1. Learner Models as Metadata to Support E-Learning: The Ecological Approach Gord McCalla ARIES Laboratory Department of Computer Science University of Saskatchewan Saskatoon, Saskatchewan CANADA

2. Take Home Message A main source of information for a system is information about user interactions with the system –often don’t need to have any other explicit metadata –don’t have to ask the user for their feedback In educational environments there is often already a synthesized model of these interactions for each user: the learner model –so, why not use this learner model as a source of insight into a learner so the educational environment can be adapted to the learner? attach learner models to educational material and mine these for patterns that can inform the educational environment in achieving various pedagogical purposes

3. Talk Outline Motivation: learner models as metadata The ecological approach, formalizing the idea of learner models as metadata Some ARIES research into issues important to the ecological approach –the data source: iHelp –active learner modelling: “just in time” purpose-based learner modelling finding patterns in data –metrics –data mining purpose-based modelling –hierarchies of purposes –purpose-based open modelling MUMS middleware for customizing interpretation of interaction data –agents alternative agent negotiation paradigms agent modelling Two other SWEL projects social tagging: CommonFolks, OATS Implications of the ecological approach

4. Motivation Learner Models as Metadata

5. Trends and Challenges for the Semantic Web Ongoing huge growth in –amount of information available electronically –number of users wanting to access this information –range of purposes these users have for wanting to access this information –social connections among these users –tools for accessing and massaging this information and for connecting to the users This has major challenges for the semantic web –how can metadata be attached to all of this information? –how can you know what kinds of metadata to attach, given the variety of purposes to which any resource can be put? –how can you deal with change: new information, changing old information, new users, etc.? G.I. McCalla, “The Fragmentation of Culture, Learning, Teaching and Technology: Implications for the Artificial Intelligence in Education Research Agenda in 2010”. Special Millennium Issue on AIED in 2010, Int. J. of Artificial Intelligence in Education, 11, 2000, 177-196.

6. The Semantic Web and E-Learning E-Learning is a more tractable place than the open web to explore these semantic web issues –the learning environment is (more) closed –the material to be learned is (more) stable –the users are (better) known –the purposes of these users are (more) explicit –there are humans (learners, teachers, tutors, etc.) embedded in the system who can take on various roles to support the system to support each other –and yet, a learning environment is a fully representative microcosm of the more open web so lessons learned are likely to be generalizable beyond e-learning SWEL is where it’s at!

7. A Generalized E-Learning Architecture: Learning Object Repository We can envisage a generalized e-learning system: a learning object repository –the repository consists of a wide variety of learning objects, including articles, web pages, discussion forums, simulations, quizzes, intelligent tutoring systems, etc. –learners, teachers, tutors (collectively users) can also be considered as “embedded” in the repository –learners spend much of their learning effort interacting with these learning objects and with each other –these users can form virtual communities –inside the repository, users are tracked in detail –the repository is closed, but can “project” much more widely, as users are, of course, free to look outwards into the vast expanse of the web –the environment has many tools to support its users Many types of e-learning system can be mapped into this learning object repository architecture

8. An Example Learning Object Repository System Tiffany Tang’s Ph.D. project –the context: graduate students trying to learn a new area of research by reading papers and articles about the area –the goal: for a given learner, recommend paper(s) appropriate to that learner at that stage in their understanding of the domain –the learning object repository: research papers are the learning objects, kept in a repository attached to the learning objects can be –attributes of the learners (learner models) –characteristics of the papers (tagging) –history of the interactions of the learners with the repository (interaction data) –the research goal: to examine how graduate students choose and evaluate research papers to discover relevant pedagogical features see Tang and McCalla papers in E-Learning J., 2005; AIED conference, 2005; book chapter in Romero and Ventura’s collection “Data Mining in E-Learning”, WIT Press, 2006; IEEE Internet Computing Journal special issue on e-learning, 2009

