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Aligning Student Learning, Faculty Development And Engineering Content: A Framework For Strategic Planning Of Engineering Instruction And Assessment
Conference proceeding

Aligning Student Learning, Faculty Development And Engineering Content: A Framework For Strategic Planning Of Engineering Instruction And Assessment

Arunkumar Pennathur and Louis Everett
2008 Annual Conference & Exposition, pp.13.166.1-13.166.11
ASEE Annual Conference & Exposition (Pittsburgh, Pennsylvania, 06/22/2008–06/25/2008)
06/22/2008
DOI: 10.18260/1-2--4101

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Abstract

This paper outlines an innovative framework for modeling and planning engineering education assessment interventions. The theoretical bases for the framework are primarily derived and integrated from research methods and findings in several different disciplines - human engineering, engineering education, human communication sciences and, mathematical modeling using statistical and neural network approaches. The framework consists of four key elements – the task of instruction, the players including students, faculty, and other stake-holders such as employers, the tools used in the learning enterprise including traditional and modern technology tools, and the environment for learning. Using the framework presented, variables associated with the task, the players, the tools, and the environment can be visualized and analyzed in 3-dimensional space using multidimensional scaling and neural network methods. One aspect of the framework, reflections from an engineering faculty member, is analyzed to demonstrate how strategic planning can be facilitated through assessment and analysis with the framework. 1. Model for strategic assessment planning Adapted from the Task, Operator, Machine, Environment (TOME) framework from the human factors engineering discipline1, the main elements of the proposed model for assessment of engineering education (figure 1) are: (1) the task of instruction: The purpose of the proposed model is to design the task of instruction for achieving the desired outcome of learning and development. All other model elements are intended to study and design the task of instruction. Therefore, the task element is a superset of all other model elements and is not represented in figure 1. At a more detailed level of modeling and analysis, task-related variables such as task sequencing (precedence- relationships among instructional tasks for example), task frequency (how often should an instructor use a certain tool for instruction), task duration (how long should an instructor teach a certain piece of instruction), task criticality (how critical is one task for success of the entire instructional piece), task discretion (e.g., what amount of discretion does the instructor have in using a certain instructional technique), and task content (what is the content of instruction), are some of the key task-related factors that need consideration. Of particular importance is task content, because the goal of formally designing the instructional task is to narrow the distance between the learners and the task content. Hence, content is explicitly included in our model. We consider all other task-related variables as part of a large strategy pool to optimize the distances between the content and the learners. (2) the players in the task: The main players with significant roles in the proposed model are the students, the faculty, and employers of students. Because student learning is primarily modeled, students are stakeholders; because faculty deliver instruction and facilitate student learning, they play a role in the model; and engineering employers influence the model by
 Education Engineering Engineering Education Strategic Planning Dimensional analysis Employers Human communication Human engineering Human factors Learning Multidimensional methods Neural networks Players Sequences Students Tools

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