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2013 03 14 (educon2013) emadrid ucm elearning standards learning analytics
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E-Learning Standards E-Learning Standards and Learning Analyticsand Learning Analytics
Can Data Collection Be Improved by Using Standard Data Models?
IEEE EDUCON Conference 2013 Berlin, March 14th, 2013
Ángel del Blanco [email protected]
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OutlineOutline
Learning Analytics
E-Learning Standards (GBL)
Learning analytics + e-learning standards
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Learning Analytics: a tendency on the rise!Learning Analytics: a tendency on the rise!
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Students can use these analysis results as guidance and self-awareness tools;
Teachers can use them to identify issues and try to tackle them;
Schools can use results as a domain-specific variant of Business Intelligence
to detect and address learning problems, assess students, and
predict learning results
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Learning Analytics: Source of dataLearning Analytics: Source of data
Collect, report, predict, act and refine
Learning Management Systems (LMS): Wide adoption
Lots of different tools
MOOCs Increasing acceptance
Store large amounts of data about the students’ performance..
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Learning Analytics: Issues gathering dataLearning Analytics: Issues gathering data
LMSs lack standardized data structures;
LA tools tend to be tied to specific implementations of LMS and databases.
This has a number of negative consequences: data gathered across different LMSs, are hard to move and compare;
cross-institution data comparison is impeded, due to installation-specific data model differences
LA tool adoption remains relatively low.
“analytics need to be broad-based, multi-sourced, contextual and
integrated” Siemens et al. [14],
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Type of data gatheredType of data gathered
Analytic Tool Platform Data for analysis
SNAPP External tool Forum activity
LOCO-Analyst External tool Resource views, resource contents, forum contents
Course Signals LMSStudent age, residency, credits attempted, academic
history, course grades to date, interactions with the LMS
Desire2Learn Students Success System
LMSStudent grades, login frequency, discussion posts, results
and number of quiz attempts.
Open Learning Initiative MOOCs Knowledge Components achieved and failed
Khan Academic MOOCs Performance in exercises
Student-performed actions with a given outcome
Student Profile: age,
interests, etc.
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IEEE LTSC 1484.11.1IEEE LTSC 1484.11.1
A.K.A. CMI data model (SCORM) “Bag” of records and fields
Student degree of progress “End State” (cmi.completion_status)
“State of Success” (cmi.success_status)
“Overall student performance” (cmi.score.raw)
Objectives degree of completion and success. progress
measurement, score…
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IEEE LTSC 1484.11.1IEEE LTSC 1484.11.1
Interactions Store fine-grained information
Different kinds of interaction (relationship, true-false, etc. )
Multiple correct answers
Tagging particular entries with identifiers that link them to sets of related learning objectives
Journalist vs State
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IEEE LTSC 1484.11.2IEEE LTSC 1484.11.2
API
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Experience APIExperience API
Data Model Like Activity Stream
Extended for educational purposes
High flexibility
Verbs: key elements
<I> <did> <this>
<actor> <verb> <object>, with <result>, in <context>
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LRS and APILRS and API
Learning Record Store
API
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E-Learning standards + Learning AnalyticsE-Learning standards + Learning Analytics
Reduce development costs, protect investments…
Data reuse and broadens the pool of data that can be analyzed and explored
Move to other issues….
Requirements: Data model structure
Access and share data among systems
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1484.111484.11
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Experience APIExperience API
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GraciasGracias
Ángel del Blanco Aguado
More information http://e-adventure.e-ucm.es
http://www.e-ucm.es
Publications http://www.e-ucm.es/publications/
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