Active/Collaborative Learning Student Teams Integrating Technology Effectively Women and Minorities Assessment and Evaluation EC2000 Emerging Technology Foundation Coalition Curricula Concept Inventories
Outcome b an ability to design and conduct experiments, as well as to analyze and interpret data

Introduction and Invitation

Constructing resources for assessment and instruction related to the eleven student outcomes contained in Criterion 3 of the ABET Engineering Criteria requires contributions across the entire engineering community. If you have one or more resources (for example, helpful papers, survey forms, assessment materials, instructional materials) for assessment and/or instructional related to outcome b click here. Please indicate whether and how you would like your contribution to be acknowledged. Thanks for contributing the growing understanding of how we might help engineering students develop knowledge and skills that they will draw upon throughout their careers.

Learning Objectives

The first step in selecting assessment and instructional approaches for a learning outcome is to formulate learning objectives that support the outcome. Learning objectives describe expectations associated with the outcome in terms of expected and observable performances. Several researchers have already constructed learning objectives and these may provide worthwhile starting points for others.

A team of researchers (Larry Shuman, Mary E. Besterfield-Sacre, Harvey Wolfe, Cynthia J. Atman, Jack McGourty, Ronald L. Miller, Barbara M. Olds, and Gloria M. Rogers) working a NSF-supported project, Engineering Education: Assessment Methodologies and Curricula Innovation, used Bloom's Taxonomy to develop and organize a set of learning objectives for outcome 3b (desiging and conducting experiments and analyzing and interpreting data) [1]. They developed learning objectives for the six levels of learning in Bloom's taxonomy for four different outcome elements:

  • Designing experiments
  • Conducting experiments
  • Analyzing data
  • Interpreting data

Felder and Brent [2] offer sample learning objectives for each of the four preceding outcome elements:

  • Designing Experiments: Design an experiment to (insert one or more goals or functions) and report the results (insert specifications regarding the required scope and structure of the report). Variants of this objective could be used in traditional lecture courses as well as laboratory courses.
  • Conducting Experiments: Conduct (or simulate) an experiment to (insert specifications about the goals of the experiment) and report the results (insert specifications regarding the scope and structure of the report).
  • Analyzing Data: Develop a mathematical model or computer simulation to correlate or interpret experimental results (insert specifications regarding the experiment and the data). The results may be real data from a laboratory experiment or simulated data given to students in a lecture course.
  • Interpreting Data: List and discuss several possible reasons for deviations between predicted and measured results from an experiment, choose the most likely reason and justify the choice, and formulate a method to validate the explanation.

McCreanor [3] described how faculty members in the School of Engineering at Mercer University constructed profeciencies for the same four outcome elements:

  • Designing experiments: Develop a methodology which will produce high quality data that can be used to evaluate a specific process or parameter.
  • Conducting experiments:
    • Operate laboratory equipment appropriate to the course or discipline
    • Demonstrate appropriate laboratory technique and etiquette, collect data
  • Analyzing data:
    • Organize data
    • Perform appropriate data manipulations and calculations
    • Ppresent final data in an appropriate format
  • Interpreting data:
    • Use good engineering judgement to determine if data is reasonable
    • Draw appropriate conclusions from data,

Assessment Approaches

Under construction (18 January 2005)

Instructional Approaches

Under construction (18 January 2005)

References for Further Information

  1. Learning Outcomes/Attributes, ABET bDesigning and conducting experiements, analyzing and interpreting data, accessed 18 January 2005
  2. Felder, R.M., and Brent, R. (2003). Designing and Teaching Courses to Satisfy the ABET Engineering Criteria. Journal of Engineering Education, 92:1, 7-25.

    Abstract: Since the new ABET accreditation system was first introduced to American engineering education in the middle 1990s as Engineering Criteria 2000, most discussion in the literature has focused on how to assess Outcomes 3a3k and relatively little has concerned how to equip students with the skills and attitudes specified in those outcomes. This paper seeks to fill this gap. Its goals are to (1) overview the accreditation process and clarify the confusing array of terms associated with it (objectives, outcomes, outcome indicators, etc.); (2) provide guidance on the formulation of course learning objectives and assessment methods that address Outcomes 3a3k; (3) identify and describe instructional techniques that should effectively prepare students to achieve those outcomes by the time they graduate; and (4) propose a strategy for integrating programlevel and course-level activities when designing an instructional program to meet the requirements of the ABET engineering criteria.

  3. McCreanor, P.T. (2001). Quantitatively Assessing an Outcome on Designing and Conducting Experiments and Analyzing Data for ABET 2000. Proceedings, Frontiers in Education Conference

    Abstract: The Mercer University School of Engineering (MUSE) identified eight outcomes to assess for the accreditation process. MUSE Outcome #4 stipulates that students should be able to design and conduct experiments and analyze data.

    The committee charged with assessment of Outcome #4 identified four separate skills associated with this outcome; conducting experiments, analyzing experimental data, interpreting experimental data, and designing experiments. The committee determined that assessment of this outcome required documentation of the number of student experiences with each of the four skills and the overall student performance level on each of these skills. A skill assessment worksheet was developed for use in the grading of any activity related to Outcome #4. The worksheet quickly identifies which of the four skills the activity incorporates as well as the performance of the students on each of the individual skills. This worksheet was distributed to instructors teaching courses that contain a significant content related to this outcome.

    Data collected from courses in Industrial Engineering, Biomedical Engineering, and Electrical Engineering taught during the Fall of Semester of 2000 suggests that MUSE has been successful at meeting Outcome #4. The data also indicates that the skill assessment worksheet was an efficient and accurate method for collecting quantitative data and identifying weakness in the assessment process. Modifications made to the worksheet by professors to accommodate their personal grading scheme demonstrates that the tool has enough flexibility to be used across multiple disciplines and grading styles while still providing the data required for assessment of Outcome #4.

    This paper presents the skill assessment worksheet, data collected using the worksheet, and instructor comments on use of the worksheet.


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