First Quantitative Measurement of Motivation. Study of the Effects of Active Learning Strategies

Scientific Study, 2013

28 Pages
















Most freshmen in engineering departments link an equation learned in a course as a unique theory specific to the subject and fail to realize that it is part of a more general notion that can be applied to a wide variety of natural phenomena. The students are able to use formulas related to the theory perfectly, but sometimes fail to understand what the basic concepts hidden behind the applications are. As a result, many students do not know how to apply similar formulas in other courses in the department. On the other hand, engineers are problem solvers; they need good critical and creative thinking skills to increase the performance of a process or design a new plant under technical, social, economic, regulatory, and environmental constraints. By consequence, how can engineering students be taught to achieve these goals? Literature has shown that effective teachers have succeeded in making students feel good about school and learning, thus increasing student achievement. Moreover, students in an actively taught class do a better job of learning (memorizing) the material they are exposed to, compared to those in a passively taught section. It is also agreed that motivation is probably the most important factor that educators can target in order to improve learning.

The main objective of this investigation is to quantify the effects of an active learning strategy on the motivation of students in a process control course. Different from the qualitative methodologies previously presented in the literature, the objective of this first quantitate method is an attempt to measure the impact of an active learning strategy on the motivation of students by introducing a motivation factor for each student calculated from the Final Grade Point (FGP) and the Cumulative Grade point average CGPA. In the first part of the investigation, the Relative Performance (RP) of students is used as a new tool to gauge the effects of the active learning strategy on the performance of students. For the second part of this quantitative method, the Dadach Motivation Factor “DMF” is introduced in order to measure the effects of the active learning strategy on the motivation of students. For the validation of this first quantitative method, the final results will be compared to the student survey as a qualitative method.


According to Williams and Williams [1], to improve their motivation, students must have access, ability, and interest, and must value education. The teacher must be well-trained, must focus and monitor the educational process, be dedicated and responsive to his or her students, and be inspirational. The content must be accurate, timely, stimulating, and pertinent to the student’s current and future needs. The method or process must be inventive, encouraging, interesting, and beneficial, and provide tools that can be applied to the student’s real life. The environment needs to be accessible, safe, positive, personalized as much as possible, and empowering. In the same perspective, Case and Fraser [2] recommended reducing content coverage, promoting active learning in the classroom, and using assessment methods that require students to demonstrate a high level of understanding and ability. For example, Turner and Patrick [3] examined how a mathematics student’s work habits (i.e., classroom participation) are related to a combination of both student factors (math achievement, personal achievement goals, perceptions of classroom goal structures, and teacher support) and features of the classroom context (teachers’ instructional practices and average perceptions of classroom goal structures). Their study provided some evidence that teachers’ instructional behaviors can contribute to the development of student work habits by encouraging and supporting them to participate in classroom activities.

Active Learning is generally described as a process in which students engage in doing things and thinking about what they are doing in the classroom [4]. Active learning includes a variety of activities, such as pausing in lectures for students to consolidate their notes, interspersing short writing exercises in class, facilitating small group discussions within the larger class, incorporating survey instruments, quizzes, and student self-assessment exercises into the course, leading laboratory experiments, taking field trips, and using debates, games, and role plays [4,5]. Some of the benefits of active learning are: a) students are more involved than in passive listening; b) students may engage in higher order thinking, such as analysis, synthesis, and evaluation, and c) student motivation is increased [4]. In addition, Hattie [6] and Marzano [7] have independently used statistical methods to average the findings of many thousands of the most rigorous studies on active learning. Their findings show that, for the best active methods, if a student is put in the active-learning group, then on average, s/he will do more than a grade and a half better than if s/he had been placed in the traditional learning group.

To support the efficiency of the active learning strategies, Figure 1 shows that students could retain up to 90% of what they learn through direct experience.

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Figure 1: Dale’s Cone of Experience [8]

Since engineering students need to work with real process applications, charts, diagrams, hands-on practices, and demonstrations concurrently with theory, equations, and words, they are encouraged to become active rather than passive learners by developing collaborative and co-operative skills, and lifelong learning skills [9, 10]. In recent years, the Accreditation Board for Engineering and Technology (ABET) has increased the pressure on engineering schools to produce graduates who are prepared to engage in unstructured problem solving and to work in groups. Indeed ABET now requires institutions to demonstrate that their graduates have developed eleven competencies including the abilities to design a system, component or process to meet certain needs, to function in multidisciplinary teams and to communicate effectively [11]. In group-work activities, engineering students have the opportunity to learn from and to teach each other when applying a newly learned concept in a short application such as problem solving. Group activities include design projects, in-class presentations, computer simulations, and lab experiments [11-13]. For example, Niekerk et al. [14] used Pair Problem Solving (PPS), a co-operative learning strategy, to enhance the conventional teaching method used in Thermodynamics, a third year module in the Mechanical Engineering curriculum. During the interviews, an important indicator of the success of PPS is that a large majority of students (80%) felt that they gained insight and knowledge from working in pairs. Eighty-seven percent of the students indicated that they would prefer to work in pairs again. Also, five of the six students were positive about working in pairs. The sixth student was already studying with a friend and was therefore not against working in pairs – only against the fact that she could not choose her partner.

