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ITS Data Collection Framework Capturing data based on agent communication standard Olga Medvedeva, Center for Pathology Informatics, University of Pittsburgh July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference Outline • Need for communication standard for Intelligent Tutoring Systems • Existing standard for multi-agent communication • Implementation in SlideTutor – Communication protocol – Data collection – Database query tool – Lessons learned • Comparison with recent standardization effort • Advantages of using the the existing standard July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference Intelligent Learning Environment Common Base • Underlying theory – Cognitive tutors (Anderson et al. 1995) – Adaptive hypermedia (Brusilovsky et al. 1996) – Constraint-based (Mitrovic et al. 2001) • Modules – Expert, Student, Interface, Pedagogic • “Single-purpose” development approach July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference Keystone – communication standard • Previous efforts: – Inter-tutor communication (Ritter, Koedinger 1996; Brusilovsky et al. 1997) one-to-one translators, strict channel, no real protocol – Shared resources (Koedinger et al. 1999) – limited use: lack of standard – DORMIN protocol (developed at CMU) – used in commercial product • Our approach – Multi-agent technology – Use existing inter-agent communication standard July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference Foundation for Intelligent Physical Agents (FIPA) FIPA (www.fipa.org) - collection of standards for inter-agent communication: • Agent Management System – manages an agent life-cycle, maintains a registry with unique Agent Identifier (AID) • Transport – describes message exchange protocol: transport type and specific address for an agent • Agent Communication Language (ACL) – communication specifications FIPA was officially accepted by the IEEE as one of its standards committees on 8 June 2005 July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference FIPA Design Principals Envelope: Sender (locator) Receiver (locator) Timestamp Message (ACL): Sender (AID) (ACL): Message Receiver (AID) Performative (String) Sender (AID) Content: ( ACR) Receiver (AID) Performative (String) Reply-to(Message ID) Content: ( ACR) Reply-to (Message ID) July 10, 2005 • Forms abstract basis for concrete architecture • Sets minimum required elements • Permits introduction of new elements • Permits arbitrary content language, uses Abstract Content Representation (ACR) for ACL as key-value pairs Educational Data Mining Workshop 20th AAAI-05 Conference FIPA ACL Message Structure :sender – identity of the sender :receiver – identity of the recipient :content – the object of the action :performative – the type of the communicative act Optional: :reply-with :replay-to :in-replay-to :replay-by– replay constraints :language – encoding schema of the content of the message :encoding – encoding identifier :ontology – is used to give a meaning to symbols/concepts in the content :protocol – gives additional context for the interpretation of the message :conversation-id – identifies the ongoing sequence of communicative act, manages the conversation strategies July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference FIPA Performatives • • • • • • • • • • • Accept-proposal Agree Cancel Call-for-proposal Confirm Disconfirm Failure Inform Inform-if Inform-ref Not-understood July 10, 2005 • • • • • • • • • • • Propagate Propose Proxy Query-if Query-ref Refuse Reject-proposal Request Request-when Request-whenever Subscribe Educational Data Mining Workshop 20th AAAI-05 Conference FIPA Implementation in Java • Java Agent Services (JAS) (www.jcp.org) defines a set of objects and service interfaces to support the deployment and operation of the agents. • Contains interfaces for building messages, directory services and a factory for message transfer services. • JAS is a base for multi-agent communication in our system July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference SlideTutor Architecture http://slidetutor.upmc.edu SlideTutor - an agent-based model tracing ITS for visual classification problem solving in surgical pathology Reasoning GUI-Tutor Communications Client GUI Download with Java WebStart WEB SERVER Protocol Collection Filter Login Servlet Java Webstart Download Manager Internet Project DB Tutor Servlet Student Modeling System Pedagogic Model Production Rules Jess Pedagogic Production Rules Domain Ontology Slide Pedagogic Ontology Protégé-2000 WholeSlide Images Pedagogic Ontologies Case DB July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference Reasoning GUI-Tutor Probabilistic Student Model Image Pump Application Download Student Files Expert Module Viewer GUI-IDS Dynamic Solution Graph Internet IMAGE DELIVERY SYSTEM Viewer Servlet 2 Student Interfaces & 1 Authoring Interface July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference Generic Representation of Problem-Solving Space Student Model Case Database Student Student Model Model Student Model State Data State Slide Slide Slide Representation Representation Representation Case Data CaseCase DataData Pedagogic Task Structure Visual Classification Task Structure Domain Behavior Refiner Domain Task Problem Solving Methods Pedagogic Layer Expert Model July 10, 2005 Pedagogic Behavior Refiner Problem Solving Methods