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education for chemical engineers 1 9 ( 2 0 1 7 ) 1–12

Contents lists available at ScienceDirect

Education for Chemical Engineers

jou rn al hom epage: www.elsev ier .com/ locate /ece

n inverted classroom approach to educateATLAB in chemical process control

ianhua Li, Zuyi (Jacky) Huang ∗

epartment of Chemical Engineering, Villanova University, Villanova, PA 19085, USA

r t i c l e i n f o

rticle history:

eceived 27 April 2016

eceived in revised form 30 June

016

ccepted 29 August 2016

vailable online 12 October 2016

eywords:

nverted-classroom

ATLAB

imulink

DE simulation

aplace transform

a b s t r a c t

The inverted-classroom teaching format and the application of MATLAB/Simulink have

recently generated considerable research interest in chemical engineering education. MAT-

LAB/Simulink was introduced in mathematics-intensive courses due to its user-friendly

interface for mathematical model simulations. Inverted classroom approach has been

reported to be generally beneficial for engineering courses, but it has never been applied

to MATLAB/Simulink education in a single course. The aim of our study is to examine the

effectiveness of the inverted-classroom approach in developing MATLAB/Simulink skills

of upper-division undergraduates in Villanova’s chemical process control course. Teaching

modules include solving ODE models, performing Laplace transform, and designing PID con-

trollers. Surveys of students’ evaluation revealed that the three inverted-classroom teaching

modules were effective in enhancing students’ understanding of mathematics-intensive

process control concepts and improving their MATLAB simulation skills. Students’ over-

all feedback on the inverted-classroom format was positive as they gradually adapted to

hemical process control inverted-classroom learning format.

© 2016 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

grams from first principles (Ibrahim, 2011; Brenner et al.,

. Introduction

ntegrating mathematical simulation software such as MAT-AB in chemical engineering curricula has been attractingidespread interest during recent decades, especially for

ourses involve process design (Komulainen et al., 2012;hacham, 1999; Martin-Villalba et al., 2008). Process controlesign deals with important techniques that can optimizehemical processes to achieve sustained and accident-freeerformance. It has also been predicted that there will be

growing need in chemical industry for synthesizing plant-ide process control systems which fully integrate discreterocesses and safety functions in the regulatory process con-rol (Grossmann and Westerberg, 2000). Thus it is beneficialor chemical engineers to develop mathematical models onomputers for process control simulation. For example, theuzzy logic model (Charpentier, 2002) and neural network

odel (Chen and Huang, 2004) have already been reported

o be of great help in process control. The advantages of

∗ Corresponding author.E-mail address: zuyi.huang@villanova.edu (Z. Huang).

ttp://dx.doi.org/10.1016/j.ece.2016.08.001749-7728/© 2016 Institution of Chemical Engineers. Published by Elsev

using software tools are characterized by their high accu-racy, easy adaptation or modification, high problem-solvingefficiency and cost-effectiveness. As computer technologiesare playing a more and more important role in chemicalresearch, implementation of computer simulations in virtuallaboratories becomes a more and more popular method toexpose students to practical research problems as a substi-tute for bench-top experiments (Ibrahim, 2011). Moreover, therapid increase in computational capabilities enables chemi-cal engineers to build more complex mathematical modelsfor numerous chemical processes, ranging from molecular-scale (e.g., reactions and synthesis) to industrial productionscale (Grossmann and Westerberg, 2000; Charpentier, 2002).Since courses developed to educate students with pro-cess modeling and control techniques instead of computercoding skills, utilizing commercially available simulationsoftware packages like MATLAB is preferable to writing pro-

2005).

ier B.V. All rights reserved.

2 education for chemical engineers 1 9 ( 2 0 1 7 ) 1–12

Research has been conducted in comparing differentsoftware packages including POLYMATH, Mathcad, MATLAB,Mathematica, Maple and Excel for educational use in chemicalengineering, and results indicated that MATLAB is especiallyexcellent for demonstrating matrix operations and multi-variable iterative processes (Shacham, 1999). In addition,MATLAB-based simulation has been verified for promot-ing deeper understanding of process modeling techniques(Ekaputra and Huang, 2015). To solve problems in MATLAB,students are required to derive mathematical models in MAT-LAB syntax and input the numerical data so that MATLABcarries out all the calculation and shows the solution. It wasreported that MATLAB/Simulink has been used for decades inresearch and teaching in the field of chemical process control(Luyben, 1989). Additionally, a study summarizing extensiveuse of computational simulation in control education reportedMATLAB/Simulink as the preferred platform (Edgar et al.,2006).

