Teaching complicated conceptual knowledge with simulation videos in foundational electrical engineering courses

Journal of Technology and Science Education JOTSE, 2016 – 6(3): 148-165 – Online ISSN: 2013-6374 – Print ISSN: 2014-5349 http://dx.doi.org/10.3926/jotse.174 TEACHING COMPLICATED CONCEPTUAL KNOWLEDGE WITH SIMULATION VIDEOS IN FOUNDATIONAL ELECTRICAL ENGINEERING COURSES Baiyun Chen , Lei Wei , Huihui Li University of Central Florida (United States) [email protected], Lei.Wei@ucf .edu, [email protected] Received June 2015 Accepted June 2016 Abstract Building a solid foundation of conceptual knowledge is critical for students in electrical engineering. This mixed-method case study explores the use of simulation videos to illustrate complicated conceptual knowledge in foundational communications and signal processing courses. Students found these videos to be very useful for establishing concepts, understanding course content and increasing general knowledge in electrical engineering. We hope that the findings can help inform best practices for producing engaging and effective instructional videos for engineering courses; inspire instructors of electrical engineering to transform their teaching practice; and eventually benefit students by building a solid conceptual understanding that fosters the development of further engineering competencies. Keywords – Conceptual knowledge, Electrical engineering, Simulation, Telecommunication, Instructional video, Undergraduate education. ---------- -148- Journal of Technology and Science Education – http://dx.doi.org/10.3926/jotse.174 1. Introduction Engineers rely heavily on conceptual knowledge to understand the interrelationships of basic engineering concepts used during their professional practice. While working, they need to solve problems and make judgments quickly and efficiently without having to refer to their textbooks for complex models or physical prototypes. This mode of operation is called engineering judgment or heuristic thinking (Streveler, Litzinger, Miller & Steif, 2008). For example, electrical engineers need to quickly and accurately diagnose problems from digital transmission signals of an eye diagram (Lauterbach, 1997), a common indicator of the quality of signals in high-speed digital transmissions on an oscilloscope, just like medical doctors need to quickly figure out potential heart problems from an electrocardiogram (ECG). Therefore, building a solid foundation of conceptual knowledge is a critical element in an electrical engineering curriculum. The conceptual knowledge foundation consists of two major components: the fundamental physical principles and the analytical procedures (Kapli, 2010). Fundamental physical principles involve understanding how a system operates and why. Analytical procedures are the mathematical equations that are essentially symbolic representations of the physical principles. An incomplete conceptual understanding hinders the development of central engineering competencies such as problem-solving and decision-making. While conceptual knowledge is critical in the engineering curriculum, acquiring accurate understanding of these abstract and complicated concepts has been found to present substantial challenges to students. It is especially challenging in the field of electrical engineering since the concepts and procedures are complex and not directly observable. As learning scientists suggested, emergent processes that are not directly observable are the most difficult concepts to learn (Chi, 2005). For example, students can quickly learn how to convert x(t) to x(-t), or to x(2t)or to x(t-3), but they have great difficulty in converting x(t)to x(-2t-3). Students also experience difficulties in developing concepts about signal waveforms and understanding operations such as correlations and signal matching. Researchers and educators have been researching teaching methods to help students foster a better understanding of complex conceptual knowledge. The cognitive theory of multimedia learning (Mayer, 2002) has suggested that students could learn more deeply using multimedia explanation, rather than mere verbal or textual explanation. Instructional videos have the ability to convey meanings through both auditory and visual channels, creating a multisensory learning environment. In engineering research, Streveler et al. (2008) also suggest that well-designed -149- Journal of Technology and Science Education – http://dx.doi.org/10.3926/jotse.174 curricular materials coupled with computer simulation technologies could lead to substantial learning gains in conceptual knowledge acquisition. Therefore, simulation videos could be especially beneficial for students in engineering disciplines (Marques, Quintela, Restivo & Trigo, 2013). Yet much remains unknown about what makes engaging and effective simulation videos (Hibbert, 2014; Guo, Kim & Rubin, 2014). What characteristics of simulation videos influence students’ learning? What characteristics engage students in learning? These are important questions to consider as instructors and course designers strategize the best ways to allocate design and production resources. 2. The context of the study To investigate best instructional practices for using videos in engineering curriculum, we conducted a mixed-method case study to evaluate the use of short simulation videos in a foundational electrical engineering course. 