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Implementing Motivational Features in Reactive Blended Learning: Application to an Introductory Control Engineering Course Juan Albino Méndez and Evelio J. González
Abstract—This paper presents a significant advance in a reactive blended learning methodology applied to an introductory control engineering course. This proposal was based on the inclusion of a reactive element (a fuzzy-logic-based controller) designed to regulate the workload for each student according to his/her activity and performance. The contribution of this proposal stands on the inclusion of elements related to motivational factors in the students. Student motivation has been widely identified as a key factor for the academic success of every teaching–learning activity. Index Terms—Applications in subject areas, classroom teaching, distance education, simulations, teaching/learning strategies, telelearning.
I. INTRODUCTION
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HIS paper presents a significant advance in the proposal presented by the authors in [1] for reactive blended learning applied to an introductory control engineering course. In that paper, a control strategy was designed to regulate the participation of every student according to a desired target (for instance, the desired level of participation in the course was set to 4 h per week). To achieve that goal, a control mechanism monitors the activity of each student and reacts by changing the assignment loads. Thus, two variables are regularly measured throughout the course: the performance of the student (P) and the participation index (PI). According to these variables, the fuzzy controller obtains the best workload for each student and also sends qualitative information to each student about its performance (“you are doing well,” “you need to work harder,” etc.). The results showed that the students easily achieved the proposed target skills, exhibited a marked degree of satisfaction, and got better results in exams with a well-distributed workload. Although statistically the increase in performance was not too significant, the results seemed promising. In this paper, the inclusion of motivational factors in the system and their validation results are presented. It has been shown that control engineering has become increasingly mathematical in the last decades [2]. This implies that many mathematical concepts must be explained before having Manuscript received July 22, 2010; revised October 28, 2010; accepted December 06, 2010. Date of publication January 13, 2011; date of current version November 02, 2011. The authors are with the Departamento de Ingeniería de Sistemas y Automática y ATC, Universidad de La Laguna, La Laguna 38206, Spain (e-mail:
[email protected];
[email protected];
[email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TE.2010.2102028
students begin elementary control system design. In a course like that proposed, many students have difficulties in assimilating all the concepts taught and, as a consequence, a decrease in the student motivation usually ensues. Motivation can be defined as those processes that can arouse and bring about behavior, give direction and purpose to behavior, allow behavior to continue, and lead to choosing or preferring a particular behavior [3], [4]. In other words, motivation is an orientation (positive, negative, or ambivalent) toward a goal. The presence or absence of this internal force in a student is a key factor from a pedagogical point of view since an unmotivated student will not perform effectively [5]. As can be deduced, motivation clearly influences the quality of learning since it acts as an enabler for learning and academic success [6], [7]: “motivation affects effort, effort affects results and positive results lead to an increase in ability” [8]. However, in spite of this concept having been identified as a critical success factor [9], motivation has come to be called “the neglected heart of our understanding of how to design instruction” [10], [11] since motivational factors are often ignored when a new course is planned. This paper is organized as follows. Section II describes the educational framework that supports additions to the applied methodology, particularly with respect to motivational aspects of learning. Section III describes the course in which the methodology is applied. Sections IV and V describe the structure of the regulation mechanism proposed in the paper, with a brief guide to fuzzy logic control. Student feedback from the validation phase is reported in Section VI. Finally, some conclusions and recommendations are offered in Section VII.
