• Continuous Improvement in Teaching Strategies through Lean Principles

      Ely, Susan
      Faculty attempt to accommodate numerous learning styles to aid individual students in their comprehension and retention of course content, however, formal feedback from students is rarely gathered in a timely fashion. Most formal student course evaluations are conducted at the end of the semester, with survey results available to faculty after the semester has ended. While feedback from these surveys can be integrated into future offerings of the course, this type of survey prohibits adjustments during the semester, which could enhance learner outcomes. Lean manufacturing principles are used in a wide variety of professional sectors to create opportunities for continuous improvement by embedding systems for regular feedback and executing improvements. The Plan-Do-Check-Act (PDCA) cycle is a lean manufacturing technique providing a framework for continuous feedback and analysis of a system paired with a mechanism for implementing changes and monitoring their success. The cyclical nature of the system accommodates reflection of the changes and the potential adjustments. This system aligns well with reflective teaching strategies, as it integrates ongoing student feedback, analysis of the data, reflection of classroom practices based on student perceptions, and timely adjustments to course delivery techniques to aid students in their learning. To test the effectiveness of this lean technique in a classroom, the researcher followed the PDCA cycle using GoogleForms and Blackboard as a method for collecting feedback from the students, multiple times throughout the semester. For this experiment, the course was divided into three modules, aligned with the administration of the three non-cumulative exams of the semester. At the completion of each module, a GoogleForms was made available to students via Blackboard. Participation was optional and anonymous, and the directions stated the purpose of the survey and the use of the data. The GoogleForms included questions using a Likert-format response to indicate their perceived effectiveness of teaching strategies and key learning objectives of the course, as well as an open response for any additional feedback. Once data was gathered, the researcher reviewed the data and reflected, with particular focus on how the course format and delivery could be adjusted to better meet student needs. Based on the results from Fall 2019, the PDCA cycle proved to be an effective tool for gathering meaningful feedback from students during the semester, while allowing for adjustments to be made in a way that increased students perceived effectiveness of teaching methodologies. Over the semester, the researcher made systematic changes to content delivery based on feedback received from the GoogleForms. The data from the surveys showed a statistically significant increase of student perceived effectiveness in teaching strategies, as well as an increase in perceived knowledge for key content areas. As such, the PDCA cycle was a valuable framework for facilitating continuous feedback, improvement and a measurable increase in student learning. The researcher also noted that students voiced their appreciation of the instructor being willing to make mid-semester adjustments to content delivery, based on their feedback. Students commented that the changes made significantly improved their understanding and retention of course materials. At this time, the researcher plans to expand the use of the PDCA cycle for continuous improvement in other courses and continue to evaluate the effectiveness of this tool in quantifying learner preferences and learning outcomes throughout the semester. References: Balzer, W., Francis, D.E., Krehbiel, T., & Shea, N. (2016). A review and perspective on Lean in higher education. Quality Assurance in Education, 24 (4), 442-462. http://dx.doi.org/10.1108/QAE-03-2015-0011 Doman, M. (2011). A new lean paradigm in higher education: a case study. Quality Assurance in Education, 19 (3), 248-262. http://dx.doi.org/10.1108/096848811111158054 Lu, J. & Laux, C. (2017). Lean Six Sigma leadership in higher education institutions. International Journal of Productivity and Performance Management, 66 (5), 638-650. http://dx.doi.org/10.1108/IJPPM-09-2016-0195 Stupnisky, R., Hall, N.C., Daniels, L.M., & Mensah, E. (2017). Testing a model of pretenure faculty members teaching and research success: Motivation as a mediator of balance, expectations and collegiality. The Journal of Higher Education, 88 (3), 376-400. http://dx.doi.org/10.1108/00221546.2016.1272317
    • What’s the problem? Perceptions in Problem Solving Methodologies

      Ely, Susan