According to recent surveys conducted by engineering fac-ulty at Villanova University (Lee et al., 2014, 2015), manyuniversities such as University of Texas at Austin, TexasA&M University, Rensselaer Polytechnic Institute, Univer-sity of Massachusetts Amherst, and University of Delaware,have included MATLAB training in their chemical engineer-ing curricula. It was also revealed that chemical engineeringalumni of Villanova University have little knowledge of MAT-LAB programming. Given the knowledge gap, it is importantto provide Villanova chemical engineering students the neces-sary training in MATLAB programming, in order that studentscan efficiently handle problems in modeling and process con-trol and remain competitive in the market place. Therefore,a MATLAB controller design module was introduced to thechemical process control course CHE4232 at Villanova Uni-versity in 2013. This course provides an experience in whichsenior students must apply their modeling skills to quantifythe transient dynamics of chemical processes and thus designcontrollers with optimal performance. However, despite all thebenefits mathematical simulation software provides, there isa significant obstacle for the spread of MATLAB in education:the mathematics-intensive property of computational model-ing methods are challenging for both teachers and students topick up in class. According to the end-of-course feedback fromstudents in course CHE4232, challenges mainly lie in threeaspects: (1) senior students in chemical engineering have nobackground in MATLAB, (2) students cannot review the ‘live’lecture after class, and (3) students have limited opportunitiesto work with the instructor on their MATLAB programs. Inno-vative teaching approaches are demanded to address theseissues. Considering that chemical education research empha-sizes active learning and problem-solving skills (Roxanne Toto,2009), a newly emerged approach called inverted classroomapproach was developed in this work. The inverted classroomis a teaching format where video lectures are watched outsideof the classroom, while in-class time is designed for individ-ual and group problem-solving activities with assistance of theinstructor.

Inverted classroom approach provides an excellent plat-form for active learning as well as supports the needs ofstudents with a variety of learning preferences. The invertedformat has been widely tested in different engineering pro-grams at various universities, and it is continuously gainingpopularity (Komulainen et al., 2012; Mason et al., 2013a,b;

Bishop and Verleger, 2013). For example, in a senior mechan-ical engineering course taught via the inverted-classroom

approach, not only was the instructor able to cover morematerial, but also students demonstrated equal or betterperformance on quizzes, exams and design problems andshowed equal or greater satisfaction for the course (Masonet al., 2013b). In a first year engineering honors course, theconclusion was drawn that the inverted classroom formatpromoted the students’ deeper learning (Morin et al., 2013).Some studies demonstrated that students who participatedin the inverted classroom format achieved higher scores onhomework assignments, tests and projects, especially projectsrequiring design (Mason et al., 2013a; Day and Foley, 2006;Moravec et al., 2010). Other benefits also include: making effec-tive use of class time, making good use of technology, helpingstudents become self-directed learners, fostering life-longlearning skills, and providing collaborative learning experi-ences (Kecskemety and Morin, 2014). Based on a survey of 24published inverted classroom studies with a combination ofself assessment and performance test reporting method, theinverted classroom format tended to be perceived positivelyby students (Bishop and Verleger, 2013). Students preferredin-class interactive learning activities to lectures and scoredhigher in flipped classes on assignments, projects and tests(Bishop and Verleger, 2013). In general, inverted classroomscan have a great impact on modern engineering educationby facilitating students to become active learners and teamplayers.

In 2015, the inverted classroom approach was employedin course CHE4232 for MATLAB programming. Although MAT-LAB is regarded as an excellent tool intended for mathematicalmodeling, very few studies were performed previously aboutapplying inverted classroom on courses involving MATLAB.Nevertheless, previous studies neither focus on engineer-ing courses (Talbert, 2012) nor emphasize MATLAB as themajor computational tool (Mason et al., 2013b; Morin et al.,2013; Kecskemety and Morin, 2014; Grzybowski, 2015). Con-sequently, they did not directly reveal the effect of invertedclassroom on MATLAB employed engineering courses. It isthus our aim in this work to investigate the effectivenessof implementing inverted-classroom specifically in MATLABemployed process control course. Several inverted classroomMATLAB training modules were developed in this course,which include: (1) simulation of ODE models in MATLAB,(2) Laplace transform and transfer function operation, and(3) process controller design. The training modules demon-strate how to apply fundamental principles and techniquesstudents acquired in former courses to develop models anddesign controllers for real-world chemical processes. Threeanonymous surveys were conducted for the three teachingmodules to evaluate the improvement of students’ technicalknowledge and skills through the implemented inverted-classroom approach. Results indicate that inverted classroomis a promising approach that solves aforementioned issuesof MATLAB teaching and therefore could foster the spread ofMATLAB utilization in Chemical Engineering curricula.