II. EDUCATIONAL FRAMEWORK: MOTIVATIONAL ASPECTS OF LEARNING According to self-determination theory (SDT), human motivation is a function of three psychological needs for self-determination (experiencing oneself as initiator and regulator of one’s own actions), competence (producing behavioral outcomes and understanding their production), and relatedness (experiencing satisfactory relationships with others and with the social order in general). According to this theory, the concept of psychological needs makes motivation a dynamic concept [12]. Two kinds of motivation are usually cited: intrinsic and extrinsic. Intrinsic behavior refers to the personal desire to fulfill one’s own needs; it is identified with factors such as attitude and expectation, goals, and emotions [13]. In other words, it refers to what people will do without external inducement [14]. Intrinsic motivational factors are rather complex to identify. The
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authors would cite as significant studies those factors identified by Malone and Lepper [14] (challenge, curiosity, control, fantasy, competition, cooperation, and recognition) and in the ARCS model by Keller (attention, relevance, confidence, and satisfaction) [15]. In contrast, extrinsic motivation usually depends on rewards, which can take the form of praise, privileges, or tangible objects [16], [17]; it is identified with factors such as social pressure (especially by parents), clear direction in the learning process, reward and recognition, punishment, facilities, school environment, and more. However, based upon SDT, motivation should rather be described as a gradient of the perceived locus of causality (PLOC) of specific behavior instead of disjointed sets of extrinsic and intrinsic motivations [9]. Several authors have plotted some strategies to improve student motivation. Thanasoulas states the need of a motivational repertoire of strategies, including those of increasing the learners’ self-confidence and creating learner autonomy [18], since a strategy based exclusively on rewards focuses on performance outcomes rather than the more favorable and productive process of learning itself. For Crookes [19], it is important to provide variety and to avoid too-regular patterns of classroom routine; at the same time, the learner should “perceive that important personal needs are being met by the learning situation.” Taking these statements into account, a list can be provided of concrete proposals to improve students’ intrinsic motivation. This list, which is not intended to be exhaustive and can be adapted to other courses/institutions, recommends the following. — Including a repertoire of teaching/learning strategies and activities, especially those that involve quality assessments [20]: classroom questioning, written assignment, oral presentations, project work, journal or log writing, portfolios, fieldwork, debate, panel discussion, research paper writing. — Dealing with the questions “Why learn this subject?” and “What related disciplines are taught at this university?” during the first session of the course. For this purpose, some students from more advanced courses can be invited to give their impressions and talk about their experience. — Including historical references in the learning process to awaken students’ interest. — Inviting professionals/teachers to answer students’ uncertainties. — Giving real examples of applications that use the course material, which could include external activities. — Giving effective detailed feedback [21]. — Involving students in the learning process, for example by having them propose requirements for hypothetical pedagogic software tools that could be implemented for subsequent courses. — Modifying instruction in the light of the assessment data (difficulty level of work, more/less work load). Unfortunately, motivation is often difficult to manage since it involves a complex facet of human behavior [13]. Often, one single cause can demotivate an entire group of students. Alternatively, it is not uncommon that the same learning activity can simultaneously motivate some students and demotivate others. For example, increasing the difficulty of a task is supposed to increase the motivation of high-performing students, but might cause poor students to give up trying. Even
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highly motivated students can become demotivated if the actual learning process does not fulfill their expectations. Furthermore, motivation is not a physical variable that can be measured, but a psychological state that can only be estimated, usually through tests administered to the students. Several authors have developed different proposals for these tests [22]–[28]. They mainly consist of a set of statements that students score in a Lickert-like scale and are frequently integrated in more complete studies that include other aspects such as self-testing, time and study environment, anxiety, concentration, attitude, critical thinking, and organization. Examples, taken from [27], of statements from motivation tests are the following. In a class like this, I prefer course material that really challenges me so I can learn new things. If I can, I want to get better grades in this class than most of the other students. It is important for me to learn the course material in this class. Getting a good grade in this class is the most satisfying thing for me now. Motivational features are even more important when