factors affecting students’ satisfaction in engineering disciplines: traditional vs. blended...

European Journal of Engineering EducationVol. 36, No. 5, October 2011, 473–483

Factors affecting students’ satisfaction in engineeringdisciplines: traditional vs. blended approaches

Eva Martínez-Caro* and Francisco Campuzano-Bolarín

Departamento de Economía de la Empresa, Escuela Técnica Superior de Ingeniería Industrial,Universidad Politécnica de Cartagena, Spain

(Received 8 February 2011; final version received 25 August 2011)

In this paper a two-year field study was carried out to analyse how satisfaction differs across the traditionaland blended learning methods. Altogether, 21 courses for graduate and postgraduate engineering studentswere evaluated. Several variables and their relationship with student satisfaction in the first year, with allcourses delivered in traditional mode, were compared with student satisfaction in the second year, whichhad the same courses delivered in blended mode. Results suggest that student satisfaction is greater inblended courses than in face-to-face courses. This can be explained because the levels of class attendance,motivation and collaboration with classmates were higher in blended learning than in classroom instruction.In addition, class attendance, access to teachers, collaboration with classmates and motivation were foundto be leading predictors of student satisfaction in blended environments.

Keywords: blended learning; e-learning; student satisfaction; engineering disciplines

1. Introduction

In recent years, educational institutions are evolving toward new learning models in their effortsto address student and institutional needs in an increasingly competitive higher educationalenvironment (Symonds 2003). Universities have to deal with the challenge of evolving fromteacher-centred to student-centred instruction, introducing information and communication tech-nologies and satisfying the needs of lifelong learners who have irregular working schedules andboth family and employment commitments. It is difficult to meet these demands with traditionalface-to-face learning. During the last few years, universities have been experimenting with the useof e-learning as an alternative to traditional learning. Although acceptance of e-learning as a majorand viable component of higher education has grown dramatically (Allen and Seaman 2003), thishas not led to a significant increase in engineering degrees granted (Bourne et al. 2005). Previousresearch explained that e-learning is generally most effective when used as a supplement to, ratherthan a replacement for, engineering education (Lux and Davidson 2003). E-learning environmentspose such disadvantages as hindrance of the socialisation process of individuals, lack of sufficientrecognition between the teacher and the learner and limitations concerning the communicationamong learners (Akkoyunlu and Soylu 2008). These disadvantages led to several difficulties and

*Corresponding author. Email: [email protected]

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474 E. Martínez-Caro and F. Campuzano- Bolarín

inconveniencies (lack of student motivation, isolation, etc.) that must be overcome. In particular,in engineering disciplines, there are special needs when offered in a distance mode. Engineeringeducation is, in the majority of cases, science- and mathematics-based courses that are tradition-ally the hardest to teach online because of the need for laboratories and equation manipulation(Bourne et al. 2005).

Blended learning – a combination of e-learning and face-to-face delivery modes – may help toopen up new channels for traditional teaching of engineering. Although there are some researcharticles on blended learning focused on engineering disciplines (e.g. Lux and Davidson 2003,Sommaruga and de Angelis 2007, Limniou and Smith 2010), they do not provide much evidenceon whether or not blended learning is a more satisfactory method than purely traditional face-to-face courses or purely online courses. In addition, the effect of blended models on outcomes,such as satisfaction, is still not well understood (Webb et al. 2005). On the other hand, most ofthe studies currently published in the academic literature focus on a single course and findings arebased on relatively small samples of students (Benbunan-Fich and Hiltz 2003). This affects theexternal validity, the statistical power and the independence of instructor-specific characteristics(Phipps and Merisotis 1999).

The research described herein was designed to address these gaps in the literature by examiningthe results of a field experiment with a sample of students in multiple engineering courses. Thesecourses were taught in both the modes: traditional and blended. The focus of this research is toanalyse how the different delivery modes impact on student satisfaction and to determine thefactors associated with student satisfaction in the blended environment.

2. Theoretical background

In the following sections, the literature on blended learning and student satisfaction is reviewedto develop a conceptual foundation for the research questions.