9. How Can Annotating with Learner Models be Used to Achieve Various Pedagogical Purposes? One “pedagogical” purpose: recommend a learning object for a learner –assume: there is a learner model for each learner with various characteristics: personal, affective, learning style, etc (can build up over time, like an e-portfolio) –assume: every time a learner has interacted with a learning object a copy of the model is attached to the learning object along with a record of the interaction between the learner and the learning object: a learner model instance –then: an appropriate learning object for a learner might be one that other learners (with similar characteristics to the learner who have interacted in similar ways with similar learning objects) have found to be useful: collaborative filtering

10. personal affective learning/cognitive style current goal(s) previous learning objects CHARACTERISTICS EPISODIC trace of learner’s interactions learner’s evaluation of object learner’s view of content outcomes Learner Model Instance

11. x x x x x x x An Example ? ?

12. Other Pedagogical Purposes There are many other pedagogical purposes that could employ similar methodologies –to find a sequence of learning objects of relevance to a learner: instructional planning –to find out which learning objects are useful, not useful, or no longer useful: intelligent garbage collection –to find peers with appropriate characteristics: help finding –to find groups of learners with appropriate shared attributes: building learning communities –to find out what happened to a learner or learners interacting with a learning object repository: empirical evaluation –to attach relevant pedagogical information to learning objects based on actual end use experience: metatagging –………

13. The Ecological Approach Formalizing the Idea of Learner Models as Metadata

14. The Ecological Approach How to support learning and users in a learning object repository? the ecological approach An ecological architecture has the following characteristics –the learning environment: learning object repository –the computing environment: multi-agent system –user and object modelling: active agent modelling –achievement of user and system purposes: mining agent models G. I. McCalla, “The Ecological Approach to the Design of E-Learning Environments: Purpose-based Capture and Use of Information about Learners”. Journal of Interactive Media in Education, Special Issue on the Educational Semantic Web (eds. T. Anderson and D. Whitelock), May 2004. http://www-jime.open.ac.uk/2004/1http://www-jime.open.ac.uk/2004/1

15. The Ecological Approach Characteristics of an ecological architecture –the learning environment all learning materials are created as learning objects learning objects can range from relatively inert text objects through fully interactive immersion environments learning objects may be at various grain sizes, with one learning object potentially breaking down into subsidiary learning objects the learning objects are in a learning object repository new learning objects can be incorporated into, and old objects retired from, the repository the learning objects can have many associative links to each other and to the outside world learners have final control over which learning objects they select and how they interact with them

16. The Ecological Approach Characteristics of an ecological architecture –the computing environment learning objects are represented by agents users (learners, tutors, and teachers) are represented by personal agents various goals in the learning object repository are carried out by these agents –learner agent finds a learning object(s) for a learner –learner agent finds a helper for a learner, a tutor or peer –learner agent finds a study group for a learner –teacher agent evaluates the effectiveness of learning object(s) –learning object agent finding related learning object(s) achieving these goals often requires agent negotiation –learner agent negotiating with learning object agent about whether a learning object meets the learner’s pedagogical goals –two learner agents negotiating whether one learner can help another learner overcome a learning impasse –one learning object agent trying to discover if another learning object agent contains pre-requisite knowledge

17. The Ecological Approach Characteristics of an ecological architecture –active agent modelling each agent keeps its own models of users and other agents these models are continuously updated as the agents interact with each other and with users: active modelling –each agent keeps track of its current goal and accesses models of other agents/people implicated in helping it to achieve this goal –in achieving the goal, new modelling information can be computed “just-in-time” as needed and as resources allow –in achieving the goal, interactions with other agents and with users can also be tracked –the models are thus continuously updated with fine-grained information, but this information is contextualized by the goals being undertaken, the agents and people involved, and the resources available –an agent’s overall model of another agent or person is thus a compendium of many such purpose-based active computations

18. The Ecological Approach Characteristics of an ecological architecture –mining the models the models do not have to be synthesized and summarized into one all encompassing model instead can keep the raw data captured during interaction and mine it for interesting patterns or patterns useful for achieving various pedagogical purposes, for example –to look for changes in a learner’s knowledge over time –to abstract general characteristics of learners –to find sub-classes of stereotypical learner behaviour –to see changes over time in the way a learning object is used by learners –to discover the effectiveness of a learning object for different learners

