a constructivist pbl approach in teaching embedded systems hands-on

GC 2009-45: A CONSTRUCTIVIST PBL APPROACH IN TEACHING EMBEDDED SYSTEMS HANDS-ON COURSE, COMPARATIVE STUDY Mahmoud Abdulwahed, Loughborough University Mahmoud Abdulwahed’s main research interest is in the integration of IT and Cybernetics into social sciences. Mahmoud has his background in electrical engineering and cybernetics. He has published extensively in the engineering education field. He is currently a graduate researcher associated with the Engineering Center of Excellence in Teaching and Learning and the Chemical Engineering Department, Loughborough University, United Kingdom. Walid Balid, Aleppo University Walid Balid is a principal developed, senior research engineer, and co-manager of the R&D department at Al-Awail Co. for electronics based in Aleppo, Syria. Walid is also a teaching associate with the automatic control department of the electrical engineering faculty at Aleppo University and a part-time postgraduate researcher. He has extensive experience in developing and delivering professional and academic training courses. His main research interests are into embedded systems development and novel constructivist pedagogical methods for engineering education. Walid is a member of the Syrian engineers syndicate and an active support member of the Syrian Computer Society (SCS).

© American Society for Engineering Education, 2009

A Constructivist PBL Approach in Teaching Embedded Systems Hands-on Course, Comparative Study Abstract Laboratory education is a concrete part in engineering and science degrees, however, there has been little attention paid for it during the past four decades. Many research papers refer to poor constructivist learning during the laboratory sessions, indicating the need for reforming the laboratory education in a way that facilitates constructivist learning as well as conceptual understanding. The later two seems to dramatically impacting the engineering students attitude and engagement in the learning process. One modern pedagogical constructivist approach is Problem Based Learning (PBL), or Project Based Learning. Some researchers indeed distinguish between the two, but others lump them in one category. Indeed, both approaches emphasizes on learning through experience. Hence, the PBL can be classed under experiential learning methods. The latter is particularly suitable for engineering education because engineering itself is inherently experiential. The paper reports on a study where the PBL was investigated whether it would enhance the students learning and engagement of an MCU laboratory course or not. The students were divided into equivalent groups, experimental and control. The control group students were taught the lab in the classical way, i.e. attending the lab session only. The experimental group was taught with assessment rich PBL pedagogical methodology where they were weekly assigned problems to solve during and after each laboratory session, these were discussed and corrected by the lecturer and feedback was sent to the students, also they were asked to prepare and evaluation quizzes were conducted each week to measure the impact of the assignments and preparation benefit. The control group students were free to communicate the lecturer for any feedback, if they wanted. After four weekly laboratory sessions, both groups were examined unexpectedly. The exam included questions covering the whole four weeks. As for statistically analyzing the exam results, the Null Hypothesis was laid down. The latter stated that “There is no statistically difference between the control and the experimental group due to the assessment rich PBL approach”. The statistical analysis of the exam results showed very strong statistical evidence to reject the Null Hypothesis. The experimental group students outperformed significantly the control group students. The results showed that a pedagogically rooted didactic reform could lead to radical enhancement of the learning outcomes. The lecturer observed significant engagement and motivation enhancement for the experimental group students. Furthermore, the students’ survey has shown better attitude of the experimental group students over the control group students. The paper discusses also the logistical issues associated with the new approach such as the extra work load deemed from the lecturer. Suggestions of further pedagogically informed restructuring to eliminate the latter downside of the approach are discussed.

