Supporting Models

The pedagogical agent, as well as the immersive and intelligent learning layer, is supported by various models (see Figure 40). Those supporting models are pedagogical-oriented and are detailed below.

The Learner Model

The learner model is central to the conceptual framework supported by learning theories that mandate understanding the learner (see Section 2.1.3) for a learner-centered approach. The model gives input to answer inference questions about learners such as preferences, style, knowledge, evaluation results, or even difficulties or misconceptions. As thus it gives input knowledge about the learner needs, its status to infer about adapted methods of instruction. Methods relevant to realizing the learner model are previously discussed in Chapter 5, finding the use of Bayesian networks, concept maps, mental models, or BDI models are used for realizing learner models. However, several other methods are possible given its relevance to the research theme of user modeling.

Adopting a learner model supports concepts from different learning theories that are learner-centered that promote the importance of understanding the learner. The user model supports personalization concepts, adaptation, and learning styles. Several learning theories including the experiential learning model of Kolb (1984) which gives importance to learning in virtual worlds endorse the

Virtual World

Immersive & Intelligent Learning Layer Intelligent Pedagogical Agent

Demonstration Mentoring & A. Embodiment &

Affective Support

Reasoning Visual Adaptation Ped. Support

Models

Learner Model Task Model Goal Model Pedagogical Model

World Model

Int. Objects

Affective Model Learner/User Avatar

process of reflection (see Section 2.1.5). Mabott and Bull (2004) argue that open learner models enable reflection processes leading to increased learning. The argument is based on that learner models enable learners to inspect and question their performance. Supporting the target of pedagogical agents’ adoption, Kerly, Hall, and Bull (2006) conclude that using a chatbot is a method that enhances interaction with the user model and makes it more accurate.

The learner model gives input to intelligent functions of the intelligent agent paradigm. It is important in relation to agent goals to set what goals have been reached, which are not, or being dropped. Thus it gives input to the reasoning functions of the IPA in learning paths; where to start, where to stop, and what is missing. The learner model gives input to the IPA of which methods of preference are to be used in a learning activity, in relation to learning styles for example discussed in Chapter 2. In contrary to the traditional learning setting of the classroom, the learner model is machine readable and hence it adds advantage to automated methods of instruction such as the IPA in a new form that is not possible in learning from a human.

The learner model is also important to the added immersive and intelligent learning layer for a virtual world given a gap in virtual world implementations and research in virtual worlds adoption for learning. Visual adaptation or any learner-oriented function can be based on the learner model. Even in absence of a pedagogical agent, a remote teacher represented by an avatar can know about remote learners through their learner models.

If the implementation of a pedagogical agent provides explanations of behavior such as the case for the explainable agents, explaining the reasoning behavior of the agent should have a basis that is in relation to the learner abilities available with the learner model. The explained behavior of the pedagogical agent is thus made logical to both the learner and the learning community member of interest.

The learner model can also support the general user of the virtual world. This is to include the learning community members who can contribute to learning activities and processes in the virtual world such as defining learning paths or setting learning scenarios for the IPA. Examples are educators, instructional designer avatars, or those in interest of evaluation reporting.

The learner model is further related to the affective model, discussed further below. As the learner affective characteristics are relevant to the IPA strategy, the IPA will convey what is suitable to the context only but what is suitable to the learner abilities performing tactics that increase motivation.

The Pedagogical Model

In the visited research, the pedagogical model is found to be required for supporting learning in several architectures for virtual learning environments, from different aspects. For example, PEGASE (Buche & Querrec, 2011) uses the pedagogical model to provide the knowledge for taking teaching decisions in relation to both the learner and the context. Buche and Querrec (2011) propose an important requirement of the pedagogical model: to be able to easily add instructive concepts so that it becomes generic and exploitable independent of the learning task. It is formed in PEGASE as a set of rules at different abstraction levels. The pedagogical model of Amokrane, Lourdeaux, and Burkhardt (2008) provides training rules of when and why to intervene in the activity, and to explain learner errors.

It is viewed that the pedagogical model ties different learning aspects and resources in the environment together to construct instructional design that is pedagogical aware and consistent with learning aspects of virtual worlds and the intervention of IPAs in the learning activity. It is to support a learning path for a learner in the virtual world.

Given the pedagogical objectives of the IPA, the pedagogical model is central to the IPA in providing several pedagogical aspects. Those pedagogical aspects are in relation to the following constituents:

 Learning activities - Such as a physical experiment linking to its learning objects, learning content, time constraints, and its relation to a pedagogical goal.

 Activity model can provide pedagogical details that help in learning and assessing learner interaction with the objects and environment. It can also provide activity plan recognition to identify what the learner is doing (supports collaboration and idle time behavior management)

 Group learning rules to match learners in teams - team formation rules, group activity description, and group assessment rules.

 Assessment, feedback, and reporting: to provide assessment, feedback, and reporting functions. It is relevant to the discussed error models. It is used to provide feedback to the learner and update the relevant learner model of progress. It can also give summative reporting about learning effectiveness in general.

