustering to group the text segments by similarity so that the same feedback can be applied to all segments within the same cluster.

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Open-ended textual exercises facilitate the comprehension of problem-solving skills. Students can learn from their mistakes when teachers provide individual feedback. However, courses with hundreds of students cause a heavy workload for teachers: providing individual feedback is mostly a manual, repetitive, and time-consuming activity.

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Paper "A Machine Learning Approach for Suggesting Feedback in Textual Exercises in Large Courses" 

The third and last paper I want to toot about is titled "A Machine Learning Approach for Suggesting Feedback in Textual Exercises in Large Courses" and was presented at the 8th ACM Conference on Learning @ Scale (L@S) in 2021. DOI: doi.org/10.1145/3430895.346013 Preprint: brn.is/las21

This paper presents two things: (1) CoFee (approach) and (2) Athene (reference implementation). 🧵

We have evaluated the algorithm qualitatively by comparing automatically produced segments with manually produced segments created by humans. The results show that the system can produce topically coherent segments. The segmentation algorithm based on topic modeling is superior to approaches purely based on syntax and punctuation.

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The goal is to reduce the workload for instructors, while at the same time creating timely and consistent feedback to the students. We present the design and a prototypical implementation of an algorithm using topic modeling for segmenting the submissions into smaller blocks. Thereby, the system derives smaller units for assessment and allowing the creation of reusable and structured feedback.

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Employing tutors in the process introduces new challenges. Feedback should be consistent and fair for all students. Additionally, interactive teaching models strive for real-time feedback and multiple submissions.

We propose a support system for grading textual exercises using an automatic segment-based assessment concept. The system aims at providing suggestions to instructors by reusing previous comments as well as scores.

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Abstract–Growing student numbers at universities worldwide pose new challenges for instructors. Providing feedback to textual exercises is a challenge in large courses while being important for student’s learning success. Exercise submissions and their grading are a primary and individual communication channel between instructors and students. The pure amount of submissions makes it impossible for a single instructor to provide regular feedback to large student bodies.

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Paper "Towards the Automation of Grading Textual Student Submissions to Open-ended Questions" 

The second paper I want to share today is titled “Towards the Automation of Grading Textual Student Submissions to Open-ended Questions” and was published at the European Conference on Software Engineering Education (ECSEE) in 2020. DOI : doi.org/10.1145/3396802.339680 Preprint: brn.is/ecsee20
In this paper we present an algorithm using topic modeling for segmenting the submissions into smaller blocks.

The paper “Toward the Automatic Assessment of Text Exercises” is available open-access: ceur-ws.org/Vol-2308/isee2019p

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Paper "Toward the Automatic Assessment of Text Exercises" 

I published my first short paper titled “Toward the Automatic Assessment of Text Exercises” for the 2nd Workshop on Innovative Software Engineering Education (ISEE) in 2019.

The paper argues that automated assessment provides more individual feedback for students, combined with quicker feedback and grading cycles. We introduce a concept for automatic assessment of text exercises using machine learning techniques.

Following my introduciton, I will post my most important publications to share what I am working on with the academic fediverse. Maybe this helps to discover peers working on similar topics or starts some interesting conversations.

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I have been on Twitter for 13 years but mostly read stuff. My recent tweets have mostly been about my papers or conferences, so I decided to join scholar.social. With this account, I want to give the another go. I am happy to connect with scientists worldwide, especially in the areas of , , , and related fields.

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