2. Course content, learning goal andinverted classroom approach

2.1. Undergraduate chemical process control course atVillanova University

The initial consideration of designing a chemical engineer-ing process is to maintain steady state. Besides, no design or

education for chemical engineers 1 9 ( 2 0 1 7 ) 1–12 3

Oper ati ng paramete rPID controll er Proc ess

yset-point + y

-ytrueymea sur ed Measuremen t

component

Fig. 1 – The schematic diagram of the negative feedbackloop regulated by a PID controller that minimizes thedv

pasfc

iotctltss

bt(bppso(KK

u

gi(adfdscmtcm

2t

TmEemii

ifference between the process output and its set-pointalue.

rocess analysis in practice is acceptable without consider-tion of its dynamic performance and the required controlystem. A typical project for engineers is to design controllersor a chemical reactor in order that desired quality productan be obtained in a timely and safe manner.

The course at Villanova, Chemical process control CHE4232s designed to introduce the topics of dynamic responsef chemical processes and basic control concepts, such asransfer functions that represent the dynamics of individualomponents (e.g., heating, pressure change, chemical reac-ions, mixing and separation) in a chemical system, feedbackoops that link the influence from one component process tohe other in the system, and process stability with the con-ideration of all component processes in the loop as a wholeystem.

A closed-loop control system can be generally representedy linking the transfer functions of individual components inhe loop shown in Fig. 1. The proportional-integral-derivativePID) controller, given in Eq. (1), minimizes the differenceetween the measured output ymeasure and the designed set-oint value yset-point (i.e., �y) by manipulating the operatingarameter to regulate the process output ytrue. The three termshown in Eq. (1) indicate how the PID controller regulates theperating parameter u on the basis of the current error value

i.e., the Kp term), the accumulation of historical error (i.e., the

i term), and the predicted change of error over time (i.e., the

d term).

(t) = Kp�y(t) + Ki

∫ t

0

�y(�)d� + Kdd�y(t)

dt(1)

The course’ learning objectives state that students shouldain the following technical skills: (1) develop mathemat-cal and transfer function models for dynamic processes;2) implement dynamic models with or without controllersnd perform simulations; (3) empirically determine processynamics from step response data; (4) evaluate dynamic per-ormance of processes via benchmarks and statistics; (5) knowifferent types of PID feedback controllers; (6) analyze processtability and dynamic responses; and (7) analyze and tune PIDontrollers to desired performance. Since this course is veryathematics-intensive, students find it challenging to learn

hese technical skills and perform the corresponding mathalculations, and that is the reason why MATLAB program-ing is necessary throughout the course.

.2. Implementation of the inverted-classroomeaching approach

he inverted-classroom instructional approach was imple-ented in all three teaching modules previously outlined.

ach video was designed to last less than 20 min, with thexception of the first teaching module that contained a 55-in video that was specially designed to introduce a complete

mplementation of Simulink to determine the temperaturen a water-cooling CSTR with an exothermal reaction. The

inverted-classroom approach entailed the following process:(1) videos were sent to students to watch one week beforeclass; (2) a quiz was given at the beginning of every classto gauge students’ content comprehension and to provideaccountability; (3) following the quiz, a question and answersection helped students to clarify questions about the videocontent; (4) students were required to reproduce the MAT-LAB programs shown in the teaching videos in class and turnin their work by the end of class; (5) students were givenanother class period to do homework when the instructorwas available for answering students’ questions in class; (6)the instructor graded students’ homework and gave sugges-tions for students to optimize their programs. The quiz givenat the beginning of the each class included quick questionsabout each teaching video, especially on how to set up theprograms in MATLAB. Students had to watch the entire videoin order to answer all the questions. Once the students wereable to reproduce the MATLAB programs shown in the teach-ing videos, they would be familiar with the skills and be ableto apply them. The knowledge and practice helped students indoing their homework, as the homework was designed to trainthem for solving problems introduced in the teaching videos.Around 70% students were able to reproduce the MATLAB pro-grams shown in each teaching module during class. Therefore,most students had more than one class section available to dohomework. The instructor was also able to answer students’immediate questions so that they received prompt feedbackand were able to resolve their problems in a timely fashion.