Dermatology Knowledge Base Domain Model Pedagogic Task Dynamic Solution Graph Interface Student Educational Data Mining Workshop 20th AAAI-05 Conference Pedagogic Knowledge Base Pedagogic Model Pedagogic Model Collected Data Protocol Agent ProblemEvent start problem AP_77 InterfaceEvent(s) Client Agent Tutor Agent ClientEvent TutorResponse ProblemEvent finish problem AP_77 July 10, 2005 Project Database Log Files • InterfaceEvent – lowlevel human-computer interactions • ClientEvent – collection of InterfaceEvents that represents an elementary subgoal, understood by tutor • TutorResponse – system response to a ClientEvent Educational Data Mining Workshop 20th AAAI-05 Conference Message Example ClientEvent Envelope: Sender: Client_1 Receiver: PROTOCOL TimeStamp = 1114444377783 Message: Sender: Concept2 Receiver: PROTOCOL Performative: X-Created In-reply-with: 1114444378242 Content: Type = Finding Label = blister Id = Concept2 ObjectDescription = Finding.blister.Concept2 Parent = null Input: name = text value = blister name = y value = 11808 name = x value = 38048 name = z value = 0.03 InterfaceEventIDS = [1114444374333, 1114444375546, 1114444376304, 1114444376798, 1114444377444] July 10, 2005 • Envelope indicates the locators of client and protocol agents • 4 required key-value pairs for a message • Performative defines a type of communicative act • List of preceding InterfaceEvent Ids: – click on Finding button – Click on image – Selecting 3 times down a tree of findings Educational Data Mining Workshop 20th AAAI-05 Conference Message in Depth ClientEvent • Envelope: Sender: Client_1 Receiver: PROTOCOL TimeStamp = 1114444377783 Message: Sender: Concept2 Receiver: PROTOCOL Performative: X-Created In-reply-with: 1114444378242 Content: Type = Finding Label = blister Id = Concept2 ObjectDescription = Finding.blister.Concept2 Parent = null Input: name = text value = blister name = y value = 11808 name = x value = 38048 name = z value = 0.03 InterfaceEventIDS = [1114444374333, 1114444375546, 1114444376304, 1114444376798, 1114444377444] July 10, 2005 • • Widget object (agent) description parameters – Type (“Button”, “Finding”) – Label (“Next”, “Blister”) – Id – unique within a session – ObjectDescription – combination of Type+Label+Id (“Finding.blister.Concept2) – Parent – list of all parent ObjectDescriptions for hierarchical structures Common for ITS user action triplet – Action = Performative – Selection = ObjectDescription+Parent – Input = list form Content Input Message encoded in XML is easy to translate into other languages (RDF, KIF, SL, etc.) Educational Data Mining Workshop 20th AAAI-05 Conference TutorResponse Example • Student performance data Envelope Sender: TutorEngine0 Receiver: PROTOCOL TimeStamp: 1114444379378 Message: Sender: TutorEngine0 Receiver: PROTOCOL Performative: FAILURE Conversation_ID: 1114444378242 Content: ErrorCode = 15 NextStepType = Evidence NextStepLabel = blister NextStepID = Concept2 NextStepParent = null NextStepAction = DELETE Input: name = Messages value = "[TEXT:There is BLISTER present, but not where you have pointed in the image. See if you can find where. POINTERS:[PointTo:Concept2 IsPermanent:false Method:setFlash Args:[true]]]“ name= TutorAction value = "PointTo:Concept2 IsPermanent:false Method:setBackgroundColor Args:[RED]" – Performative: FAILURE – user took incorrect step – ErrorCode = 15 – user incorrectly located existing finding – Input: - contains a description of an error message to be presented to user • Tutor performance data July 10, 2005 – Best possible next step – action expert model would take in this problem state Educational Data Mining Workshop 20th AAAI-05 Conference Database Schema • High-level static tables similar to Mostow et al. 2002 contains Experiment, CaseList, Student, etc. • Low-level tables for captured events, including start/end of problem and session closely follow the FIPA standard, generic with any number of event parameters stored in corresponding Input tables July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference Web-Based Protocol Query Tool • Allows the user to obtain data sets specific to a wide range of constraints • Outputs to HTML file that can be transferred to Excel • Query can be saved and viewed in SQL • Interface, Client and Tutor events data can be joined in different ways July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference Query Tool Results for Identifying Blister InterfaceEvents ClientEvents TutorResponses July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference Advantages of Event-Based Data Representation • Usability Perspective: InterfaceEvents linked to ClientEvents (Saadawi et al. 2005) – How many actions were performed – How much time was required to achieve a particular subgoal, such as identification of Blister – How many InterfaceEvents were unrelated to any ClientEvent • Student Performance over time: ClientEvents linked to TutorResponses – Number of hints requested – Depth of hints – Error frequency and distribution • Tutor Performance: NextStep fields in TutorResponses – Compare next student actions to those predicted by tutor July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference SlideTutor Data Sharing Limitation • This paper and presentation have been approved by Institutional Review Board (IRB) • Researcher needs to sign a Limited Use Agreement • There might be one agreement with consortiums