Introduction Project, or Problem, Based Learning (PBL) is one prominent constructivist pedagogy practice. One of the recent constructivist pedagogy practices is Project Based Learning (PBL). Project based learning is an educational methodology draws on the constructivist pedagogy philosophy, it transforms education from teacher-centred into student-centred approach by designing curriculum emphasizing more on projects than classroom lectures, hence, the student has the principal role in constructing the knowledge. The PBL projects are normally defined in the literature as follows, projects are tasks based on challenging problems that involves the students in design, problem solving, decision making, give the students an opportunity to work in rather autonomous way, and results in a realistic product (Jones et al.,1997; Thomas et al.,1999). Projects should include an authentic content, authentic and effective assessment, clear objectives, and a teacher role as facilitator (Moursund, 1999). Many researchers emphasize that projects should include elements for reflection, cooperative learning, and adult skills (Diehl et al., 1999). Normally the assigned projects are real or quasi-real, hence, relevance of the provided tuition to the students in higher education is facilitated. This has particular impact on increasing students motivation to the studied subject (Thomas and Rafael, 2000), students can master the specific learning outcomes of the curriculum through the PBL efficiently as they will do through the classical classroom based tuition. There is no one unique model of PBL, the literature on this subject varies considerably; however, there are some generalities. For instance, the PBL projects are not trivial tasks (Thomas and Rafael, 2000), projects should have clear goals (Moursund,1999), should improve student autonomy and foster the experiential learning skills (Torp and Sage, 1998), they develop problem-solving skills which are necessary for life long learning (Thomas and Rafael, 2000), projects are complex by nature and they are emphasizing on non trivial challenges (Jones et al., 1997). Thomas emphasizes that the PBL assignments must involve students in constructivist work and they are student-centred in nature (Thomas and Rafael, 2000). PBL engages the students in authentic experience fosters self regulation learning, students get more involved in the learning process because they must define their own specific objectives within the limits imposed by the general trends provided by the instructors of the course (Macias-Guarasa et al., 2006). There are similarities between models referred to as Project-Based Learning and models referred to with other labels, for example, "intentional learning" (Scardamalia & Bereiter, 1991), "design experiments," (Brown,1992) and "problem based learning' (Gallagher et al., 1992). PBL is particularly suitable for engineering education because an engineering career is to large extent is based on projects. Hence, early stage training on such form activities during the university life would result in much effective future engineers who are autonomous, problem solvers, and equipped with rich skills needed for solving complex problems such as teamwork, decision making, design, and innovation. Many researchers have indicated that modern societal challenges requires graduates who can solve complex problem in effective way (Engel, 1997; Gagné; Poikela & Poikela, 1997; Segers, 1997). PBL has been applied in engineering education and has been frequently reported of leaving a positive impact, examples can be found in many studies (Cawley, 1989; Mills and Treagust, 3003; Gonzalez and Musa, 2005; Chu and Lu, 2008). In this paper, the application of the PBL approach with rich assessment components on an embedded systems laboratory course is reported. Empirical findings are detailed after

introducing the laboratory kit. This is followed by extended discussion of the findings with relation to pedagogical and cognitive theories. The Embedded Systems Laboratory Kit The used MCU laboratory kit for the laboratory teaching in this study was an outcome of a postgraduate research project aimed at enhancing the embedded systems education of the electrical engineering students (Balid and Abdulwahed, 2009). The kit development aimed at designing a rather universal training board that can cover a wide range of experiments for the electronics, communication, control, and power departments of the electrical and electronics engineering. A survey of available commercial kits has been done, the main advantages of each one has been classified and based on this, and a set of generic requirements of the new board has been admitted. The kit can utilize any AVR MCU or any SPI protocol compatible one. The MCU programming can be done by all GNU based compilers with booth low-level (ASM) and highlevel (Basic, Pascal, C, or C++.) MCUs programming languages Many peripherals have been placed on the board to enrich its universality. More than 70 different MCU programming and interfacing exercises (on basic, intermediate, and advanced level) have been developed. Since a good planned student-centric approach has proven effectiveness in engaging students, the experiments manual was designed in a student-centered manner, so that the students can proceed with the experiment and develop the aimed skills with minimal supervision. To extend its didactic generality, the developed kit contains double peripherals than similar commercial kits (Balid and Abdulwahed, 2009), sample of the possible experiments exercises to be conducted with kit is shown in Table 1. Table 1. Selected set of the possible experiments to be conducted with the used laboratory kit Com Elect. Hands-on Experiment Comp. Cont. m. Digital Frequency counter/meter 1HZ – 4MHz X X X Programming SmartCard with high-security software algorithms using the AES and DES symmetric-key algorithm Storing Data using MMC/SD card in FAT23 format