3. Simulation software and teachingmodules

3.1. Introduction of MATLAB and Simulink

MATLAB has functions for Laplace domain operations andODE simulation. The user-friendly and intuitive applicationsof Simulink allow students to solve ODEs without extensiveknowledge in numerical methods. A well-developed modulelibrary in Simulink enables students to link the function mod-ules of ODEs in Simulink, and the process resembles buildingcars using LEGO. For example, the liquid level in a tank (Fig. 2A)can be represented in Simulink as shown in Fig. 2C, in whichthe mathematical operations in the ordinary differential equa-tion (e.g., minus, square root, and differentiation in Fig. 2B)are implemented via the operation modules in the Simulinklibrary. In addition to ODE model simulation, Simulink alsoprovides powerful functions and modules for constructingthe closed loop control system, designing and evaluating theperformance of process controllers. For example, Simulinkhas been used to develop fuzzy logic controllers (Altas andSharaf, 2007), ITAE-criterion-based PID controllers (Martins,2005), feedback controller for switching converters (Su et al.,2002), digital signal processor and DSP-based motor con-troller (Hercog and Jezernik, 2005), and autopilot controllersfor unmanned aerial vehicles (UAV) (Lucio and Ribeiro, 2010).

3.2. Teaching modules for simulating ODE models inchemical engineering

In accord with the aforementioned learning objectives, threeinverted-classroom teaching modules were developed in thisstudy, including: (1) simulation of ODE models in chemical

engineering (illustrated in three videos of 91 min in total); (2)Laplace transform and transfer function (illustrated in eight

4 education for chemical engineers 1 9 ( 2 0 1 7 ) 1–12

Fig. 2 – An example to represent an ODE model in Simulink: (A) the water tank; (B) the ODE model to quantify the liquidlevel in the tank; (C) the ODE model represented in Simulink.

Table 1 – Summary of the examples developed in the three teaching modules.

Module Example # Tasks

Simulation of ODE models 1 Simulate an ODE model to investigate the temperature profile in a CSTR reactor with anexothermal reaction.

Laplace transform andtransfer function

2 Represent transfer functions and perform the inverse Laplace transform using the MATLABcommand ilaplace.

3 Solve a linear ODE model using the Laplace transform approach and produce the time profileof a species concentration upon the change of the inlet flow rate in a chemical reactor.

4 Compare the Simulink-based approach with the command-line coding based approach insolving the ODE model in Example 3.

5 Generate the required input signal for a liquid storage system in Simulink Signal Builder andinvestigate the influence of the input on the dynamics of the output, i.e., the liquid level inthe storage tank.

6 Study the behavior of second order systems with different denominator roots and understandoscillatory dynamics.

7 Evaluate of the influence of time constant values in a lead-lag system.8 Understand inverse response through variation of input factors by studying the liquid level in

a reboiler in which the steam pressure is disturbed.9 Simplify the transfer functions for sequential processes in an open loop system (i.e., the

reboiler in a distillation column).

PID controller design andevaluation

10 Evaluate the influence of the controller gain Kc on the stability of a closed-loop system model.11 Compare the influence of three controller modes (proportional, integral, and derivative

contr

the two different cooling water temperature Tc.

Table 2 – Nominal operating conditions for the CSTR.

Parameter Value Parameter Value

q 100 L/min E/R 8750 KcA,i 1 mol/L k0 7.2 × 1010 min−1

�i 350 K UA 5 × 104 J/min KV 100 L Tc (0) 300 K� 1000 g/L cA (0) 0.5 mol/L

components) in PID

videos of 140 min in total); (3) PID controller design and evalua-tion (illustrated in two videos of 35 min in total). These trainingmodules address the most commonly required skills for usingMATLAB to solve modeling and controller design problemsthat students may meet in chemical engineering. Generally,one example is given per video. A total of 11 examples weredeveloped and summarized in Table 1. Instruction handoutsfor each of these inverted-classroom teaching modules areprovided in Supplementary Materials 1, 2, and 3. Due to thespace limitation, only 3 representative examples (i.e., Exam-ples 1, 5, 10), one from each teaching module, are described indetail.

Example 1: An exothermal reaction, A → B, takes place in acontinuous stirred-tank reactor (CSTR) reactor which is cooledby water with temperature Tc. The concentration of species A(cA), the bulk flow temperature (T), and the reaction rate con-stant (k) are determined by the following equations (Seborget al., 2010), and values of parameters in the equations areshown in Table 2.

dcA

dt= q

V(cA,i − cA) − kcA (2)

dT

dt= q

V(Ti − T) + (−�HR)k

�CcA + UA

�VC(Tc − T) (3)

k = k0 exp(−E

RT

)(4)

ollers on the system output dynamics.

The assignment for students is to develop a Simulinkmodel to determine the bulk flow temperature T profiles fortwo scenarios: (1) Tc decreases to 290 K; and (2) Tc increase to309 K. In addition, T profiles should be plotted for these twoscenarios in the same figure.

