1. A. Mørch, 30.03.2006, 2.1 1 1) Empirical-Based, Evolutionary Design of FLE/Agents Background Knowledge building environments Method Empirical study and design Pedagogical agent system

2. A. Mørch, 30.03.2006, 2.1 2 DoCTA NSS project Design and use Of Collaborative Telelearning Artefacts – Natural Science Studios Goal: Study social, cultural and pedagogical aspects of shared artefacts in distributed collaborative learning and apply the findings to the design of new learning environments

3. A. Mørch, 30.03.2006, 2.1 3 Knowledge building A model for collaborative learning (Scardemalia & Bereiter, 1994) Students learn and interact by “talking” (reasoning aloud) with peers to develop explanations of scientific phenomena Formulate research questions, answering them independently, and finding arguments Computer supported knowledge building environments: –CSILE, Knowledge Forum –FLE

4. A. Mørch, 30.03.2006, 2.1 4 Method Empirical-based design –Identify need for improved knowledge building based environment based on data from using an existing system Evolutionary design –Identify ways to integrate software agents with existing system

5. A. Mørch, 30.03.2006, 2.1 5 Empirical study Two secondary school classes in Norway (9 th grade) 3 week pilot; 4 week field trial (2001, 2002) Collaborative learning in small groups Discussing science problems Knowledge domain: Ethical aspects of biotechnology Computer supported environment without software agents (FLE system)

6. A. Mørch, 30.03.2006, 2.1 6 FLE (Future Learning Environment) Developed at Media Lab, University of Art and Design Helsinki (http://fle3.uiah.fi/) Open-source groupware implemented in the Python language using SOAP platform Designed based on pedagogical and psychological research in networked learning and knowledge building at University of Toronto and University of Helsinki (Hakkarainen, Lipponen & Järvelä, 2002)

7. A. Mørch, 30.03.2006, 2.1 7 FLE interface Writing/reply mode (editor with message categories) Viewing mode (threaded list of previous postings)

8. A. Mørch, 30.03.2006, 2.1 8 FLE message categories Categories of progressive inquiry: –Problem –My working theory –Deepening knowledge –Summary –Meta-comment

9. A. Mørch, 30.03.2006, 2.1 9 Physical setting in one of the schools

10. A. Mørch, 30.03.2006, 2.1 10 Method Observation Video recording Data logging Interviews Interaction analysis

11. A. Mørch, 30.03.2006, 2.1 11 Data excerpts: Interview When asked about the usefulness of the FLE categories, a student said: “It was kind of smart! Because you can see what it [the message] is about. That’s reliable knowledge and that’s a summary [pointing to two KB notes on the screen]. You know immediately what it is.” However, when later asked to demonstrate his understanding of the difference between “My Working Theory” note (MWT) and a “Summary” note he says (modifies his initial misunderstanding of two categories): “… if we had sent this to them [pointing to a note he has labeled MWT] and you ask what it is supposed to mean - is it a working theory or is it a summary, right? But you see it first by its small [category abbreviation] … oh -it is a summary after all, okay!”

12. A. Mørch, 30.03.2006, 2.1 12 Summary of findings Students had difficulties choosing knowledge building categories Instructors had difficulties following the distributed collaboration process and guiding the students Tentative conclusion: Need to find alternative ways of facilitating distributed knowledge building

13. A. Mørch, 30.03.2006, 2.1 13 Design implications Software agents can be useful as scaffold in semi-structured knowledge domains Pedagogical agents Agent system features

14. A. Mørch, 30.03.2006, 2.1 14 Pedagogical agents “Pedagogical agents can be autonomous and/or interface agents that support human learning in the context of an interactive learning environment.” –Johnson, et al. (2000)

15. A. Mørch, 30.03.2006, 2.1 15 Agent system features Agent as an observer –Collect information Participant, activity, timestamp Last log on, last contribution (for each participant) –Compute statistics Agent as an advisor –Present statistics –Encourage non-active students to be more active –Advice students on the use of knowledge- building categories

16. A. Mørch, 30.03.2006, 2.1 16 System architecture Observation: Student activities Instructor activities DB:log Advice feature analysis: Message feature Student feature Instructor activity Confidence factor Learning CN2 KB:rules Advice Generation Statistic Generation Updates & Who-is-online Assistant FLE3 Knowledge Building WebTop ChatAdmin User Interface Assistant GUI

17. A. Mørch, 30.03.2006, 2.1 17 Technical detail Languages –Python (Fle3) and Java (agent applet interface) Database –MySQL Learning algorithm –CN2 (Clark & Niblett, 1989) Knowledge representation –RuleML

18. A. Mørch, 30.03.2006, 2.1 18 Student Assistant Agent

19. A. Mørch, 30.03.2006, 2.1 19 Instructor Assistant Agent

20. A. Mørch, 30.03.2006, 2.1 20 References Jondahl, S. and Mørch, A. (2001). Simulating Pedagogical Agents in a Virtual Learning Environment, Proceedings IRIS- 24, pp. 15-28. Chen, W. and Wasson, B. (2002) An Instructional Assistant Agent for Distributed Collaborative Learning. Proceedings ITS- 2002, pp. 609-618 Dolonen, J., Chen, W. and Mørch, A. (2003). Integrating Software Agents with FLE3. Proceedings of CSCL 2003, Bergen, Norway. Kluwer Academic Publishers, pp. 157-161. Mørch A, Dolonen J, Omdahl K. (2003). Integrating Agents with an Open Source Learning Environment. Proceedings of International Conference on Computers in Education 2003 (ICCE 2003), Dec. 2-5, Hong Kong: AACE Press, 393-401.