Problem-Based Learning (PBL) is another active learning activity and has been considered by a number of higher educational institutions in many parts of the world as a method of delivery. Through PBL, engineering students can acquire creative thinking skills and professional skills as they tackle complex, interdisciplinary and real-life problems. PBL has also been linked with increased student motivation and interest in a subject [15].

Another effective teaching style that could enhance the students’ intrinsic motivation and achievement is to adopt a deep approach to learning by trying routinely to relate course material to known situations. Many science and engineering teachers successfully used analogies to build conceptual bridges for students between what is familiar (an analogy concept) and what is new (a target concept) [16, 17, 18, 19, 20]. According to Yelamarthi et al. [21], some of the immediate positive outcomes in using analogies are increased student motivation, better participation in class and laboratory exercises, better rapport between the student and instructional group, increased creative thinking of the students and active student participation in providing valuable course feedback. Finally, open-ended questions are also a useful tool to promote creative thought, problem-solving skills, and the cognitive abilities of engineering students because they inherently build a stronger bond with better memory and a more engaged conversation [22].


There are many ways to collect evidence of student learning. To simplify the options, assessment efforts are categorized as direct and indirect measures. According to Maki [23], direct methods prompt students to represent or demonstrate their learning or produce work so that observers can assess how well student texts, responses and skills fit program level expectations. The strength of direct measurement is that faculty members are capturing a sample of what students can do, which can be very strong evidence of student learning. A possible weakness of direct measurement is that not everything can be demonstrated in a direct way, such as values, perceptions, feelings, and attitudes [24]. Some typical examples of direct measurement done by faculty include [25]:

1) Grades

2) Standardized tests

3) Pre/post tests

4) Analysis of assignments designed to test conceptual understanding (e.g., concept maps, pro/con grids)

5) Observations of students performing a task

6) Analysis of student work products (e.g., exams, essays, oral presentations)

7) Senior thesis

8) Portfolios compiled over the course of undergraduate study

Indirect methods capture students’ perceptions of their learning and the educational environment that supports that learning, such as access to and the quality of services, programs, or educational offerings that support their learning [23]. Typical examples of indirect measures of learning outcomes done by faculty include [24]:

1) Grades

2) Course evaluations (during the semester and end-of-semester)

3) Concept questions, “muddy cards,” and other in-class techniques

4) Surveys of student attitudes about new pedagogy, curriculum, etc.

5) Surveys asking students for reflections on their learning

6) Exit interviews

Grading is the “process by which a teacher assesses student learning through classroom tests and assignments, the context in which teachers establish that process, and the dialogue that surrounds grades and defines their meaning to various audiences. As a consequence, grading could have four different roles: a) evaluating the quality of a student’s work; b) communicating with the student, as well as employers, graduate schools, and others; c) motivating how the students study, what they focus on, and their involvement in the course; and d) organizing to mark transitions, bring closure, and focus effort for both students and teachers [25]. According to Breslow [24], grades provide a measure of how much students have learned. However, the validity of grades as an assessment measure is dependent upon how systematically and rigorously assignments, exams, and so forth, are analyzed for evidence of Student Learning outcomes (SLOs).

As an indirect measure of SLO, student surveys have become increasingly important tools for understanding the educational needs of students. When combined with other assessment instruments, many departments have successfully used surveys to produce important curricular and co-curricular information about student learning and educational experiences [26]. The different indirect measures can provide additional information about what students are learning and how this learning is valued by different stakeholders. However, as evidence of student learning, indirect measures are not as strong as direct measures because we have to make assumptions about what self-reporting actually means [27]. Because each method has its limitations, an ideal assessment program combines direct and indirect measures from a variety of sources. This triangulation of assessment can provide converging evidence of student learning [27].


Process Control is applying the principles of automatic control within the process industries. It implies that two disciplines are involved, Chemical Engineering and Control Theory. The Process Control Course ( CHEM N 304) described in this paper is a four-hour lecture course offered during the winter term of the third year students of the Chemical & Petroleum Engineering Department of Abu Dhabi Men’s College (UAE). The course has forty sub-learning outcomes within seven distinct learning outcomes and was taught to fifty-five students divided in three sections.

Since engineers are mainly involved in solving technical problems or innovating new processes, critical and creative thinking skills need to be developed. In order to reach this objective and enhance the intrinsic motivation of the students, the teaching style was based on active learning [4]. The objective of the utilization of this strategy was to help students make relevant connections among course materials; transforming course them from opaque language into something they could visualize and integrate into their own knowledge network. In this perspective, a workbook was given to the students during the first class. This workbook provided relevant material being covered in the lectures, worksheet exercises, case-studies and labs that offered opportunities to build upon knowledge and apply basic process control principles. The teaching strategy included the use of analogies, interactive, cooperative, and inductive learning techniques.