Instructional video content: Three videos were recorded forthis example. The first video (12 min) introduces basic com-mands and functions used in MATLAB. The second video(24 min) provides general introduction of Simulink, includ-ing signal source blocks, math operation blocks, integratorblocks, defined function blocks and sink blocks provided in theSimulink module library. The third video (55 min) shows stu-dents step by step how to develop the Simulink model (shownin Fig. 3A) for the CSTR models (shown in Eqs. (2)–(4)) and per-form the simulation to derive the reactor temperature T for

C 0.239 J/gK T (0) 350 K−�HR 5 × 104 J/mol

education for chemical engineers 1 9 ( 2 0 1 7 ) 1–12 5

Fig. 3 – The simulation of the CSTR model shown in Eqs. (2)–(4): (A) the Simulink model in which different colors representthe hierarchical levels we can decompose differential equations into; (B) the simulation results from the Simulink modelshowing the influence of the cooling water temperature Tc on the reactor temperature.

6 education for chemical engineers 1 9 ( 2 0 1 7 ) 1–12

0.8

0.6

0.4

0.2

00 5 10 15 20

Time (minutes)

q (

m3 /

min

)in

Fig. 4 – The profile of inlet flow-rate qin of the liquid storagesystem.

3.3. Teaching modules for Laplace transform andtransfer function

Most chemical processes can be represented by the first orsecond order models. The Laplace transforms of these mod-els, represented by transfer functions, can be used to study thedynamics of chemical processes upon the change of operationor disturbance variables. In addition, the transfer functions ofindividual components in the closed loop of the entire systemare essential to studying the stability of the loop and designPID controllers. In this module, eight examples were givenfor students to master the basic skills on the transfer func-tion operations. Example 5 is shown below while the otherexamples are provided in Supplementary Material 2.

Example 5: Liquid storage system with a long transmissionline can be described by a first order plus time delay model:

dh

dt= qi

A− h

RvA(5)

where Rv is the valve resistance coefficient 0.1 min/m2, A isthe cross-section area of the tank 4 m2, qi is in-let flow rate.The steady state value for qi and h are 0.4 m3/min and 0.4 m,respectively. qi follows the profile shown in Fig. 4. Determinethe time profile of h.

This example aims to show students the procedure of usingthe Signal Builder from Simulink to build the input signal asshown in Fig. 4.

Instructional video content: A 17-min video illustrated theexample step by step. Most input signals in chemical engineer-ing are step, ramp, or pulse signals. The Signal Builder fromSimulink allows students to create those input signals withoutdriving the mathematical formula for them. The detail of theSignal Builder’s function was given to the student (Fig. 5A). Onthe basis of the Simulink model with the Signal Builder as theinput shown in Fig. 5B, the output profile (i.e., the liquid levelin the storage tank) can be predicted in Fig. 5C. The studentsare encouraged to change the setting of Signal Builder to studythe influence of these setting on the dynamics of the outputs.

3.4. Teaching modules for PID controller design

The examples in the previous two teaching modules aremainly focused on the modeling and dynamic analysis for

open-loop systems. In this teaching module, two examples(i.e., Examples 10 and 11) are given to educate students on howto construct closed-loop models in Simulink and evaluate theperformance of PID controllers. While the PID tuning methodsare introduced using traditional in-class teaching methods,these two inverted teaching modules have been designed tofacilitate students’ learning in close loop stability and PID con-troller design. While Example 10 is given below, Example 11 isprovided in Supplementary Material 3.

Example 10: A chemical process is represented by Fig. 6. Thetransfer functions for the components in the diagram are: GP =Gd = 1

5s+1 , GV = 12s+1 , GM = 1

s+1 , KM = 1, Gc = Kc. Plot theoutput profile of the output for Kc equal to 2, 6, and 15 for aunit step change in the set point (i.e., Ysp is changed from 0 to1) for 20 min.

This example educates students the procedure of develop-ing a closed-loop model in Simulink and allows students toevaluate the influence of the controller gain Kc on the stabilityof the closed-loop system.

Instructional video content: A 19-min video was developed toprovide this training. The closed-loop model is represented inSimulink in Fig. 7A. It can be seen from Fig. 7B that a larger con-troller gain may lead to unstable oscillatory output. Studentswere encouraged to change the disturbance magnitudes sothat they could understand the role of the negative feedbackloop in rejecting disturbance and stabilizing the closed-loopsystem.