2.1. Blended learning

Kerres and de Witt (2003) define blended learning as ‘a mix of several didactic methods anddelivery formats’. Probably the type of blended learning that appears more often in the literatureis the type that combines e-learning with other forms of traditional learning (Oliver and Trigwel2005) so that the teaching–learning process takes place in the classroom and online. Blendedlearning models are commonly used in universities by combining face-to-face lectures and tutorialswith online teaching. Online systems are used for a range of purposes, including distributinglearning materials, making timely announcements, making available online learning modulesand allowing discussion and feedback through tools such as discussion forums and chat-rooms(Farley et al. 2011). The important consideration is to ensure that the blend involves the strengthsof each type of learning environment and none of the weaknesses. Osguthorpe and Graham (2003)identified six goals to aim for when designing a blended learning course: (1) pedagogical richness(use class time to their advantage); (2) access to knowledge (use more resources, connect toexperts, etc.); (3) social interaction (in class and online); (4) personal agency (learner control);(5) cost effectiveness; (6) ease of revision.

There are many reasons why an instructor or learner might pick blended learning over otherlearning options. Graham et al. (2005) found that, overwhelmingly, people chose blended learningfor three reasons:

(1) Improved pedagogy: In higher education, the lecture is used as the predominant teachingstrategy. E-learning often suffers from making large amounts of information available for

European Journal of Engineering Education 475

students to absorb independently (Waddoups and Howell 2002). Moreover, it is indicated thatsome students experienced difficulty adjusting to the structure of online courses, managingtheir time in such environments and maintaining self-motivation (Marino 2000). Some haveseen blended learning approaches increase the level of active learning strategies, peer-to-peerlearning strategies and learner-centred strategies used (Collis et al. 2003).

(2) Increased access and flexibility: Online learning environments have been criticised for theirlack of human interaction.For this reason, there has been an increasing movement towardblended learning approaches where students can have opportunities for both online and offlineinteraction with their instructors and classmates (Allen and Seaman 2003).

(3) Increased cost-effectiveness: Blended programmes may help universities to explore ways ofusing technology to achieve quality enhancements and cost savings simultaneously (Graham2005).

2.2. Factors affecting satisfaction

Student satisfaction in the classroom is an inherently desirable goal and a benefit of teaching.Although plenty of research exists on the many benefits of student satisfaction, there is lessresearch that identifies and tests the effectiveness of methods that teachers may use for increasingsatisfaction (Nath and Anderson 2007). The above-mentioned advantages of blended learningmust influence positively on student overall satisfaction. However, to help teachers to design theircourses by taking advantage of such characteristics with the aim of enhancing the student learningexperience, it is crucial to identify the specific set of factors that influence how blended learningaffects student satisfaction.

The factors identified and analysed in previous literature are mainly related to the system,the individuals (students and teachers) or the group (course or class) (Benbunan-Fich and Hiltz2003). In particular, the effects of several variables representing the categories of the previousclassification are examined in this study:

• Type of course: Engineering education is, in the majority of cases, science- and mathematics-based courses that are traditionally the hardest to teach online because of the need forlaboratories and equation manipulation (Bourne et al. 2005). However, in a blended learn-ing environment that problem could be solved by assembling students in person for the mostcomplex mathematical explanations and the laboratory sessions. Still, one may think of possiblereasons why more or less technical content would be more suited to the blended environment(Benbunan-Fich and Hiltz 2003).

• Class size: In traditional education, reduction in class size has been associated with morepositive student attitudes (Smith and Glass 1979). Likewise, in online environments, increasein class size has been found to affect negatively student satisfaction (Arbaugh and Duray 2001).In a smaller class, there are more opportunities to adapt learning programmes to the needs ofindividuals. Students are more directly and personally involved in learning. Hence, reductionin class size could be related to satisfaction improvement in both the blended and the traditionalmode.

• Class attendance: Previous research suggests that class attendance leads to success (e.g. Urban-Lurain and Weinbank 2000, Chung 2004). Students who miss too many classes may be lesslikely to have a successful learning experience because they perform poorly, withdrawing orrequiring significant help in the form of tutoring to keep abreast with the rest of the class(Chung 2004). Hence, higher attendance would be desirable in traditional as well as in blendedlearning (in both face-to-face and online sessions) to promote student satisfaction. In onlinesessions, class attendance can be considered as engagement and participation in the variousonline activities.