distance activities are developed, both in e-learning and blended learning (BL), since this type of activity involves the concept of self-regulation of learning. This is an important characteristic that contributes to student motivation [27] since learning is a personal process of assigning meaning through personal impact. Several studies [29], [30] affirm that online students are more intrinsically motivated than face-to-face (F2F) students in similar courses. Martens et al. [31], in agreement with Thanasoulas and Crookes, showed that high levels of intrinsic motivation on e-learning are indicative not of higher levels of achievement, but rather of different learning activities, especially those related to exploratory behavior. More in-depth research in BL suggests that blended courses produce a stronger sense of community among students than do either traditional or fully online courses [32]. In this sense, Schmidt [33] proposes a conceptual framework for motivation and cognition in blended learning, based on the work by Pintrich and Schrauben [34] for traditional face-to-face interaction. In this way, student involvement in learning is a result of both motivational and cognitive components. III. COURSE DESCRIPTION AND STUDENT BACKGROUND The improvement proposed here was tested in the same control engineering course presented in [1]. This course is taught in the first term in the third course (third year) of the Electrical Engineering degree; its syllabus is given in Table I. The course has two different content blocks: System Analysis and Control Design. The former gives an introduction to system theory, dealing with the description and analysis of single-input–single-output (SISO) linear systems. The latter deals with the design of controllers, PID control, compensation, and basic state-feedback designs. The skills to be instilled in the students (in the terms of the European Higher Education Area) are that they should be the following:
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TABLE I SYLLABUS OF THE INTRODUCTORY CONTROL ENGINEERING COURSE
TABLE II SCHEDULING FOR AN INTRODUCTORY CONTROL COURSE
— capable of understanding the problems and time constraints related to the design and analysis of computer control systems; — capable of designing and implementing control systems; — capable of using mathematics and software tools and having good programming skills; — knowledgeable about introductory control technology; — able to lead projects, comfortable in a team environment, able to sell ideas through persuasion, and having good communication and relationship skills; — able to work independently, assume responsibility for their work, and complete tasks on time. These competences are inculcated by having the students carry out the range of activities shown in Table II. Students enrolling in this course should have met some mathematical tools in previous courses and seminars: Laplace transforms, eigenvalues/eigenvectors, interpretation of matrices,
basic differential equations, difference equations, state transition matrices and solutions to discrete-time state models, -transforms, discrete Fourier transforms, and Fourier analysis. The course continues with a BL methodology, whose success clearly depends on the adaptation of the students to the use of Web-based systems for learning, which allow the students to build personal relationships. The course employs several resources for online activities: Moodle (Modular Object Oriented Development Learning Environment), a widely used Content Management System (CMS), and a simulator (designed by the authors) called ControlWeb [38] as a learning support for process control topics. The application of online activities in degrees like this is supposed to be easier and more satisfactory since the students have had previous computing experience, which is one of the issues identified by Mason and Weller [36] that positively affect student satisfaction in this type of learning
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Fig. 1. Fuzzy logic controller scheme.
environment. Garrison and Kanuka [37] point out that BL environments are complex in nature and demand a rethinking and redesigning of the teaching and learning relationship based on the specific needs of the learning situation. In this sense, a reactive blended learning proposal could be a solution. In the new proposal presented in this paper, the authors continue the use of the ControlWeb [38] educational simulator and the stimulus it provides to the student to practice many topics of the course: SISO plants, stability, closed-loop configurations, etc. The student thus has a free and easy access to the tool, without any spatial and/or temporal restriction. IV. FUZZY LOGIC CONTROL The engineering problem of regulating the output of a particular system is accomplished by proposing a closed-loop system. The key point in this structure is the feedback of the variable of interest. This variable is used by the controller to decide the input to be applied to the system. Among the various possibilities, the use of fuzzy logic control (FLC) is particularly useful when the model of the system to control is either unknown or too complex to provide reliable predictions [39]. Instead of using a strict quantitative approach to describe the process, FLC emphasizes empirical knowledge of the process behavior. This knowledge, translated into a linguistic form, constitutes the basis of fuzzy logic control. Taking into account the complexity of