| duygu-bektik |
primary topic |
cf3adb70b2e9a3385e8cca52b2c43bd7 |
| 6-18622 |
Creator |
cf3adb70b2e9a3385e8cca52b2c43bd7 |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
jobTitle |
PhD Research Student |
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jobTitle |
PhD Research Student |
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research overview |
I joined KMi as a full-time PhD Student in October 2012. My PhD is being supervised
by KMI's Simon Buckingham Shum, Anna De Liddo and IET's Rebecca Ferguson.
The main question that this PhD aims to answer is: "To what degree can computational
text analysis and visual analytics be used to support the academic writing of students
in higher education?"
This research investigates:
1) whether computational techniques can automatically identify the presence or absence
of attributes of good academic writing, as correlated with grades and as identified
in the literature
2) if this proves possible, how best to feed back actionable analytics to support
students and educators
3) and whether this feedback has any demonstrable benefits.
To answer the main research question, the following four supplementary research questions
are aimed to be addressed:
RQ1: To what extent is the rhetorical parser XIP accurate and sufficient for identifying
the presence of attributes of good academic writing within student writing, as judged
by the grade, and by educators?
RQ2: In what ways should XIP output be delivered to end users (students and educators)?
RQ3: To what extent do educators value the results of XIP’s analysis of an individual
student or cohort’s work when the primary focus is on assessment?
RQ4: To what extent do students value the results of XIP’s analysis as formative feedback
on their writing?
One contribution of this PhD will be a conceptual framework bridging between educational
research into academic writing (the features deemed to be important to quality writing),
and research into language technologies (the features which can be automatically detected).
This framework will occupy the ‘middle ground’ between learning and computation, helping
members of both communities articulate, in precise terms, the opportunities for pedagogically
sound learning analytics. This provides the rationale for investigating rhetorical
parsers, whose feature extraction capabilities overlap with key features of academic
writing.
A second contribution will be the evaluation of a particular rhetorical parser, XIP,
against several measures of quality (RQs 1-4), both quantitative and qualitative,
engaging both educators, and students from undergraduates to PhDs. Since this is
a technology developed outside education, the PhD will exemplify a holistic learning
analytics approach to the evaluation of commercial analytics products when migrated
into learning contexts, which should be of interest to the wider LAK community given
the rapid growth of this market. |
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hasMembership |
kmi |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
hasMembership |
knowledge-media-institute |
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biography |
<p>Duygu Bektik holds a Ph.D. in Learning Analytics from The Open University, UK.
Her Ph.D. focuses on the use of writing analytics, particularly automated analysis
of meta-discourse in student writing, to support tutors’ essay assessment practices.
Besides her Ph.D., she holds B.A.&M.A. degrees in computer and instructional technologies,
and MSc. in software engineering. She also holds a teaching certificate to practice
teaching ICT in primary and secondary schools, with various experiences both in Turkey
and in the UK. Duygu is a mixed-methods researcher with an experience of holding one-to-one
interviews, focus groups, questionnaires, and carrying out quantitative studies (regression
and Jaccard analyses).<br /><br />Duygu is currently acting as a postdoctoral researcher
at The Open University, UK. Her research interests currently lie primarily in the
fields of learning analytics, data visualisation, and e-assessment.</p> |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
holder_of |
ResearchStudent |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
type |
Person |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
label |
Duygu Bektik |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
label |
Dr Duygu Bektik |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
account |
dsimsek |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
account |
ds23274 |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
account |
duygubektik |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
account |
duygubektik |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
account |
duygubektik |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
depiction |
duygu-simsek.jpg |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
familyName |
Bektik |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
Given name |
Duygu |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
sha1sum of a personal mailbox URI name |
c8bc5cc775721fbf9b04fecbcffb528e2867d92a |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
name |
Duygu Bektik |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
title |
Dr |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
topic_interest |
information retrieval |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
topic_interest |
technology-enhanced learning |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
topic_interest |
natural language processing |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
work info homepage |
duygu-bektik |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
work info homepage |
ds23274 |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
Description |
<p>Duygu Bektik holds a Ph.D. in Learning Analytics from The Open University, UK.
Her Ph.D. focuses on the use of writing analytics, particularly automated analysis
of meta-discourse in student writing, to support tutors’ essay assessment practices.
Besides her Ph.D., she holds B.A.&M.A. degrees in computer and instructional technologies,
and MSc. in software engineering. She also holds a teaching certificate to practice
teaching ICT in primary and secondary schools, with various experiences both in Turkey
and in the UK. Duygu is a mixed-methods researcher with an experience of holding one-to-one
interviews, focus groups, questionnaires, and carrying out quantitative studies (regression
and Jaccard analyses).<br /><br />Duygu is currently acting as a postdoctoral researcher
at The Open University, UK. Her research interests currently lie primarily in the
fields of learning analytics, data visualisation, and e-assessment.</p> |
| cf3adb70b2e9a3385e8cca52b2c43bd7 |
in dataset |
kmifoaf |
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in dataset |
profiles |