• Motivation: Motivation is key for the success of any educational programme. Students worklonger, harder and with more intensity when they are motivated than when they are not. Thisattitude leads students to a more satisfactory learning experience. Therefore, the actions aimedat increasing student motivation are crucial in any delivery mode. However, in blended learningenvironments, the importance of student motivation may increase because there is less in-classtime and more emphasis on self-regulated learning (So and Brush 2008).

• Interaction with teachers:A learning environment in a typical classroom can be characterised asactive interactions between learner and instructor. In online environments, opportunities for suchinteractions are often limited because of physical separations (Nel and Wilkinson 2006). Forthis reason, there has been an increasing movement toward blended learning approaches wherestudents can have opportunities for both online and offline interaction with their instructors(Allen and Seaman 2003).

• Collaboration with classmates: Interaction between participants has been found to be a strongpredictor of student satisfaction. Palloff and Pratt (2001) emphasise that effective collaborationcan enhance the learning experience and can therefore be regarded as one of the determiningfactors in measuring the success of any online course. The students may learn as much, or more,from each other as they do from the professor and the textbook (Brower 2003). When workingwith peers instead of alone (or just with the instructor), students find their way together throughcomplex or new tasks (Harasim et al. 1995). In some cases, students have even expressed apreference for online dialogue over traditional classroom discussion because they can partic-ipate more fully and can reflect upon other students’ comments and responses before sharingtheir own (Clark 2001).

2.3. Research questions

From the research and theory presented, the following research questions were derived:

(1) Are there significant differences in student satisfaction with traditional and blended learning?(2) Are there relationships between the influential variables (‘type of course’, ‘class size’, ‘class

attendance’, ‘motivation’, ‘interaction with teachers’and ‘collaboration with classmates’) andthe ‘satisfaction’ outcome in traditional and in blended learning?

(3) Do these variables influence satisfaction differently depending on the delivery mode?(4) Are there significant differences in the level of ‘class attendance’, ‘motivation’, ‘interaction

with teachers’ and ‘collaboration with classmates’ among traditional and blended learning?(5) When the influential variables are combined, what set of variables best predicts satisfaction

in blended courses?

3. Method

The following sections deal with the experimental design, participants, procedure and measure-ment instruments.

3.1. Design

To obtain a real operative situation that might make it possible to generalise the results, a field studywas carried out. A quasi-experimental post-test design was adopted with a non-equivalent controlgroup. In such design two pre-existing groups are compared, one being the control group and theother the experimental group. Only the experimental group receives an intervention. Every course


Table 1. Breakdown of the respondents by instructional mode

Instructional mode FtF Blended Total

Respondents (n) 419 377 796Total enrolment 1363 1295 2658Response ratea 30.74 29.11Sample (%)b 52,74 47,36 100

Courses (n) 21 21

FtF = face-to-face.aPercentage of students who completed the questionnaire.bProportion of the sample corresponding to each instructional mode.

is measured after intervention occurs. In the present study, the experimental group was taught ina blended learning environment and the control group in a traditional learning environment.

3.2. Sample

The participants of this research were the students enrolled in the Universidad Politécnica deCartagena (Spain) in 21 courses for graduate and postgraduate engineering students. The popu-lation comprised all the students officially enrolled in these courses in two consecutive academicyears, that is, 2658 students.

Enrolments in these courses ranged from eight to 148 students. The students enrolled in thefirst academic year formed the control group (all courses delivered in a traditional mode) andthose enrolled in the second academic year constituted the experimental group (the same coursesdelivered in a blended mode).

Data were obtained from 796 students and the response rate was 29.94%. The male studentsmade up 64.2% of the participants. The average age of the students was in the range of 21–22years old. As shown in Table 1, the response rate was similar in both instructional modes.

