The Data Learning Problem Arises also in Cognitive Architectures other than Soar

The data learning problem is a phenomenon that arises when an agent employing a cognitive architecture faces the task of acquiring declarative information from an external source, such as the “answer ” to a “question”. Because the agent has to pay attention to both question and answer in order to learn the association between them, it is problematic for the agent to learn to produce the answer in response to the question alone. This observation helps shape the basic characteristics of human memory. The problem was first reported with the Soar architecture, but it arises also in ACT-R, and this paper argues that it will occur in any cognitive architecture, connectionist as well as symbolic, which is specified in a sufficiently explicit manner to avoid having the theorist act as an implicit homunculus for the agent. The Data Learning Problem The data learning problem is a phenomenon that arises when an agent faces the task of acquiring declarative information provided by an external source. Paradigmatic tasks are, in the laboratory, paired associates, where in response to a stimulus word such as shelf, the agent has to learn to say the digit eight; or in everyday life, learning the correct answer to a question, such as that the capital of France is Paris. In such situations, both the question and the answer (or the “stimulus ” and the “response”) must be present and attended to by the agent, in order to learn the connection between them. Thus the agent must process both the question “capital of France? ” and the answer “Paris ” if it is to acquire the relevant fact. It is therefore problematic for the agent to acquire a directed association from the question to the answer, such that it will be able to retrieve the answer in response to the question alone, without the answer also being present. The data learning problem was first identified in work with the Soar cognitive architecture. However, this paper shows that the problem occurs with ACT-R as well, and argues that it arises in any architecture which is sufficiently theoretically explicit. The Data Learning Problem in Soar The data learning problem was first noticed and discussed (and termed the data chunking problem) by Rosenbloom

The Data Learning Problem Arises also in Cognitive Architectures other than Soar

The data learning problem is a phenomenon that arises when an agent employing a cognitive architecture faces the task of acquiring declarative information from an external source, such as the “answer ” to a “question”. Because the agent has to pay attention to both question and answer in order to learn the association between them, it is problematic for the agent to learn to produce the answer in response to the question alone. This observation helps shape the basic characteristics of human memory. The problem was first reported with the Soar architecture, but it arises also in ACT-R, and this paper argues that it will occur in any cognitive architecture, connectionist as well as symbolic, which is specified in a sufficiently explicit manner to avoid having the theorist act as an implicit homunculus for the agent

Augmented Conversation and Cognitive Apprenticeship Metamodel Based Intelligent Learning Activity Builder System

This research focused on a formal (theory based) approach to designing Intelligent Tutoring System (ITS) authoring tool involving two specific conventional pedagogical theories—Conversation Theory (CT) and Cognitive Apprenticeship (CA). The research conceptualised an Augmented Conversation and Cognitive Apprenticeship Metamodel (ACCAM) based on apriori theoretical knowledge and assumptions of its underlying theories. ACCAM was implemented in an Intelligent Learning Activity Builder System (ILABS)—an ITS authoring tool. ACCAM’s implementation aims to facilitate formally designed tutoring systems, hence, ILABS―the practical implementation of ACCAM― constructs metamodels for Intelligent Learning Activity Tools (ILATs) in a numerical problem-solving context (focusing on the construction of procedural knowledge in applied numerical disciplines). Also, an Intelligent Learning Activity Management System (ILAMS), although not the focus of this research, was developed as a launchpad for ILATs constructed and to administer learning activities. Hence, ACCAM and ILABS constitute the conceptual and practical contributions that respectively flow from this research. ACCAM’s implementation was tested through the evaluation of ILABS and ILATs within an applied numerical domain―the accounting domain. The evaluation focused on the key constructs of ACCAM―cognitive visibility and conversation, implemented through a tutoring strategy employing Process Monitoring (PM). PM augments conversation within a cognitive apprenticeship framework; it aims to improve the visibility of the cognitive process of a learner and infers intelligence in tutoring systems. PM was implemented via an interface that attempts to bring learner’s thought process to the surface. This approach contrasted with previous studies that adopted standard Artificial Intelligence (AI) based inference techniques. The interface-based PM extends the existing CT and CA work. The strategy (i.e. interface-based PM) makes available a new tutoring approach that aimed fine-grain (or step-wise) feedbacks, unlike the goal-oriented feedbacks of model-tracing. The impact of PM—as a preventive strategy (or intervention) and to aid diagnosis of learners’ cognitive process—was investigated in relation to other constructs from the literature (such as detection of misconception, feedback generation and perceived learning effectiveness). Thus, the conceptualisation and implementation of PM via an interface also contributes to knowledge and practice. The evaluation of the ACCAM-based design approach and investigation of the above mentioned constructs were undertaken through users’ reaction/perception to ILABS and ILAT. This involved, principally, quantitative approach. However, a qualitative approach was also utilised to gain deeper insight. Findings from the evaluation supports the formal (theory based) design approach—the design of ILABS through interaction with ACCAM. Empirical data revealed the presence of conversation and cognitive visibility constructs in ILATs, which were determined through its behaviour during the learning process. This research identified some other theoretical elements (e.g. motivation, reflection, remediation, evaluation, etc.) that possibly play out in a learning process. This clarifies key conceptual variables that should be considered when constructing tutoring systems for applied numerical disciplines (e.g. accounting, engineering). Also, the research revealed that PM enhances the detection of a learner’s misconception and feedback generation. Nevertheless, qualitative data revealed that frequent feedbacks due to the implementation of PM could be obstructive to thought process at advance stage of learning. Thus, PM implementations should also include delayed diagnosis, especially for advance learners who prefer to have it on request. Despite that, current implementation allows users to turn PM off, thereby using alternative learning route. Overall, the research revealed that the implementation of interface-based PM (i.e. conversation and cognitive visibility) improved the visibility of learner’s cognitive process, and this in turn enhanced learning—as perceived