Info & Library Science: stances in move 3

Stances in 'Introduction': Info & Library Science - Introduction 3 - Move 3

(1) Select an 'Introduction' (2) Select a move in that 'Introduction' (What is this?)

Learning Objectives & Strategies:
Explore stances used (A) to make move (B) to support move-making in Move 3
1. Understand what the 3 moves are? ('Introduction' & 3 Moves).
2. Look at the sentences that make move and the stances used.
3. Look at the supporting sentences and the stances used.
4. Compare why supporting sentences are differnt from move-making sentences.
5. Check out the ratios of stances used (A) to make move only & (B) overall in Move 3.

Title: A Machine Learning Approach for Identification of Thesis and Conclusion Statements in Student Essays
Author(s): Jill Burstein and Daniel Marcu
Journal: Computers and the Humanities (37), 2003, 455–467.

Clause

Making Move?
(Y/N)

Stances

Move 3, "Present the present work," Introduction 3 (*green = Stance Keywords)

(Y) announcing present research purposively

Non Argumentative

This study builds on previous work that reports on the identification of a single sentence associated with the thesis statement text segment, using Bayesian classification (Burstein et al., 2001).

(Y) announcing present research purposively

High Argumentative

It relates specifically to system performance with regard to a system’s recognition of the possible multiple text segments corresponding to thesis and conclusion text segments in student writing.

(Y) announcing present research purposively

Non Argumentative

A machine learning decision tree algorithm, C5.0 with boosting, was used for model building and labeling.

(Y) announcing principal outcomes

Non Argumentative

The results indicate that

19.1

(Y) announcing principal outcomes

Med Argumentative

the system can automatically identify features in student writing and can be used to identify thesis and conclusion statements in student essays.

(Y) presening RQs

Non-Argumentative

In this article, we address the following questions:

(Y) presening RQs

1) Can a system be built that reliably identifies thesis and conclusion statements?,

(Y) presening RQs

2) Moreover, how does system performance compare to a baseline, and inter-annotator agreement between human judges?,

(Y) presening RQs

3) Will the system be able to generalize across genre and grade level to some extent?, and

(Y) presening RQs

4) How well does the system generalize to unseen essay
responses?

(Y) presening RQs

That is, can the system identify thesis and conclusion statements on essay topics that it has not been trained on?