Hello! My name is Thiemo Wambsganss and I'm a research associate at the University of St.Gallen!
Today, I will present you my research and my Ph.D. thesis about the design of adaptive argumentation
learning systems based on Artificial Intelligence. If you're interested in my research, please scan
the barcode or visit my website below (thiemowa.github.io).
What we see in discussions with students or other scientists is,
that not only facts are the key ingredient to persuade the opponent but rather the way how
you organize the factual knowledge in the lines of argumentation. This becomes beware by the fact
that knowledge is not anymore stored in large books like this one but is rather constantly
accessible by smart devices everywhere and every time. This leads to the fact that skills
such as argumentation become more important for the future education.
This is also depicted in the OECD learning framework 2030, where metacognition skills such as augmentation are one of the
three pillars for the 21st-century learner.
However, to teach argumentation and train thee skills usual one-to-one pedagogy scenarios are necessary,
where students apply a certain skill and receive adaptive feedback and tutoring from the lecturer.
However, this one-to-one relationship between student and lecturer is hardly possible, since in public universities
we have large-scale university lectures or due to the rising field of massive open online courses.
My research is about how to use technology-based on recent advantages in artificial intelligence
to improve these traditional learning scenarios based on adaptive learning tools, so that students
can learn independent of an instructor time and location. And, lecturer can focus
on the more important tasks. Therefore, I aim to design adaptive augmentation learning systems
based on the recent advantages of artificial intelligence where argumentation mining, a field
from natural language processing and machine learning, has been a proven technology to mine argumentation
and argumentation levels of text. In order to design these adaptive learning tools, I aim
to answer three research questions. First, I aim to investigate what are the requirements for
adaptive learning tools to teach students how to argue. Second, I aim to answer how can an adaptive
learning tool based on artificial intelligence be designed to support the development of students
argumentation skills. And third, I am to investigate what is the effect of argumentation learning
tools on students argumentation skills. In order to answer this questions, I aim to follow a design
science research approach according to Hevner 2007, where I aim to investigate the requirements
based on theory and user interviews. Moreover, I aim to develop and design and adaptive augmentation
learning tools based on recent advantages of natural language processing and machine learning
and test them in low fidelity prototypes. Last, I aim to investigate what are the effects
and the perception of students of these adaptive learning tools. The backbone of the adaptivity of
the argumentation learning tools is argumentation mining. In order to train an argumentation mining
model, we need annotated corpora based on student written text. Since no corpora were available for
these tasks, the first step was to annotate a new corpora based on student-generated peer reviews in
German which are annotated for argumentative components and argumentative relation. bBased
on this corpus, I was able to train different models which now detect the augmentation level
of the student texts. The corpora are embedded in two different learning tools first an adaptive
argumentation writing support tool called AL, where students receive adaptive argumentation feedback
based on their text. And second, a pedagogical conversation agent called ArgueTutor, which tutors
students with theory input, task explanation and adaptive feedback. For my third research question,
i evaluated the tools in different settings. I could evaluate the influence of the
augmentation learning tools on the short term argumentation skills in a laboratory experiments.
Here, the results for AL show that students who use AL wrote their text with significantly better formal
quality of argumentation compared to a reference discussion scripting approach. For my future
research, I aim to evaluate two tools and their effect on the influence on long-term argumentation
skill. Overall,I expect to contribute to the understanding of teaching meta cognition skills
in large or distance learning scenarios and enrich the literature by user-centered design principles
for the design of adaptive augmentation learning tools. Overall, I aim to contribute to the OECD
learning framework 2030 for more metacognition based education. If you are interested in my research,
don't hesitate to contact me for any further collaboration. Thank you very much for your time!