Modeling How Students Learn to Answer Interactive Online Questions with Knowledge Tracing Models

MPhil Thesis Defence


Title: "Modeling How Students Learn to Answer Interactive Online Questions 
with Knowledge Tracing Models"

By

Mr. Wai Lun CHAN


Abstract

Knowledge tracing (KT) is a research topic that seeks to model the 
knowledge acquisition of students through analyzing their past performance 
in answering questions, based on which their performance in answering 
future questions is predicted. Each question involves a knowledge 
component (KC) such as the topics concerned or the skills required. 
However, existing models only consider whether a student answers a 
question correctly at the end, but not the process of how the student 
attempts to answer it. It is anticipated that the interaction process can 
at least partially reveal the thinking process of the student, and 
hopefully even the competence of acquiring or understanding each of the 
KCs. By analyzing fine-grained clickstream events recorded for each 
question, we can understand better the student’s ability and performance 
or even the learning process, just like a personal tutor observing how a 
student solves a problem.

Based on real student interaction data including clickstream events 
collected from an online learning platform on which students solve 
mathematics problems, we conduct clustering analysis for each question to 
show that clickstreams can reflect students' behavior such as the steps 
and order of answering a question, time allocation, and score acquiring 
ability. We then propose the first clickstream-based KT model, dubbed the 
Clickstream Knowledge Tracing (CKT) model, which augments a basic KT model 
by modeling the clickstream activities of students when answering 
questions. We apply different variants of CKT and compare them with the 
baseline KT model that does not use clickstream data. Despite the limited 
number of questions with clickstream data and noisy nature of the 
clickstream data which may compromise the data quality, we show that 
incorporating clickstream data leads to performance improvement. This 
pilot study will likely open a new direction in KT research by analyzing 
finer-grained interaction data of students on online learning platforms.


Date:  			Tuesday, 25 August 2020

Time:			3:00pm - 5:00pm

Zoom meeting:		https://hkust.zoom.us/j/95877658018

Committee Members:	Prof. Dit-Yan Yeung (Supervisor)
 			Prof. Raymond Wong (Chairperson)
 			Dr. Brian Mak


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