      Engineering students in every discipline complete numerous courses that require complex problem- solving methodologies based on quantitative reasoning. While engineering courses have various areas of focus, the nature of problem-solving is often found to be similar in approach. Many faculty find that errors in student work are due to fundamental flaws in the problem-solving approach, rather than mistakes in computation. Engineering faculty express frustration when grading papers that incorrectly interpret the problem statement or fail to follow the problem-solving approach demonstrated frequently in class. Students are also frustrated by their lack of success and inability to independently solve problems that seemed clear during lecture. In a review of engineering coursework at the University of Southern Indiana, it was found that many of the courses that require extensive quantitative analysis also had a low completion rate: that is, many students who took the course withdrew prior to completion or had to take the course again due to receiving a failing grade. Faculty from a wide variety of engineering programs across the nation have voiced similar frustrations – students come to class with a preconceived notion of the course being difficult but fail to follow the established problem-solving methods provided during instruction. From a faculty perspective, if students followed the methodology presented, the course would not feel so difficult. This difference in perception between faculty and students, as well as the pre-conceived notion of difficulty seems to hinder student success. This research aims to look more closely at the differences in perceived difficulty of the problem-solving process to aid in student persistence and success in quantitative based engineering coursework. Therefore, this research aims to answer the following questions: do students and faculty rate the perceived difficulty and importance of problem-solving steps and does the students problem-solving process (order, perceived difficulty and importance) correlate to overall course success. Research has found that self-efficacy for self-regulated learning has a critical relationship to persistence within a class, a program of study and completion of a degree [1]. That is to say, a student’s ability to take control of their learning and view their learning potential with confidence will positively impact their academic success for both specific courses and academic progress as a whole [1]. Self-efficacy has been shown to directly impact anxiety within STEM coursework, decreasing student success and persistence in courses with a high perceived level of difficulty [2]. This perceived difficulty correlation to poor academic performance supports other work on expectancy-value theory, where academic achievement is limited by students’ perceived ability for success. In fact, some studies have shown perceived difficulty as a predictor of overall course performance [2, 3]. While studies have found students rate their faculty as effective, despite perceived levels of difficulty in the coursework, it is noted that perceived difficulty in coursework can relate to workload, number of exams given or the actual course material, making it difficult to determine what specific course feature or subject was experienced as “difficult” from the student’s perspective [3]. This variability in perceived difficulty can confound faculty who are trying to aid students in learning or make adjustments to their curriculum. However, a student’s fear of academic failure can cause them to procrastinate in assignments, not attend class or otherwise hinder their academic preparation, resulting in the poor performance and poor persistence [4]. This creates a difficulty in the classroom for both teachers and learners: course materials can help to support student learning, but student perceptions about course content can negatively influence their learning outcomes. Therefore, course developers and faculty need to understand student perceptions of the course material to better promote positive learning [5]. To further explore this issue, research was conducted to understand differences in approaches to problem-solving, as well as perceived importance of each step within the process. Furthermore, students were surveyed to understand the relationship between the problem-solving process steps, and the differences in perceived importance and difficulty for each process step. To accomplish this, students completed a survey in which they created a process map of problem-solving steps and rated the perceived difficulty and importance of each step using a Likert-scale. Faculty completed this same survey with their own process maps and Likert ratings. Comparison between student and faculty process steps, as well as Likert response data, were analyzed. Finally, correlations were made between the students’ final course grade and their problem-solving approach. When analyzing the data, it became apparent that students did not uniformly begin with examining the problem. This brings into question both how problem-solving is taught in engineering and whether traditional teaching methodologies place undue emphasis on computation rather than qualitative analysis of a given situation. This presentation will review the results of the research as well as discuss potential classroom implications. References [1] M. Morelli, A. Chirumbolo, R. Baiocco, E. Cattelino, “Academic Failure” Individual, Organizational and Social Factors,” Educational Psychology, vol. 27, no. 2, pp. 167-175, 2021. [2] B. England, J. Brigati, E. Schussler and M. Chen, “Student anxiety and perception of difficulty impact performance and persistence in introductory biology courses,” CBE – Life Sciences Education, vol. 18, no. 21, pp. 1-13, Summer 2019. [3] A. Joyce, “Course difficulty and its association with student perceptions of teaching and learning,” Kentucky Journal of Excellence in College Teaching and Learning, vol. 14, pp.54-62, 2016. [4] T. Bledsoe and J. Baskin, “Recognizing student fear: The elephant in the classroom,” College Teaching, vol. 62, pp. 32-41, 2014. [5] J. Wall and J. Knapp, “Learning computer topics in undergraduate Information Systems courses: Managing perceived difficulty,” Journal of Information Systems Education, vol. 25, no. 3, pp. 245-259, Fall 2014.