 Other pedagogical elements such as learning styles, strategies, and preferences that can be allocated to learners based on their models and the possible offering.

Educational space aspects such as visual settings or constraints relevant to learning

The pedagogical model gives a higher level of abstraction of conditions in forms of rules relevant to what to do in learning situations. Hence it has the potential to give much support for the intelligent realization from the knowledge level to the pedagogical agent and to enable taking intelligent pedagogical attitudes and strategies. In visited work, the pedagogical model includes elements that are listed separately in this conceptual model. The reason is to highlight the separated models and to allow further functions. For example, the learner model discussed above gives knowledge and rules which can also support the learning community.

In relation to the theoretical foundation of learning, the pedagogical model should support learning by doing in virtual worlds such as the four cycles of experiential learning (Kolb, 1984; Figure 3) are enabled. Further work will reveal the mapping between the cycles and the learning activities with the pedagogical agent with different activities. Learning scenarios with pedagogical agent in a virtual world are further elaborated in Sections 8.3, and 8.4.

In relation to the implementation of the pedagogical model, the intelligent agent approach is considered and hence its intelligence support is to be investigated in accordance to how it can fulfill the demand to model and achieve the pedagogical model accordingly.

The pedagogical model triggers the need for a pedagogical module that realizes pedagogical process and put them into the service of the pedagogical agent and the learning community in the virtual world as well as other processes. For example, the module can search in the currently deployed learning objects that can satisfy a learning objective in according to current pedagogical situation. This is considered as part of the immersive and intelligent learning layer to accompany the virtual world.

The World Model

The world model represents the immersive environment, its functions and services, its relevant 3D scenes, arrangement of objects, physics, navigation paths, purpose of the environment, users, events, and services provided in the environment. It is needed for the IPA to physically navigate the environment and infer about the context that the learner avatar is interacting in. The environment model gives world state to serve the system request in relation to its constituents and their interrelationships. The virtual world is generally composed of a set of objects that can be either pedagogical or not as well as visual scenes that the learners are immersed in. The world model facilitates knowledge inference about world objects and their state, logged users, security rules, and users’ activity identification. Users’ activity identification generally helps to identify who is interacting with which learning objects and what the learner is doing. It can help as well to put rules of learner-learner interaction and reason about it. The latest

function is important for the educator or the IPA to either prevent or promote interaction depending on the learning situation.

Reasoning about world events can be viewed to be part of the world model.

It provides semantics of the events occurring in the environment. And hence, it facilitates: capturing relevant events to the learning situation, notify IPA of events of interest, and notify intelligent agent module.

The Learning Object Model (Intelligent Objects)

The learning object is important to learning design for immersive environment when it encompasses learning by doing. The learning object in a virtual world builds on its 3D rich visualization abilities. An example of a learning object is a physics simulation experiment module in a virtual world. In this setting, the learning object promotes the learning in virtual worlds with given visualized simulation in 3D.

Schmeil and Eppler (2009) characterize the learning object to be static, automatic, or interactive. All types of objects are required expecting the ones which stress interaction and resulting visualization and animation in 3D to give rise to experiential and explorative learning. Learning objects which add a pedagogical value to learners in the virtual world are of interest. This means learning objects should follow an instructional design method to know in advance the expected pedagogical value as a result of interaction. For automated pedagogical methods to incorporate a learning object in a learning activity or recommend that resource, it should be able to reason about its pedagogical value and relevance.

The semantics relevant to being be machine understandable, observable, and controllable are important for the IPA to run an experiment, with no human aid and conduct activities with the learner and reason about those activities. The IPA requires ability to understand the object and its behavior so as to provide explanations to the learner about it with knowledge from the pedagogical module. The IPA should be able, with knowledge from the learning object model, to observe the output so it can explain it to the learner and know its correctness for assessment. The IPA also requires controlling the learning object so as to provide tutorials on running it The IPA might intercept the interaction between the learner and the learning object to create situations for learning and observe the consequent learner behavior. It is viewed that the smart learning object is learning artifact that gives ability to obtain pedagogical information that helps in assessing the task completion status. Furthermore, its adoption for learning purposes and facilitating IPA goals are essential. Modeling the smart object with the above properties in a learning object model, in relation to learning situations gives operation and pedagogical knowledge to conduct effective learning activities.

The selection of the size of learning objects in virtual worlds is related to significant research in intelligent tutoring systems (ITS). ITS suggested the use of modular small units that can be grouped to form a learning path. Thus it gives the opportunity to provide sequencing and personalization to suit different needs in addition to make the realization feasible and scalable. In comparison with a large learner object that does not give such flexibility. In presence of such approach, the IPA should be able to understand and synthesize learning paths that form larger learning goals than learning with a single learning object. The methods of synthesizing a learning path forms intelligent guidance by the IPA to lead to the appropriate resources according to the goal model, the pedagogical model, and the learner model in relation to the available objects.

