An information-based approach to integrating empirical and explanation-based learning

We describe a new approach to integrating explanation-based and empirical learning methods for learning relational concepts. The approach uses an information-based heuristic to evaluate components of a hypothesis that are proposed either by explanation-based or empirical methods. Providing domain knowledge to the integrated system can decrease the amount of search required during learning and increase the accuracy of learned concepts, even when the domain knowledge is incorrect and incomplete and there is noise in the training data

In defense of compilation: A response to Davis' form and content in model-based reasoning

In a recent paper entitled 'Form and Content in Model Based Reasoning', Randy Davis argues that model based reasoning research aimed at compiling task specific rules from underlying device models is mislabeled, misguided, and diversionary. Some of Davis' claims are examined and his basic conclusions are challenged about the value of compilation research to the model based reasoning community. In particular, Davis' claim is refuted that model based reasoning is exempt from the efficiency benefits provided by knowledge compilation techniques. In addition, several misconceptions are clarified about the role of representational form in compilation. It is concluded that techniques have the potential to make a substantial contribution to solving tractability problems in model based reasoning

Varieties of operationality

In recent papers on machine learning, the term 'operationalization' has been used to describe the purpose of the learning process. In particular, explanation-based learning systems are said to 'operationalize' the given target concept. Unfortunately, the exact meaning of this term has varied from one paper to another, and frequently the term has been used without being precisely defined. In general, the term 'operational' has encompassed many different aspects of problem solving including correctness, efficiency, and effectiveness. Although one might argue that a precise definition of the term is neither possible nor desirable, this paper takes the opposite position. Without a precise definition of the goals of the learning process, it is difficult to evaluate particular learning systems. The following sections identify several different aspects of 'operational' and propose a collection of more specific terms, such as 'testable', 'achievable', and 'efficient', to clarify this all-too-vague term. These more precisely defined terms can then be applied to evaluate current learning systems and guide future research

Analytical learning and term-rewriting systems

Analytical learning is a set of machine learning techniques for revising the representation of a theory based on a small set of examples of that theory. When the representation of the theory is correct and complete but perhaps inefficient, an important objective of such analysis is to improve the computational efficiency of the representation. Several algorithms with this purpose have been suggested, most of which are closely tied to a first order logical language and are variants of goal regression, such as the familiar explanation based generalization (EBG) procedure. But because predicate calculus is a poor representation for some domains, these learning algorithms are extended to apply to other computational models. It is shown that the goal regression technique applies to a large family of programming languages, all based on a kind of term rewriting system. Included in this family are three language families of importance to artificial intelligence: logic programming, such as Prolog; lambda calculus, such as LISP; and combinatorial based languages, such as FP. A new analytical learning algorithm, AL-2, is exhibited that learns from success but is otherwise quite different from EBG. These results suggest that term rewriting systems are a good framework for analytical learning research in general, and that further research should be directed toward developing new techniques

A knowledge level analysis of learning programs

This chapter develops a taxonomy of learning methods using techniques based on Newell’s knowledge level. Two properties of each system are defined: knowl­edge level predictability and knowledge level learning. A system is predictable at the knowledge level if the principle of rationality can be applied to predict its behavior. A system learns at the knowledge level if its knowledge level de­scription changes over time. These two definitions can be used to generate the three-class taxonomy. The taxonomy formalizes the intuition that there are two kinds of learning systems: systems that simply improve their efficiency (symbol-level learning SLL) and systems that acquire new knowledge (knowledge-level learning; KLL). The implications of the taxonomy for learning research are explored. Automatic programming research can provide ideas for SLL. Devel­opment of methods for KLL must rely either on the development of a principle of plausible rationality or OIL the construction of learning methods that work well only for certain kinds of environments. Explanation-based generalzation and chunking methods address only SLL and do not provide solutions to the problems of KLL

A comparison of student perceptions of academic, social, and emotional self-efficacy in classrooms with divergent approaches to integrating instructional technology

As society advances in technology, it is important that our educational systems have a unified understanding of how technology should be used inside the classroom (Bitter & Pierson, 2001; Oppenheimer, 2003). However, literature is mixed on whether technology impacts the learner positively or negatively (Brusca, 1991; Cassil, 2005; Cuban & Cuban, 2009; Kulik, 2003; Li & Ma, 2010; Strong, Torgerson, Torgerson, & Hulme, 2011; Torgerson et al., 2004; Waxman, Connell, & Gray, 2002). A number of researchers state that technology in schools can have a positive impact on achievement (Brusca, 1991; Cuban & Cuban, 2009; Li & Ma, 2010) while other researchers concluded that the distractions provided by technology decrease achievement and the habits it instills are harming students’ development, both academically and socially (Cassil, 2005; Kulik, 2003; Strong et al., 2011; Torgerson et al., 2004; Waxman et al., 2002). Various findings on the impact of technology as it relates to learning achievement suggest that there is a variable beyond the technology itself that may affect student learning (Cassil, 2005; Kulik, 2003; Strong et al., 2011; Torgerson et al., 2004; Waxman et al., 2002). Despite a large amount of literature on the impact of technology on educational achievement, there is a lack of literature related to the impact of technological approaches on learner self-efficacy, a strong predictor of achievement (Bandura, Barbaranelli, Caprara, & Pastorelli, 2001). This study aimed to fill the gap by determining if a relationship exists between students’ academic, social, and emotional self-efficacy and their classroom’s approach to integrating technology. Classrooms involved in the study where separated based on their approach to integrating technology and assessments where administered to each student. The first assessment was a specialized measure of self-efficacy, developed by Peter Muris (2001). The second was a measurement of technological competence, developed by the researcher. The results of the study showed significant relationships between self-efficacy and several factors involved in integrating technology