4. Evaluation methods of student learning

At the end of each teaching module when graded homeworkwas returned to students, an anonymous survey was given tostudents. Among 50 senior students taking this course, 84%,72%, and 32% of them participated in Surveys 1, 2, and 3,respectively. Each survey included three sections: in Section1, a numerical rating was used to evaluate the improve-ment of students’ knowledge in process control and modelingand MATLAB/Simulink skills; in Section 2, a numerical ratingwas designed to evaluate the effectiveness of the inverted-classroom approach in facilitating students’ learning; and inSection 3, a written feedback was required, which includedtwo open-ended questions which asked students to list theaspects they liked about the inverted-classroom teachingapproach and provide suggestions for further improvement.Four numerical items were designed for Section 1 of the sur-veys (as shown in Tables 3–5). The first two questions inSection 1 were designed to evaluate the effectiveness of theseteaching videos and MATLAB practice to enhance students’knowledge in solving ODE (Teaching Module 1), performingLaplace transform and solving transfer functions (Teach-ing Module 2), and analyzing the closed-loop stability anddesigning PID controllers (Teaching Module 3). In these twoquestions, it is hypothesized that the MATLAB teaching mod-ules enhance students’ understanding of knowledge learnedin this course. On the other hand, the last two questions ofSection 1 (shown in Tables 3–5) were used to evaluate theimprovement in students’ MATLAB skills to solve the model-ing and controller design problems. The students were askedto compare their skills or knowledge before and after viewingthe teaching modules. The comparison results were then used

to evaluate the teaching effectiveness of the implementedinverted-classroom approach.

education for chemical engineers 1 9 ( 2 0 1 7 ) 1–12 7

Fig. 5 – The example to use the Simulink Signal Builder to generate a specific input signal: (A) the setting of the SignalBuilder to build the signal shown in Fig. 4; (B) the Simulink model to solve Eq. (5); (C) the output profile (i.e., the liquid leveli

soStiimtrt

Fa

n the storage tank) for the input shown in Fig. 4.

While questions in Section 1 of all the three surveyshown in Tables 3–5 were used to evaluate the improvementf technical students’ skills or knowledge, the questions inection 2 (as shown in Table 6) were designed to evaluatehe effectiveness of the inverted-classroom format in facil-tating students’ learning. Since students’ feedback on themplemented inverted-classroom format for the first teaching

odule was positive in general (shown in the next section),he same inverted-classroom format was integrated in theemaining teaching modules. The questions for Section 2 werehus the same for all three teaching modules.

ig. 6 – The diagram for an example chemical process with close-loop. Gc is the gain of a PID controller.

5. Survey results and discussion

As shown in Tables 3–5, Section 1 of the surveys includedfour questions: Questions 1 and 2 were used to evaluatethe improvement of students’ knowledge levels, while Ques-tions 3 and 4 were designed to evaluate students’ abilityin solving technical problems with MATLAB. On the basisof students’ answers, the two-sample Kolmogorov–Smirnovstatistic method was used to analyze the data. The resultsshown in Fig. 8 indicate that students’ knowledge in pro-cess modeling and control taught in this course was improvedafter they took the training. In particular, the average for stu-dent’s knowledge levels before taking the teaching moduleswas around or below 3 out of 5, while the average rose toabove 4 out of 5 after they finished the teaching modules andhomework. This means that the implementation of the pro-cess modeling and control knowledge in MATLAB can enhancestudents’ understanding of knowledge taught in the class-room. The comparison of the average shown in Questions 3and 4 of Fig. 8 indicates that students’ ability in using MAT-LAB to simulate ODE models, perform Laplace transform, anddesign PID controllers was significantly improved. Specifically,

the average score rose from around or below 2 to above 4 outof 5 in Questions 3 (before taking the teaching modules) and

8 education for chemical engineers 1 9 ( 2 0 1 7 ) 1–12

Fig. 7 – Evaluation of the role of controller gains in rejecting disturbance and stabilizing the closed-loop system: (A) theSimulink model for a closed-loop system with different controller gains applied; (B) the system outputs for the closed-loop

system with different controller gains.

4 (after taking the teaching modules). This is expected, asMATLAB and Simulink are not used in other courses in theDepartment of Chemical Engineering at Villanova University.

Nevertheless, the survey results shown in Fig. 8 proved that the

designed inverted-classroom teaching modules were effectivein improving students’ knowledge in process modeling andcontrol, as well as students’ skill in conducting simulation in

MATLAB Simulink.

education for chemical engineers 1 9 ( 2 0 1 7 ) 1–12 9

Table 3 – Questions in Section 1 of the survey designed for Teaching Module 1 on students’ skill in solving ODE modelusing MATLAB Simulink.

Table 4 – Questions in Section 1 of the survey designed for Teaching Module 2 on students’ skill in solving Laplacetransform and transfer functions using MATLAB Simulink.

