• Taxonomies of pedagogy
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Different kinds of pedagogical content

  • Collaboration
  • Critiquing
  • Designing

Taxonomy of pedagogical challenges

  • INDIVIDUAL AND GROUP INTERACTIONS
    • Coordinating fluid switching between groups
    • Fluid control of learning activities in class, outside, at home
    • Managing individual and group activities and exchanges
    • Coordinating access to and organization of resources
  • VISUALIZATIONS
    • Simulations, multi-user or synthesized environments
    • Representations of designs, arguments, etc
  • INTERACTIVE LEARNING ELEMENTS
    • SAIL learning content
    • Management of workflow - dynamic learning process
    • Connect to other technology elements (from collaborators, commercial products, etc)
  • SOCIAL ELEMENTS
    • Capture social "flow" (metatags, etc)
    • Connect to wider community of students, citizens
    • Classroom social preferences, questions, issues feeds back into itself
    • Enable group functionality (SAIL or other groupware)
  • MANAGEMENT OF WORKFLOW
    • offline activities such as hands-on or lab activities, integrate with probeware
    • peripheral devices, printing, homework, etc.
    • portal, teacher feedback, student peer exchanges

Grouping and regrouping of students for purposes of curriculum

  • Grouping would be done either into a set of categories (e.g., groups A, B, C, D), or into groups with a certain number of students (3, 4, 5, 6).
  • The curriculum author could also have groups rotate what category they are assigned to (i.e., the same group of 4 start out working on category A together, while the other categories do their thing, then the A folks go into category B, the B group moves into C, etc. I suppose this doesn't change the grouping at all, but illustrates that groups may be manipulated in terms of what they are working on.
  • Another kind of grouping would be what is known as "jigsaw" - where you start out grouping everyone into "specialization groups" (e.g., the questioners/hypothesizers, the background researchers, then data analyzers and the concluders) - then after they've done some focused activities on their specialization, you re-group people so that a new set of groups is determined where each group has one person from each of the previous specialization categories. I think there's an even more stringent view of this, where then you re-do the whole thing successively until you've made sure that every kid gets a chance to play every specialization role - but I suppose if we can solve the first case we could do that monster (not sure what curnit would ever have enough TIME to allocate to such a design...)
  • Another kind of group would be the dynamic grouping, where students are sorted into their group based on content from previous pods in the curnit (e.g., assessment responses, or just "select your preferred group") -
  • dynamic self-selected grouping, where students go to a kind of chat room or web tool and find partners and create their own groups.

Architecture for pedagogical flow

Building Blocks for Smart Space Learning (Simon et al. 2006)

  • Teachers, learners, and learning managers might miss the latest critical developments due to the fact that the existence of an external learning object is hidden from them
  • While proprietary semantics can lead to effective search on the local repository, the lack of semantic alignment with the outside world reduces usability.

Roles for pedagogical agents

Conceptualizing Intelligent Agents (Jafari, 2002)

  • This article deals with distance learning but this doesn't have to be a limiting factor
  • Intelligent agents could relieve "the instructor from manual monitoring and management of course activities" (p.29)
  • This could be especially useful in our classrooms as teacher could spend more time on helping students who are having real difficulties rather than simply being a 'task warden'
  • On-the-fly-reflection rather than on-the-fly-monitoring
  • Intelligent agents Def.
  • An intelligent agent is a set of independent software tools linked with other applications and databases running within one or several computer environments.
Interesting break down of different PIA's

Digital TA (teaching assistant)

Digital Tutor

Digital Secretary

Send overdue notices to the students for assignments
The agent could provide background info on a student, when reporting problems, etc.

Assists students with specific learning needs
Could be a "smart search engine"
Could "learn" through repeated use and feedback
It is assumed that the Digital Tutor has access to students? learning profiles. Accessing student profiles and knowing students? strengths and weaknesses on a learning objective empowers the Digital Tutor to provide more useful resources. (p. 31)
System could alert students when group members or other students who might be helpers (scoffolding helpers) log into the system

Running calendar and event co-ordination
Communication routing and organizing
Connecting relevant team members, group mails, chats

Possible challenges for data collection for PIA's
  • Two major obstacles could inhibit the collection, analysis, and use of learning data in an educational institution. First, the technology and the software engine necessary for collection and analysis of learning data do not yet exist. Second, collecting and using learning data could create legal challenges for educational institutions.
  • TWO GOOD EXAMPLES OF SCENARIOS AT THE END OF PAPER
Good References
  1. N. Negroponte, Being Digital (London: Hodder & Stroughton, 1995), pp. 152-156.
  2. M. Dehn and S. van Mulken. "The Impact of Animated Interface Agents: A Review of Empirical Research". International Journal of Human-Computer Studies, 52 (1), 2000, pp. 1-22.

