This document summarizes previous research on categorizing discussion posts in MOOCs and proposes analyzing the linguistic resources used in MOOC discussions. It presents initial keyword analysis comparing the language of facilitators versus learners and different types of learner posts. The document outlines linguistic resources that could be analyzed such as pronouns, hedging, stance, and discourse organizers. Initial findings on the linguistic resources used by facilitators are presented. The document proposes using natural language processing techniques like part-of-speech and semantic tagging to further examine characteristics of posts and turn-taking patterns by analyzing the collocation of linguistic features between posts and replies.

1. Shi Min Chua, shimin.chua@open.ac.uk
Background
Most Massive Open Online Courses (MOOCs)
have a dedicated discussion space for learners to
interact with each other.
Several automatic categorizations of the
discussion postings have been implemented in in
learning analytics research:
• Supervised Machine Learning (O’Riordan et
al, 2016) to categorize postings based on
frameworks such as
o Community of Inquiry (Garrison,
Anderson, & Archer, 2001): social,
cognitive, teaching
o Bloom’s Taxonomy (Bloom et al, 1956):
remember, understand, apply, analyse,
evaluate, create
• Content related vs. non-content related (Cui
& Wise, 2015)
• Sentiment Analysis (Wen, Yang, Rose, 2014)
These categorizations are useful for evaluation
yet disregard the dialogic nature of the
discussion forums and underpinning linguistic
resources.
Work-in-Progress
Keyword Analysis &
Lexical Bundles
• Facilitators (816058) vs. Learners
(11206220)
• Learners’ lone posts (2401795) vs.
initiating posts (6162230)
• Learners’ replies (6162230) vs.
initiating and lone posts (8564025)
Linguistic Resources
for Dialogic Learning
• Pronouns (Oliveira et al, 2007)
• Personalized framing (Csomay, 2017)
• Hedging (Brennan & Ohaeri, 1999;
Concannon, Healey & Purver, 2003)
• Stance expression (Hyland, 2011)
• Interactivity (Kleinke, 2017)
• Experience talk (Kaanta & Lehtinen, 2016)
• Discourse organizers (Conrad & Biber, 2004)
• Referential (Conrad & Biber, 2004)
• Story-like (Alsop & Nesi, 2018)
• Questions (Tracy & Robles, 2009)
• Conditionals (O’Keeffe & Walsh, 2016)
• Agreement and disagreement (Baym, 1996)
• ?
Research Questions
What linguistic resources
are used in MOOC
discussions?
• What linguistic resources are used by
facilitators to create dialogic learning in
MOOC discussions?
• Why do some posts receive replies but
some don’t? (learners’ posts)
• What happens within a conversation
thread? (learners’ replies)
Findings: Facilitators’ Linguistic Resources
Interactivity
Names
Pronouns you, your, yourself, we, us
Discourse Particles hi, yes, thanks, please, sorry
Meta-language
Discussion-related point, points, pointing, comment, comments, question, discussion, post,
feedback, answer, questions, pointing, reply, discussed, posted
Logistics and Learning
Materials
click, check, button, materials, download, page, link, videos, section,
text, pdf, fixed, sections, website
Course and MOOCs mooc, futurelearn
Conceptual Objects issue, issues, topic, case, research, researchers
Referential week, weeks, later, next, coming
Stance Expression
Modality might, 'll, can, will, want, 'd,
Booster indeed, just, exactly, directly
Positive Evaluation right, fine, good, great, interesting
Emotions glad, worry, afraid
Hedging Expression sounds, sure, thoughts, find
Speech act suggest, mean, ask, suggestion, refering, asking
Uncategorized
Connectors if, e.g., example, terms, i.e., meantime, then, examples, depends
Punctuation ),'(-:!?"
Grammatical particles here, this, that, there, the, these, are, is, be, 's, do, on, for
Uncategorized option, b, different, what, two, available, free
Uncategorized Verbs let, hear, hope, note, see, look, using, try, collect, uses
Facilitator: A disappointing news item on the ALT
mailing list today, ….<url>… Sadly this will also set a
US legal precedent, so we'll probably see a great deal
more free and open content disappearing. So now its
all completely inaccessible - to everyone :-( So a
question <...> Do you think such freely-provided 'open
content' should be taken down if it isn't captioned
<…>?<...>
Learner A: I think this is rather unfair. I can
understand that the UoC wants to make sure all
their content is accessible but surely there could be
a statement to say <…>
Learner B: Hi <…>
Possible for Corpus NLP
To examine characteristics
- lone posts vs. initiating posts
- popular posts (most liked)
POS & Semantic Tagging or Biber’s Tagger for multidimensional
analysis
Use the dimensions to categorize the postings, similar to previous
learning analytic research yet from the linguistic point of view.
To examine turn-taking
Collocation of linguistic features between posts and replies, rather
than by N-positions?