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Learning Analytics
Implementation in a Multidomain
Computer-Based Learning
Environment
Professors and Students
>
Educational software is supporting a diversity of domains at all the
educational levels. However, there is still a lack...
>
MDLE Architecture
Local users
Identity
Provider (idP)
Services (SP)
 KnowledgeTree, an adaptive e-Learning architecture that
integrates distributed servers hosting educational services.
Mul...
MDLE
implementation
UCol IPN
UABC
SAML allowed flow
DB Node’s users database
SAML metadata file
DB
DBidP
idP
idP DB
Use of...
MDLE Interfaces
 First experience with MDLE takes place using the Preliminary
Evaluation system.
 This system is intended to identify ed...
 Considering the four key elements included in the learning analytics
definition: measurement, collection, analysis and r...
Preliminary Evaluation System
L.A. Student View
Average result is depicted in yellow,
domain intervention is recommended.
...
Preliminary Evaluation System
L.A. Professor View
Very high
High
Normal
Low
Very low
0
10
20
30
40
50
60
70
80
90
100
0 10...
 Pre-university Mathematics System (PreMath), is an Intelligent Tutoring
System (ITS) supporting high-school and universi...
 Students are provided with theoretical content and then requested
to solve a minimum of three math exercises for each to...
 This system is responsible for characterizing the level of student
math knowledge and the different exercises complexity...
 In order to display the information visually, users can navigate
between the different tabs and access different configu...
 We have presented MDLE, a multidomain computer-based learning
environment, as a proposal for students’ data sharing amon...
DONE….
Learning Analytics
Implementation in a Multidomain
Computer-Based Learning
Environment
OMAR ALVAREZ XOCHIHUA
Professors an...
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VII Jornadas eMadrid "Education in exponential times". Learning Analytics Implementation in a Multidomain Computer-Based Learning Environment. Omar Alvarez-Xochihua Universidad Autónoma de Baja California. 04/07/2017.

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VII Jornadas eMadrid "Education in exponential times". Learning Analytics Implementation in a Multidomain Computer-Based Learning Environment. Omar Alvarez-Xochihua Universidad Autónoma de Baja California. 04/07/2017.

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VII Jornadas eMadrid "Education in exponential times". Learning Analytics Implementation in a Multidomain Computer-Based Learning Environment. Omar Alvarez-Xochihua Universidad Autónoma de Baja California. 04/07/2017.