19. The Ecological Approach Why is this approach “ecological” –the environment is populated by many agents and learning objects (possibly changing over time) –the agents and objects constantly accumulate more and more information –there is natural selection as to which objects are useful: could “prune” useless objects –there are ecological niches based on purposes: certain agents and learning objects are useful for a given purpose, others aren’t –the whole environment evolves and changes naturally through interaction among the agents and on-going attachment of learner models to learning objects

20. The Ecological Approach The ecological approach impacts many computational issues in AI and other areas of CS –various traditional AIED topics, especially learner modelling and instructional planning –various application level agent topics, especially agent negotiation and agent modelling –various system level agent topics, especially scalability and adaptivity –data mining and clustering, especially to actively compute patterns connecting particular types of learner to particular types of outcomes –collaborative filtering and case-based reasoning, a frequently used technique in reasoning about user activities –semantic web, especially alternative strategies for tagging based on patterns found in end-use data

21. Current Ecological Projects ARIES Laboratory Investigations into Issues of Relevance to the Ecological Approach

22. ARIES Projects Impacting the Ecological Approach The data source –iHelp Active learner modelling: “just in time” computation of important learner attributes for various pedagogical purposes –finding patterns in data metrics data mining –purpose-based modelling hierarchies of purposes purpose-based open modelling –MUMS middleware Agents –alternative agent negotiation paradigms –agent modelling

23. Collecting Data: iHelp iHelp aims to support peer-peer interaction in courses –Greer, McCalla, Vassileva, Deters, Cooke, Hansen, Brooks, Winter, Kettel, Bull, Collins, Meagher, students, technical staff, over 10 years ITS-98 paper has most general architecture description; many other papers iHelp has several components –iHelp courses: on-line courses embedded in iHelp –iHelp discussions: open peer forum –iHelp chat: real time on-line chat –many tools and capabilities: search, preferences, code sharing, … Deployed with many computer science courses –thousands of users over many years; two complete on-line courses –keystroke level data collection; many experiments using the large and growing interaction database Future directions –take further advantage of huge end-use database to investigate issues in learner modelling, personalization, visualization –privacy tools –re-institute the 1-on-1 expertise location tool (from earlier versions) –possibly look at re-instituting personal agents (from earlier versions)

24. iHelp Courses

25. iHelp Discussions

26. Active Learner Modelling Exploring active learner modelling –idea of active modelling is that the learner model is computed as needed from system-user interaction data based on Self’s observation: “don’t diagnose what you can’t treat” model as a “verb” not a “noun” –research agenda is then to explore how to do relevant just-in-time computations from interaction data one wing: finding patterns in learner interaction data another wing: working out types of computations that might be carried out for various pedagogical purposes, i.e. creating purpose-based programming clichés third wing: providing tools to support pedagogical engineer in customizing processes to interpret interaction data J. Vassileva, G.I. McCalla, and J.E. Greer, “Multi-Agent Multi-User Modelling in I- Help”. User Modeling and User-Adapted Interaction J., Special Issue on User Modelling and Intelligent Agents (E. André and A. Paiva, eds.), 13 (1), 2003, 1-31.

27. Finding Patterns in Data: E-Learning Metrics and Measurements Generation of metrics and measurements from learner data for automatic learner modelling –Liu, McCalla (see Brooks, Liu, Hansen, McCalla, Greer in AGILeViz workshop at AIED-07) –idea is to generate metrics and measurements that may be pedagogically useful based on an analysis of raw student interaction data –basic approach: raw data is pre-processed into “facts”, then these facts are mined for patterns (using several clustering algorithms), the patterns put together into measurements (such as page dwell time, number of chat messages, etc.), and the measurements finally coalesced into high level pedagogical metrics (such as activity level, social ability, learning style, knowledge level, etc.) –metrics and measurements can then be applied to new data, often in real time –two experiments, both inconclusive (due mostly to small numbers) system-generated metrics compared to metrics generated by human experts system-generated metrics compared to alternative questionnaire-based metrics