the student behavior in the learning process, fuzzy logic seems an interesting tool to advise the lecturer of the actions that should be taken during the course. In fuzzy logic, the description of linguistic terms is done with fuzzy sets. For each variable , a partition is done in which several sets are defined covering the whole universe of discourse of . The definition of each set is done by means of a membership function that defines how each point in the input space is mapped to a degree of membership to that set. The membership function can be an arbitrary curve taking values in the range [0, 1] and whose shape can be defined by the user. Typical examples are piecewise linear functions, the Gaussian distribution function, the sigmoid curve quadratic, or cubic polynomial curves. An FLC consists of three steps: fuzzification, inference, and defuzzification (Fig. 1). In the fuzzification stage, the crisp values are converted into degrees of membership for linguistic terms of fuzzy sets. Thus, for an input value , the fuzzification will produce the degree of membership to the fuzzy sets that will be denoted by , . In the inference, the output fuzzy set is generated. This set is obtained using the rule-base system. Different methods to construct this set can be found in the literature. In this paper, the minimum was chosen as T-norm and the Mamdani approach was used for implication [41]. The rules are generated by an
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expert who understands, from his/her own experience, student behavior in that specific course. As noted, the result of the inference is also a fuzzy set, so it is necessary to convert this fuzzy output into a crisp output value. This is done in the defuzzification stage of the procedure. The method chosen here is the center of gravity. This method calculates the crisp value by computing the center of area under the curve defining the output fuzzy set. In its discrete form, the output value is computed as (1) To illustrate the whole procedure, consider that the controller and , and one output . has two inputs, For each of the universes of discourses, and , a set of fuzzy partitions are defined: , , and . Each of these fuzzy partitions will correspond to a linguistic concept (very large, large, small, ). In the fuzzification, the degree of membership of the variables and to each partition is obtained. If , then the degree of membership to the set large is low. The inference of the fuzzy controller is based on a rule-base system as : IF
is in
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: IF
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Thus, if are the input crisp values at a given time, the evaluation of these rules will produce the crisp output value (defuzzification). In general, the algorithm of the fuzzy controller will consist of offline and online tasks. The offline tasks are to define the following: — input and output variables (linguistic variables); — fuzzy sets for each variable (large, very large, ); — membership functions of the sets; — rule base; — defuzzfication mechanism. The online tasks that have to be performed at each sample instant are the following: — obtaining the input crisp values; — fuzzification: computation of the degrees of membership to each fuzzy set; — inference: computation of the output fuzz set; — defuzzification: computation of the output crisp values. V. MOTIVATIONAL ASPECTS IN REACTIVE BLENDED LEARNING A. Student Learning Model As already stated, the aim of this paper is to provide a mechanism to regulate some important variables affecting a student’s learning mechanism. The methodologies used are based in system theory and control engineering. The focus is on the motivational aspects that directly affect the involvement and academic achievement of the student. The approach adopted to
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TABLE III SAMPLE TEST TO ESTIMATE STUDENT MOTIVATION
Fig. 2. Model of the student learning process.
model the student learning process is presented in Fig. 2. Two interconnected main blocks are considered in this approach: the motivational model and the involvement and achievement model. The variables measured are the performance (P) and the participation index (PI) of the student. The performance is measured through weekly tests and laboratory exams. The PI is related to the activity of the student in the course and is obtained from the analysis of the last week’s statistics taken from the Web tool. In [1], a first approach to regulate these variables was proposed. Although the results obtained with the proposed system offered promising results in the actual implementation, the method proposed did not affect the problem of motivation in the learning process. As mentioned in Section II, motivation has a direct influence on the involvement and achievement of the student. In this paper, this aspect is considered explicitly, and a closed-loop regulator was designed to maintain the motivational state of the student at a satisfactory level throughout the course. Observe that, in Fig. 2, disturbances affect both the motivational model and the involvement and achievement model. To simplify the model, only three types of disturbances were considered. “Emotional factors” refer to variations in the psychological state of the student. “Stressful environment” refers to the degree of stress due to the influence of socioeconomic aspects. “Global workload” represents the workload for all the courses taken.