  1. 1. Learning Analytics Implementation in a Multidomain Computer-Based Learning Environment Professors and Students
  2. 2. > Educational software is supporting a diversity of domains at all the educational levels. However, there is still a lack of effective integration or communication among systems. Introduction Design and implementation of a Multidomain Learning Environment (MDLE) that integrates and interconnects different heterogeneous educational applications. Three main objectives:  Interconnect heterogeneous web-based educational applications.  Allow students access to the set of educational applications by using a unique/secure user account.  Share the students’ data, in order to be used as an integrated learning analytics environment.
  3. 3. > MDLE Architecture Local users Identity Provider (idP) Services (SP)
  4. 4.  KnowledgeTree, an adaptive e-Learning architecture that integrates distributed servers hosting educational services. Multiple instances of KnowledgeTree can collaborate and interchange students’ data with each other, however, there is no specific functionality intended to conduct LA.  eLAT, an exploratory Learning Analytics Toolkit that uses data from different systems to conduct a more comprehensive teaching analysis.  Authors of this paper stressed that “Current Learning Analytics tools should be interoperable with different platforms, learning environments and systems.”  Recently, the LAK community organized the Cross-LAK workshop, encouraging participants to explore blended learning by researching and implementing LA across physical and digital spaces (learning analytics across digital spaces). >Related Work
  5. 5. MDLE implementation UCol IPN UABC SAML allowed flow DB Node’s users database SAML metadata file DB DBidP idP idP DB Use of the LA service SPDB SP SP DB DB LA serviceSP Federated Identity (FI) architecture that allows secure access to MDLE, and serves as a repository of educational computer-based applications. Implemented using the enterprise federation protocol supported by SAML authentication standard (Security Assertion Markup Language). Each node may have an identity Provider (idP) and a set of Service Providers (SP). The idP is in charge of the access control and the SP manage specific app. Each institution decides which services would be shared, and which users’ attributes are going to be available, such as name, age, email, etc.
  6. 6. MDLE Interfaces
  7. 7.  First experience with MDLE takes place using the Preliminary Evaluation system.  This system is intended to identify educational gaps of freshman students in the fields of: 1) mathematics and 2) reading and comprehension, as well as evaluates aspects regarding 3) study habits and 4) self-esteem.  Through the use of four specific assessments, the system evaluates students’ knowledge and their academic behavior.  Students are asked to answer all of the assessments in order to gain access to the rest of the educational services in MDLE.  The information obtained is processed and used as a starting point for the assignment of activities to each student; based on his/her specific knowledge gaps and learning habits. Preliminary Evaluation System
  8. 8.  Considering the four key elements included in the learning analytics definition: measurement, collection, analysis and reporting of data about learners, the preliminary evaluation module mainly works as an early alert system for the whole MDLE environment.  First, by using the domain specific assessments the system evaluates, collects and stores the students’ data.  Second, the system measures students’ background knowledge and learning behavior. This information is stored in the MDLE shared database (LA service), and is available to be used for any other application on the network.  Then, a deep analysis is conducted to determine those students that require leveling courses, and in what particular domains.  Finally, this module generates and displays information about students’ performance. Preliminary Evaluation System
  9. 9. Preliminary Evaluation System L.A. Student View Average result is depicted in yellow, domain intervention is recommended. Mandatory domain intervention is displayed in red The green color indicates a good or very good student performance.
  10. 10. Preliminary Evaluation System L.A. Professor View Very high High Normal Low Very low 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 Self-esteem Students
  11. 11.  Pre-university Mathematics System (PreMath), is an Intelligent Tutoring System (ITS) supporting high-school and university students to address gaps in their math current knowledge.  Embedded in a Moodle environment, includes a set of instructional content (instruction and practice), considering a set of 20 math topics such as: multiply-divide monomials, fractions, decimals, percentages, etc.  The PreMath system aims to reduce the university failing grades and drop- outs rates. PreMath System
  12. 12.  Students are provided with theoretical content and then requested to solve a minimum of three math exercises for each topic.  The system provides feedback in a proactive (when a student commits a mistake) and reactive (under student request) way.  The inputs of students and feedback provided by the system are used to conduct learning analytics; providing information on the performance of students and quality of the educational content. PreMath System
  13. 13.  This system is responsible for characterizing the level of student math knowledge and the different exercises complexity using a series of formulas based on the Item Response Theory (IRT) model. PreMath System
  14. 14.  In order to display the information visually, users can navigate between the different tabs and access different configurable graphs.  While the student can visualize only his own information, teachers are able to analyze individual and group learning performance. PreMath System Difficulty of Exponents Topic Student Ability in Exponents Topic Exercise 20 Topic Student 5 Group Very easy Easy Normal Hard Very hard Very high High Normal Low Very low Very High High Normal Reading & Comprehension Study Habits Self Esteem
  15. 15.  We have presented MDLE, a multidomain computer-based learning environment, as a proposal for students’ data sharing among interconnected educational software systems.  MDLE allows the integration of educational systems, hosted in different locations, and sharing generated users-data to implement a comprehensive learning analytics service.  Conduct a further investigation about the benefits of this environment interoperability, generating dashboards that integrates learning analytics views from systems attending multiple domains.  Use the OAuth standard as communication and authentication protocol. As a complementary method for data transfer between nodes, instead of using a centralized database (implementation of a communication protocol used for data transfer). >Conclusions and Future Work
  16. 16. DONE….
  17. 17. Learning Analytics Implementation in a Multidomain Computer-Based Learning Environment OMAR ALVAREZ XOCHIHUA Professors and Students

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