Slides of my presentation at DCLA13 (1st International Workshop on Discourse-Centric Learning Analytics) Leuven, April 8, 2013

1. OpenEssayist: Extractive Summarisation
and Formative Assessment
of Free-Text Essays
Nicolas Van Labeke, Denise Whitelock ,
Debora Field , Stephen Pulman, John Richardson 
 Institute of Educational Technology – The Open University
 Department of Computer Science – University of Oxford

2. SAFeSEA: Research Questions
• How can an automated system detect passages
on which a human marker would usually give
some feedback ?
• Can existing methods of information extraction,
summarization be adapted to select content for
such feedback ?
• How effectively can these methods deliver
feedback ?
• What effect does these techniques have on essay
improvement? On current essay and in future
ones ? On self-regulation and metacognition ?

3. Context
• Essays: Open University (UK) postgraduate assignments
– Distance learning, adult learners
– 1500+ words, free-text & open-ended questions
• No “Gold Standard”, wide range of content
– Perfect test ground for extractive techniques
– Impact of lack of (or limited) domain knowledge?
• Bulk of activity (i.e. writing) takes place outside system
– Usage of drafts “varies a lot” among students
– Nature, scope and timing of feedback?
• Limited possibility for “mock” experiments:
• testing & evaluation on “live” material
• Connection with summative (tutor-based) assessment ?

4. Education Postgraduate Course H810
Accessible online learning: supporting disabled students
TMA1 (Tutor-Marked Assignment) – 1500 words
Write a report explaining the main accessibility challenges for disabled learners
that you work with or support in your own work context(s).
Critically evaluate the influence of the context (e.g. country, institution, perceived
role of online learning within education) on the: (1) identified challenges; (2)
influence of legislation; (3) roles and responsibilities of key individuals; (4) role of
assistive technologies in addressing these challenges.
TMA2 – 3000 words
Critically Evaluate your own learning resource in the following ways:
1. Briefly describe the resource and its accessibility features.
2. Evaluate the accessibility of your resource, identifying its strengths and
weaknesses.
3. Reflect on the processes of creating and evaluating accessible resources.

5. Context
• Essays: Open University (UK) postgraduate assignments
– Distance learning, adult learners
– 1500+ words, free-text & open-ended questions
• No “Gold Standard”, wide range of content
– Perfect test ground for extractive techniques
– Impact of lack of (or limited) domain knowledge?
• Bulk of activity (i.e. writing) takes place outside system
– Usage of drafts “varies a lot” among students
– Nature, scope and timing of feedback?
• Limited possibility for “mock” experiments:
• testing & evaluation on “live” material
• Connection with summative (tutor-based) assessment ?

6. openEssayist
localhost:8065
phaeros.open.ac.uk:80
openEssayist
PHP, Epiphany
[Symfony2]
User
openEssayist
RESTful API
PHP, Epiphany
User
User
pyEA
RESTful API
Python, Flask
localhost:8064
AfterTheDeadline
Spell/Grammar
checker
Java
User
localhost:9998
Apache Tika
Text Extractor
Java
Orchestrator
(Open)
Learner Model
pyEssayAnalyser
Python, NLTK

7. Extractive Summarisation
• Hypothesis
– quality and position of key phrases and key sentences
within an essay (i.e., relative to the position of its
structural components) give idea of how complete and
well-structured the essay
– provide a basis for building suitable models of feedback
• Experimenting with two simpler summarisation
strategies
– key phrase extraction : identifying individual words or
short phrases are the most suggestive of the content of a
discourse
– extractive summarisation: identifying whole key
sentences.
• Rapid implementation and testing

8. Summarisation Processes
1. NL pre-processing of text
2. unsupervised recognition of structural
elements
3. unsupervised extraction of key
words/phrases
4. unsupervised extraction of key sentences.

9. Pre-processing
• Using NLTK (Python-base Natural Language
Processing Toolkit)
– tokenisers,
– lemmatiser,
– part-of-speech tagger,
– List(s) of stop words.
• Experimenting different approaches to define
suitable stop word list(s)
– domain-independent list?
– Generated from appropriate reference materials
(using TF-IDF, for example)?