Fig. 3. Student environment in the learning process.
One of the main difficulties in controlling the degree of student motivation is that of first estimating his/her motivation state. Here, that estimation was made on the basis of 10 questions to be answered by students at the end of each unit, on a 5-point Likert scale (from “strongly disagree” to “strongly agree”). These tests, based on the questionnaires cited in Section II, can vary slightly depending on the unit in question. Table III shows a typical questionnaire for a unit. From this test, an estimation of the degree of motivation (M) and level of anxiety (A) of the student can be obtained. As can be seen in Fig. 2, two kinds of actions are used to control the motivational level. — Positive reinforcement (PR). This actions refers to explicit recognition of work, awards, quantitative increase in marks, reduction in difficulty of tasks, . — Motivational activities (MA): Workgroup sessions, project design, laboratory experiments, simulation activities, . Other elements in the student learning process are shown in Fig. 3. As can be observed, the main elements correspond to academic activities (F2F or Web-based), motivational activities, positive reinforcements, and disturbances. In this paper, disturbances are assumed not to be measurable, and they are not
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Fig. 4. Model of the student learning process. Disturbance terms have not been included for the sake of clarity.
included directly in the control loop. The aim of the control actions proposed will be to reject or minimize the negative effects of these disturbances on the performance of the student during the course. B. Regulation of Motivation Using this model, a two-controller fuzzy system was proposed (see Fig. 4, where disturbance terms have not been included for the sake of clarity). The controllers were implemented with a sample time of one week (although two weeks can also be used). In the Assignment Fuzzy Controller, the output variables are the week assignment load (WAL) and the student flag status (Flag) [1]. The Flag is a stimulus intended to alert the students to the general progress of their learning, to elicit reactions, and to change their attitude (if necessary). Three values are possible (red, orange, and green) according to the student state. Green indicates that the student’s progress is satisfactory, while red means that a problem was detected and some action is required to correct this. The WAL advises the lecturer about the correct number of exercises and problems and the objectives for the current week. The controller will propose assignment loads that can be very high, high, medium, low, or very low depending upon whether the participation of the student in the course that week was very active, active, medium, little active, or very little active. Performance is also taken into account in this decision. The contribution of this paper is focused on the second fuzzy controller, the Motivational Fuzzy Controller, which is responsible for suggesting appropriate actions to the lecturer to maintain the desired level of student motivation. The information used in this controller is the estimated level of anxiety and motivation and the participation index of the student. Depending on the values of these variables, the controller decides to maintain, decrease, or reduce the motivational activities and positive reinforcements for the next sample time. It is important to take into account that, in the approach considered, variations in MA will not affect the WAL. For instance, increasing MA implies a new planning of all the activities so that WAL stays constant. The rule base for the Motivational Fuzzy Controller was set up according to the experience reported in the literature [12]–[30] using a 13-rule base system. The goal is to
activate the control mechanisms only when necessary, as their excessive use can reduce their efficiency (idealization of the objectives, scare of failure, undesired effects on the curve of learning speed, intrinsic motivation undermining, etc.) [41]–[43]. The rules considered are the following. R1: If M(k) is HIGH and (PI(k) is ACTIVE or MEDIUM) THEN MA(k) is LOW. R2: If (M(k) is HIGH and PI(k) is INACTIVE) THEN MA(k) is MEDIUM. R3: If M(k) is MEDIUM and PI(k) is ACTIVE THEN MA(k) is MEDIUM. R4: If M(k) is MEDIUM and (PI(k) is MEDIUM or INACTIVE) THEN MA(k) is HIGH. R5: If M(k) is LOW THEN MA(k) is HIGH. R6: If A(k) is HIGH THEN PR(k) is HIGH. R7: If A(k) is MEDIUM and M(k) is HIGH and (PI(k) is ACTIVE or MEDIUM) THEN PR(k) is MEDIUM. R8: If A(k) is MEDIUM and M(k) is HIGH and PI(k) is INACTIVE THEN PR(k) is