Since students were not familiar with control theory, it was beneficial to utilize as many analogies as possible to explain the basic concepts of control theory. The final aim of using analogies was to give students different ways to visualize the abstract concepts of control theory that could help them understand better the physical phenomena hidden behind each equation in order to perform the calculations properly. The analogy between process control systems and brain/body interactions was extensively used to help the students create a link between what they already know about brain/body mechanisms and the sophisticated concepts of control theory.

During the first half hour of the first class of each week, students were asked to answer questions related to the previous lecture. A discussion between the students was encouraged and a final conclusion, that clarified the key points of the precedent chapters and connected the students with the new topic, was also presented. Secondly, in order to encourage curiosity to discover the unknown, all the questions about the new lectures were open-ended questions. In this perspective, the question “Why?” was very often used. In their smiles, I could guess that some students accepted the challenge to think deeply about the topic to formulate answers. In addition, the question “What happens if…?” was used instead of the question "Do you have any questions?" The discussion with the students generally provided an indication of their level of understanding of the material.

To grasp the concepts better, five selected videos (20 minutes each) from YouTube with exercise books were used whenever students lost some focus and it was needed to recreate images in their mind that could help them follow the difficult theory of process control. Very often, videos had to be stopped and students were asked open-ended questions for general discussions about the key points of the subject covered. After each video, students were asked to work in groups to fill in the blanks in the corresponding exercise book. Students were also invited to review these videos at their convenience.

Class activities of two hours were usually organized after three or four lectures. As defined in the literature [29], class activities were based on Pair Problem Solving (PPS), a co-operative learning strategy. Through PPS, three or four students had opportunities to explore and solve problem situations. They were encouraged to use whatever solution strategies they wished. Students were also given opportunities to share their various strategies with each other and decide together about the best solution to solve short problems or the selected options for more complex process control situations.

Six lab experiments (two to demonstrate and four to conduct experimental investigation in groups of three students) were part of the active learning strategy to help students understand in depth the theory of process control and learn how to apply it. Lab experiments in this course were meant to help students to work in teams and teach them how to carry out experiments in a safe manner, collect data using an investigative strategy, analyze experimental values and compare them to theory, present results in a professional manner and learn to use process control software tools.

Problem-based learning (PBL) is another activity used in the active learning strategy. The objective of the project was to encourage curiosity and hunger for exploration in students by using all the library resources to search for the latest technologies and applications of process control for a specific application.


In this process control course, a variety of assessments were used throughout the semester-long course. First, two written exams (30 marks and 2-hour exams) were organized respectively in the middle and the end of the semester. Secondly, the assessment of the active learning strategy (lab experiments, cases studies, and project) represented 40% of the total mark. The non-exam activities that were assessed are:

a) Team-Work as Pair Problem Solving (PPS) (10 marks)

b) Inductive Learning (20 marks)

c) Individual Final Project as Problem Based learning (PBL) (10 marks)

In conclusion, the assessment strategy used in this process control course is shown in Table 1.

Table 1: Assessment Strategy of the Course CHEM N 304

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The following assumptions are used in this investigation: (1) The grade obtained for each activity in Table 1 is taken as an indicator of student achievement for the learning outcomes covered by the corresponding assessment. (2) Since students had been assessed on different activities that covered all the learning outcomes, the final grade of a student can then be used as a direct measure of his average achievement for the process control course. (3) The fifty five students took the same thirty five (35) courses of three credits including twenty six (26) technical courses (74%). Consequently, it is assumed that the Cumulative Grade Point average (CGPA) of all the courses provided by the college is a good approximation of the average performance of each student for the technical courses taken in the department. (4) It is assumed that no external factor (family, health, etc.) affected the performance of the students. (5) As presented in Table 2, the grading system of the college is the reference for this investigation.

Table 2:Grading System of the College

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The goal of the first part of the quantitative analysis is to compare the performance of each student in the process control course with his average performance related to all the courses taken in the department. For this purpose, Equation (1) is presented in this paper as a tool to define, in percentage, the relative performance RP of each student:

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A positive or a negative value of the RP means that a student performance in this process control course was higher or lower than his average performance for all the courses taken in the department. The distribution of the performance of all students is shown in Figure 2.

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Figure 2: Relative Performance of Each Student in Relation to CGPA

The overall analysis of Figure 2 indicates that 38 students (69%) had a positive RP. Figure 2 shows also that the highest values of the positive RPs are located in the lower CGPA region. This finding could be explained by the fact that it is easier for students in the lower CGPA region to increase their grade. Finally, the sum of the positive and negative relative performances of all the students indicates that, in average, every student had a positive RP of +6.86%.

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First Quantitative Measurement of Motivation. Study of the Effects of Active Learning Strategies
Process Control
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enginering education, active learning, motivation, first quantitative method
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Zin Eddine Dadach (Author), 2013, First Quantitative Measurement of Motivation. Study of the Effects of Active Learning Strategies, Munich, GRIN Verlag,


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