31. Finding Patterns in Data: Mining iHelp Data Data from on-line and in-class versions of introductory computer science courses mined to see if there is any difference between them –Peckham: started as graduate course project –use of contrast-set association rule mining, particularly useful for comparing groups –compared 7 sections of introductory CS course Cmpt. 100, 2 on- line and 5 in-class, over the 3 years 2005-2007, looking for differences between on-line and in-class versions –some marginally interesting patterns, but we are now looking for more pedagogically useful and consistent patterns Long term big question: how much can we actively compute the learner model from log-file data of learner interactions?

32. Finding and Using Purpose-Based Program Clichés Another track of research into active learner modelling is finding and using purpose-based program clichés that actually carry out the learner modelling computations: “model as verb” –Niu, Vassileva, McCalla (Computational Intelligence J., 2003): hierarchies of purpose-based program clichés to compute user modelling information for an agent-based portfolio management system experiments with a prototype system in a simulated context were promising not clear if the methodologies can be extended to messy real world –Hansen, McCalla (open learner modelling workshop at AIED conference, 2003; also see Brooks, Liu, Hansen, McCalla, Greer in AGILeViz workshop at AIED-07): purpose-based open learner modelling computing learner models for learners/teachers in Cmpt. 100 course

33. Hierarchies of Purpose-Based Program Clichés

36. Purpose-Based Open Learner Modelling Purpose-based open learner modelling –Hansen’s M.Sc. project –learners, teachers, tutors have a need to find out information about themselves, for reflection, evaluation, motivation, etc. –provide a tool that allows such end users to generate summaries that suit their purposes: key is that the information is generated as needed, actively, and not kept in a learner model –prototype has been implemented and tested with students and teachers in several classes Hansen, McCalla, Open Learner Modelling workshop at AIED conference, 2003

37. Teacher Query

38. Answer to Teacher Query

39. Peer Comparison

40. MUMS - User Modelling Middleware MUMS - Massive User Modelling System –Brooks, Winter, Greer, McCalla (ITS-04 conference) –idea is that the e-learning system builder can customize processes to interpret raw data about learner interaction –naturally de-couple data from its use Opinions are used by three computational entities –evidence producers: observe user interaction with an application and produce and publish opinions about the user –modellers: are interested in acting on opinions about the user, usually by reasoning over these to create a user model –broker: acts as an intermediary between producers and modellers, providing routing and quality of service functions for opinions From this, we can derive fourth entity of interest (adapter design pattern) –filters: act as broker, modeller, and producer of opinions. By registering for and reasoning over opinions from producers, a filter can create higher level opinions

41. MUMS – Architectural Overview Evidence Producers BrokerModeller 1 Modeller 2 Filters 1. Observe user interaction 2. Form opinion about user 3. Publish opinion to broker 4. Store opinion 5. Route opinions to interested modellers and filters 5. Route opinions to interested modellers and filters 6. Reason over opinions forming higher level statements 7. Route higher level Opinions to interested modellers and filters 8. Reason over opinions 9. Act!

42. Agents Agent interaction in the ecological approach provides interesting challenges to agents research –alternative agent negotiation protocols: Winoto, Vassileva, McCalla agents in e-learning systems often don’t bargain monotonically or strictly rationally, and must negotiate a multi-dimensional pedagogical space non-monotonic offers: agents don’t always monotonically reduce/increase their offers: AAMAS-2004; JAAMAS-2005 strategic delay, strategic ignorance: AAMAS-2007 –agents modelling other agents: Olorunleke, McCalla in order to negotiate and communicate with one another agents need to model each other (actively?) controlling spread of delusion in agent societies: AAMAS-2003 agent modelling in real time (RoboCup domain)