HIGH. R9: If A(k) is MEDIUM and M(k) is MEDIUM and (PI(k) is ACTIVE or MEDIUM) THEN PR(k) is MEDIUM. R10: If A(k) is MEDIUM and M(k) is MEDIUM and PI(k) is INACTIVE THEN PR(k) is HIGH. R11: If A(k) is MEDIUM and M(k) is LOW THEN PR(k) is HIGH. R12: If A(k) is LOW and (PI(k) is ACTIVE or MEDIUM) THEN PR(k) is LOW. R13: If A(k) is LOW and PI(k) is INACTIVE THEN PR(k) is MEDIUM. The system was implemented through Moodle modules, specifically test modules for motivation estimation, whose results are exported to datasheets. These datasheets are directly accessed by the fuzzy controller, implemented in Java, whose actions are included in the Moodle platform. This platform informs itself about how long the student has been connected to the platform as well as his/her performance status. VI. COURSE EVALUATION This proposal provides the lecturer with a helpful tool to monitor and regulate the student learning process throughout the course. As an example of a real application of this tool, the
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TABLE IV DEGREE OF ACHIEVEMENT OF THE PROPOSED SKILLS (%) WITH AND WITHOUT THE PROPOSED METHODOLOGY
particular case of a student in the seventh week of the course is described. The values measured in this week were: Perfor, Participation Index h , Anxiety mance and Motivation . The assignment controller calculates that WAL has to be changed to medium and the flag has to be changed to orange. The motivational controller tries to correct the decrease in motivation by proposing two actions: Positive reinforcement (PR) is set to low, and motivational activities factor (MA) is set to high. As a result, the lecturer decided to have this student (and all students in the same state) carry out a practical project in the laboratory as a motivational activity. For the WAL, the lecturer had to reduce the corresponding workload of this student to keep the WAL medium. This was achieved by reducing the programmed individual problems from four to one. From an evaluation point of view, it is interesting to analyze the effect of introducing the motivational aspects described in the reactive blended learning proposal. In this sense, the results from the application of the original proposal in [1] can be considered as having been taken from a control group for this purpose. As in that case, the evaluation of the methodology was carried out in three categories: — degree of learning and achievement of targeted skills; — degree of satisfaction with the proposed methodology; — performance of the student in the exams. In the three phases (conventional methodology, reactive BL, and reactive BL with motivational features), the participants were all the students matriculated in the course. In this way, any possible biasing in the results obtained should be mitigated. A. Degree of Learning and Achievement of Target Skills This part of the evaluation analyzes the degree of learning and achievement of target skills listed in Section III. This analysis consists of an individual report by the lecturer at the end of the course, processed jointly with a self-evaluation carried out by each student. As can be deduced, the innovation in the teaching–learning methodology does not yield a significant improvement in skills in the ability to work independently and the capacity for designing and implementing control systems
(3 and 1 points, respectively). However, there is an improvement of 7 points in both the development of mathematical skills, introductory control concepts, and control technology knowledge and communication, relationship, and ability to lead projects (Table IV). B. Degree of Satisfaction With the Proposed Methodology In order to quantify this degree of satisfaction, students were asked to complete a subset of the questionnaire described in [1], indicating their degree of agreement with a set of statements, using a 5-point Likert scale from “strongly disagree” (1) to “strongly agree” (5). The rest of that questionnaire was removed from the study since the questions were related to the Controlweb simulator. As can be seen in Table V, there was not a significant statistical impact on statements S1 (general satisfaction about the methodology), S2 (getting a deeper knowledge of the topics), and S6 and S7 (both related to workload during the course). The answers to S4 and S5 are especially interesting. These statements are related to the improvement in communication skills and confidence in mathematical topics. Both statements show better values, paralleling the results shown in Section VI-A on the improvement in the development of mathematical and communication skills. In this phase, when the course is about