July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference Lessons Learned For the past year our data collection framework was used in 4 small HCI studies and one large experiment with a total of 50 students. • Keep data clean: ended up maintaining ‘raw’ and ‘clean’ copies of database • Granularity of captured data: capturing of detailed data slows the system • Separate database for assessment: no explicit mapping of performance on tests and in the tutoring system July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference Data Collection Framework Advantages • Advantages of relational database (Mostow et al. 2002) – Eases the analysis of the enormous volume of complex data • Generic framework that might be adapted to other model-tracing ITS – Adapted in the extension of SlideTutor – ReportTutor that teaches how to write the pathology reports • Flexibility of FIPA-based communication protocol – Flexibility to describe interaction events – Extendable set of performatives – Multiple messages in one envelope, unrestricted number of input parameters – Potential to reference ontologies within the message – Can be easily reused in the Data Shop July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference Data Shop Project, Pittsburgh Science of Learning Center (http://www.learnlab.org ) • Logging and Analysis: Tools and reports to aid PSLC researchers and course developers – Log the activities of the experiments to a database – Provide the reports and queries on that experiments • Goal: Standardize the messaging format among tools, tutoring translators and agents – Message types: tool_message, tutor_message, curriculum_message, message • Data Shop Tutor Logging v3 released in June 2005 July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference Data Shop Tool Message and SlideTutor Interface/Client events tool_message attempt_id meta (0 or 1) user_id session_id time time_zone problem_name (0 or 1) semantic_event id semantic_event_id name trigger event_descriptor (0+) event_id selection (0+) id type action (0+) id input (0+) id step (0+) probability ui_event id July 10, 2005 (1+) Educational Data Mining Workshop 20th AAAI-05 Conference Data Shop Tutor Message and SlideTutor TutorResponse meta (0 or 1) user_id session_id time time_zone event_descriptor (0+) event_id problem_name (0 or 1) semantic_event id semantic_event_id name trigger selection (0+) id type action (0+) id input (0+) id step (0+) probability ui_event id action_evaluation (0+) current_hint_number total_hints_available classification tutor_advice (0+) skill (0+) probability production (0+) July 10, 2005 step_interpretation (0+) name (1) value (1) custom_field (0+) name (1) value (1) Educational Data Mining Workshop 20th AAAI-05 Conference FIPA Advantages • FIPA as a information exchange underlying standard – Develop a set of performatives – a controlled vocabulary for ITS communication – Create sharable ontologies for domain knowledge, hint content, error categories and use ‘:ontology’ FIPA parameter to give a meaning to the message content – Use ‘:protocol’ parameter to identify the translator and to preserve the internal component structure • Syntactically aligned systems – Ease meta-analysis for tutors with the identical performatives – Reuse data for simulations – Shared services for real-time interoperability • Identifying particular help-seeking behavior • Calculating knowledge tracing probabilities July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference Acknowledgements Grants: • National Library of Medicine • National Cancer Institute People: • • • • • • Rebecca Crowley Girish Chavan Eugene Tseytlin Elizabeth Legowski Katsura Fujita Maria Bond July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference References • • • • • • • • Anderson JR, Corbett AT, Koedinger KR, and Pelletier R. Cognitive Tutors: Lessons learned. Journal of the Learning Sciences 4(2): 167-207, 1995 Brusilovsky, P., Kommers, P. & Streitz, N. (Eds.) (1996) Multimedia, Hypermedia, and Virtual Reality (LNCS Vol. 1077). Berlin: Springer-Verlag, 1996 Mitrovic A, Mayo M, Suraweera, P and Martin, B. Constraint-Based Tutors: A Success Story. In Monostori, L. and Vancza, J. (Eds). Proceedings of the 14th International Conference on Industrial & Engineering Applications of Artificial Intelligence and Expert Systems, Budapest, Hungary, Springer, pp. 931-940, 2001 Ritter, S. and Koedinger, K. R. (1996). An architecture for plug-in tutor agents. Journal of Artificial Intelligence in Education, 7, 315-347 Brusilovsky, P., Ritter, S., & Schwarz, E. Distributed intelligent tutoring on the Web, Proceedings of AIEDâ97, the Eighth World Conference on Artificial Intelligence in Education. 1997 Koedinger KR, Suthers DD, & Forbus KD. Component-based construction of a science learning space: A model and feasibility demonstration. International Journal of Artificial Intelligence in Education: 10, 392-31, 1999 Mostow J, Beck J, Chalasani R, Cuneo A, and Jia P. Viewing and Analyzing Multimodal Human-computer Tutorial Dialogue: A Database Approach. Proceedings of the ITS 2002 Workshop on Empirical Methods for Tutorial Dialogue Systems, 75-84 Saadawi G, Legowski E, Medvedeva O, Chavan G, and Crowley RS. A method for automated detection of usability problems from client user interface events. Accepted to Proceedings of the American Medical Informatics Association Symposium 2005 July 10, 2005 Educational Data Mining Workshop 20th AAAI-05 Conference