This document discusses how learning analytics and e-learning standards can work together to improve data collection. It describes how learning analytics is used to analyze student performance data from sources like learning management systems (LMS) and MOOCs. However, LMSs lack standardized data structures, making it hard to compare data across systems. The document proposes that using e-learning standards like IEEE 1484.11.1 (SCORM) and the Experience API could help standardize data collection and allow data to be more easily accessed, shared, analyzed and compared across different systems. This could help reduce costs and broaden the pool of data available for learning analytics.

1. E-Learning StandardsE-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 Aguado
Angel.dba@fdi.ucm.es

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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 LMS
Student age, residency, credits attempted, academic
history, course grades to date, interactions with the LMS
Desire2Learn Students
Success System
LMS
Student 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
angel.dba@fdi.ucm.es
 More information
 http://e-adventure.e-ucm.es
 http://www.e-ucm.es
 Publications
 http://www.e-ucm.es/publications/

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