Download As discussed earlier, OELEs provide a learning context and

3. Learner modeling in Open ended learning environments
As discussed earlier, OELEs provide a learning context and a set of tools for learning and solving complex problems. To be successful in these environments, students have to develop a number of different skills and strategies to become effective learners and problem solvers. From a
self-regulation and metacognitive perspective, the complex nature of the problems, requires students to develop strategies for decomposing their learning and problem solving tasks into subtasks, and develop and manage their plans for accomplishing these tasks. The open-ended nature
of the environment also implies that students have choice in the way they decompose, plan, sequence, and solve their given tasks. Along with the choice, comes the responsibility for managing, coordinating, monitoring, evaluating, and reflecting on relevant cognitive processes and
metacognitive strategies as they search for, interpret, and apply information to construct and test
potential problem solutions. On the one hand, this presents significant challenges to novice
learners, who may lack both the proficiency to use the system’s tools and the experience and understanding necessary to explicitly regulate their learning and problem solving in these environments. On the other hand, the large solution spaces implied by the open-ended nature of the environments and the complexities of the search space, clearly makes the application of traditional
overlay and perturbation modeling techniques intractable. Learner-based modeling approaches
that focus more on learning behaviors and their impact on learning and evolution of the problem
solution are likely to be more appropriate. To facilitate learner-based modeling, and provide a
framework that encompasses the cognitive and metacognitive processes associated with students’
learning and problem solving tasks, we have developed a task- and strategy-based modeling
framework to interpret and analyze students’ actions and activity sequences in the learning environment.
Task Model
Strategy Process Model
Binary
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Task
Temporal
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Strategy
Unary
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Task
Domain-General
OELE Tasks
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Temporal
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Task
Task
Subtask
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Figure 1: A task- and strategy-based modeling framework for OELEs
At the core of this representational approach is a hierarchical task model, illustrated in the right
half of Figure 1. The task model relates specific OELE activities to relevant tasks and ultimately
to general tasks applicable across a variety of domains. Thus, the highest layers in this model in-
clude domain-general tasks that the learner has to be proficient in to succeed in a variety of
OELE environments; the middle layers focus on approaches for successfully executing a set of
subtasks, which may be specific to a particular OELE or genre of OELEs, to achieve the higherlevel tasks; and lower levels map onto actions that are defined with respect to the tools and interfaces in an individual OELE. These actions are directly observable, and are typically captured in
log files, because they are performed through the OELE interface. Thus, the task model, which is
represented as a directed acyclic graph, provides a successive, hierarchical breakdown of the
tasks into their component subtasks and individual OELE actions. However, the task model does
not indicate whether (or in what circumstances) multiple subtasks need to be completed to effectively perform a higher-level task, nor whether there are any necessary relations (such as ordering) among them. Similarly, links from a task/subtask to actions do not directly indicate whether
all actions have to be executed to complete the task or a subset of the actions might suffice.
Instead, it is the strategy model, illustrated in the left half of Figure 1 that captures this information in a form that can be directly leveraged for online interpretation of a student’s actions.
Thus, the strategy model complements the task model by describing how actions, or higher-level
tasks and subtasks, can be combined to provide different approaches or strategies for accomplishing learning and problem-solving goals. By specifying a temporal order and conceptual relationships among elements of the task model that define a strategy, the strategy model codifies
the semantics that provide the basis for interpreting a student’s actions beyond the categorical information available in the task model.
Strategies have been defined as consciously-controllable processes for completing tasks (Pressley et al., 1989) and comprise a large portion of metacognitive knowledge; they consist of declarative, procedural, and conditional knowledge that describe the strategy, its purpose, and how
and when to employ it (Schraw et al., 2006). How to apply a particular procedure in the OELE
describes a cognitive strategy, while strategies for choosing and monitoring one’s own cognitive
operations describe metacognitive strategies. In this task-and-strategy modeling approach, strategies manifest as partially-ordered sets of elements from the task model with additional relationships among those elements determining whether a particular, observed behavior can be interpreted as matching the specified strategy. Figure 1 illustrates unary relationships that describe
specific features or characterizations of a single strategy element, binary relationships among
pairs of elements, and the temporal ordering among elements of the strategy. Further, if a relationship is not specified between any two elements in a strategy, then the strategy is agnostic to
the existence or non-existence of that relationship. Because the elements of the task model used
in the definition of strategies are hierarchically related, strategies may also naturally be related
from more general strategy definitions to more specific variants. In this representation, specifying additional relationships, additional elements, or more specific elements (e.g., a specific action replacing a more general task/subtask) derive a more specific strategy from a general one, as
illustrated in Figure 1.
An important implication of the hierarchical relationships among the strategy process definitions
is that multiple variations on a more general process can automatically be related to each other.
In particular, this enables relating a set of desired and suboptimal implementations of a general
strategy process for use in the learner model. As illustrated in Figure 1, the general outline of the
strategy is hierarchically linked to a variety of more detailed versions of the process that represent either desired variants or suboptimal ones. By analyzing a student’s behavior, the system
can compare matches to desired versus suboptimal variants in order to estimate the student’s proficiency (and need for scaffolding) with respect to the strategy, as illustrated in Figure 2.
Task Model
Strategy Process Model
General
OELE Tasks
Strategy A
Strategy B
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Strategy C
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Student X’s OELE Activities
Figure 2. Strategy matching in OELEs
Dynamic Learner
Model
Action