learning analytics ppt workshop Eisa rezaei Assistant Professor of Virtual University of Medical Sciences , Tehran, Iran. Ph.D. in educational technology Introduction A Brief History of Learning Analytics Ethics & Privacy DELICATE Checklist What is Learning Analytics? What and How The Process Of Learning Analytics  siemens (2010) learning analitces dashboard Sample of Edx, khan academy Learning Analytics: What It Can Do? reflections

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A Brief History of Learning Analytics
DELICATE Checklist
What and How
siemens (2010)
Sample of Edx, khan academy
reflections
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Dentist’s Recommendation
Internet of things
Big Data Analytics

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‫توانمن‬ ‫از‬ ‫فراتر‬ ،‫آن‬ ‫اندازه‬ ‫که‬ ‫هایی‬ ‫داده‬ ‫مجموعه‬‫دی‬
‫خ‬ ،،‫ضبط‬ ‫در‬ ‫معمولی‬ ‫داده‬ ‫پایگاه‬ ‫افزارهای‬ ‫نرم‬،‫یبره‬
‫باشد‬ ‫می‬ ‫تحلیل‬ ‫و‬ ‫مدیریت‬(،‫مکنزی‬2011)
‫برمط‬ ‫گیری‬ ‫تصمیم‬ ‫که‬ ‫اند‬ ‫داده‬ ‫نشان‬ ‫ها‬ ‫پژوهش‬‫داده‬ ‫نبای‬
‫م‬ ‫بهطود‬ ‫را‬ ‫سازمانی‬ ‫های‬ ‫فراورده‬ ‫و‬ ‫پیامدها‬ ،‫شواهد‬ ‫و‬ ‫ها‬‫ی‬
‫بخشد‬(،‫زیمنس‬ ‫و‬ ‫النگ‬2011)
The Student’s Interactions With Their Online Learning Activities Are Captured And Stored.
These Digital Traces (Log Data) Can Then Be ‘Mined’ And Analyzed To Identify Patterns
Of Learning Behaviour That Can Provide Insights Into Education Practice.
(Siemens & Gašević, 2012)

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‫شده‬ ‫تفسیر‬ ‫های‬ ‫داده‬
‫شده‬ ‫تلفیق‬ ‫اطالعات‬
‫مهارت‬ ‫سطح‬ ‫در‬ ‫دانش‬
‫ها‬ ‫نشانه‬ ‫و‬ ‫اعداد‬

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Google analytics,
Yahoo Web Analytics,
Twitalyzer, Facebook
Insights , cookies
classification,
regression, clustering,
factor analysis, social
network analysis
business
intelligence data for
education
data about learners and
their contexts
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V U M S
Siemens, Gasevic , Dawson ,Baker, Pardo

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Learning
Analytics
Educational
Data
Mining
Academic
Analytics
Predictive modelling
Extract value from big data sets
Business Intelligence applied
to education at an institutional,
regional and national level
Understand how students are
learning and optimise the
learning process

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“Learning analytics is the measurement, collection, analysis and
reporting of data about learners and their contexts, for the
purpose of understanding and optimising learning and the
environments in which it occurs.”
(George Siemens 2011)

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Siemens, Long, 2011. EDUCUASE Review