X

X

X

Resistance and Capacitance Digital Meter

X

Wireless data transfer using IR 38KHz (Infrared) based on IrData Protocol Wireless data transfer using RF 433MHz (Radio Frequency) based on FSK modulation Wide area data transfer using the industrial CAN protocol Digital to Analog conversion using 8-bit Ladder network Programming 32x8pixel LED-Matrix scrolling Display Interfacing with LM35 analog temperature sensor (-45ºC ~ +125ºC)

X

X X

X X

X

X

X

X

X

X

X

Each experiment is indicated with a potential teaching for the computer engineering department students, the control engineering department students, the communication engineering department students or the electronics engineering department students. The MCU ATmega128 is the kit core and it is linked with the kit peripherals such as a crystal display LCD through I/O ports. It is linked with the kit serial communication units, e.g. USB or CAN, through the serial interfaces. It is linked with the analog sensors, e.g. temperature or pressure, through Analog to Digital (AD) convertors. The used kit in this paper is shown in Figure1.

Figure 1. The MCU embedded systems laboratory kit The Lab Experiments This study was conducted on four lab sessions, out of ten sessions in total. In each session, the students conduct a number of experiments to master a defined set of MCU programming and interfacing skills. In the 1st session, the students conduct the following programming exercises: - Ex1: Programming the MCU ports for displaying a light movements with a set of LEDs - Ex2: Interfacing switches with the MCU ports. - Ex3: The consideration of Interfacing the MCU ports with external peripherals and the important role of the Pull up/down resistors. - Ex4: Using the MCU digital pins for Timing control of power relays.

Figure 2. BASCOM Code with schematic of one of the laboratory exercises In the 2nd session, the students conduct the following exercises: - Ex1: Interfacing 4x4 Hexadecimal Array Keypad (16key) with the MCU and expand the array to 4x8 (32Key). - Ex2: Interfacing and programming the MCU with a 20x4 LCD (Liquid Crystal Display). - Ex3: Interfacing and programming the MCU with a 128x4 GLCD (Graphical Liquid Crystal Display). - Ex4: Interfacing a speaker and programming the MCU for generating musical tones. - EX5: Programming the MCU for generating Dual Tone Multi Frequency (DTMF) [used in telephone]. In the 3rd session, the students conduct the following exercises: - Ex1: Programming with the MCU Shift and Rotate Commands. - Ex2: A trick for Using a digital input pin as an analog input for Reading the capacitor charge constant.

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Ex3: Interfacing and programming the MCU with an Infrared (IR) receiver based on RC5 code and RC5Extended protocol. Ex4: Interfacing and programming the MCU with an IR transmitter based on RC5 and RC6 protocols. Ex5: Data Transfer using IR sender/receiver.

