The Valladolid Presentation - Nov, 16, 2011Presentation Transcript
Ubiquitous Orchestration Perspectives from the Pedagogical Space and Tools for the CSCL teacher Stavros Demetriadis Assistant Professor [email_address] http://mlab.csd.auth.gr/sdemetri @sdemetri Aristotle University of Thessaloniki , Department of Informatics Greece
...boiled down to a Special Theory of Orchestration
A General Theory of the Universe
A more modest objective...
(a) Ubiquitous Orchestration
A comment on Orchestration linking to the Pedagogical Space
Implications (linking to EEE...)
Flexibility: “Adaptation Patterns”
(b) Orchestrating the CSCL classroom
Extending IMS-LD capabilities to cater for adaptation
MentorChat: Orchestrating students’ dialogue
Augmented Reality: The PROTEAS project
Scripting: What happens in scripted collaboration when it is not scripted?
The C&B vs. the CSCL classroom I
Did the “chalk & blackboard” teacher have orchestration problems?
No chalk (sends a student to bring some)
Wet sponge Wet board (has to wait before writing)
No curtains (striking sun writings on the board difficult to read)
But look at the poor CSCL teacher:
A student is missing Three students (instead of two) have to work together
– Is the script efficient for a group of three also?
Some students have problems logging in the web system
– What exactly is the problem?
Students surfing the Internet / others playing a computer game
– What do I do? (while trying to help the others with logging in)
Supervising the computer-supported activity
– Do they work as instructed/scripted?
A comment on orchestration
The C&B vs. the CSCL classroom II
... the teacher can easily overcome problems in the C&B classroom
Interaction in the C&B classroom is low
Students sit quiet and read or write individually
No communication .....
But ...you need Collaboration and Technology to make it really a mess!
A comment on orchestration Example: Nick misses the Monday lesson because he is ill. The teacher has designed a dyad-based script but now student number is odd (23). The system supporting the activity adapts the activity so that a triad can also work productively. Orchestration duties are off-loaded to the system. Example: T he teacher asks the students to form 4-member small groups and enact a jigsaw-based scenario. The teachers says “We work in groups of four as we did the other time”. Students experienced with the scenario understand the hint for the Jigsaw activity. Orchestration duties are off-loaded to the students
Type I Reflection
TEACHER Mapping PS onto TS A comment on orchestration Technology Spaces (TS) Data-based Pedagogical Space (PS) Pattern-based Cool technology! I know a good pattern that I want to implement. How can this technology help me? Type II Reflection Cool technology! I want to use it. But what patterns fit well with these tools? Mapping TS onto PS
An example: Twitter
Type II Reflections : Twitter allows students to send short messages. Is this of some use?
...Well, I put another screen in my classroom and project there students’ Twitter messages. Lets see what happens...
Type I Reflections : a good learning “pattern” is that my students communicate with domain experts. I can use Twitter to support this expert-novice communication by enabling students to “follow” experts.
A comment on orchestration
Linking Spaces: Opportunities to teachers to reflect on
PS (Physical + Multiple TSs) relationship
in at least three modes:
(a) “Complementary” Mode Distribute a pattern over various Spaces
(b) “Constrain” Mode Good practices “constrain” the use of novel tools/services/representations in a TS
(c) “Construct” Mode Reflect on activities successfully distributed over many Spaces and construct an abstract understanding enriching the Pedagogical Space and possibly the affordances of TSs
A comment on orchestration
A Model of Reflections Complement? (Type I) Construct? (Type I & II) Constrain? (Type II) A comment on orchestration Reflections Data-based Continuity Discontinuity Technology-based Space (TS) Pedagogical Space (PS) Pattern-based TEACHER Technology-based Space (TS) Physical Space
Support Type I Reflection
Simple – easy to handle – tools to allow intuitive integration into pattern enactment (i.e. support mapping of PS onto TS)
cf. “Awareness” tools (Dillenbourg in Stellar Report, Dillenbourg et al. 2011)
Implications Example : Simple interaction analysis tools can be easily used by teachers for monitoring and adaptation purposes
Support Type II Reflection
Provide teachers with good practices to make optimal use of new tools/technologies (i.e. help mapping TS onto PS)
(cf. Dimitriadis in STELAR Report - Dillenbourg et al. 2011)
Implications Example : Provide scenarios (“scripts”) for using educational robots in the classroom to help improve (a) domain learning, (b) students’ collaboration and metacognitive skills development
Implications III An Architecture for Orchestration Tools
Easy to use (low complexity) pattern-supported, tools
“ Pattern-intuitive” level
Interface Level Specific to Spaces/Tools Specific to Spaces/Tools Rule Level
Advanced users define the Rules for Interface Level tools
Defining DataFlow (protocols, models, etc.)