10. Essay Structure
• Restructure text as paragraphs/sentences
• Automatic Identification of each paragraph’s structural role
– Summary, Introduction, conclusion, body, references, …
– Regardless of presence of content-specific headings
– No clues from formatting markup (plain text submission)
• Decision trees developed through manual experimentation
– corpus of 135 student essays submitted in previous years for the
same module that the evaluation will be carried out on.
• Still need formal evaluation but output good enough for
first rounds of OpenEssayist testing, and continually
improving

12. Key words, lemmas and phrases
• Unsupervised extractive summarisation using graph-based ranking
methods (TextRank, Mihalcea & Tarau 2004, 2005)
• Each unique word is represented by a node in the graph, and co-
occurrence relations (specifically, within-sentence word adjacency) are
represented by edges in the graph.
• Compute a 'key-ness' value for each word in the essay ('Key-ness' can be
understood as 'significance within the context of the essay‘)
• Centrality algorithm used to calculate the significance of each word
– betweenness centrality (Freeman 1977) and PageRank (Brin & Page 1998)
– Roughly speaking, a word with a high centrality score is a word that sits
adjacent to many other unique words which sit adjacent to many other unique
words which…, and so on.
• The words with high(est) centrality scores are the key words.
– Decision needs to be made as to what proportion of the essay's words qualify
as key words.
• Sequences of keywords in the surface text identify within-sentence key
phrases (bigrams, trigrams and quadgrams).

13. Key words, lemmas and phrases

14. Key Sentences
• Similar graph-based ranking approach used to compute
key-ness scores for whole sentences.
• Instead of word adjacency (as in the key word graph),
co-occurrence of words across pairs of sentences is the
relation used to construct the graph.
– similarity measures of every pair of sentences.
• The similarity scores become edge weights in the
graph, while whole sentences become the nodes.
• TextRank key sentence algorithm (based on PageRank
but with added edge weights) is then applied.

15. Extractive Summarisation - Sentences

16. Extractive Summarisation – Overview

17. Exploring The Design Space
❶Researcher-centred Design
– Data-driven
– Architecture setup, integration & refinement of
tools
– From discourse to summarisation
– Emerging properties, hypotheses building

25. • Multiple External Representation
• Mash-ups, reports, summaries, …
• Highlighting co-occurrence of terms (or lack of)
• Exploration & discovery, hypotheses building,
eliciting recommendations & heuristics

26. Exploring The Design Space
① Researcher-centred Design
– Data-driven
– Architecture setup, integration & refinement of tools
– From discourse to summarisation
– Emerging properties, hypotheses building
❷ Learner-centred Design
– Task-driven
– Hypotheses testing & validation, refinement
– From summarisation to formative feedback
– Live evaluation

27. Question: What kind of feedback?

28. Section of
essay
Purpose of section
Title
Write the full question (title) at the
top of your assignment. It will
contain keywords (known as
content and process words). See
the 'Understanding the question'
webpage for these.
Introduction
A paragraph or two to define key
terms and themes and indicate
how you intend to address the
question.
Main body
A series of paragraphs written in
full sentences that include specific
arguments relating to your answer.
It’s vital to include evidence and
references to support your
arguments.
Conclusions
A short section to summarise main
points and findings. Try to focus on
the question but avoid repeating
what you wrote in the
introduction.
References
A list of sources (including module
materials) that are mentioned in
the essay.
• Introductions
– An introduction provides your reader
with an overview of what your essay
will cover and what you want to say.
– Essays introductions should
• set out the aims of the assignment and
signpost how your argument will unfold
• introduce the issue and give any
essential background information
including a brief description of the
major debates that lie behind the
question
• define the key words and terms
• be between 5% and 10% of the total
word count
– Some students prefer to write the
introduction at an early stage, others
save it for when they have almost
completed the assignment. If you write
it early, don't allow it to constrain what
you want to write. It's a good idea to
check and revise the introduction after
the first draft.
• The body of your essay
– …
Open University - Skills for OU Study
http://www.open.ac.uk/skillsforstudy/essays.php

29. Question: Reflective activities?
• “Advice for action”
– Expectation vs. intention
– Reflection, self-report, validation of advices, …
• Introducing user interventions in the system
• Feeding back to the system? To the Essay Analyser?

30. Question: Drafts, History & Changes

31. Question: “Quality” of output?

32. Current and Future Work
• Three lines of experimentations:
– improve the different aspects of the essay analyser (e.g.
different “key-ness” metrics, introduce domain-specific
lists of stop-words)
– Analyses of summarisation output (e.g. factor analysis) to
run on existing corpus of essays
• 5 years of essays on the H810 course, all marked and annotated by
human tutors),
• identify trends and markers to be used as progress/performance
indicators;
– Iterative, user-centred, design and testing of openEssaysit
(refine possible usage scenarios, test pedagogical)
• Currently proceeding with second design phase
• First live evaluation, in authentic context, by a new
cohort of students on the H810 module (Sept 2013)