An analysis of help seeking in course discussion forums

Thakkar, Vyom

Permalink

https://hdl.handle.net/2142/115782 Copy

Description

Title

An analysis of help seeking in course discussion forums

Author(s)

Thakkar, Vyom

Issue Date

2022-04-26

Director of Research (if dissertation) or Advisor (if thesis)

Bhat, Suma

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• NLP
• Discussion Forums
• Text Classification

Language

eng

Abstract

In the last few years, online learning has become increasingly prevalent in the student learning experience. With the advent of Massive Open Online Courses (MOOCs) and online courses at colleges and universities, course discussion forums have become an important medium for students to get help and ask questions. These discussion forums are also a medium to get to know other students and participate in class discussion activities. The critical feature of large-scale course discussion forums is that, as the number of students learning online scales, the number of help-seeking questions scale, which can be met with the following options: (1) an increase in the number of course staff attending to these questions, (2) presence of an engaged community of students to actively help out each other or (3) to develop a technique in a way that these critical help-seeking discussion posts can be filtered out from the non-help-seeking posts in order to allow the course staff to deal with them in an efficient manner. In this work, we used a discussion forum dataset from a chemistry course as the primary source of data for the investigations and experiments. We explore the use of Natural Language Processing (NLP) techniques in order to train models to classify a given text as help-seeking or non-help-seeking. We will explore the use of labeled text data from related domains to expand the primary dataset and experiment with transfer learning to improve classification performance. We also performed a Social Network Analysis (SNA) to determine the correlation between the amount of student interaction on the discussion forums to the course outcome that they received. This work is followed up with a UI/UX exploration of integrating the findings from our work into the existing online course discussion forum experience.

Graduation Semester

2022-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/115782

Copyright and License Information

Copyright 2022 Vyom Thakkar

Owning Collections