Katharina Maitz

dblp:260/5277 · DBLP profile ↗
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4ranked-venue papers
0as first author
3since 2021 · last 2023
0000-0001-6943-7057ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 4 · 3 since 2021Applied, interdisciplinary, general and emerging computing · 4 · 3 since 2021
YearPublicationVenuePosition
2023 Transferring Digital Competences for Teaching from Theory into Practice Through Reflection
Angela Fessl, Monica Divitini, Katharina Maitz
EC-TEL3
2023 Interactive Web-Based Learning Materials Vs. Tutorial Chatbot: Differences in User Experience
Behzad Mirzababaei, Katharina Maitz, Angela Fessl, Viktoria Pammer-Schindler
EC-TEL2
2021 The Impact of Explicating Learning Goals on Teaching and Learning in Higher Education: Evaluating a Learning Goal Visualization
Angela Fessl, Katharina Maitz, Sebastian Dennerlein, Viktoria Pammer-Schindler
EC-TEL2
2020 Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy
abstract
This paper describes the analysis of temporal behavior of 11--15 year old students in a heavily instructionally designed adaptive e-learning environment. The e-learning system is designed to support student's acquisition of health literacy. The system adapts text difficulty depending on students' reading competence, grouping students into four competence levels. Content for the four levels of reading competence was created by clinical psychologists, pedagogues and medicine students. The e-learning system consists of an initial reading competence assessment, texts about health issues, and learning tasks related to these texts. The research question we investigate in this work is whether temporal behavior is a differentiator between students despite the system's adaptation to students' reading competence, and despite students having comparatively little freedom of action within the system. Further, we also investigated the correlation of temporal behaviour with performance. Unsupervised clustering clearly separates students into slow and fast students with respect to the time they take to complete tasks. Furthermore, topic completion time is linearly correlated with performance in the tasks. This means that we interpret working slowly in this case as diligence, which leads to more correct answers, even though the level of text difficulty matches student's reading competence. This result also points to the design opportunity to integrate advice on overarching learning strategies, such as working diligently instead of rushing through, into the student's overall learning activity. This can be done either by teachers, or via additional adaptive learning guidance within the system.
Leon Fadljevic, Katharina Maitz, Dominik Kowald, Viktoria Pammer-Schindler, Barbara Gasteiger-Klicpera
LAK2