In the 4th session, the students conduct the following exercises: - Ex1: Interfacing seven-segment display with MCU for Displaying Digital numbers and values. - Ex2: Interfacing Real-time clock chip with MCU and displaying the real time and date on LCD. - Ex3: Interfacing 8-bit ladder network with MCU for Digital to Analog Conversion (DAC). - Ex4: Programming the AVR analog comparator unit. For the exercises, the BASCOM programming environment has been used. BASCOM is an Integrated Development Environment (IDE) that supports the 8051 microcontrollers family and other derivatives such as Atmel’s AVR microcontrollers (Kuhnel, 2001). The BASCOM-AVR IDE is based over the BASIC programming language compiler for AVR MCU family. Students who are exposed to embedded systems hands-on course for the first time may be exposed to a high cognitive load (Sweller, 1999) due to the nature of embedded systems which requires knowledge on many dimensions such as hardware, electronics, computer architecture, programming and algorithms, sensors and actuators, etc. Choosing a simple programming language such as BASIC could be significantly a contributing factor of reducing the students cognitive load. Figure 2 shows a BASCOM code for one of the exercises and the circuit interfacing diagram with the MCU. Building Virtual Experiments of the Hands-on Lab Sessions Simulated versions of the developed hands-on experiments with the kit in a form of virtual experiments were designed. The virtual experiments were developed using the evaluation version of Proteus VSM Co-Simulation Software (LabCenter, 2009). There have been two main aims of designing virtual experiments with Proteus: - Supplementing the hands-on experience with assistive tool for preparation, reflection and extended experimentation. - Providing the students who are unable to buy the kit with alternative free solution to practice MCU programming. The virtual experiments were the main vehicle of implementing the problem based learning approach during the course. The students conduct the hands-on lab session and they are assigned projects to be implemented using the Proteus VSM Co-Simulation Software. Furthermore, using a co-simulation tool such as Proteus can result in high economical and time savings during the design process of embedded systems engineering applications. With the simulation tools, prototypes can be tested in simulation mood, modified, and redesigned as much as needed before the final physical prototype is done. This is a common practice in designing analog and digital

electronics circuits where SPICE is extensively used. Introducing the students to a novel cosimulation tool for embedded systems design such as Proteus that includes MCUs is essential to get them used to this practice and understand the tool limitations as well and the differences between simulations and physical prototypes. Figure 3 shows a virtual MCU experiment designed in Proteus.

Figure 3. Virtual Lab Version of one of the Lab Experiments

The Pedagogical Effectiveness Measurement Methodology In total, 62 students have taken the MCU lab. The students were divided into two groups, a control group which follows the lab teaching and learning in the classical approach that has been followed in the department where the students attend the hands-on lab and follow the manual, no preparation, assignments, or tests are applied for this group. The second group is the experimental group where the students follows the PBL approach. In this approach, preparation, solving weekly assignments and laboratory testing of the students are the applied elements. The experimental group students are given assignment projects every week to work on. The projects are related to the concepts and skills that should have been acquired during the already conducted lab session. Normally, two to three projects for each session are delivered. The projects complexity varies from simple for the first towards a rather complex. Most students will be able to achieve at least one project. The course lecturer collects the stocking issues (which are normally similar among the students) that prevent the students from achieving the complex tasks and provides them with relevant feedback. The tests were conducted at each lab session, mainly to deeply measure the students understanding of the previous lab session, and with some explicit components related to the current lab session, no feedback has been given to the students related to their answers, and the tests sheets were kept with lab lecturer. The students answer on the same sheet and are not allowed to take the questions with them after they finish. These tests

