Online Finite Element Tutorials as Active Learning Tools

Engaging in active learning promotes deep conceptual understanding and possibilities to cultivate valuable aptitudes for synthesizing, analyzing, and evaluating ideas and creating new ones. Over the last few decades, a plethora of research supporting active learning pedagogy indicates that this approach to learning needs to be incorporated in teaching. Multiple literacy practices, participating in discussions, hands-on activities, collaborative learning, and real world problem solving are among the many ways to facilitate active learning. The skills acquired during active learning tend to go above and beyond basic comprehension of information covered during a lecture. In fact, the goal of active learning is to not only develop student comprehension, but also to a) increase the learner’s investment, motivation, and performance, b) empower the learner to make real world connections, c) promote independent, critical, and creative thinking, and d) facilitate collaboration. One model for active learning takes the form of tutorials, or more accurately described as active learning modules (ALMs), aimed at improving student learning in historically difficult subject areas in engineering through the application of finite element analysis. The tutorial set developed here includes learning modules for various subject areas in Mechanical, Electrical, and Biomedical Engineering courses. The purpose of this study is to determine if ALMs of this type are effective active learning tools. In each participating course, after the student completes their traditional lecture series, they are introduced to a computer-based ALM. In order to perform a baseline study, students are administered content quizzes before and after the completion of the module. These quiz results are statistically analyzed to determine if subject aptitude, including comprehension, is improved. The incorporation of a novel assessment methodology reinforces the project goals as we are able to evaluate if these modules afford all students, regardless of learning style or personality type, with an equitable active learning process experience. The ALMs are shown to be a successful step towards improving aptitude and comprehension of challenging engineering content in an active learning environment. P ge 22121.3 2 Introduction In the quest to improve engineering education, the active learning methods must be designed, assessed, and implemented effectively. Even though active learning is frequently used in other disciplines, these pedagogical techniques have not yet been fully developed in engineering curriculum, especially within core courses [1-3]. For this current work, we consider active learning to be anything that goes beyond the traditional model of students passively listening to a lecture. Hands-on activities, problem based learning, interactive software and collaborative learning are all specific pedagogical techniques that are integrated into our learning module-based active learning repertoire in order to enhance students’ experiences in engineering education. Such active learning approaches have the potential to improve student comprehension and knowledge retention and, also, to increase students’ interest in the material [4]. The main goal in this current work is to present the design, development, and assessment of one type of active learning tool, i.e. finite element (FE) learning modules. These Active Learning Modules (ALMs) fit in the general category of problem based learning [5,6]. Effectiveness of these ALMs is assessed based on improvement in student performance in general coupled with equitability of learning enhancement across a variety of student demographic groups. Twelve ALMs were designed based on active learning pedagogical research and were then evaluated in various classroom settings. Traditional lectures in selected engineering courses in the Mechanical, Electrical and Biomedical Engineering fields of study were supplemented with these ALMs. Two overall project goals drive the details of the design, implementation and assessment of the ALMs. These overall project goals are: 1. Use the ALMs to provide a method to enhance students’ understanding of conceptually difficult engineering concepts, 2. Use the ALMs to provide a baseline exposure to the finite element (FE) method of engineering analysis. The following process is used to implement and assess the ALMs. Participating students are given a quiz to evaluate their baseline understanding of historically difficult engineering topics. This is labeled the “pre-quiz”. Then the FE-based ALMs are administered and the same quiz retaken. This is called the post-quiz. This procedure is used, from a holistic viewpoint, to assess if these ALMs are accomplishing the goal of improving student learning. P ge 22121.4 3 Noting that the quizzes are designed to evaluate students’ ability to accomplish specific learning objectives, the effectiveness of the ALMs is measured by the increase in post-quiz (taken after the ALM) scores over pre-quiz (taken before the ALM) scores. Additionally, improvements in quiz scores are correlated to learning styles, personality types and other demographic variables, followed by the application of basic statistical analysis. The end goal is to assess the effectiveness of the ALMs in two specific manners. First, the general effectiveness of the ALMs is measured by considering the overall improvement of the students’ post-quiz scores over their pre-quiz scores. Second, quiz improvements are categorized based on demographic variables and the improvement levels of different demographic groups are compared. This second assessment technique involves correlation studies between quiz performance and student demographic type which provides understanding of whether the enhancement from the ALMs is equitable across these different demographic groups. These two assessment procedures lead to two project assessment objectives: 1. Determine the overall effectiveness of the ALMs. This is primarily based on the delta between the pre-quiz and the post-quiz. 2. Determine the effectiveness of the ALMs across different demographic groups. This is primarily based on the quiz deltas of the different demographic groups. This paper presents the overall results of the implementation and assessment of twelve FE modules. To provide context for this work, active learning approaches are reviewed in a following section. The literature review reports an overview of the research to date in active learning and provides some details on state-of-the-art active learning aspects that are particularly applicable to our work. Our FE analysis learning modules are seen in this context to be viable active learning tool. After the active learning literature review, the focus of this work will shift to our assessment approach. The innovative assessment/demographic type correlation method is discussed below and in even greater detail in the precursor to this work [7, 8]. Our assessment focus, in this current work, will delve into the specific demographic correlation results as well as the global results of the ALMs as a whole. After the results portion, possible improvements to the learning modules are discussed, focusing on how the ALMs may be iteratively refined and implemented as improved active learning tools. Page 22121.5 4 Active Learning Approaches Once the ALMs have been created, their effectiveness in enhancing learning is assessed in a closed-loop fashion using the Felder-Soloman Index of Learning Styles (ILS) and the Myers Brigg Type Indicator (MBTI). Specifically, the students’ preand post-ALM quiz scores are grouped according to their ILS and MBTI data. If one student group is determined, from their preand post-ALM quiz scores, to benefit more from the ALM than a different group, the ALM can be modified accordingly. For example, if the MBTI-introverts are found to benefit more from the ALM than the MBTI-extroverts, then additional collaborative learning (which tends to energize the extroverts) could be added to the ALM. The two pedagogical background theories (ILS, MBTI) used to develop this work are not unique to this research, but combining their foundations to design and assess these ALMs is an original effort. Because ILS and MBTI are likely familiar to the reader, they are not described in detail here. However, details can be found at [p] for ILS and [10] for the MBTI. As a reference for the different types of data produced by the ILS and MBTI instruments, see the tables 1 and 2 below. Table 1. ILS Learning Style Categories