In truth most of the references in this article seem useful

Conceptualising Smart Spaces for Learning (Simon et al. 2004)

  • "The lack of interoperability of knowledge repositories, for instance, does not allow for a unique view on the learning services offered. As a result, a user's search costs increase and the transparency of learning resources offered is reduced with each repository added to the environment." (p.2)
  • There has to be some way to connect different knowledge sources together... combined with "Building blocks" article
  • Neither potential learners nor their mentors have all the goal - driven business tools and information available to concisely select the right learning service for closing a particular knowledge gap (p.2)
  • A Smart Space for Learning is a distributed system, which provides management support for the retrieval and consumption of heterogeneous learning resources . (p.3)
Types of intelligent agents
  1. A content brokerage service can be used for preparing the delivery of a course or for providing a learner with related information in a particular subject area (p4)
  2. Assessment services can be used to identify knowledge gaps.
  3. Reputation services attempt to quantify the reputation of a learning service provider within the network (p.5)
Personalization Domain - Cool wording and idea... can we build on this???
Good References
  1. Simon, B., Retalis, S., & Brantner, S. (2003). Building Interoperability among Learning Content Management Systems. In Proceedings of the 12th World Wide Web Conference. Budapest .

E-learning agents (Gregg, 2007)

E-learning Core features
  • What makes e-learning content different from other educational materials is that it can be disassembled as individual learning objects, tagged, and stored for reuse in a variety of different learning contexts (need page #)
  • These learning objects can be assembled into different configurations depending on the requirements of an individual educational situation. This reuse of educational content is one of the core values of e-learning systems (need page #)
  • "Agents can hide the complexity of diffcult tasks, perform tasks on the user's behalf, train or teach the user, help users collaborate, or monitor events and procedures (Maes, 1994)" (p.3)
  • "These agents do not attempt to map or understand the entire web. Instead, they attempt to process specific types of content about which the agent has some prior knowledge." (p.3)
  • This is a good point... there should be practical constraints on the scope of the system... perhaps we can design the system to grow slowly... through the natural expansion of the users' actions
Challenges
  1. Open intelligent e-learning infrastructures that can be used with standard web technology
  2. Deliver just-in-time learning materials required by the individual learners
  3. Personalizing course materials based on learning objectives, learner characteristics, and prior learner knowledge, and facilitating learner interaction.
E-Learning systems example
Virtual Mentor(From Zhang et al., 2004)
  • Testing of a prototype Virtual Mentor system indicates that learners that used the Virtual Mentor system had better learning outcomes than learners in traditional classroom settings *
Requirements of the system (taken directly from text)

E-learning agents

Ongoing research into effective learning support systems suggests that in order to support ubiquitous, collaborative, experiential, and contextualized learning in dynamic virtual communities, an e-learning environment should provide the following features for learners (Allison et al., 2005)

Experiential active learning

Learning resources should be interactive, engaging, and responsive, with active learning and knowledge formation emphasized above simple information transfer.

Personalized

The learning environment should be customized to the individual learners learning styles and educational needs with the quality of the learning experience continually validated and evaluated. This includes customizing accessibility to meet unique learner needs (e.g. to support screen readers, language translation or alternative devices automatically), and dynamically creating appropriate learning contexts.

Collaboration socio-constructivist

Both solitary and group work should be supported.

Lesson planning

Agents can be used to perform information gathering and sophisticated reasoning necessary to determine appropriate learning sequences (Woolf and Eliot, 2005; Cassin et al., 2003, Sicilia, 2006).

