Orchestration graph: Difference between revisions
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== Introduction == | == Introduction == | ||
Orchestration Graphs depict the structure (what is done when by whom), the | Orchestration graphs are a formalism developed by Pierre Dillenbourg to model [[CSCL script]]s and similar complex learning designs. | ||
Orchestration Graphs depict the structure (what is done when by whom), the pedagogical rationale behind the activity flow, and the work flow created by data transformations and student groupings (“social structure”). An “Orchestration Graph Engine ”allows the teacher to run educational scenarios with her students following the activities as specified in the OG – or as modified real-time by her as she monitors their work– to provide input data and obtain output products. Finally, products and traces of the learning process are collected and processed; a stochastic model of the scenario uses these traces to predict (or confirm) learner states" (Dillenbourg, 2015:95-100)<ref name="dillenbourg2015">Dillenbourg, Pierre (2015). Orchestration Graphs: Modeling scalable education. EPFL Press.</ref> | |||
== Definition == | |||
An Orchestration Graph defines a [[pedagogical scenario]] in terms of learning activities at three "planes": class, group and individual. | |||
It is organized in a similar way as a GANTT chart, i.e. one has to read it from left to right. Each learning activity starts at given time and lasts for a given time. | |||
Activity time can be short, intense and precise, e.g. in a class setting or last over days where students can engage asynchronously. | |||
{{quotation|Activities range from reading a text or watching a video, to contributing ideas in a brainstorm, or experimenting with a simulation. They can take input data and social structures from previous activities and operators, and produce student products (such as students answers and designs), as well as detailed student trace data. See an example of a running activity. Activities (nodes) are connected through edges. These can contain pedagogical justifications (e.g., activity 1 is an advanced organiser for activity 2), learning analytics information (e.g.,student success is activity 1 is 34% correlated with/predictive of success in activity 2), and operators.}} <ref>Håklev, S., Faucon, L., Hadzilacos, T., & Dillenbourg, P. (2017). Orchestration Graphs: Enabling Rich Social Pedagogical Scenarios in MOOCs. In Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale - L@S ’17 (pp. 261–264). New York, New York, USA: ACM Press. https://doi.org/10.1145/3051457.3054000 </ref> | |||
== Example == | |||
The following diagram taken from [https://www.slideshare.net/StianHklev/frog-embeddable-tools-for-rich-collaborative-learning-lbeck?next_slideshow=1 FROG: Embeddable tools for rich collaborative learning] talk, defines the [[ArgueGraph Script|ArgueGraph]] learning activity where learners first fill in a questionnaire, then discuss results that positions them according to their beliefs, then engage in group argumentation. After a debriefing they have to write a summary. | |||
[[image:orchestration-graphs-enabling-rich-learning-scenarios-at-scale-21-638.jpg|600px|thumb|none|Orchestration | |||
Graph example (ArgueGraph). [https://www.slideshare.net/StianHklev/frog-embeddable-tools-for-rich-collaborative-learning-lbeck?next_slideshow=1 Source: Stian Håklev,] ]] | |||
Each activity is usually supported by a tool. E.g. Argumentation can use a structured argumentation tool. | |||
Activities can be connected by simple sequencing lines. However, most often the scenario requires workflow operators to be applied. | |||
The following diagram shows that between activity a1 and activity a2, something must synthesize the information in the questionnaire. In order to create an argument (a3) after discussion (2), students must be organized in groups. | |||
[[image:frog-embeddable-tools-for-rich-collaborative-learning-lbeck-22-638.jpg|600px|thumb|none|Operators in orchestration graph example (ArgueGraph). [https://www.slideshare.net/StianHklev/frog-embeddable-tools-for-rich-collaborative-learning-lbeck?next_slideshow=1 Source: Stian Håklev,] ]] | |||
== Software == | == Software == | ||
* [[Fabricating and running orchestration graphs|FROG]] | * [[Fabricating and running orchestration graphs|FROG]] (Fabricating and running orchestration graphs). | ||
* More traditional software, e.g. [[learning management system]]s can be used to partly support some types of [[CSCL script]]s, the same is true for workflow systems like [[LAMS]]. | |||
== Bibliography == | == Bibliography == | ||
=== Cited with footnotes === | |||
<references /> | |||
[[Category:Pedagogic strategies]] | [[Category:Pedagogic strategies]] | ||
| Line 15: | Line 47: | ||
[[Category: Collaborative learning]] | [[Category: Collaborative learning]] | ||
[[Category:Computer-mediated communication]] | [[Category:Computer-mediated communication]] | ||
[[category:Educational modeling languages]] | |||
[[category: workflow]] | |||
{{stub}} | |||
Latest revision as of 21:08, 31 March 2020
Introduction
Orchestration graphs are a formalism developed by Pierre Dillenbourg to model CSCL scripts and similar complex learning designs.
Orchestration Graphs depict the structure (what is done when by whom), the pedagogical rationale behind the activity flow, and the work flow created by data transformations and student groupings (“social structure”). An “Orchestration Graph Engine ”allows the teacher to run educational scenarios with her students following the activities as specified in the OG – or as modified real-time by her as she monitors their work– to provide input data and obtain output products. Finally, products and traces of the learning process are collected and processed; a stochastic model of the scenario uses these traces to predict (or confirm) learner states" (Dillenbourg, 2015:95-100)[1]
Definition
An Orchestration Graph defines a pedagogical scenario in terms of learning activities at three "planes": class, group and individual.
It is organized in a similar way as a GANTT chart, i.e. one has to read it from left to right. Each learning activity starts at given time and lasts for a given time.
Activity time can be short, intense and precise, e.g. in a class setting or last over days where students can engage asynchronously.
“Activities range from reading a text or watching a video, to contributing ideas in a brainstorm, or experimenting with a simulation. They can take input data and social structures from previous activities and operators, and produce student products (such as students answers and designs), as well as detailed student trace data. See an example of a running activity. Activities (nodes) are connected through edges. These can contain pedagogical justifications (e.g., activity 1 is an advanced organiser for activity 2), learning analytics information (e.g.,student success is activity 1 is 34% correlated with/predictive of success in activity 2), and operators.” [2]
Example
The following diagram taken from FROG: Embeddable tools for rich collaborative learning talk, defines the ArgueGraph learning activity where learners first fill in a questionnaire, then discuss results that positions them according to their beliefs, then engage in group argumentation. After a debriefing they have to write a summary.
Each activity is usually supported by a tool. E.g. Argumentation can use a structured argumentation tool.
Activities can be connected by simple sequencing lines. However, most often the scenario requires workflow operators to be applied.
The following diagram shows that between activity a1 and activity a2, something must synthesize the information in the questionnaire. In order to create an argument (a3) after discussion (2), students must be organized in groups.
Software
- FROG (Fabricating and running orchestration graphs).
- More traditional software, e.g. learning management systems can be used to partly support some types of CSCL scripts, the same is true for workflow systems like LAMS.
Bibliography
Cited with footnotes
- ↑ Dillenbourg, Pierre (2015). Orchestration Graphs: Modeling scalable education. EPFL Press.
- ↑ Håklev, S., Faucon, L., Hadzilacos, T., & Dillenbourg, P. (2017). Orchestration Graphs: Enabling Rich Social Pedagogical Scenarios in MOOCs. In Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale - L@S ’17 (pp. 261–264). New York, New York, USA: ACM Press. https://doi.org/10.1145/3051457.3054000