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‫ذی‬‫نفعان‬ ‫واکاوش‬ ‫هدف‬ ‫یا‬ ‫سطح‬ ‫انواع‬‫واکاوش‬
‫یادگیرندگان‬‫عل‬ ‫هیات‬ ‫اعضای‬ ‫و‬‫می‬ ‫دوره‬ ‫سطح‬:،‫اجتماعی‬ ‫های‬ ‫شبکه‬
‫گفتم‬ ‫تحلیل‬ ،‫مفهومی‬ ‫توسعه‬،‫ان‬
‫هوشمند‬ ‫درسی‬ ‫برنامه‬
‫یادگیری‬ ‫واکاوش‬
‫یادگیرندگان‬‫عل‬ ‫هیات‬ ‫اعضای‬ ‫و‬‫می‬ ‫دپارتمان‬:‫بینی‬ ‫پیش‬ ‫الگوهای‬،
‫موفقیت‬ ‫الگوهای‬/‫شکست‬
‫بازاریابی‬ ،‫موسسان‬ ،‫مدیران‬ ‫آموزشگاهی‬:‫یادگیرند‬ ‫پروفایل‬،‫ه‬
‫دانش‬ ‫جریان‬ ،‫آموزشی‬ ‫عملکرد‬
‫تحصیلی‬ ‫واکاوش‬
‫موسسان‬ ،‫مدیران‬ ‫ای‬ ‫منطقه‬(‫ایالت‬/‫استان‬:)
‫ها‬ ‫نظام‬ ‫بین‬ ‫مقایسه‬
،‫ملی‬ ‫های‬ ‫دولت‬‫آموزش‬ ‫مسئولین‬‫ی‬ ‫المللی‬ ‫بین‬ ‫و‬ ‫ملی‬

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Learning Analytics Reference Model (chatti et al, 2014)

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‫اکبر‬ ‫سيدعلي‬،‫احمدی‬‫کری‬ ‫داوود‬،‫مزادگان‬‫خيراتي‬ ‫تورج‬‫کازروني‬(1394)‫داده‬‫دانشگاه‬ ‫انصرافي‬ ‫دانشجویان‬ ‫کاوی‬
‫بر‬ ‫تمرکز‬ ‫با‬ ‫تهران‬‫حفظ‬‫دانشجویان‬‫اطالعات‬ ‫ناوری‬ ،‫پرداز‬ ‫شهریه‬،‫اطالعات‬ ‫فناوری‬ ‫،مدیریت‬‫دورة‬7‫شمارة‬ ،2

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siemens (2010)

16. 1. Data Trails

17. 2. Machine-human readable content

18. 3. Learner Profile Development

19. 4. Analytics tools and Methods

20. 5. Prediction & Intervention

22. 6. Adapting and personalizing

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Data Sources Repositories Tools and
Monitoring
Analytics
Methods
Permissions
LMS, library,
social media,
support
services,
mobiles,
profile,
attendance
Data
warehouse
(institutional,
national)
Dashboards,
visualization,
query & drill
down,
automated
monitoring,
“quantified
self”
monitoring
Predictive,
course-path,
social network,
data mining,
learner profile
Admin, faculty,
learners,
reporting
agencies

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LearningAnalyticsinAdobePresenter9

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pathgather.com
pathgather.com

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pathgather.com
blackboardanalytics

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http://research.uow.edu.au/learningnetworks/s
eeing/snapp/index.html

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based on past patterns of learning across
diverse student bodies
via socialization, pedagogy and technology
to provide unique feedback tailored to their
answers
for each and every student, playing to their
strengths and encouraging improvement

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D-etermination: Decide on the purpose of learning analytics for your institution.
E-xplain: Define the scope of data collection and usage.
L-egitimate: Explain how you operate within the legal frameworks, refer to the
essential legislation.
I-nvolve: Talk to stakeholders and give assurances about the data distribution and
use.
C-onsent: Seek consent through clear consent questions.
A-nonymise: De-identify individuals as much as possible
T-echnical aspects: Monitor who has access to data, especially in areas with high
staff turn-over.
E-xternal partners: Make sure externals provide highest data security standards
Drachsler, H. & Greller, W. (2016). Privacy and Analytics – it’s a DELICATE issue. A Checklist to establish trusted Learning Analytics.
6th Learning Analytics and Knowledge Conference 2016, April 25-29, 2016, Edinburgh, UK.

41. V U M S Hype Cycle for Education (University of Minnesota )
www.hypecycle.umn.edu

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Timeline of significant milestones in EDM (R.S. Baker and P.S. Inventado, 2014)

43. Archived Course
https://courses.edx.org/courses/UTAr
lingtonX/LINK5.10x/3T2014/info

44. V U M S

45. http://tech.ed.gov/wp-content/uploads/2014/03/edm-la-brief.pdf