Application of Brain-based Learning Principles to Engineering Mechanics Education: Implementation and Preliminary Analysis of Connections Between Employed Strategies and Improved Student Engagement

In a recent study, an instructional model that converts principles from cognitive neuroscience learning principles into instructional protocols has been developed and successfully yielded statistically significant learning outcomes in the Fluid Mechanics course in an HBCU. Motivated by that success, we extended a similar intervention to the Dynamics course in the same department. In this work in progress paper, we report preliminary data from this intervention. The main strategies implemented in this intervention include the following: organization of the course into smaller-grain concepts and sub-concepts, which are concisely presented by short (limited to 2-6 minutes) content-rich lectures (diagrams and animations), active learning through in-class worksheets, and prompt feedback. The design of these instructional materials incorporated protocols derived from cognitive neuroscience, such as ‘connect to relevant old/prior knowledge’, ‘creating of neural networks’, and ‘repeated use of neurons’. Results from this new implementation in the Dynamics course indicate that students’ engagement and learning were significantly enhanced by this approach in a manner consistent with the Fluid Mechanics course. The data not only confirms the findings of our previous study but also suggests that the model’s effectiveness may be independent of the developer and implementer of the model, if the instructional protocols are followed. Additionally, this study shed some light on the relative contribution of each of the strategies implemented towards the measured positive impact. According to student opinions, it was found that the greatest positive impact can be attributed, by far, to carefully designed in-class activities, followed by the quality of the lecture content. Introduction and Background A great deal of research has shown that engineering students who are more engaged in their class activities are more likely to succeed academically and professionally than those who are disengaged or distracted in class. There is ample evidence that the academic achievement of today’s students falls below desired levels and that the lack of academic engagement is a major contributor [1, 2]. Devising effective solutions to the lack of engagement can be challenging, due the multiplicity and complexity of the factors affecting it. Such factors include student preparation, socioeconomic background and teaching style effectiveness [3-5]. In this study, we extend our previous work that proposes a solution to this problem by specifically addressing two significant contributors to disengagement: the inadequate preparation of students for their courses and the traditional teaching style. Although our approach is conceived at and for an HBCU school students, it originates in cognitive neuroscience and the learning sciences and is applicable in any STEM field to any student population. Research on teaching and learning has long suggested that the traditional approach to teaching, which faculty still commonly practice across the nation, could be a major factor contributing to the lack of engagement, motivation, and learning of today’s students [6, 7]. The traditional approach is generally marked by the instructor giving lectures and demonstrating the solution of example problems to students who (in theory) listen and take notes while occasionally asking questions for clarification. For student learning through practice, the instructor assigns weekly homework problems from a prescribed textbook which are like the ones whose solutions were demonstrated in class. Typically, students turn in their homework, which is then graded and returned within a week. This approach is acknowledged by researchers to be ineffective and incapable of engaging students collectively or individually, in part because it promotes both inherent student passivity during class and feedback delay, and it is incompatible with students’ the learning styles, among others. All these aspects of traditional learning are at odds with findings of modern cognitive and behavioral learning research, which overwhelmingly agree that active learning is essential for any approach aiming at effective engagement and learning [8-11]. Researchers have developed different approaches to active learning including experiential learning [12], problem-based learning [13], case studies-based learning [14], and peer learning [15]. Another factor leading to disengagement of students in the upper level engineering courses is inadequate preparation. This issue is evident in deficiencies and/or misconceptions of prerequisite concepts as well as foundational weakness in mathematical skills commonly cited by educators as essential for complex problem-solving. These weaknesses play an important role in student disengagement and ineffective learning in the upper level courses. The foundational deficiency and resulting disengagement can be particularly at play and self-perpetuating for students coming from underserved communities. An NSF-funded study performed at the mechanical engineering department at an HBCU school confirmed that foundational deficiency exists at upper level classes and this impedes engagement and their achievement of desired learning outcomes [16,17]. Mandating pre-requisite course has not been successful at preventing ill-prepared students from making their way to the upper level engineering courses. The study concluded that students who reach junior and senior level classes require a novel approach that is more systematic, engaging and tailored to the specific needs of individual students. Using the principles of brain and learning sciences [18,19], Solomon et al. [16] proposed a novel instructional framework titled “Knowledge and Curriculum Integration Ecosystem” (KACIE) to improve student engagement and learning outcomes. The framework is based on a set of systematic cognitive neuroscience learning procedures (we call them protocols) to be followed during classroom interactions and in designing and delivering instructional materials. In this approach, the course is presented as a set of well-defined interconnected concise concepts and sub-concepts, which can be presented in about 5 minutes to leverage the typical focused attention span of a learner. Another important motivation for this concept-approach is the breakdown of complex topics into small manageable pieces, which in turn can be scaffolded to build larger understanding. For the effective teaching of these concepts, the instructor should follow several protocols to guide the classroom interaction as well as to design the lecture content. Examples of such protocols include: P1 Connect to old/prior information, P2 Create neural connections, P3 Active learning component and P4 Repeated use of neurons. More details of the nine protocols appear in [16]. The framework does not require that all the protocols must be used in any particular implementation. While it appears that the more protocols implemented the better, normally 4-6 protocols have proven sufficient for observable impact. The ultimate vision is for this framework is to extend beyond individual courses and to consider curriculum as a set of connected concepts and fundamental mathematical skills. Such a system may be useful for students to review and connect to concepts at higher levels more systematically and in a selfregulated manner. In a larger perspective, KACIE is designed to provide versatile framework for course structure, tools, and content, a framework into which fundamental principles from cognitive neuroscience learning can be implemented. These principles are the same as those that are the basis for other learning models, such as active learning, participatory teaching, and peer learning. In this work, we extend the KACIE framework to the fundamental mechanics course Dynamics within the same department. Dynamics is a core mechanics course in departments like mechanical engineering and aerospace engineering. It covers the fundamentals of particle and rigid body dynamics. Engineering students struggle with this course as it requires high level of analytical skills and strong foundation of basic physics concepts. Besides examining the effectiveness of the KACIE framework in a different course and setup, this work represents a step forward towards our goal of a systematic approach to connecting the whole curriculum by this framework. In spite of the different instructor (developer), implementation specifics, and course, the results from this first implementation of the KACIE framework in Dynamics indicate that student engagement and learning improved when compared to the control group taught using the traditional teaching, as described above. This provides additional evidence that teaching guided by cognitive neuroscience learning principles has the potential to improve student engagement and learning. It also shows that this framework is flexible and versatile. Finally, the questions of which and how many of the cognitive neuroscience learning principles to use to guarantee a positive outcome is a subject of further research. Objectives and Research Questions The goal of this research is to improve student engagement and learning by creating a systematic and flexible instructional framework which transcends individual courses to tie the whole curriculum as a set of interconnected concepts delivered through a hybrid face-to-face and virtual environment. The framework particularly targets the disengagement due to inadequate preparation (pre-requisite and math skills) and traditional teaching. Following the promising results of the pilot implementation in Fluid Mechanics, the objective of this work is to develop, implement, and test the effectiveness of KACIE framework in the course Dynamics. Due to the different conditions under which it is developed (still within the fundamental requirements of the framework), this implementation will consider a few aspects of KA