Table 5 – Questions in Section 1 of the survey designed for Teaching Module 3 on students’ skill in closed-loop analysisand PID controller design using MATLAB Simulink.

eseetcktaato

Section 2 of the surveys includes six questions on theffectiveness of the inverted-classroom approach to improvetudents’ overall learning and ability to solve process mod-ling and simulation problems. Question Q1 was used tovaluate the improvement of students’ overall learning whenhey compared the inverted-classroom format to the classi-al in-class lecture format. Fig. 9 shows that the score for Q1eeps rising from 3.6 to above 4.1 from Teaching Module 1o Teaching Module 3. This suggests that students graduallydapted to the inverted-classroom format after they got morend more familiar with this format. Question Q2 was usedo determine whether students spent substantial investment

f their time compared to a classical in-class lecture format.

Table 6 – Questions in Section 2 of the survey designed for all Tinverted-classroom format in enhancing students’ learning.

The score for this question keeps decreasing from 3.5 to 3.0from Teaching Module 1 to Teaching Module 3. This result indi-cates students were able to manage their time more efficientlyin the inverted-classroom format when they got more famil-iar with this teaching format. Question Q3 was designed todetermine whether the inverted-classroom format improvedstudents’ conceptual understanding of the principles and the-ories of this course over a classical in-class lecture format.The average score for this question keeps rising from 3.6 to4.0 from Teaching Module 1 to Teaching Module 3. This con-firms that the inverted-classroom format was able to enhancestudents’ learning of the principles and theories. Similar

trends can be observed for questions Q4 and Q5, which were

eaching Modules on the effectiveness of the implemented

10 education for chemical engineers 1 9 ( 2 0 1 7 ) 1–12

Fig. 8 – Survey results on the improvement of students’ knowledge in process control and modeling (Q1 and Q2, the firsttwo questions in Tables 3–5) and students’ skill in MATLAB Simulink simulation (Q3 and Q4, the last two questions in

.

Tables 3–5). The error bars represent one standard deviation

designed to evaluate whether the inverted-classroom formatimproved students’ ability to apply knowledge in solving prob-lems over a classical in-class and whether students receivedmore personal attention (individual faculty-student interac-tion) in the inverted-classroom format, respectively. QuestionQ6 was used to determine whether students preferred more oftheir technical classes to use the inverted-classroom format.The score for this question stays relatively stable at around3.4, which indicates that in general, students’ perceive theinverted classroom approach as positive.

Two opening-ended questions were provided in Section3 of the surveys for the three teaching modules. Studentswere asked to list the aspects that they liked in the inverted-classroom teaching approach and provide suggestions forfurther improving the teaching modules. The aspects stu-dents like most include: (1) they were able to watch the videosrepeatedly at the time and pace they felt comfortable with(around 80% of students); (2) the instructor was able to answertheir questions on homework immediately in class (around

60% of students); (3) the videos offered another chance for

Fig. 9 – Survey results on the effectiveness of the inverted-classrQ1–Q6 are listed in Table 6. The error bars represent one standar

them to understand the concepts introduced in the in-classformat from the perspective of MATLAB simulation (around60% of students). These positive comments are consistentwith the high scores of questions Q1, Q3, and Q4 shown inSection 2 of all the three surveys (Fig. 9). The aspects studentssuggested for further improvement include: (1) 4% studentsmentioned that they spent more time compared to the in-classformat; (2) 10% students mentioned in the survey for TeachingModule 1 that the size of some equations seemed to small andunclear in videos; and (3) 10% students mentioned in the sur-vey for Teaching Module 1 that one video for solving the ODEmodel was too long. The first aspect of spending more time inthe inverted-classroom than the traditional format is reflectedin the results for question Q2 in Section 2 (Fig. 9). Reasons pro-vided by students for additional time spent include: (1) theycannot ask the instructor questions when watching the videosso that they have to watch the videos repeatedly (10% of stu-dents); (2) they get distracted by other things when watchingthe videos so that they spend more time than expected (5%

of students); (3) they have to watch all the videos and then do

oom approach in facilitating students’ learning. Questionsd deviation.

education for chemical engineers 1 9 ( 2 0 1 7 ) 1–12 11

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omework in class (4% of students). Therefore, students felthey spent more time in the inverted-classroom approach. Onetudent mentioned in the survey that it would be challengingf he took more than two inverted-classroom courses at theame time. In spite of this time-consuming issue, studentstill recommended to apply the inverted-classroom approacho other technical courses (as shown in Fig. 9 for question Q6f Section 2 in the surveys).