together with assigned projects feedback were designed to implement a PBL approach with rich assessment components. After the four lab sessions have ended, an examination of both the control and experimental group was conducted. A lab achievement measurement was taken for the students of both groups by observing their behavior and the experimentation progress during the lab session. This observation was made originally to measure any performance difference between the control and experimental group of conducting the hands-on experiments. Each group included 31 students. To guarantee equivalence as much as possible, the students were divided into the groups equally based on their average of the last year. The median average of the control group was 67.31% while the median average of the experimental group was 67.12%. All students were males, and with very similar age. The average age of the control group students was 22.81 years, while the experimental group student age average was 22.58 years. Performance comparison was based on the following measures (M) taken for both groups: - M1: Evaluation exam at the start of the course to examine the initial knowledge of the students of both groups of MCU prior taking the course. - M2: The taken lab measurements of the students performance during the hands-on session. - M3: Laboratory exam at the end of the four lab sessions and a Project exam two weeks later on after the Laboratory test. - M4: Qualitative observations. The first three measures are quantitative and can be empirically analyzed using statistical approaches, the fourth measure is qualitative based on the lab lecturer observations. For comparing the results, a statistical hypothesis testing approach is followed. In statistics, the only way to support a true hypothesis is by rejecting its opposite. It is commonly agreed among the statistics community that it is impossible to prove that something is true, but it is possible to show that something is false (Howell, 1999). This is why statistical hypothesis testing is usually achieved through laying down a “Null Hypothesis” which is of opposite meaning to the intended hypothesis for test. The reason why it is called with this name is because it states normally that “ … there is no difference (or null) between the control and the tested groups”. When the null hypothesis is proved false, then the intended hypothesis for study would be percept as a true (Conover, 1998). The null hypothesis is normally rejected if the statistical test has resulted in a significance probability of 95% or more. This is expressed by significance value of 0.05 or less, it is referred normally in the literature as the p- value. The 0.05 value has been historically suggested as arbitrary one, but it is accepted as a standard among the statistical community, a historical review on this issue can be found in (Dallal, 2003). If the threshold p- value is lowered, i.e. to 0.02, this will result in higher confidence of accepting or rejecting the null hypothesis test results minimizing a so called type I error when the null hypothesis is rejected when it is indeed a true one. However, such low p- values may result in a so called type 2 error when the null hypothesis is not rejected when it is indeed false.

Hypothesis tests are classified in two categories, parametric tests such as t-test and non parametric tests such as Mann-Whitney test. Parametric test requires valid assumption of the data to be tested, in particular they assume normally distributed data. On the other hand, nonparametric tests does not require such conditions and hence they are more applicable than parametric tests. Using non-parametric test instead of a parametric test is recommended when the sample number is small (Conover, 1998). The Mann-Whitney U test is a non-parametric test that can be used in place of an unpaired t-test for comparing two independent groups of sampled data. It is used to test the null hypothesis that two samples come from the same population (i.e. have the same median) or, alternatively, whether observations in one sample tend to be larger than observations in the other such as will be the case through this chapter. In this study, the standard p=0.05 threshold value is admitted. For the evaluation of the statistically significant difference between the control and the experimental group (if any) in response to the treatment the null hypothesis was used. The null hypothesis in this case states that: “There is no statistically significant difference between the learning outcomes (measured by tests) in between the control and the experiment group as a result of the PBL approach”. Groups Equivalence Measure The hypothesis tests applied on the students previous year average and age average has given a statistically non-significant difference with p-=0.899>0.05 for the previous year average and p=0.314>0.05 for the age leading to the conclusion that the control and the experimental groups are rather equivalent. However, to further guarantee equivalence, an evaluation exam (M1) has been taken to test the students of both groups prior knowledge of the course content. Both groups performed very poorly (about 5%) in the test as expected with a p-=0.60>0.05 indicating that there is no significant difference in the prior knowledge in between the groups. The control group score in the evaluation exam was slightly higher, the statistics of the groups equivalence measures are shown in Table 1. Table 2. Statistics of Equivalence Measures Between the Control and the Experimental Groups, Sample Number is 31/31 Measure Exact Significance (the pMeans Value) Mann-Whitney Test (Control/Experimental) Previous Year Average 0.899 22.81/22.58 Students Age 0.314 67.31/67.12 (%) Evaluation Exam 0.600 5.51/4.55 (%) Measure 2: The lab sessions were offered as a closed ended type (or expository) where the students follow the explained steps in the lab manual and apply them on the kit. The lab lecturer has been marking the students performance of following the hands-on session and applying the steps described in the manual. Generally, the students of both groups achieved close performance with slightly higher capability for the experimental group. As the sessions complexity increased with

the weekly progress of the course, the gap in performance began to increase as shown in the “Means Difference” in Table 3. The first three sessions measures did not show a statistically significant difference, however, it is interesting to observe that the p-value started to decrease with the course progress until finally it crossed the threshold in the fourth session (p=0.041