Linking Spaces Modified across Spaces
Implications IV More on the proposed Architecture ...
What Representations ?
What Patterns ?
DataFlow streaming between spaces
What Data ?
How to stream data across multiple Spaces ?
Semantics to describe Data?
Flexibility : Cater for future extensions of data-relevant protocols?
Linking Spaces Modified over Spaces Specific to Spaces/Tools Rule Level
What Information ? (from Data)
What Rule? (from Information)
Tools to define Rules
Interface Level Specific to Spaces/Tools
Relevant to EEE
Exemplifying the reflection-based approach & Tool architecture
Pre-defined : I can integrate in my design alternative activity flow to be enacted if conditions occur
On-the-fly : I (or the system) can trigger alternative activity flow during the activity alerted by specific events
A core idea of pedagogical value on how to adapt the collaborative learning activity when specific conditions occur
… an AP suggests a valuable alternative (to the whole or part of the initial design) depending on conditions
IF [ something specific occurs] THEN [ adapt your design]
Example: Students work in dyads on a collaborative task. An “advanced” student works in a dyad with a “beginner” student. The task is easy for the advanced and has lost interest. Adaptation : the teacher adjusts the task to address the advanced level of student, i.e. makes the task more demanding and interesting for the advanced student (of course without failure of the beginner). Adaptation Pattern: “Advance the Advanced” Adaptation patterns
Adaptation Pattern Life-Cycle Adaptation patterns
List of Adaptation Patterns Karakostas & Demetriadis, 2010 Adaptation patterns
Identifiable through Teacher activity analysis & Interaction analysis
Tools like FlexCoLab & PPR aim to help teachers identify favorite adaptations and integrate them in their design (pre-defined)
The tools support mainly Type I Reflections at Interface & Rule level
known patterns to implement with technology
Better interface: Difficulties with complex pattern setting interface
Evaluation in context
Clearly define Interface vs. Rule vs. DataFlow levels
What about On-the-fly adaptations?
IMS-LD Enhancing IMS-LD to cater for adaptivity
Adaptation Pattern Specification
Input, Rule(-s), Model(-s), Output (IRMO)
RULES INPUT Interaction Analysis MODELLED ENTITIES OUTPUT On Screen Representation A D A P T A T I O N P A T T E R N or db manifest
Define monitored parameters (e.g. from interaction analysis tools)
Rules (the adaptation model of the pattern) are hard-wired to the pattern
Define Output (form, content, etc.)
Extending IMS-LD capabilities
IMS-LD modeling language has limited capabilities in expressing and implementing complex information processing (necessary when complex adaptive interventions need to be made by the CSCL system)
How IMS-LD, a de-facto standard in the CSCL area, could facilitate the required adaptive behavior of a CSCL system through communication with external software components?
such as web services, communication tools or virtual learning environments (VLEs).
MAPIS: A reference architecture for implementing ACLS systems with IMS-LD IMS-LD
MentorChat Orchestrating Students’ Dialogue
MentorChat Design Rationale
Dialogues between students and tutors: a prominent component of effective tutoring.
In CSCL: a supportive conversational agent may model and intelligibly trigger the peer dialogue.
The MentorChat agent acts as a peer discourse facilitator enabling the teacher to define what type of intervention to implement.
Not a teacher substitute, but a teacher‘s tool to orchestrate intervention/support strategy during students’ discussions.
MentorChat Architecture MentorChat
Teacher Orchestration using MentorChat
Define a set of rules to guide the agent’s behavior
A rule can be divided into three parts:
(a) an event
A set of keywords that can be used in conjunction with regular expressions in order to recognize specific phrases and language structures
(b) a triggering prompt
When an event occurs the agent posts an appropriate triggering prompt as a participant that joins the dialogue
(c) a final prompt
is posted by the agent at the end of the conversation
to display some important aspects of the problem‘s solution that have not been mentioned in the group discussion
(Yannis and Nelly discuss online issues of cognitive theory for Multimedia learning)
<Y ANNIS >: ... in the brain some connections are formed with the prior knowledge and stored in long-term memory
<Tutor>: What do you think Nelly about the role of long-term memory?
<N ELLY >: In order to store information in the long-term memory, it should...
<Y ANNIS >: ... we should also mention the concepts of Selecting and Organizing ...
<Tutor>: Do you think Nelly that the concept of Organizing can be part of your answer? Why?
<N ELLY >: Of course, because the better the two models (verbal and pictorial) are organized in memory the better the integration with prior knowledge. Is this a satisfactory answer?
<Y ANNIS >: Wait Nelly , I will write down the answer and then you can check if something is missing
<Tutor>: Before you submit your final answer please consider commenting on the concepts of: verbal and pictorial model.
