First Steps in Understanding Engineering Students' Growth of Conceptual and Procedural Knowledge in an Interactive Learning Context

Journal of Engineering Education, Jan 2007 by Taraban, Roman, Anderson, Edward E, DeFinis, Alli, Brown, Ashlee G, Et al

ABSTRACT

The development of procedural knowledge in students, i.e., the ability to effectively solve domain problems, is the goal of many instructional initiatives in engineering education. The present study examined learning in a rich learning environment in which students read text, listened to narrations, interacted with simulations, and solved problems using instructional software for thermodynamics. Twenty-three engineering and science majors who had not taken a thermodynamics course provided verbal protocol data as they used this software. The data were analyzed for cognitive processes. There were three major findings: (1) students expressed significantly more cognitive activity on computer screens requiring interaction compared to text-based screens; (2) there were striking individual differences in the extent to which students employed the materials; and (3) verbalizations revealed that students applied predominantly lower-level cognitive processes when engaging these materials, and they failed to connect the conceptual and procedural knowledge in ways that would lead to deeper understanding. The results provide a baseline for additional studies of more advanced students in order to gain insight into how students develop skill in engineering.

Keywords: cognitive processing, instructional software, skill development

I. INTRODUCTION

A. Cognitive Influence on Engineering Education Research

Some recent initiatives in engineering education research have adopted a cognitive framework for designing and implementing studies of student learning behaviors and outcomes [1]. This is an encouraging development because it allows engineering education reform to benefit from the basic research on cognition and learning that has been going on since the 1970s. According to cognitive theories, learning results in changes in mental representations and processes and depends critically on learners' prior knowledge and their ability to effectively synthesize and store what they gained from problem-solving episodes [2]. Indeed, curriculum reform efforts affirm the centrality of students' experiences and attempt to identify those experiences that maximize student gains within the available time for learning.

As one significant manifestation of the cognitive approach, there are several ongoing efforts to advance classroom practice and outcomes by identifying and understanding misconceptions held by engineering students regarding basic engineering concepts, like rate and energy [3]. One way this research is being pursued is through the development and implementation of concept inventories [4, 5]. In recent work, Streveler et al. [6] have expanded this effort and have proposed to identify misconceptions that occur across multiple curricular areas and to repair these misconceptions through remediation involving changes in mental schemas.

Misconceptions are part of a learner's prior knowledge-more technically, declarative knowledge-in a specific knowledge domain. In cognitive theories of skilled problem solving [2, 7], acquiring and using declarative knowledge is crucial for effective performance. However, problem solving is largely procedural, i.e., action-oriented knowledge, and draws on a distinct form of memory that stores procedural knowledge [2].

The development of procedural knowledge in students, i.e., the ability to effectively solve domain problems, is the goal of many instructional initiatives in engineering education. Gray et al. [8] outline five steps for solving equilibrium and kinetics problems that they assert should be followed "without exception." These steps are guided largely by the problem statement and indude (1) stating what needs to be found, (2) considering the problem assumptions, (3) finding the equations needed for solution, (4) computing the solution, and (5) verifying the solution. Litzenger et al. [7] also stress the structured nature of setting up, solving, and checking the equations involved in problem solving. Importantly, their Integrated Problem Solving Model explicitly combines declarative knowledge with procedural knowledge by treating the activation and application of prior knowledge as a definite step at each stage of problem solving. Other research, like the work of Zywno and Stewart [9], has incorporated additional cognitive factors. They investigated computer-based learning using measures of cognitive complexity based on Bloom's taxonomy [10], and learning styles based on the Felder-Solomon Index of Learning Styles [11].

B. Theory Behind the Practice

Structured approaches to problem solving, like the Integrated Problem Solving Model [7], are well-aligned with cognitive theories of the development of expertise at the postsecondary level and beyond [12, 13, 14, 15]. These theories assert that skill is the result of declarative knowledge-facts associated with the domain-being integrated and transformed into procedures in a continuous process of refinement over time, as the result of deliberate practice on the part of the learner [14]. It needs to be emphasized that from a cognitive theoretical perspective, one's skill is not fully characterized by problem solving performance in one's area of training, but also by how one's conceptual knowledge expresses itself in problem solving, and ultimately by one's grasp of the deep principles governing the domain [16, 17].