collaborative study on the cloud

Presented by:- Nileshwari Desai
Under the guidance of:- Alpa reshamwala

What is cloud/ cloud computing?
Introduction
Background
Architecture of iWrite
Intelligent feedback components
Glosser, WriteProc, AQG
Evaluation Process
Conclusion
References

 Cloud Computing describes a new supplement,
consumption and delivery model for IT services based on
Internet protocols.
 It typically involves provisioning of dynamically scalable
and often virtualized resources. It is a byproduct and
consequence of the ease-of-access to remote computing
sites provided by the Internet according to their own
needs.
 This may take the form of web-based tools or applications
that users can access and use through a web browser as if
the programs were installed locally on their own
computers. There are five known ways of providing cloud
computing currently viz. public, private, community,
combined and hybrid cloud computing.

 The cloud focuses on maximizing the effectiveness of the shared
resources. Cloud resources are usually not only shared by
multiple users but are also dynamically re-allocated per demand.
This can work for allocating resources to users.
 For example, a cloud computer facility, which serves European
users during European business hours with a specific application
(e.g. email) while the same resources are getting reallocated and
serve North American users during North America's business
hours with another application (e.g. web server).
 This approach should maximize the use of computing powers
thus reducing environmental damage as well since less power, air
conditioning, rack space, etc. is required for a variety of
functions.

 WRITING is important in all knowledge-intensive
professions. Engineers, for example, spend between 20% to
40% of their workday writing, a figure that increases with
the responsibility of the position [1]. It is often the case that
much of the writing is done collaboratively [2] motivating
and helping students to learn to write effectively before
they graduate, particularly in collaborative scenarios. It
poses many challenges, many of which can be overcome by
technical means.
 Over the last 20 years, researchers within universities have
been developing technologies for automated feedback in
academic writing and for enabling collaborative writing but
work combining both automated feedback and CW has
been scant.

iWrite effectively allows researchers and instructors
to learn more about the students’ writing activities,
particularly about features of individual and group
writing activities that correlate with quality outcomes.
The evaluation provides data collected in general
classroom activities and writing assignments (individual
and collaborative), using mainstream tools yet allowing
for new intelligent support tools to be integrated.

These tools include –
 automated feedback.
 document visualizations.
 automatically generated questions to trigger
reflection.

From a software engineering and artificial intelligence perspective, the
iWrite architecture is built around new cloud-based technologies that are
likely to change the way people write together.
Google docs, Microsoft live, and etherpad among others allow writers to
work concurrently on a single document.
While google docs provide application programming interfaces that allow
developers to build applications on top of such writing environment, cloud
based technologies are expected to change the technological landscape in
many area, including computer-supported collaborative learning (CSCL).
While wiki engines, blogging tools and web-service oriented document
architectures such as Google Docs have simplified the task of CW, they are
not designed for supporting novice writers, or for addressing the challenges
of learning through CW. In particular, these technologies still fall short of
supporting writers with scaffolds and feedback pertaining to the cognitively
and socially challenging aspects of the writing process.

 There are features to manage writing activities in large
cohorts, particularly the management and allocation of
groups, peer reviewing, and assessment which include a
combination of generic and computer generated
personalized feedback. The generic feedback includes
interactive multimedia animations and content.
 The architecture incorporates features for feedback forms
such as Argument quality, features of text (such as
coherence), automatic generation of questions, and
feedback on the writing process. A combination of
synchronous and asynchronous modes of CW.
 The use of computer-based text analysis methods to
provide additional information on text surface level and
concept level to writing groups.

 CW is an iterative and social process that involves a team
focused on a common objective that negotiates,
coordinates, and communicates during the creation of a
common document.”
 1).Research that analyzes CW in terms of group work
processes-focusing on issues such as process loss,
productivity, and quality of the outcomes [2], [3].
 2).Research that studies CW in terms of group learning
processes, focusing on topics such as establishing common
ground, knowledge building and learning outcomes [3]. In
this, writing is seen as a means to deepen students’
engagement with ideas and the literature and for
knowledge building by jointly developing a text or
hypertext.

 A number of reasons have been identified to explain why writing
is an important tool for learning. Cognitive psychologists make
the general argument that writing requires the coordination of
multiple perspectives (content and audience) and the
linearization thought, which might not be linear [1].
 For subject matter learning, this means that writing requires
deep cognitive engagement with content, which will lead to
better learning. From a discourse theory perspective, it has been
argued that students must learn to understand and reproduce.
 Pedagogy-based arguments for the value of writing assert that
writing is an important medium for reflection and, in the
context of higher education, also a medium for developing
epistemic orientations [2].