3.3. Experimental situation

In blended learning, face-to-face sessions were combined with an online environment. The tech-nological platform WebCT 4.1 was used to create that online environment. Through this platform,the teachers placed general information on the course (syllabus, tutoring timetables, recommendedbibliography, etc.) at the disposal of the students, together with basic and complementary docu-mentation. Moreover, students had several communication tools (email, forums and chat) at theirdisposal. These tools represent different channels of interaction with teachers and with peers.In some courses, tasks were being done online individually or in groups requiring collaborativework. Online laboratories were also used and online exams were taken. Face-to-face sessionswere employed for explanations of the most difficult topics and for the laboratory sessions thatrequired the direct operation of instruments. Students had the support of technical staff, who gavethem advice concerning any technical problems.

In the traditional environment, all activities were carried out with the attendance of the students.The identical syllabus was developed with the same contents.

Following recommendations by Alavi (1994) and Awargal and Day (1998), the same teacherdelivered the classes to the two groups in every course. In this way, any difference that could becaused by a different teaching style was eliminated.

At the end of the courses, the data were collected through a survey filled in by the studentsanonymously in the classroom.


3.4. Variables

Throughout literature, there are multiple studies that considered student satisfaction as a dependentvariable. There are many ways to measure this variable, using a single item (e.g. Ponzurick etal. 2000, So and Brush 2008) or multiple indicators (e.g. Arbaugh and Duray 2001, Nath andAnderson 2007). In this case, because of the heterogeneous nature of the courses analysed andthe multi-teacher and multi-course nature of the study, it was decided to choose the formula ofPonzurick et al. (2000), which measures the general satisfaction with the course through a singleitem. A response scale of 5 points (1 = very unsatisfactory, 5 = very satisfied) was used. The useof single-item measures is recommended by Hayduk (1996), Rossiter (2002) and Bergkvist andRossiter (2007). Their findings suggest that single-item measures minimise respondent refusal,reduce data collection and data-processing costs and avoid common methods bias. Bergkvist andRossiter (2007) state that theoretical tests and empirical findings would be unchanged if goodsingle-item measures were substituted for constructs, such as beliefs, perceptions or satisfaction,in place of commonly used multiple-item measures.

Regarding the influential variables, 13 courses were classified as ‘more technical’ (coursesincluding substantial mathematical analysis, e.g. Physics) and eight courses as ‘less technical’(courses more oriented toward qualitative analysis and discussion, e.g. Business Management).Motivation, the perceived level of interaction with teachers and the perceived collaboration withclassmates were measured through a response scale of 5 points (1 = very low, 5 = very high).Class attendance was measured through a response scale of 4 points according to the percentageof classes attended along the course (1: <25%; 2: 25–50%; 3: 50–75%; 4: >75).

Finally, the student’s age, gender and initial interest in the course were considered to be variablesreplacing the pre-test measures. The initial degree of interest in the course was measured througha response scale of 5 points (1 = very low, 5 = very high).

3.5. Analysis of the data and results

To analyse the data, the SPSS 15.0 statistical pack was used. An analysis of variance (ANOVA)was made to detect possible differences between the courses in the two groups as regards threepre-test variables defined (age, gender and initial interest in the course). No significant differenceswere found between the groups as regards any of the variables; therefore, the aspects that threatenthe validity because of possible bias in the selection were controlled.

Once this was verified, the first research question was approached. An ANOVA was used tocompare satisfaction across the delivery modes (Table 2). It was found that the level of satisfactionof the students is greater in the blended learning environment than in the traditional one.

The second and third research questions called for the exploration of the relationship betweenthe influential variables (type of course, class size, class attendance, motivation, interaction with

Table 2. Analysis of variance of satisfaction and influential variables by instructional mode

FtF Blended F-value

Satisfaction 3.46 (1.30) [419] 3.74 (0.96) [377] 11.49∗∗Class attendance 1.68 (1.02) [419] 3.52 (0.95) [377] 692.19∗∗∗Motivation 3.04 (1.25) [413] 3.41 (1.04) [373] 20.167∗∗∗Access to teacher 3.79 (1.20) [410] 3.66 (1.07) [370] 2.42Collaboration 3.59 (1.19) [411] 3.76 (1.02) [373] 4.55∗

FtF = face-to-face.Significance (∗p > 0.5; ∗∗p < 0.01; ∗∗∗p < 0.001).Cells show mean (SD) [sample size].


teachers and collaboration with classmates) and satisfaction in both the instructional modes.Table 3 shows the ANOVA of the qualitative variables on satisfaction and Table 4 shows theanalysis of correlation of the quantitative variables with satisfaction. All the variables have asignificant positive influence on satisfaction in both traditional and blended environments. Theonly exception is the variable ‘type of course’, which has no significant influence on satisfaction.In the case of class size, a post-hoc comparison of the means via a Waller-Duncan testing revealstwo mean groups in both instructional modes: one formed by the courses containing a maximum of20 students and another formed by the courses with more than 20 students, with greater perceivedsatisfaction by the first group.