43. Two Other SWEL Projects Research into Other Kinds of Metadata for Learning Objects

44. OATS and CommonFolks It may be possible to annotate educational material in pedagogically relevant ways with explicit metadata that is easily captured from learners and teachers –CommonFolks: combining social and semantic tagging of learning objects: Bateman, Farzan, Brusilovsky, McCalla, SW-EL @AH 2006 –OATS: building a tool that allows learners to highlight and annotate text-based learning objects, discuss these highlights with one another, and add semantic and social tags: Bateman, Farzan, Brusilovsky, McCalla, I2LOR 2006

45. CommonFolks: Combining Social and Semantic Tagging Semantic annotation is difficult, pedagogically focused metadata is important for (re) using learning objects, but authors, librarians or instructors will readily apply it CommonFolks investigates a process that is similar to simple social tagging (applying and sharing simple keywords), but to create semantic tags CommonFolks tags contain a predicate (property) and a value from an evolving dictionary, but has no predefined vocabulary like the LOM Perhaps the process still requires too much effort of learners and/or teachers?

46. OATS: Open Annotation and Tagging System How can we encourage students to create and annotate content? OATS applies annotations in a simple way, using highlights, created by clicking-and-dragging over text in a webpage

47. OATS: Open Annotation and Tagging System Users can organize and annotate a highlight by clicking on it, revealing a popup interface Tags and notes can be applied and are shared, so learners can see what others think about a particular part of a learning object

48. OATS: Open Annotation and Tagging System Highlights are also shared. Learners can see what their peers have determined is the most important part of the text (emerges from highlights created). The higher the pink highlight the more all other students have highlighted that part of the text A class study showed that learners liked and were excited about creating annotations using OATS, especially being able to see what others thought was important But as a self-directed organizational scheme, users would need to be able to widely use the system over many courses and multiple years

49. Implications Is the Ecological Approach Actually Tractable? If So, What Does it Imply?

50. Is the Ecological Approach Tractable? Computational issues –how much can be done actively? –space-time trade-offs: how much to save, how much to pre-compute? –can purposes and learner models constrain the mining, clustering, and filtering algorithms? –can purposes cover a domain and be re-used in other domains? –can learner models be standardized and shared? Social issues –what kinds of pedagogy can be supported? –can social capital be enhanced? (Daniel, Schwier, McCalla, AIED conference, 2005) –advantages of e-learning application environment can be constrained learner can be constrained feedback from learner is natural and serves a pedagogical purpose

51. Broader Implications? Are there lessons of the ecological approach beyond AIED? Some wild speculation! –the semantic web is it only necessary to tag objects (web pages, etc.) with user trace data? –impossibility of tagging everything –user model instances and interaction data can be naturally gathered and then mined according to purpose (OATS community tagging and visualization tool may be a step in this direction) –focus is on end use and users, not content; maybe it should be the pragmatic web? maybe there needs to be effort spent on determining how to negotiate differences between ontologies? –impossibility of universal agreement upon standard tag sets, ontologies –agents representing each ontology could perhaps negotiate content and social differences, much as internet service protocols negotiate differences in technical environments

52. Broader Implications? Wild speculation –user modelling how much user modelling can be done actively? user model maintenance through re-computation, not belief revision life long user modelling –a person’s hard disk contains a huge amount of information about them over the long term –what kinds of patterns can be found by mining this for what kinds of purposes? –what about information about the person kept in other hard disks? –life long user modelling would have to be largely active –references: UMAP 2009 workshop organized by Judy Kay, Bob Kummerfeld; workshop hosted October 2008 at Institute for Creative Technologies by Chad Lane

53. Broader Implications? Wild speculation –artificial intelligence AIED as a challenge problem for AI that is not concerned with formal issues of soundness, completeness and consistency, but with the practical issues of –robustness –effectiveness –context –change –resource constraints ecological approach as a challenge paradigm for AI applications –integrating many AIED, AI, computer science, and social science issues –applicable to many e-learning paradigms –can it be applied in less constrained domains than AIED?