. Limitation and implication

2% and 74% students participated in the first two teachingurveys, while only 32% students finished the last survey. Theeason for this is that the teaching surveys were not manda-ory. Survey questionnaires were emailed by the instructoro students, and students turned in the anonymous surveysn class or to the instructor’s mailbox. Since all MATLABmplementation trainings were offered after the related theoryas introduced in class, the last teaching module was given

round the end of the semester after PID controller design wasntroduced. Senior students were busy with job searching andraduation activities during that time period, and those activ-ties distracted them from taking Survey 3. This is the majoreason for the low number of students participating in Sur-ey 3, who were thus a down-sampled population of studentsrom those taking Surveys 1 and 2. Therefore, the comparisonf the results from Survey 3 to those from Surveys 1 and 2 onhe inverted-classroom teaching approach (shown in Table 6nd Fig. 9) may not be of statistical significance. Although only

small portion of students taking Survey 3, the results shownn Figs. 8 and 9 indicated that the majority of these studentsad made large improvement in their skills on PID controlleresign and that these students were positive with the invertedlassroom approach.

The teaching effectiveness was evaluated by student per-eption data shown in surveys. The quizzes given at theeginning were designed to test students’ understanding ofhe teaching videos. They were not used as direct measure-

ent on students’ improvement in knowledge and skills,lthough self-assessment strategies have been shown to beerifiable measures of student learning in other studies (seeeference Elmer et al., 2015 for an example). In future, stu-ents will be asked to work on those problems shown inhe training examples before they watch videos. They willork on the same problems in MATLAB after they view the

raining modules. The instructor will grade students’ twoets of solutions so that he can directly evaluate students’mprovement in their knowledge of process modeling andontrol and their skills of using MATLAB in solving prob-ems in process simulation and control. In addition, usingomparison cohort to compare the performance of differ-nt groups of students under different teaching methodswithout MATLAB, with MATLAB in class, with MATLAB inhe flipped-class format) is another way to provide betterndication of students’ improvement in their knowledge andkills.

Whether we should use the inverted-classroom approacho teach MATLAB is another debated problem. Teaching MAT-AB in class has the advantage that students’ questions cane resolved by the instructor in time. On the other hand, the

nverted-classroom approach provides a flexible time frame-

ork for students to repeatedly access the teaching videos.he instructor (Huang) has taught a graduate-level course

in which students have advanced MATLAB experience. Thepersonal observation from the instructor is that the inverted-classroom approach benefits the students with less MATLABexperience more. This is because MATLAB beginners canfollow the videos step by step to build their own MATLAB pro-grams and they can watch the videos repeatedly until theyfinish their programs. They may not be able to follow the paceof the instructor in the traditional teaching format. We fur-ther hypothesize that the inverted-classroom approach maybe more suitable for the courses introducing techniques withwell-established routines. In addition to courses introducingSimulink simulation, the course for chemical unit lab exper-iments may be another good course for implementing theinverted-classroom approach. Training videos for bio-lab skillshave been developed at Villanova University in BiochemicalEngineering Courses and students are positive with the virtualexperiment teaching approach (Weinstein, 2015).

As mentioned in Reference (Elmer et al., 2015), the besttime length for flipped-class videos is less than 15 min tocapture students’ attention and improve students’ under-standing. Most of teaching videos were thus made to fit in thetime windows of 15–20 min. Some videos were slightly longerthan 15 min because MATLAB Simulink operation is consistedof sequential steps and a complete video for one example mayfacilitate students in following the steps better.

7. Conclusion

We proposed the first inverted-classroom teaching approachin chemical engineering for educating MATLAB programmingskills to simulate ODE models, perform Laplace Transform,and design PID controllers for chemical processes. Threemodules containing eleven examples were designed and13 videos were employed. Three student surveys were con-ducted separately for all the three teaching modules. Surveyresults indicated that these inverted-classroom teachingmodules significantly enhanced students’ ability of usingMATLAB to solve process modeling and control problems inchemical engineering. In addition, the inverted-classroomteaching modules enhanced students’ knowledge levelson mathematics-intensive concepts in process modelingand control. Surveys also showed that students’ experi-ence became more positive with the inverted-classroomapproach as they became more familiar with MATLAB andthe inverted-teaching approach. Students indicated that theyspent more time in the flipped-class teaching format. We needto address this issue in the future application of the inverted-teaching approach to teaching MATLAB programmingskills.

Acknowledgements

We appreciate the help from Dr. Gabriele Bauer, the director ofVillanova Institute for Teaching and Learning (VITAL), in offer-ing suggestions on designing teaching survey questionnaireand providing feedback on the manuscript. We appreciate Dr.Michael Smith’s help in proof-reading the manuscript. Weappreciate the financial support from Villanova VITAL.

Appendix A. Supplementary data

Supplementary data associated with this article can be found,in the online version, at doi:10.1016/j.ece.2016.08.001.

12 education for chemical engineers 1 9 ( 2 0 1 7 ) 1–12

Weinstein, R.D., 2015. Improved performance via the inverted

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