to finish, the students answered a single question about motivation. In particular, the students were asked to locate the source of their motivation (Table VI) in the terms used by Malhotra and Galletta [9]. The students tended to locate their motivation in somewhat external factors. This is a significant result for the methodology since students implicitly recognize its effect (among others) on their motivation. In SDT terms, the proposed methodology helps students to understand the product of their behavioral outcomes. C. Student Performance on Exams The students’ marks were compared to those from previous courses that used conventional methodologies focused on F2F lectures and reactive BL without motivational features. The results showed an improvement in the percentage of students
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TABLE V RESPONSES TO THE QUESTIONNAIRE: DEGREE OF SATISFACTION WITH THE PROPOSED METHODOLOGY. THE TWO NUMBERS ARE FOR REACTIVE BLENDED LEARNING WITHOUT (BEFORE THE SLASH) AND WITH (AFTER THE SLASH) THE MOTIVATIONAL FEATURES
TABLE VI RESPONSES TO THE QUESTIONNAIRE: PERCEIVED LOCUS OF CAUSALITY OF STUDENT’S MOTIVATION
giving them a clear direction as an extrinsic-like motivation (external regulation) and promoting their personal learning process and improving their self-confidence. VII. CONCLUSION AND RECOMMENDATIONS
passing the course (results are for the final exam at the end of the course), especially when compared to those obtained using conventional methodology: — Conventional methodology: 63%; — Reactive BL: 79%; — Reactive BL with motivational features: 88%. D. Discussion From the data collected, it seems that the new methodology has a clear impact in two areas: communication skills and confidence in mathematics, although more work, over more years and/or subjects, is be needed to corroborate this conclusion. Both improvements are to be expected since many of the reinforcement activities for unmotivated students are oriented to these objectives (in particular to the confidence in mathematics),
This paper has presented a significant advance in the fuzzylogic-based proposal presented by the authors in [1] for reactive blended learning applied to an introductory control engineering course. The results for that proposal were promising: easier achievement of the proposed target skills, positive degree of satisfaction, and better results in exams, although statistically the increase in performance was not so significant. This advance is based on the inclusion of motivational factors in the control loop, suggesting appropriate actions to the lecturer to maintain the desired level of student motivation. The information used in this controller is the estimated level of anxiety and motivation and the participation index of the student. Depending on the values of these variables, the controller decides to maintain, decrease, or reduce the motivational activities and positive reinforcements for the next sample time. The inclusion of these factors has yielded further improvement in the results obtained, with the exception of those related to workload, and especially for those related to mathematics confidence and to communication skills. At the same time, this study serves as a new validation of the original proposed methodology for the first year. However, a study over
MÉNDEZ AND GONZÁLEZ: IMPLEMENTING MOTIVATIONAL FEATURES IN REACTIVE BLENDED LEARNING
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Juan Albino Méndez received the B.S. degree in applied physics and Ph.D. degree in computer science from the University of La Laguna, La Laguna, Spain, in 1992 and 1998, respectively. He is currently a Full Professor with the Department of System Engineering and Automation, University of La Laguna. He has experience in coordinating projects in the field of control engineering. His main research project is on automation of anesthesia infusion in collaboration with the Hospital Universitario de Canarias, Santa Cruz de Tenerife, Spain. His research interests include advanced control methodologies, predictive control, fuzzy logic control, and applications of control methodologies to engineering education.
Evelio J. González received the M.S. degree in applied physics and Ph.D. degree in computer science from the University of La Laguna, La Laguna, Spain, in 1998 and 2004, respectively. He was a Research Student with the Department of Applied Physics, Electronics and Systems, University of La Laguna, from 1998 to 2001, and is currently an Assistant Professor. His areas of interest include simulation, digital control, computer architecture, artificial intelligence, and intelligent agents.