2.1. Course description Analog and Digital Communication Fundamentals (EEL3552C) is an undergraduate-level course required for third-year students in the Bachelor of Science degree in electrical engineering at the University of Central Florida. It is a 4 credit hour class that runs over a 16-week semester in the spring, summer and fall semesters. The enrollment is approximately 65-75 students every semester. This class covers fundamental theories and design principles behind telecommunication systems. In telecommunications, machines talk in mathematical languages, i.e., signal wave forms. One of the key learning outcomes of this class is for students to understand signal waveform representations and operations. Since underlining signal waveforms are not readily observable and very different from human communications, students have had special challenges grasping these abstract concepts. The class has been delivered conventionally through face-to-face lectures and paper-based examinations. In class, the instructor traditionally drew various signal waveforms on paper or printed still images for demonstration. However, the pen-and-paper method only presents snapshots of complex operations but does not accurately reflect various transformations of signal waveforms. In addition, the demonstration happened only in a short period of time in face-to-face lectures. If students did not understand or were absent-minded at the moment, they -150- Journal of Technology and Science Education – http://dx.doi.org/10.3926/jotse.174 could miss these concepts completely and would struggle with further analytical procedures. As a result, more than half of the students in EEL3552C were struggling to interpret these waveforms in precise mathematical formula, and could not further develop skills to identify or solve related problems. Based on conversations with other instructors, the same issue happened in other electrical engineering classes and in other universities or institutions. In 2012, the instructor of EEL3552C participated in a professional development program for designing and teaching courses with online technologies (IDL6543). During the program, he had the opportunity to reflect on his teaching strategies and course delivery. As a result, he integrated Canvas, the learning management system (LMS) to supplement his face-to-face teaching. He posted the course materials in a modular format inside the LMS and created a series of simulation videos to reinforce difficult concepts. 2.2. Simulation video production Each video was produced using hundreds of simulation images generated by Matlab. Matlab is a basic but efficient mathematical tool for programming and simulation that is widely used in the engineering industry for research and development. Unlike other programming software, the programming language in Matlab is simple and the syntax is easy to remember. Equipped with thousands of internal functions, Matlab is convenient for mathematics calculation and figure plotting. After the instructor selected the topics and drafted the outlines of these videos, a teaching assistant defined the variables, input formulae and generated single image frames using Matlab. Figures 1 to 5 illustrate the production process of mixing three signals, in which the second signal changes in amplitude and the third one changes in frequency. We first defined the formula (Figure 1) for the first sinusoid signal as y1 = sin(2pt) (Figure 2). Here y1 is fixed and has no variable during the mixture. The second sinusoid signal is y2 = Asin(2pt) (Figure 3), where A represents amplitude, changing from -3V to 2V. The third sinusoid signal is y3 = sin(2pvt) (Figure 4), where v is radians per second changing from 0.5 rad/s to 2 rad/s. The summation of the three signals is y4 = y1 + y2 + y3 (Figure 5). -151- Journal of Technology and Science Education – http://dx.doi.org/10.3926/jotse.174 Figure 1. Define formula in Matlab Figure 2. First sinusoid signal as y1 = sin(2pt) Figure 3. Second sinusoid signal is y2 = Asin(2pt) -152- Journal of Technology and Science Education – http://dx.doi.org/10.3926/jotse.174 Figure 4. Third sinusoid signal is y3 = sin(2pvt) Figure 5. Summation of the three signals y4 = y1 + y2 + y3 The video producer then combined all still image frames with corresponding formulae and text explanation to produce an instructional simulation using a professional animation tool, Motion. Considering the complexity of the content, we produced the simulation videos with special attention to related educational design principles. •All videos are kept short, lasting between one to four minutes (Hibbert, 2014; Guo et al., 2014). Each video focuses on one concept and students view them in a sequential order, from the easiest to the more complicated. •The information is presented to students in visual and text formats as well as in narration (Hibbert, 2014). All videos are fully accessible and students could turn on the caption if needed. •The videos are directly embedded in pages in the LMS and students can easily view these videos on their computers and mobile devices. -153- Journal of Technology and Science Education – http://dx.doi.org/10.3926/jotse.174 •The videos were produced professionally through animation. In the final video, students can view the actual transformation of the signal waveforms in a continuous fashion and also simultaneously connect the waveform transformation to corresponding mathematical formulas. Six open-access videos were produced to illustrate signal waveform manipulation, understanding signal representation, mixing and decomposition. Students can gain insights on many topics; for example, how mathematics helps us to extract wanted signals from noise-corrupted received signals, and what causes fading in mobile communications. The following list includes the video links and brief descriptions of the topics: •Video one: https://vimeo.com/cdlvideo/review/129452739/d9f6048784 The first video illustrates the mixture of two signals, i.e., the sum of two sine waveforms, in which we allow the second signal to change its phase from zero to 180 degrees. •Video two: https://vimeo.com/cdlvideo/review/129451174/d036c0be74 The second video illustrates the mixture of two signals, in which the amplitude of the second signal changes from -3V to 0 and then back to 2V. •Video three: https://vimeo.com/cdlvideo/review/129451170/09544bb960 The third video illustrates the mixture of two signals, in which the frequency of the second signal changes from 0.5 Hz to 2 Hz. •Video four: https://vimeo.com/cdlvideo/review/129451165/6a4750bcbb In the fourth video, we see a mixture of three signals, in which the second signal changes in amplitude and the third one changes in frequency. •Video five: https://vimeo.com/cdlvideo/review/129451164/78c65dda9a The fifth video illustrates how to represent a signal (e.g., square waveform in y1(t)) using another signal (e.g., a sinusoid waveform in y2(t)). •Video Six: https://vimeo.com/cdlvideo/review/129451161/a7959121bb In the sixth video, we repeat what appears in video 5 for several sinusoid waveforms, which leads to a representation of a signal (e.g., square waveform in y1(t)) using Fourier series. -154- Journal of Technology and Science Education – http://dx.doi.org/10.3926/jotse.174 These videos cannot only be accessed by enrolled students but are also an open-access resource on the video-streaming website, Vimeo. 