Modelling Argument Quality in Technology-Mediated Peer Instruction

Learnersourcing is the process by which students submit content that enriches the bank of learning materials
available to their peers, all as an authentic part of their learning experience. One example of learnersourcing
is Technology-Mediated Peer Instruction (TMPI), whereby students are prompted to submit
explanations to justify their choice in a multiple-choice question (MCQ), and are subsequently presented
with explanations written by their peers, after which they can reconsider their own answer. TMPI allows
students to contrast their reasoning with a variety of peer-submitted explanations. It is intended to foster
reflection, ultimately leading to better learning. However, not all content submitted by students is adequate
and it must be curated, a process that can require a significant effort by the teacher. The curation
process ought to be automated for learnersourcing in TMPI to scale up to large classes, such as MOOCs.
Even for smaller settings, automation is critical for the timely curation of student-submitted content, such
as within a single assignment, or during a semester.
We adapt methods from argument mining and natural language processing to address the curation
challenge and assess the quality of student answers submitted in TMPI, as judged by their peers. The
curation task is confined to the prediction of argument convincingness: an explanation submitted by a
learner is considered of good quality, if it is convincing to their peers. We define a methodology to
measure convincingness scores using three methods, Bradley-Terry, Crowd-Bradley-Terry and WinRate.
We assess the performance of feature-rich supervised learning algorithms as well as transformer-based
neural approach to predict convincingness using these scores. Experiments are conducted over different
domains, from ethics to STEM. While the neural approach shows the greatest correlation between its
prediction and the different convincingness measures, results show that success on this task is highly dependent
on the domain and the type of question.