 Machine Learning have progressed substantially and
automated writing tutors have improved
simultaneously.
 Despite this progress, the value of automated feedback
and essay scoring remains contested [2]. The
increasing use of automatic essay scoring (AES) in
particular by many institutions has created robust
debates about accuracy.

Preprocessing of documents:-
 Tokenising.
 Stemming.
 Stop-word removal.
It maintains three corpora, adding each new document,
at the sentence, paragraph and document level. In order
to reduce the lag time, all these are stored in a
repository, along with the results of the text mining
operations.

 The Assignment Manager’s academic and student User
Interfaces (UI) were illustrated in diagram. The student UI
contains two panels tabling a student’s writing tasks and
reviewing tasks.
 The writing tasks panel includes links to the activity
documents in Google Docs, information about deadlines, a
link to download a PDF formatted snapshot of the
document at each deadline, as well as links to view the
feedback provided by instructors and peers and to
automated feedback from Glosser.
 The reviewing tasks panel is styled in a similar fashion to
the writing tasks panel, but instead contains a table of the
documents that a student has been allocated to review.

• Assignment Manager is administered through a web application
based on Google Web Toolkit which facilitates the creation of
courses and writing activities. Each course has a list of students
(and their contact information), maintained in a Google Docs
spreadsheet and synchronized with Assignment Manager on
request. Keeping this information in a spreadsheet allows course
managers to easily modify enrollment details in bulk, and assign
students to groups and tutorials.
• Assignment Manager maintains a simple folder structure of
courses and writing activities on Google Docs. The permission
structure of the folder tree is such that lecturers are given
permission to view all documents in the course and tutors are
given permission to view all documents of the students enrolled in
their respective tutorials. A writing activity can specify a document
type (i.e., document, presentation, or spreadsheet), a final
deadline along with optional draft and review deadlines, along
with various other settings.

• When a writing activity starts, a document is created for each student
or group from a predefined assignment template. The document is
then shared accordingly, giving students “write” permission and their
lecturers and tutors “read” permission.
• Students are notified via email when a new assignment is available and
given instructions on how to write and submit their new assignment.
When a draft deadline passes, snapshots of the submitted documents
are downloaded in PDF format and distributed for reviewing or
marking through links in the iWrite interface.
• A number of configurations can be used to automatically assign
documents to students in peer-reviewing activities, or to tutors, and
lecturers for reviewing and marking. These include assignments within
a group, between groups or manual. Students are notified via email
when a new document is assigned for them to review. When a review
deadline passes, links to the submitted reviews will then appear as
icons in the feedback column of the writing tasks panel alongside the
corresponding document they critique. Students can click on these
icons to view the feedback and revise their document accordingly.

• Lastly, when the final deadline of a writing activity passes, the
permission of the students’ documents is updated from “write”
to “read,” so documents can no longer be modified by students.
A final copy of each submitted document is downloaded in
PDF format and distributed to tutors and lecturers for
marking.
• The Assignment Manager system greatly simplifies many of
the administrative process in managing collaborative writing
assignments, making it logistically possible for educators to
provide feedback to students quicker and more often.

Intelligent feedback consists of:
1). Automatic feedback
2).Questions
3).Process analysis
Using the APIs the system has access to the revisions of any
document. This allows new functionalities such as automatic
plagiarism detection, automatic feedback, and automatic
scoring systems to be integrated seamlessly with the appropriate
version of the document. iWrite currently implements three
such intelligent feedback tools, Glosser , AQG , and WriteProc ,
to generate automatic feedback, questions and process analysis,
respectively.

• Glosser [1] is an automatic feedback tool used within iWrite
for selected subjects. It was designed to help students review
a document and reflect on their writing . Glosser uses textual
data mining and computational linguistics algorithms to
quantify features of the text, and produce feedback for the
student.
• This feedback is in the form of descriptive information about
different aspects of the document. For example, by analyzing
the words contained in each paragraph, it can measure how
thematically “close” two adjoining paragraphs are. If the
paragraphs are too “far,” this can be a sign of a lack of flow,
and Glosser flags a small warning sign. As a form of feedback
Glosser provides trigger questions and visual representations
of the document.

• The highlighted features are focused on improving a document
by relating them to common problems in academic writing.
Glosser is not intended to give a definitive answer on what is
good or bad about a document. The feedback highlights what
the writers of a document have done, but does not attempt to
make any comparison to what it expects an ideal document
should be. It is ultimately up to the user to decide whether the
highlighted features have been appropriately used in the
document.
• Glosser has also been designed to support collaborative
writing. By analyzing the content and author of each
document revision, it is possible to determine which author
contributed which sentence or paragraph and how these
contribute to the overall topics of the document. These
collaborative features of Glosser can help a team understand
how each member is participating in the writing process.