3. Methods The video series were completed in October 2013. We began our research study in spring 2014. Given that there was relatively little educational research conducted at the post-secondary level or with a focus on electrical engineering (Streveler, et al., 2008) on fostering conceptual knowledge through simulations, we designed this research as an exploratory case study of students’ experiences and perceptions. We used multiple sources of information for evaluating this instructional practice, including student survey, course evaluation, students’ learning outcomes and interviews with the instructor and the teaching assistant. Our goal was to reflect on our instructional practice and improve the instructional videos for future semesters. The specific research questions were: •How did students perceive the use of simulation videos for demonstrating key conceptual knowledge? •What are the benefits and limitations of this instructional approach? •What changes can we make to these videos to meet students’ needs? 3.1. Data collection At the beginning of the spring 2014 semester, the instructor introduced the videos to his students in class lectures. The videos were placed inside the LMS that was used to supplement the face-to-face lectures. Towards the end of the semester, we delivered an online survey facilitating students’ feedback on the use of simulation videos in this course. Of the 67 students in the class, 62 volunteered to participate in this anonymous online survey. The response rate is 92.5%. Students had the opportunity to offer further comments and suggestions at the end of the survey. In addition, the course evaluation ratings were reviewed, and the instructor and the teaching assistant were interviewed both at the beginning and at the end of the semester, using a semi-structured interview protocol that consisted of questions about the instructor perspectives. -155- Journal of Technology and Science Education – http://dx.doi.org/10.3926/jotse.174 3.2. Technology acceptance models In addition to usage and open-ended questions, the questionnaire (Table 1) consisted of 12 Likert-scale (1=”Strongly Disagree”; 5=”Strongly Agree”) multiple-choice items derived from an existing Technology Acceptance Model (TAM) instrument (Chen, Sivo, Seilhamer, Sugar & Jin, 2013) to measure students’ perception of the usefulness, ease of use, attitude and intention to use the simulation videos. The reliability measures for the survey instrument were acceptable since the Cronbach’s alpha of all variables are higher than 0.70 (The National Research Center for Distance Education and Technological Advancements, 2015). Usefulness •These videos were useful to help me understand the course content. •These videos helped me in completing the course assignments. •These videos increased my general knowledge in engineering. Ease of use •These videos complemented face-to-face lectures. •The videos are easy to understand. •The video production is high in quality. Attitudes •These videos made this course more interesting. •The video length is appropriate. •These videos encouraged me to think about the course content in a new way. Intention •I would recommend these videos to my friends in the engineering program. •I would like to see more videos like these in this class. •I would like to see more videos in other engineering classes. Table 1. Scales Used in the Survey Instrument The Technology Acceptance Model (TAM) was first used to investigate innovation adoption in the fields of information systems and engineering in the past three decades (Davis, Bagozzi & Warshaw, 1989; Davis, 1989; Venkatesh, Morris, Davis & Davis, 2003). In the last decade, TAM has also been successfully used in the instructional technology field to explain students’ behaviors in educational information systems (Chen et al., 2013; Rejón-Guardia, Sánchez-Fernández & Muñoz-Leiva, 2013, Sivo & Cheng-Chang, 2005; Sivo, Pan & Hahs-Vaughn, 2007). The TAM framework focuses on the impact of user attitudes on behavioral outcomes and predicts the -156- Journal of Technology and Science Education – http://dx.doi.org/10.3926/jotse.174 With the above design considerations in mind, we aim to continue producing more simulation videos. In addition to these six videos, the design team will work on a second series of videos on the concept of signal time domain operations and a will produce a third series of videos on eyediagram in the near future. After the pilot, all videos will be used in EEL3552C and two other classes, EEL 4515 (Digital Communication Systems) and EEL 6530 (Communication theory). Since all videos are openly-accessible on the public Vimeo site, we would like to invite engineering instructors outside of UCF to use them for teaching. We believe that students from other classes such as Signal and System or Digital Signal Processing could benefit from watching these simulation videos as well. We plan to evaluate the impact of these simulation videos systematically. A possible future direction is to examine if these simulation videos will impact students learning outcomes via experimental research. 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