A comparison and classification of grading approaches used in engineering education

Grades are intended to communicate achievement associated with a learning experience. Engineering educators in higher education often default to a particular grading approach without considering how the approach impacts student achievement. This work proposes a model for comparing and classifying commonly used grading systems in engineering higher education. Examples from the engineering education literature revealed five general categories of grading: 1) normative, score-based grading, 2) summative grading, 3) standards-based grading, 4) mastery-based grading, and 5) adaptive grading. (Note: variations in naming conventions were observed.) Each grading system was examined to determine key characteristics of the system and how student performance was ultimately assessed. A continuum of grading approaches was created after discovering that each system ranged in its intention to select and/or develop talent. The most widely adopted approaches to grading in engineering higher education, norm-based grading, were classified using purely selective processes (e.g., letter grades). Alternative, learning outcomes-based grading approaches differentiate themselves by the level in which they attempt to develop talent. This was determined by examining differences in how the grading system impacted sequencing of content, course pace, number of attempts to demonstrate achievement, scale and weight of performance, feedback provided, and basis for a final grade. The resulting continuum provides a tool for engineering educators to compare and discuss grading approaches in order to select an appropriate system for their course or program. Informed decisions on grading can have a critical impact in student retention and program improvement

Challenges to informed peer review matching algorithms

BACKGROUND Peer review is a beneficial pedagogical tool. Despite the abundance of data instructors often have about their students, most peer review matching is by simple random assignment. In fall 2008, a study was conducted to investigate the impact of an informed algorithmic assignment method, called Un-weighted Overall Need (UON), in a course involving Model-Eliciting Activities (MEAs). The algorithm showed no statistically significant impact on the MEA Final Response scores. A study was then conducted to examine the assumptions underlying the algorithm. PURPOSE (HYPOTHESIS) This research addressed the question: To what extent do the assumptions used in making informed peer review matches (using the Un-weighted Overall Need algorithim) for the peer review of solutions to Model-Eliciting Activities decay? DESIGN/METHOD An expert rater evaluated the solutions of 147 teams' responses to a particular implementation of MEAs in a first-year engineering course at a large mid-west research university. The evaluation was then used to analyze the UON algorithm's assumptions when compared to a randomly assigned control group. RESULTS Weak correlation was found in the five UON algorithm's assumptions: students complete assigned work, teaching assistants can grade MEAs accurately, accurate feedback in peer review is perceived by the reviewed team as being more helpful than inaccurate feedback, teaching assistant scores on the first draft of an MEA can be used to accurately predict where teams will need assistance on their second draft, and the error a peer review has in evaluating a sample MEA solution is an accurate indicator of the error they will have while subsequently evaluating a real team's MEA solution. CONCLUSIONS Conducting informed peer review matching requires significant alignment between evaluators and experts to minimize deviations from the algorithm's designed purpose

Engineering education and the development of expertise

BACKGROUND: Although engineering education has evolved in ways that improve the readiness of graduates to meet the challenges of the twenty-first century, national and international organizations continue to call for change. Future changes in engineering education should be guided by research on expertise and the learning processes that support its development. PURPOSE: The goals of this paper are: to relate key findings from studies of the development of expertise to engineering education, to summarize instructional practices that are consistent with these findings, to provide examples of learning experiences that are consistent with these instructional practices, and finally, to identify challenges to implementing such learning experiences in engineering programs. SCOPE/METHOD: The research synthesized for this article includes that on the development of expertise, students' approaches to learning, students' responses to instructional practices, and the role of motivation in learning. In addition, literature on the dominant teaching and learning practices in engineering education is used to frame some of the challenges to implementing alternative approaches to learning. CONCLUSION: Current understanding of expertise, and the learning processes that develop it, indicates that engineering education should encompass a set of learning experiences that allow students to construct deep conceptual knowledge, to develop the ability to apply key technical and professional skills fluently, and to engage in a number of authentic engineering projects. Engineering curricula and teaching methods are often not well aligned with these goals. Curriculum-level instructional design processes should be used to design and implement changes that will improve alignment. © 2011 ASEE