hello my name is Thiemo Wambsganss and I'm a research associate at the University of St.Gallen.
Today, I will present you our work about an adaptive dialogue-based learning system for
augmentation skills. This work has been conducted with my co-authors Tobias Küng, Matthias Söllner,
and Jan Marco Lesmeister. If you're interested in our research of AI for education please scan the
barcode or visit our website below. What we see in discussions with students or with researchers
is that not only factual knowledge is persuading the opponent of a certain
opinion but rather the way how you organize the knowledge and your lines of argumentation. This
comes beware by the fact that knowledge is not only any more saved in large books like these ones
but can be constantly accessed via smart devices. This leads to the fact that meta cognition skills
such as augmentation become more important in future education,
for the 21st-century learner which is reflected in the OECD learning framework 2030.
To train meta-cognition skills however one-to-one teaching learning scenarios are usually necessary,
where a student applies a certain skill and gets adaptive and individual tutoring or feedback
on his or her skill level. However, this is and was always very difficult since this one-to-one
relationship is hardly possible in traditional large-scale university lectures or in the rising
field of massive online courses in distant learning scenarios. Our idea was that we enrich
the current scenarios with a dialogue based agent which provides students with adaptive feedback and
ongoing tutoring during distance learning or large scale learning scenarios. Our idea was to design
a teaching-learning scenario where students write a persuasive writing exercise and receive
adaptive tutoring and argumentation feedback based on his or her skill level. Therefore, we designed
ArgueTutor based on several design requirements from students and theory-motivated design principles.
ArgueTutor consists of two different parts: One a dialogue-based front end which was based on our
design requirements of students and theory. Second, an adaptive backend which
was a trained machine learning algorithm based on an argumentation annotated corpora of student
peer reviews. We back our research on the ICAP framework which could explain why interactive
learning, meaning conversation, is more effective than constructive, active or passive learning.
The basic interaction with ArgueTutor worked like this: a student was conducting a certain writing
exercise, got his or her tasks explained, and could receive theory explanation or
task explanation at any time he or she wantes. The student would write a persuasive text
and after entering the text to ArgueTutor he or she would receive an adaptive feedback based on
his or her skill level. For example, here claims are highlighted in bold and premises are highlighted
in italic, indicating, together with the adaptive feedback message, where the augmentation is
weak or strong. We evaluated ArgueTutor in an online lab experiment with 55 participants,
where we asked them to write a persuasive text. Subjects in the treatment group were using ArgueTutor
with an adaptive tutoring based on natural language processing (NLP) and machine learning (ML) to conduct the
same writing exercise. Participles in the control group used the non-adaptive discussion scripting
application. Our results indicate, that students who used ArgueTutor wrote the text with a higher formal
quality of argumentation and a higher perceived argumentation level. Moreover, we measured the ease
of use and the level of enjoyment of participants. Here, participants of ArgueTutor rated the ease
of use and level enjoyment significantly higher compared to the reference tool.
All in all, we aim to contribute with our research to the understanding of teaching metacognition
skills in large scale or distance learning scenarios based on machine learning and natural
language processing. Moreover, we aim to contribute to the OECD learning framework 2030 for meta more
meta cognition skill-based future education of students. If you're interested in our research,
please read the paper and feel free to contact us for any collaboration. Thank you very much!