• The autosave function in Google Docs acts as a version tracking
functionality, saving documents every 30 seconds or so (as long as the
student has written something in that period). This means that, for each
single document written by a student or team, thousands of revisions are
stored. This versioning information has the potential to provide a valuable
insight into the microstructure of the process students follow while
writing. This information can be used to not only understand which
processes are most likely to lead to successful outcomes but also to give
feedback to students and instructors.
• WriteProc takes advantage of these data traces to analyze the processes
involved in writing the documents [3], as opposed to focusing on the end
product or the snapshot of the document at a given time. It uses log files of
page views generated from students using the iWrite assignment
submission system and the actual content of the document revisions to
build a profile of a student’s behavior. This analysis is performed using a
combination of text mining and process mining techniques.

 The iWrite architecture includes a novel AQG tool [3] that extracts
citations from students’ compositions, together with key content
elements. For example, if the students use the APA citation style,
author, and year are extracted. Then the citations are classified using a
rule-based approach. For example, based on the grammatical structure
and other linguistic properties, the citations are identified as an
opinion, or describing an aim, or a result, or a method, or a system.
 Finally, questions are generated based on a set of templates and the
content elements. For example, if the citation is an opinion, the AQG
could generate a question that looks like: “What evidence is provided
by X to prove the opinion?”. If the citation is used in describing a
system, the question could look like: “In the study of X what
evaluation technique did they use?”

Three different evaluation aspects are presented.
 First, show a traffic analysis of iWrite, which is a key to
understanding how the tool is used and the writing
processes involved.
 Second, analyze further how high achieving students
differed from other students with respect to the way
they worked on their collaborative writing assignment.
 Lastly, include some user feedback.

Eg. During the first semester 2010, iWrite was used
to manage the assignments of four engineering
subjects. In total, these courses consisted of 491
students who wrote 642 individual and collaborative
assignments, for which recorded 102,538 revisions
that represent over 51,000 minutes of students work.
The amount of data and detail being collected by
iWrite about students’ learning behaviors is
unprecedented.

 Here the students were categorized into three groups, according to the mark
they received for their collaborative writing assignment[1]. The author
considered a low mark to be below one standard deviation from the mean
mark, a medium mark to be within one standard deviation from the mean, and
a high mark above the mean plus one standard deviation.
 He then compared these three groups in relation to the iWrite usage variables
defined in the previous section, & revealed the following: .
 Students with low grades did more individual and group revisions compared to
those with medium grades.
 Students with low and medium grades engaged in fewer writing sessions
compared to students with high grades.
 Students with low and medium grades engaged in fewer writing days compared
to students with high grades.
 These results indicate that it is not whether, but how, students used iWrite
which made a difference in their CW performance. Students who obtained
high grades were in teams that engaged more frequently in sustained writing
sessions. Fewer bursts of document revisions were associated with lower marks.

 The author collected informal feedback from the course
lecturers of the University which used iWrite. They were
extremely positive about the experience. One course
manager commented “An online assignment submission
system will save us a lot of time sorting and distributing
assignments. In addition, we send copies of a portion of our
assessments to the Learning Centre, so online submission
really minimizes our paper usage.”
 User feedback also highlighted the risk of users having to
learn new technologies. For example, students found the
use of Google Docs and the automatic submission process
confusing, “A number of students tried to upload a word
document or created a new Google document, rather than
cutting and pasting in to the Google document we had
created for them . ”

 The system provides features for managing
assignments, group and peer-reviewing activities. It
also provides the infrastructure for automatic
mirroring feedback including different forms of
document visualization, group activity, and automatic
question generation.
 A key design aspect was the use of cloud computing
writing tools and their APIs to build tools that make it
seamless for students to write collaboratively either
synchronously or asynchronously. A real time feedback
was provided and it made easier for professors to deal
with a large number of student assignments.

1) Rafael A. Calvo,” Collaborative Writing Support Tools on the
Cloud” Senior Member, IEEE, Stephen T. O’Rourke,Janet Jones,
Kalina Yacef, and Peter Reimann, vol 1, march 2011.
2) Namrata Raut, Darshana Parab,Shephali Sontakke, Sukanya
Hanagandi “Cloud Documentation and Centralized Compiler
for Java & Php “, Student, Department of Computer
Engineering,JSPM‟s BSIOTR(W).
3) JOHN C. GOOCH, ”The Dynamics and Challenges of
Interdisciplinary Collaboration: A Case Study of “Cortical
Depth of Bench” in Group Proposal Writing.
4) C.Hong, ”Principle-Based Design of Technology Use to Support
Community Knowledge-Building”.

collaborative study on the cloud

collaborative study on the cloud

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