Thus formalizing the learning object model gives pedagogical interoperability and allows desired characteristics: Just-in-time learning, discovery of best learning resources, and synthesis of larger objects with bigger learning goals. Just-in-time learning gives rise to the active explorative learning characteristic of virtual worlds. The discovery of a best learning resource for a learner is relevant to the IPA to give intelligent guidance to the learner in presence of vast and complex learning resources in a scalable environment. While learning units are explored by ITS research, they are being adopted in virtual worlds for learning as well.

Maroto, Leony, Kloos, Ibáñez, Rueda (2011) utilize the concept of Units of Learning (UoL) to orchestrate learning activities in virtual worlds. While e-learning systems standardized e-learning objects specification in SCORM¹, Maroto et al. (2011) suggest the recent use of IMS Learning Design (IMS LD, 2012) into the virtual world. Consequently, part of the model is the semantics that should be dedicated to the context of learning. For example, to give knowledge about how can the object used in which virtual spaces or in relation to other learning objects.

The Goal model

Pedagogical goals hierarchy: The decomposition of goals in relation to learning activities with objects in the environments is linked through a pedagogical goal hierarchical model. It is important to device learning plans for instructional design. The goal hierarchy is relevant to the pedagogical agent to pursue a pedagogical goal oriented behavior and give the current status of the learner (by relating to the user model) to the required goals. The structure into goals allows organizing the relationship in a form of pre-requisites of learning levels to order a set of objectives. The formation of goals should be tied to highest levels of learning goals in relation to what is accomplishable by learning with IPA into the virtual world given the current conceptual model. Furthermore, it realizes the autonomous nature of the pedagogical. The goal-directed behavior is a nature of an intelligent agent as well being a factor in the decision to select the intelligent agent approach for the cognitive layer of the pedagogical agent. Discussed in

1 The Sharable Content Object Reference Model (SCORM).

Section 8.3.1 is the Belief-Desire-Intention model. The Desire part of BDI forms the goals that form the behavior of the agent. In the decision of the goals, the pedagogical nature of goals matches with design objectives of the IPA.

In presence of multiple IPAs, the roles of IPA is defines in relation to the goals it can pursue. When the agent is assigned a role, it follows a main pedagogical objective to pursue with the learner according to his abilities, assessment, interaction with the environment, and environment events. The BDI model of intelligent agents gives how the reasoning occurs in relation to the different parameters. Depending on the agent paradigm abilities, there are different types of goals that can be defined, such as achieve goals or perform goals. The goal model forms a goal-directed behavior of intelligent agents that also include generation of sub-goals. The goal directed behavior has benefits not only to pedagogical goal attainment for the learner but also to the potential believability of the pedagogical agent embodiment as the learner perceives its autonomous intelligent behavior.

The Task Model

In addition to the above discussed models, the task model provides knowledge about the task and its decomposition in relation to the learning activity and the context of learning. Therefore, it has relevance to the pedagogical model and the goal models discussed above. Furthermore it has importance triggered by the focus on experiential learning by doing approach in virtual worlds. The task model in Steve agent architecture (Johnson et al., 1999) gives the relationship among the different steps of the task towards a goal and to ensure its compliance to operating procedures¹. It helps Steve to perform explanations about actions in relation to their goal. The task model has been also used to support collaborative tasks by the aid of a pedagogical agent within the context of virtual worlds (Rickel & Johnson, 2000). The task model is relevant to the cognitive component of the pedagogical agent in the scope of supporting decision processes for performing a task. Furthermore, it gives standardization and input to inference about the tasks of the pedagogical agent and the learner. For example, the W3C² community standardized task models describing them as “They describe the logical activities that have to be carried out in order to reach the user’s goals”. In this standard, different task categories, with different task types, are characterized: user task, system task, interaction task, and abstract task. Therefore, in complex types of tasks within learning objects and in the context of virtual worlds, it is viewed that the task model gives details to support the pedagogical agent in performing complex tasks for tutorials and achieving pedagogical goals.

1 Therefore and in other work, it can be related to an error model. Hence it can be used to differentiate between correct operating conditions and the errors generated.

2 http://www.w3.org/TR/task-models/

However, in the question of how the task model is created, it found that it results from a task analysis process. The purpose of the task analysis process is identifying the requirements from the task that leads to accomplishment. Two main approaches of analysis methods exist: behavioral approaches and cognitive approaches. Behavioral approaches focus on external observations based on the procedural steps to perform a task. The cognitive approach stresses the importance of the mental process of doing the task. Cognitive task analysis (CTA) is hence important to instructional design that stresses mental processes.

Behavioral approaches rather give the input to operating and procedural functions. As an example, it gives reflexive behavior of what to do in response to events in the object or a device which can suit training functions. With both approaches, the task model prescribes how to do a task correctly providing input to assessment functions.

Given the above properties of the task model, it is important to investigate its relevance to intelligent agent based reasoning. Intelligence is required to allow reasoning on the tasks and about what the learner is doing at a given instance?

Given the above properties of the task model, it is important to investigate its relevance to intelligent agent based reasoning. Intelligence is required to allow reasoning on the tasks and about what the learner is doing at a given instance?