Artificial Intelligence in Education, July 2005, Amsterdam Generating Reports of Graphical Modelling Processes for Authoring and Presentation Lars Bollen COLLIDE research group University Duisburg-Essen, Germany Supervisor: H.U. Hoppe Co-Supervisor: W. van Joolingen

Artificial Intelligence in Education, July 2005, Amsterdam2 context computer supported learning environment  graph based modelling action / interaction analysis authoring by example supporting presentation and documentation (of modelling processes)

Artificial Intelligence in Education, July 2005, Amsterdam3 starting point collaborative modelling with graph based visual languages realised e.g. within learning support environment Cool Modes  System Dynamics, Petri Nets, UML class diagrams, discussion support etc.

Artificial Intelligence in Education, July 2005, Amsterdam4

5 problem learner finishes modelling task:  (usually) only the final result is stored as one artifact  process of creating and exploring a model is compressed into a single, static document

Artificial Intelligence in Education, July 2005, Amsterdam6 problem losing information about  different phases (e.g. phases of argumentation, coordination with peer, design, verification, revision,...)  design rationale (why did the user choose this solution?)  alternative solutions (that emerged on the way to the final solution)  collaboration (group result = one artifact)

Artificial Intelligence in Education, July 2005, Amsterdam7 related works, partial solutions record and replay approaches  „Authoring on the fly“ [Müller, Ottmann, 2005]  „E-Chalk“ [Rojas et. al, 2001] series of snapshots  COPRET [Petrou, Dimitracopoulou, 2003]

Artificial Intelligence in Education, July 2005, Amsterdam8 approach: generating reports! >> Reports are summaries of states / action traces from modelling processes. <<

Artificial Intelligence in Education, July 2005, Amsterdam9 approach: generating reports! How to create summaries of modelling processes? How to visualise such a summary? What are typical use cases?

Artificial Intelligence in Education, July 2005, Amsterdam10 approach: generating reports! How to visualise a report?

Artificial Intelligence in Education, July 2005, Amsterdam11 approach: generating reports! How to create a report? “capturing“ workspaces

Artificial Intelligence in Education, July 2005, Amsterdam12 approach: segmentation What are suitable “triggers“ for automated capturing? detect milestones / phases in modelling processes  classify actions that occur in modelling environment domain-indepent actions (e.g. create, delete, modify objects) domain-dependent actions (e.g. model structure, design issue) coordination level (e.g. chat, claiming / releasing key)  time aspects (e.g. clusters of actions, breaks)

Artificial Intelligence in Education, July 2005, Amsterdam13 approach: segmentation What are suitable “triggers“ for capturing? collaborative aspects  floor control  find collaborative patterns in action sequence

Artificial Intelligence in Education, July 2005, Amsterdam14 approach: meaning of edges What is the meaning of the edges in the visualisation of reports?  show possible paths of modelling processes  edges contain all information about all actions that occured between states  edges may have an implicit processual meaning (e.g. X examplifies Y, X explained by Y [Baloian, 1997])

Artificial Intelligence in Education, July 2005, Amsterdam15 reports: some use cases monitoring and analysing  automatically collect material from (collaborativ) modelling processes  apply various filters and analysis methods to collected data  supports assessment of results (and processes) “capturing“ workspaces

Artificial Intelligence in Education, July 2005, Amsterdam16 reports: some use cases authoring by example  generating reports can be used to prepare learning material  playback recorded paths into modelling tool  automated recommendation of paths? “play back“

Artificial Intelligence in Education, July 2005, Amsterdam17 reports: some use cases documentation on-the-fly  use reports to present own results  supports self-assessment, peer-assessment  analysis / classification of actions supports metacognitive skills

Artificial Intelligence in Education, July 2005, Amsterdam18 to do next find suitable classification scheme  case studies find suitable algorithms to detect phases / milestones elaborate on prototypical implementation