Developing Metacognitive Engineering Teams Through Targeted Writing Exercises And Studying Learning Preferences

Metacognition is the awareness and understanding by a student of his or her own learning own skills, performance, preferences, and barriers. This paper describes a pilot scale effort to develop metacognition in engineering teams at Rowan University through structured writing and the use of the Learning Connections Inventory (LCI). The theoretical basis for the LCI is the Interactive Learning Model, which proposes that learning processes occur through four distinct learning patterns: sequential, precise, technical, and confluent. The LCI was used to profile the learning style of each student in the Rowan Chemical Engineering department. During the Fall Semester of 2004, engineering teams in the Junior/Senior Engineering Clinics were broken into four categories. Category I teams received instruction in use of the LCI and met with a facilitator and their teammates to examine their LCI profiles. In this meeting, potential areas for future conflict were discussed and the teams developed strategies to avoid these conflicts. Category II teams received no LCI instruction but participated in a series of structured writing assignments designed to enhance their awareness of teaming. These assignments included developing and ratifying a team charter and submitting biweekly reports on barriers to success and team dynamics. Category III teams received both the LCI training and participated in the structured writing assignments, while Category IV teams served as a control and participated in none of the activities. At the beginning of the semester, each person was surveyed to determine their perception of their teaming skills, their opinion of teams, and their level of interest in learning about teaming. The participants were surveyed again at the end of the semester and were also asked to evaluate the usefulness of the strategies. In addition, final project reports were collected and evaluated using a system of rubrics in order to assess the impact of these activities on team performance. The data indicate that the students receiving LCI instruction (with or without the targeted writing exercises) both performed better, and had better attitudes towards teaming, than did the students receiving no LCI training. There was also some indication that the targeted writing exercises were beneficial but these results were less conclusive. Background and Pedagogical Theory Behavioral scientists classify thought processes into cognitive and affective domains 1 . The cognitive domain includes higher order thought processes such as logic and reasoning and is the primary (and in many cases, the only) target of engineering curricula. The affective domain includes attitudes, values, and self-concept. These attributes typically cannot be measured directly through exams and other classroom instruments, yet they are essential components of the overall developmental process. ABET itself recognizes the importance of the affective domain by including criteria in their assessment of engineering programs such as “engages in lifelong learning,” “understands the P ge 11442.2 Proceedings of the 2006 American Society for Engineering Education Annual Conference & Exposition Copyright  2006, American Society for Engineering Education impact that engineering has on society,” and “communicates effectively” 2 . Besterfield-Sacre et al. observed that students’ attitudes about engineering and their abilities change throughout their education and influence motivation, self-confidence, perception of engineering, performance, and retention 3 . The same group also found that attitudes toward engineering directly related to retention during the freshman year 4 . Seymour and Hewitt 5 examined students who left engineering programs and found that according to measures external to the engineering curriculum (high school GPA, SAT scores, IQ, etc.) they were not academically different from their peers who continued in the program. Retention did, however, correlate closely with student attitude. For many students, college challenges their level of motivation and academic aptitude for the first time, but too often provides them with little or no help in identifying and overcoming the barriers to their learning. The Study Group on the Conditions of Excellence in American Higher Education stated “there is now a good deal of research evidence to suggest that the more time and effort students invest in the learning process and the more intensely they engage in their own education, the greater will be their satisfaction with their educational experiences, their persistence in college, and the more likely they are to continue their learning” 6 . Thus, it is reasonable to conclude that an effective student must be both self-aware and self-directed, yet these issues are often ignored completely by engineering faculty. Student awareness and understanding of their learning skills, performance, preferences, and barriers is referred to as metacognition. Although different research groups emphasize different aspects of metacognition 7 , it clearly refers to two distinct, but related issues 8 : Awareness and knowledge of self as learner Conscious self-control and self-regulation of cognition In essence, a metacognitive learner must understand his or her strengths and weaknesses in learning and consciously control how he or she will approach a problem. Weinstein and Meyer 9 described the importance of students’ understanding their own learning preferences, abilities, and cognitive styles, and discussed how “learning how to learn” helps students develop knowledge of strategies required to achieve specific tasks. To provide this metacognitive awareness to our students, we used the Learning Connections Inventory (LCI), a survey instrument developed by Johnston and Dainton to profile an individual’s learning patterns 10 . The theoretical basis for the LCI is the Interactive Learning Model, which posits that learning processes occur through four distinct learning patterns: sequential, precise, technical, and confluent. The patterns are used by all learners to varying degrees; a given individual’s LCI profile is determined by the strengths of their preferences and avoidances, scored as “avoid,” “use as needed,” and “use first.” Some learners lead with one or two patterns, some avoid certain patterns, some are able to use a number of patterns on an as-needed basis, and still others exhibit strong preferences for a number of patterns. Each pattern is distinguished by a number of features. A few hallmarks are listed below: Sequential learners prefer order and consistency. They want step-by-step instructions, and time to plan, organize, and complete tasks. Page 11442.3 Proceedings of the 2006 American Society for Engineering Education Annual Conference & Exposition Copyright  2006, American Society for Engineering Education Precise learners thrive on detailed and accurate information. They take copious notes and seek specific answers. Technical learners like to work alone on hands-on projects. They enjoy figuring out how something works and insist on practical objectives for assignments. Confluent learners have a strong desire for creativity and innovation. They are not afraid of risks or failure and prefer unique, unconventional approaches. Depending on the interaction of an individual’s patterns, strong preferences associated with one pattern may coincide with strong avoidances of another pattern. For example, the sequential learner’s preference for order and consistency may be evidenced as a desire for predictability, and, therefore, as a corresponding avoidance of the risk and openness to chaos that is a characteristic of the confluent learner. In each case, knowledge of this profile provides extremely useful insights into the conditions that promote learning. The LCI is based on three assumptions about these conditions: 1) Learners learn most efficiently and successfully when allowed to use their stable-overtime patterns of cognition (intelligence, aptitude, experiences, levels of abstraction), conation (pace, autonomy, natural skills), and affectation (sense of self, values, and range of feelings) to engage in a learning task; 2) Learners learn best when given the opportunity to know their learning process, allowed to negotiate their learning environment, and provided the tools to strategize to meet the rigors of standardized and alternative methods of assessment and performance; 3) Learners receive the most effective instruction when their teachers have an appreciation for their diverse learning characteristics 10 . Other attempts to gain a better understanding of engineering students as learners have employed the concept of learning styles, using instruments such as the Myers-Briggs inventory 11,12 . The developers of the LCI explain the difference between their approach and that of learning styles in this way: Unlike learning styles, the Interactive Learning Model is an advanced learning system that provides an inward look at a learner’s internalized metalearning behaviors, an outward analysis of a learner’s actions, and a vocabulary for communicating the specific learning processes that yield externalized performance. Other measures of personality, multiple intelligences, or learning styles provide information about the learner and then leave the learner informed but unequipped to use the information. [The LCI] not only provides the learner with the means to articulate who s/he is as a learner, but then provides the strategies (metawareness) for the learner to use these learning tactics with intention. 13 The LCI survey is composed of 28 Likert scale items—descriptive statements followed by a fivepoint set of responses—and three questions requesting written responses. The 28 questions are scored according to the patterns they illustrate, and from these scores the LCI profile is generated. The three written responses are used to validate the preferences and avoidances exhibited by t