54. Broader Implications? Wildest speculation –social science a revolution in social science research methodology - the tyranny of the control group experiment may be overcome? –the increasing bandwidth of human interaction with and through technology means that a large amount of hard data can now be collected in natural social settings (mediated through ICT) and can provide evidence for cognitive and social theories –social scientists, like astronomers a few centuries ago, can use new tools and techniques to both collect and make sense of this data data mining and clustering methodologies statistical techniques representation techniques HCI techniques education is perhaps the social science best situated to explore this revolution - the high interaction bandwidth and natural constraints of educational domains makes them ideal for pioneering experiments which brings us full circle to where we started, so lets end!

55. Questions, Comments, Interactions ? Acknowledgements –my graduate students past and present –my colleagues in the ARIES Laboratory –our research associates past and present –funding from the Natural Sciences and Engineering Research Council of Canada discovery grant LORNET networks grant –the Government of Saskatchewan through the TEL program –private sector support: TRLabs, Desire-to-Learn, Parchoma Consulting Ltd Contact me at mccalla@cs.usask.ca

56. Some References –T.Y. Tang and G.I. McCalla, “Active, Context-Dependent, Data-Centered Techniques for E-Learning: A Case Study of a Research Paper Recommender System”, in Data Mining in E-Learning (eds. C. Romero and S. Ventura), WIT Press, Southampton, UK, 2006, pp. 97-116. –C. Brooks and G.I. McCalla, “Towards Flexible Learning Object Metadata”, Int. J. of Continuing Engineering Education and Life Long Learning, Special Issue on the Application of Semantic Web Technologies in E-Learning (D. Dicheva, ed.), 16, 1/2, 2006, 50-63. –G. I. McCalla, “The Ecological Approach to the Design of E-Learning Environments: Purpose-based Capture and Use of Information about Learners”. Journal of Interactive Media in Education, Special Issue on the Educational Semantic Web (eds. T. Anderson and D. Whitelock), May 2004. http://www- jime.open.ac.uk/2004/1http://www- jime.open.ac.uk/2004/1 –J. Vassileva, G.I. McCalla, and J.E. Greer, “Multi-Agent Multi-User Modelling in I- Help”. User Modeling and User-Adapted Interaction J., Special Issue on User Modelling and Intelligent Agents (E. André and A. Paiva, eds.), 13 (1), 2003, 1-31. –G.I. McCalla, “The Fragmentation of Culture, Learning, Teaching and Technology: Implications for the Artificial Intelligence in Education Research Agenda in 2010”. Special Millennium Issue on AIED in 2010, Int. J. of Artificial Intelligence in Education, 11, 2000, 177-196.

57. Learner Models as Metadata to Support E-Learning: The Ecological Approach Invited Talk at SWEL Workshop @ AIED 2009 Gord McCalla ARIES Laboratory Department of Computer Science University of Saskatchewan Saskatoon, Saskatchewan CANADA Abstract In this talk I will present an e-learning framework called the ecological approach that is based on the idea that learner models are a rich source of metadata that can be used to inform various activities of an environment that supports human learning. Learner models can provide insight into learning material that might be appropriate for a learner, guidance about other learners who might be able to help a learner who is at an impasse, useful information to create and support learning communities, or even data that can be used for empirical analysis of the effectiveness of an e-learning environment. I will discuss the characteristics and implications of the ecological approach, for e-learning and beyond. I will also mention some of the research currently underway in the ARIES laboratory that may lead to more ecological e-learning systems. Short Biography Gord McCalla is a Professor in the Department of Computer Science at the University of Saskatchewan in Saskatoon, Canada. His research interests are in applied artificial intelligence, focussed particularly on user modelling and artificial intelligence in education (AIED). Working with colleagues and students in the ARIES Laboratory at the U. of S., he has explored many issues, including granularity in learning and reasoning, educational diagnosis, learner modelling, tutorial dialogue, instructional planning, peer help, and learning object repositories. Recently, he has begun to look into the implications of “ fragmented learning systems ”, systems that are designed to support learners in diverse virtual learning communities (social fragmentation) and systems that are themselves composed of many software agents (technological fragmentation). This has led to an AIED architecture called the “ ecological approach ”, currently being explored in a number of research projects in the ARIES Laboratory.