Sarah Jane Grigg

A Coding Scheme for Analysing Problem-Solving Processes of First-Year Engineering Students.

This study describes the development and structure of a coding scheme for analysing solutions to well-structured problems in terms of cognitive processes and problem-solving deficiencies for first-year engineering students. A task analysis approach was used to assess students’ problem solutions using the hierarchical structure from a theoretical framework from mathematics research. The coding scheme comprises 54 codes within the categories of knowledge access, knowledge generation, self-management, conceptual errors, mechanical errors, management errors, approach strategies and solution accuracy, and was demonstrated to be both dependable and credible for analysing problems typical of topics in first-year engineering courses. The problem-solving processes were evaluated in terms of time, process elements, errors committed and self-corrected errors. Therefore, problem-solving performance can be analysed in terms of both accuracy and efficiency of processing, pinpointing areas meriting further study from a cognitive perspective, and for documenting processes for research purposes.

How does academic preparation influence how engineering students solve problems

In first year engineering courses, students possess a wide range of academic preparation such as their exposure to various mathematics courses and pre-engineering programs. Additionally, students bring perceptions about their abilities, and have already begun practicing preferred methods of analysis and documentation. Understanding how students with different backgrounds develop problem solving skills in first year engineering programs is of critical importance in order to close achievement gaps between diverse populations. This study examines how students solve engineering problems and identifies variations based on student factors of gender, ethnicity, mathematics preparation and achievement, and prior engineering experience. Solutions for three problems from 27 students were analyzed (n = 68 solutions). Students worked out problems using custom-designed software that digitally records ink strokes and allows researchers to associate codes to the problem solution at any point, even in portions of the problem solution that would not have been available without the use of this technology, such as in erased work. Differences in how students solve problems were assessed based on the prevalence (or absence) of elements and errors in the problem solving process, which were evaluated using task analysis. Results indicated that pre-engineering experience did not have a significant impact on successfully solving problems; however, having completed a calculus course was significantly related to successful problem solving. Future research will expand the study population to a larger sample of first year engineering students across multiple semesters, identifying key strategies that are absent for students with low mathematics preparation, and to investigate relationships between prior academic preparation and indicators of metacognition.