<N ELLY >: ok, then. Lets discuss these issues and I think also we need to mention the redundancy and contiguity principles
Two studies: School and University classes
Students discussing online in dyads in lab conditions
MentorChat posts messages whenever a concept modeled in the vocabulary appears in students’ discussion
Teacher monitoring students conversation Preliminary Evaluation I MentorChat
Preliminary Evaluation II
MentorChat interface has been evaluated as user friendly and easy to use
Students mention that agent‘s messages helped their group discussion to advance and improve their collaboration
Also: the prompts before the final answer very helpful to refresh their domain knowledge
School : The teacher found very useful the ability to monitor the discussion from a simple panel
She stated that “… a MentorChat activity, ..., can motivate students to actively participate in a collaborative task… ‘ She also said that agent‘s messages can promote the critical thinking of students .
University : Preliminary statistical analysis indicates learning benefits for the treatment condition (discussion in MentorChat)
Implications for Orchestration I
MentorChat can help teachers to orchestrate multiple students’ discussions by predefining interventions (rules)
Beneficial interventions may be offered to multiple groups
What is the necessary agent intelligence to intervene in a human-like way?
Students after using MentorChat model the behaviour of the agent and may not pay attention to what is said
Sometimes students ask the agent for further support
Unavailable at the moment
Implications for Orchestration II
Relevant to EEE?
An agent in a space can become mediator for teacher interventions and orchestration actions
Once these actions are predefined then multiple students groups could be benefited (or not?) from interacting with this agent
The PROTEAS project PRO gramming T angibl E A ctivity S ystem
Control : Students working in triads to solve an algorithm problem unscripted
Simple Prompt: “Collaborate”
Treatment : Students working also in triads on the same problem
Additionally: a peer-tutoring script assigned successively each student in the triad to become tutor and pose an issue to his/her colleagues relevant to solving the problem
Simple Prompt: “Collaborate”
When the Student-tutor posed the issue the two other peers were unscripted on how to collaborate
Alcolab tool Scripting
“ Issue” level analysis
Suppose S1 poses an issue
Control : while discussing with others
Treatment : Triggered by the script
S2 and S3 are in both conditions unscripted as to what to do next - What do they do?
Issue level analysis : Refers to the analysis of peer interaction (between S2 and S3) after S1 posed an issue
Scripting S1 S2 S3 S1 poses an issue
S1 poses an issue Peer interaction for answering: Directly to S1 Leaving the other peer (S3) in a low-participation condition Peer interaction for answering: Interaction between S2 & S3 Then answering the issue by S1 This behavior resulted to triads improved learning outcomes (port-test) Scripting S1 S2 S3 S1 S2 S3
When a script leaves student interaction unscripted then students’ internal scripts are activated
In this case internal scripts responding to prompt “collaborate” were activated
CONTROL : All Students identified themselves as peers
Therefore directly answering the issue by S1 was considered as collaboration
TREATMENT : assigning the role of tutor to S1 resulted in the other two students S2 & S3 identifying themselves as peers
Therefore “collaborate” meant interact with peer before answering to the tutor
Implications for Orchestration
Assigning roles to students may create social hierarchies and activate peer interaction among certain partners
Assigning such roles can be a teachers’ handy technique to activate peer interaction between peers
A peer tutoring script effective when implementing with dyads may also be effective when implemented with triads
Relevant to EEE?
Social hierarchies established in a space (for example with virtual partners) may direct and trigger collaboration (discource activity) between students (in this or other spaces)
Orchestration: Ubiquitous & Distributed
Pedagogical Space: establishes continuity
Support teachers’ reflections while linking (mapping) various Physical space & Technology-based Spaces on Pedagogical Space
A proposed architecture for orchestration tool entails three levels: Interface, Rule, DataFlow
...but will these ideas and guidelines help making orchestration simpler and easier for the average teacher?
Ευχαριστώ! Muchas Gracias!
(1) IEEE Learning Technology Newsletter
Next issue: Adaptive & Intelligent Systems for Collaborative Learning
Guest Editor: Stavros Demetriadis
Critical Dates: Deadline for submission of articles: 15 December 2011
1000 words submission
(2) 4 th International Conference on Intelligent Networking and Collaborative Systems (INCOS-2012)
IEEE Technical Sponsorship // Proceedings by IEEE CPS
September 19-21, Bucarest, Romania
(3) 3rd International Workshop on Adaptive & Intelligent Systems for Collaborative Learning (IWASCL-2012)
In conjunction with INCoS-2012
Previous: IWASCL-2009 in Barcelona, IWASCL-2010 in Thessaloniki