Erik Marx

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Wissenschaftlicher Mitarbeiter

Mr Erik Marx

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Professur für Didaktik der Informatik

work

Visiting address:

Andreas-Pfitzmann-Bau, Raum 2089 Nöthnitzer Str. 46

01187 Dresden

Areas of responsibility

Research and doctoral studies on teaching the topic of machine learning in schools.
Conception, implementation and evaluation of learning materials and workshop concepts on the topic of artificial intelligence
Teaching in courses of the Chair of Didactics of Computer Science with a focus on artificial intelligence
Further training events for teachers and lateral entrants with a focus on AI
Opening up research fields and associated findings in the field of AI to the general public in the ScaDS.AI project

Doctorate and research

I am concerned with the question of how the subject area of artificial intelligence (AI), in particular the field of machine learning (ML), can be taught in schools. The focus is on considerations of which ML concepts are essential for pupils' understanding and development of suitable cognitive models and how pre-conceptions that students develop before they come into contact with the topic at school influence the learning process.

A mixed methods approach is required to answer these questions. In order to identify basic concepts, literature analyses and qualitative studies are necessary to record existing preconceptions among students. In addition, a measurement instrument in the form of a questionnaire will be developed to investigate the persistence of these preconceptions and to evaluate the effect of learning interventions, which will be used in quantitative studies.

As part of my project position in ScaDS.AI, I also design learning materials and workshop concepts and test and evaluate them with pupils in our student laboratory EduInf.

Publications

13 Entries

2022

Brief Summary of Existing Research on Students’ Conceptions of AIMarx, E., Leonhardt, T. & Bergner, N., Oct 2022, p. 1-2, 2 p.Electronic (full-text) versionResearch output: Contribution to conferences > Poster
Using Matchboxes to Teach the Basics of Machine Learning: an Analysis of (Possible) MisconceptionsMarx, E., Leonhardt, T., Baberowski, D. & Bergner, N., 8 Sep 2022, Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop. PMLR, p. 25-29, 5 p.Electronic (full-text) versionResearch output: Contribution to book/conference proceedings/anthology/report > Conference contribution

2021

EduInf - Education in Informatics.Marx, E., Hamann, A., Baberowski, D., Nenner, C., Kazimiers, A., Leonhardt, T. & Bergner, N., 2021, p. 1645-1648Electronic (full-text) versionResearch output: Contribution to conferences > Paper

University teaching

Below you will find an overview of offered courses and supervised theses with a focus on artificial intelligence.

Courses

(SuSe 24) IT4Advanced - AI at school
(WiSe 23/24) IT4Advanced - AI at school
(SuSe 23) IT4Advanced - AI at school
(WiSe 22/23) IT4Advanced - In-depth aspects for computer science teacher training students
(SuSe 22) Didactics of computer science - computer science education at middle schools
(SuSe 21) Computer science didactics - selected aspects

Final theses

(SuSe 25) Evaluation of a concept inventory for machine learning using cognitive interviews
(WiSe 24/25) Conception and development of a collaborative learning game mode for the introduction to the subject area "Machine Learning"
(WiSe 24/25) Development of a project-based approach to teaching the basics of machine learning at upper secondary level
(SuSe 24) Student perceptions of recommendation systems
(SuSe 22) Didactic reconstruction of machine learning in the context of image processing - workshop concept for upper secondary level
(WiSe 21/22 ) KI Play - Didactic conception of an AI learning game for multi-touch tables

Professional career

since 04/2021	Research Associate in the project "ScaDS.AI"
10/2014 - 02/2021	Studies with 1st state examination for Teacher Training - Secondary Schools in the subjects Mathematics and Computer Science