Knowledge-based design analytics for authoring courses with smart learning content - AIED conference presentation by Laia Albó

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Presentation at the 22nd AIED conference (https://aied2021.science.uu.nl/) of the journal paper: Albó, L., Barria-Pineda, J., Brusilovsky, P., & Hernández-Leo, D. (2021). Knowledge-Based Design Analytics for Authoring Courses with Smart Learning Content. International Journal of Artificial Intelligence in Education, 1-24. https://doi.org/10.1007/s40593-021-00253-3

Bildung

Knowledge-based Design Analytics for Authoring
Courses with Smart Learning Content
Laia Albó1, Jordan Barria-Pineda2, Peter Brusilovsky2 and Davinia Hernández-Leo1
1TIDE, Interactive & Distributed Technologies for Education,
Universitat Pompeu Fabra, Barcelona (Spain)
2PAWS, Personalized Adaptive Web Systems Lab,
University of Pittsburgh, Pennsylvania (US)
AIED Conference Twitter: @LaiaAlbo / Email: laia.albo@upf.edu June 17th, 2021

2
• Lack of work in supporting educators during the design process of their
courses/activities
Knowledge-based design analytics
Visualize the concept coverage of individual learning activities to help
instructors in creating balanced learning designs
Context
Metadata
• Type of activity
• Associated concepts
• ...

Type of SLC Comprehension Construction
Examples
WebEx Anim_Ex PCEX
Problems
Quizjet PCEX_ch PCRS
Designing a JAVA course with Smart Learning Content (SLC)
3
• Each SLC activity is associated with different programming concepts
(metadata).

Knowledge-based design analytics
4
● 3 types of visualizations
Previous lecture concepts Future lecture concepts
Current lecture concepts
DESIGN ERRORS!

edCrumble: https://ilde2.upf.edu/edcrumble/
5

Within-subjects user study
6
Data collection: action logs, learning design outcomes, Cognitive load
questionnaire, post-study questionnaire

8
Using the visualizations (knowledge-based design analytics):
1. Reduced the cognitive load of the design tasks (Mental Demand).
2. Facilitated the activities selection (= Time).
3. Helped to avoid design errors: disappearance of future concepts.
4. Changed the behavior of instructors.
(previewing contribution vs. looking deeper into their content)
Results

Thank you
Tool: https://ilde2.upf.edu/edcrumble/
Demo video: http://tiny.cc/edCrumbleECTEL
Contact: laia.albo@upf.edu
@LaiaAlbo
Research collaboration supported by
the SEBAP Research Mobility Award
https://www.amicsdelpais.com/
Research partially funded by:
NSF DRL 1740775
“la Caixa Foundation”(CoT project, 100010434)
FEDER, Grant Numbers:
MDM-2015-0502, TIN2014-53199-C3-3-R,
TIN2017-85179-C3-3-R.

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Knowledge-based design analytics for authoring courses with smart learning content - AIED conference presentation by Laia Albó

1. Knowledge-based Design Analytics for Authoring Courses with Smart Learning Content Laia Albó1, Jordan Barria-Pineda2, Peter Brusilovsky2 and Davinia Hernández-Leo1 1TIDE, Interactive & Distributed Technologies for Education, Universitat Pompeu Fabra, Barcelona (Spain) 2PAWS, Personalized Adaptive Web Systems Lab, University of Pittsburgh, Pennsylvania (US) AIED Conference Twitter: @LaiaAlbo / Email: laia.albo@upf.edu June 17th, 2021

2. 2 • Lack of work in supporting educators during the design process of their courses/activities Knowledge-based design analytics Visualize the concept coverage of individual learning activities to help instructors in creating balanced learning designs Context Metadata • Type of activity • Associated concepts • ...

3. Type of SLC Comprehension Construction Examples WebEx Anim_Ex PCEX Problems Quizjet PCEX_ch PCRS Designing a JAVA course with Smart Learning Content (SLC) 3 • Each SLC activity is associated with different programming concepts (metadata).

4. Knowledge-based design analytics 4 ● 3 types of visualizations Previous lecture concepts Future lecture concepts Current lecture concepts DESIGN ERRORS!

5. edCrumble: https://ilde2.upf.edu/edcrumble/ 5

6. Within-subjects user study 6 Data collection: action logs, learning design outcomes, Cognitive load questionnaire, post-study questionnaire

7. 7

8. 8 Using the visualizations (knowledge-based design analytics): 1. Reduced the cognitive load of the design tasks (Mental Demand). 2. Facilitated the activities selection (= Time). 3. Helped to avoid design errors: disappearance of future concepts. 4. Changed the behavior of instructors. (previewing contribution vs. looking deeper into their content) Results

9. Thank you Tool: https://ilde2.upf.edu/edcrumble/ Demo video: http://tiny.cc/edCrumbleECTEL Contact: laia.albo@upf.edu @LaiaAlbo Research collaboration supported by the SEBAP Research Mobility Award https://www.amicsdelpais.com/ Research partially funded by: NSF DRL 1740775 “la Caixa Foundation”(CoT project, 100010434) FEDER, Grant Numbers: MDM-2015-0502, TIN2014-53199-C3-3-R, TIN2017-85179-C3-3-R.

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