Use Of Educational Technology To Transform The 50 Minute Lecture:

Educational technologies like web-deployed assessments and student response systems provide opportunities for formative assessment that would be expected to enhance student learning and help create a more active classroom environment. These technologies can be used in ways that might help or hinder particular types of learners, yet not much research has been done in this area. This paper describes student response to Blackboard TM -delivered “preparation assessments” and use of the Classroom Performance System TM in two offerings of a large-enrollment introductory materials science course. The Myers Briggs Type Indicator (MBTI) was used as a measure of learning style, and preand post-course questionnaires probed students’ reactions. Initial findings indicate that Judging and Perceiving students respond differently to both technologies, and that students with Extroversion preferences tend to react in particular ways to use of CPS. Effects of gender, however, are as or more pervasive than effects of type, and gender and type interact in complex ways. Introduction and Background Recent syntheses of the science of learning and assessment of learning have argued for the key role that formative assessment plays in enhancing student learning. 1,2 In this context, formative assessment refers to providing feedback to students for learning, versus a summative assessment of learning. Increasingly, educational technologies are making it feasible to provide more formative assessment to students in a relatively efficient manner, even in large enrollment courses. This paper describes ongoing experiments in the use of educational technologies in an introductory materials science course. Students are asked to prepare for class by reading the textbook and/or lecture notes and then taking a daily “preparation assessment” via Blackboard TM . The questions in these assessments are designed to reveal student misconceptions at a formative stage in the learning process. Fifty-minute class periods are then planned “just in time” 3,4 to bring these misconceptions to the forefront. Short “mini-lectures” are interspersed with frequent use of the Classroom Performance System, 5 a feedback/voting technology or “student response system” that enables instructors to pose questions and problems to students and provide them with immediate feedback on their understanding. This type of active/interactive classroom experience, along with the expectation to start the learning process on their own via preparation assessments, is novel to most science and engineering students. The dominant model on our campus and many others is still the 50-minute lecture with an expectation of listening and note-taking. While in general students respond well to a more active classroom, there is clearly a spectrum of reactions. This research addresses the question of whether there are patterns in student response according to learning style. P ge 10385.1 Proceedings of the 2005 American Society for Engineering Education Annual Conference & Exposition Copyright 2005, American Society for Engineering Education Many learning style models have been used successfully to predict or explain differences in student response to subject matter and to teaching and learning environments. 6,7 One of the more commonly used instruments with an extensive research base is the Myers-Briggs Type Indicator (MBTI), which is based on Jungian theory of psychological type. Only a brief summary of type theory will be given here; interested readers are referred to several articles and books for additional details and explanation. 8-11 According to Jungian theory, while individuals can typically operate in multiple environments and call upon a variety of skills, each of us has intrinsic preferences or tendencies— ways in which we feel most comfortable when we seek information and make decisions. Furthermore, rather than being random, there are patterns or classifications that are useful in describing the vast range of human behavior. The MBTI groups these patterns into four dimensions, with two possibilities in each dimension: • Extraversion (E): More interest in the outer world of actions, objects, and persons Introversion (I): More interest in the inner world of concepts and ideas • Sensing (S): More readily perceives immediate, real, practical facts of experience Intuition (N): More readily perceives possibilities, relationships, meanings of experience • Thinking (T): Prefers to make decisions objectively and impersonally Feeling (F): Prefers to make decisions subjectively and personally • Judging (J): Prefers to live in a decisive, planned way Perceiving (P): Prefers to live in a spontaneous, flexible way. 8 Over the past several decades, many studies have shown that some MBTI types tend to struggle in or drop out of engineering programs more than others. These findings are most often explained by mismatches between traditional teaching styles and the learning preferences of many of our students. In general, traditional engineering education is biased towards Introversion (I) over Extraversion (E), Intuition (N) over Sensing (S), Thinking (T) more than Feeling (F), and Judgment (J) over Perception (P). 7,9,12 The majority of engineering faculty tend to be Intuitors, focusing on theory, concepts and principles, while more students tend to be Sensors, perceiving information more readily from practical experience and observation of concrete events. TJ types—methodical, logical, organized— are likely to be attracted and retained well in engineering education, while we are more likely to lose those with F and P preferences-those who tend to weigh human, subjective factors first and those who prefer to be flexible and spontaneous. 7,9,12 Rather than being overwhelmed by the notion of providing an ideal learning environment for all 16 possible types, teachers have been advised to use a balanced approach and a breadth of strategies that appeal to each preference at least some of the time. 6 Furthermore, type-conscious instructors recognize that students are also well served by developing modes of learning that come less naturally to them. Often, achieving this balance and breadth can be easier said than done, even with a knowledge of one’s own type and openness to a variety of teaching strategies. Indeed, some of my previous research has shown some type-dependence in student performance in one of my introductory materials course offerings. 13 In an effort to achieve and maintain balance in my teaching, I wanted to know whether particular types of students tend to be P ge 10385.2 Proceedings of the 2005 American Society for Engineering Education Annual Conference & Exposition Copyright 2005, American Society for Engineering Education enthusiastic or unenthusiastic about, helped by or overwhelmed by, preparation assessments and the use of CPS technology. Course Description: Use of Blackboard Assessments and Student Response Technology This paper focuses on two large-enrollment offerings of Introduction to Materials Science, which at Worcester Polytechnic Institute is taught in a seven-week format with four 50-minute lectures and one conference section meeting each week. Students taking this course are quite diverse, from sophomores to seniors and from a variety of engineering and science majors, with some taking the course as a requirement and some as an elective. In this section I will describe how educational technologies were used in each offering of the course, and my expectations as to how students with different learning styles might respond to them. In the Spring 2004 offering, I used Blackboard-deployed “Reading Quizzes” for the first time, in an attempt to promote preparation for class so that class periods could be used primarily for additional assessment and feedback rather than information delivery. Because of the large enrollment (113) and only one TA, a multiple-choice format was used, which could be graded automatically by the system. Each reading quiz had five questions addressing key concepts from the assigned reading, and students had to complete the quiz on-line no later than several hours before class. In an attempt to decrease inappropriate student collaboration (i.e., copying), upon completion of the quiz students could see their score but not which answers were correct/incorrect. After the quiz deadline had passed, answers were made available on the Blackboard site. These reading quizzes contributed 10% to the overall course grade, but each student was given two “freebies” (i.e., two quizzes that could be missed without an effect on their grade.) I reviewed the quiz results prior to class and addressed common misconceptions. Note, however, that these reading quizzes were really summative in nature; students’ state of understanding from the reading was graded for its correctness. In the Fall 2004 offering of the course, there were slightly fewer students (95) and two TAs, so I transformed the quizzes into formative “Preparation Assessments” that were intended to be assessments for learning instead of assessments of learning. There were two open-ended questions on each assessment, and an optional “Muddiest Point” question. Following is an example of the assessment used for a class meeting on basic mechanical behavior: 1. Explain the difference between elastic and plastic deformation. What is the property that indicates a material’s resistance to elastic deformation, and what is the property that indicates a material’s resistance to plastic deformation? 2. Sometimes we just refer to the “strength” of a material, but it’s important to distinguish between different strength properties. What is the difference between yield strength and tensile strength? Try to think of and describe an application where you would be most concerned about the yield strength (in other words, not exceeding that stress level) and a situation where you’d actually want to be operating above the yield st