Resource location

With hundreds of thousands of educational resources available on the web, anyone assembling e-learning materials requires assistance to locate appropriate resources online (Gasevic and Hatala, 2006; Woolf and Eliot, 2005). Agents can facilitate location of a wide variety of learning materials, including those that support active learning. (p. 3&4)

Types of agents

Instruction Agent

Lesson planning agents can be used to assist with the design of the course structure as well as with the selection of appropriate learning materials (p.4)
dynamically generate recommended instructional resources and schedules, which can be customized for individual learners.(p.5)
decomposes complex topics into simpler subtopics to facilitate the ordering of the materials.(p.6)

Lesson Planning agent

 

Resource Location Agent

Used to identify additional resources for the specific topic (p.5)
Resource discovery can occur in two modes in e-learning environments. The first occurs when learners explore the digital environment on their own to assemble learning materials based on a self-perceived learning need, and the second occurs when domain experts or instructors assemble the reusable learning objects necessary to support a course or learning module.(p.6)
can utilize semantic web metadata to help locate and classify new learning materials as well as to monitor existing resources to determine if they have been changed, moved, or eliminated (p.6)
Needs: Querying, Indexing, Evaluation

Learner Centered Agent

Learner Centered Agents are responsible for making the learner's interaction with the e-learning environment smooth and effective (p.7)
The Learner Centered agent is responsible for soliciting feedback from learners regarding the effectiveness of specific learning materials; and continuously monitors learning outcomes(p.7)
It is responsible for communicating with the Personalization Agent and the Collaboration Agent to improve learning (p.7)

Personalization Agent

I think this is an especially important function... especially in light of our aim for the S3
Used to create a personalized learning model and pathway tailored to individual learner knowledge and personality traits (p.8)
Should be able to select learning materials and optimize schedules for individual learners based on cognitive style, personal preferences, and accessibility needs in addition to prior knowledge and desired knowledge. (p.8)

Collaboration Agent

Tools to support socialization between learners and their instructors, community building tools to support the process of building cohesion in a group, and discussion supporting tools (p.8)
Suggesting collaboration where appropriate
Can monitor the e-learning environment and suggest synchronous "chat" between learners working on similar problems at the same time (p.8)
Can also point learners to appropriate discussion threads throughout the learning process (p.8)
Can also identify learners that are having difficulty with a particular topic and facilitate their interaction with the instructor, before they become too lost or spend too much time misunderstanding a particular topic (p.8)

  • Many of these factors have already been highlighted in our own scenarios on the encore wiki
  • Although this paper highlights several problems with collaboration and community building I think many of these can be overcome in S3 because this is not 'true' distance learning which is the focus of many of these papers

Good References

Classroom Spaces
  1. G. Abowd, C. Atkeson, J. Brotherton, T. Enqvist, P. Gulley, and J. LeMon, "Investigating the Capture, Integration and Access Problem of Ubiquitous Computing in an Educational Setting," Proceedings of CHI '98 Los Angeles, CA (April 18-23, 1998), pp. 440-447.
  2. G. D. Abowd, "Classroom 2000: An Experiment with the Instrumentation of a Living Educational Environment," IBM Systems Journal 38 No. 4, 508-530 (1999)*
Situated Information Spaces
  1. G. W. Fitzmaurice, "Situated Information Spaces and Spatially-Aware Palmtop Computers," Communications of the ACM 36 No. 7, 39-49 (July 1993).
  2. G. W. Fitzmaurice, S. Zhai, and M. H. Chignell, "Virtual Reality and Palmtop Computers," ACM Transactions on Information Systems 11 No. 3, 197-218 (July 1993).
  3. S. Long, D. Aust, G. D. Abowd, and C. Atkeson, "Cyberguide: Prototyping Context-Aware Mobile Applications," Proceedings of CHI '96 Vancouver, BC (April 13-18, 1996).
  4. S. Long, R. Kooper, G. Abowd, and C. Atkeson, "Rapid Prototyping of Mobile Context-Aware Applications: The CyberGuide Case Study," MobiCom, ACM Press, New York (1996).
  5. M. Krueger, "Environmental Technology: Making the Real World Virtual," Communications of the ACM 36 No. 7, 36 (July 1993).
Semantics and e-learning
  1. Allison, C., Cerri, S.A., Ritrivato, P., Gaeta, A. and Gaeta, M. (2005), "Services, semantics and standards: elements of a learning grid infrastructure" Applied Artificial Intelligence, Vol. 19, pp. 861-79.
  2. Berners-Lee, T., Hendler, J. and Lassila, O. (2001), "The semantic web", Scientific American, Vol. 284 No. 5, pp. 34-43.
  3. Zhang, D., Zhao, J.L., Zhou, L. and Nunamaker, J.F. (2004), "Can e-learning replace classroom learning?", Communications of the ACM, Vol. 47 No. 5, pp. 75-9.
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