ANOVA was conducted to address the fourth research question: do influential variables affectsatisfaction in different ways depending on the delivery mode? According to the results pre-sented in Table 2, the levels of class attendance, motivation and collaboration were higherin blended courses, whereas no significant differences were found for the level of access toteacher.

Finally, to assess the fifth research question, that is, to determine the simultaneous effects of theinfluential variables on satisfaction and the set of variables that best predicts satisfaction in blendedcourses, a multiple regression analysis was employed. Table 5 summarises the analysis results.Class size and course type were not significant. The coefficients for class attendance, access toteacher, collaboration with classmates and motivation were all positive and significant. This showsthat higher levels of those variables produce more positive perception of satisfaction. Accordingto the standardised coefficients, access to teacher was the strongest predictor of satisfaction inblended learning.

Table 3. Analysis of variance of satisfaction by qualitative influential variables in each instructional mode

FtF Blended

Type subject Technical 3.54 (1.23) [236] F = 1.77 3.68 (0.88) [228] F = 2.01No Technical 3.37 (1.38) [183] 3.83 (1.06) [149]

Class size 1–20 4.69 (0.76) [23] F = 15.88∗∗∗ 4.30 (0.50) [52] F = 11.26∗∗∗21–50 3.10 (1.35) [114] 3.54 (1.16) [46]50+ 3.51 (1.24) [282] 3.67 (0.95) [279]

FtF = face-to-face.Significance (∗∗∗p < 0.001).Cells show mean (SD) [sample size].

Table 4. Correlation of quantitative influential variableswith satisfaction in each instructional mode

FtF Blended

Class attendance 0.32∗∗ 0.48∗∗[419] [377]

Motivation 0.64∗∗∗ 0.52∗∗∗[413] [373]

Access to teacher 0.68∗∗∗ 0.58∗∗∗[410] [370]

Collaboration 0.77∗∗∗ 0.44∗∗∗[411] [373]

FtF = face-to-face.Cells show correlation, significance level (∗∗p < 0.01; ∗∗∗p < 0.001),[sample size].


Table 5. Regression analysis of influential variables onsatisfaction for blended courses

Beta standardisedcoefficients t-value

Intercept 1.816Class size 0.022 0.580Type of subject −0.071 −1.922Class attendance 0.265 6.696∗∗∗Access to teacher 0.371 8.821∗∗∗Collaboration 0.157 3.388∗∗Motivation 0.191 3.999∗∗∗Model F = 64.18∗∗∗Degrees of freedom = 6Total R2 = 0.518

Significance level (∗∗p < 0.01; ∗∗∗p < 0.001).

4. Discussion

Blended learning appears as a solution to the need to update traditional engineering classes becauseof demand from a society motivated by the strong upsurge of information and communicationtechnologies. However, as in any learning environment, the question that should be asked iswhether blended learning is an effective method for increasing student satisfaction; in particular,in the field of industrial engineering. Hence, this study has examined several variables and theirrelationships with satisfaction in traditional and in blended courses, with the aim of comparing ifthose relationships differ across the instructional mode. The results of the analyses conducted toaddress the above-mentioned research questions seem to support several findings.

Student satisfaction was significantly greater in blended courses than in face-to-face courses.These results support the findings of earlier studies (e.g. Benbunan-Fich and Hiltz 2003, El-Deghaidy and Nouby 2008), which suggest that a mix of classroom and online technologiescombines the best of both worlds and seems to achieve the best results.An analysis of the behaviourof influential variables can be useful for understanding why blended learning would be moreeffective than classroom instruction.

Some teachers think that courses that are more technical could be harder to teach online,whereas less technical courses would be well-suited to the blended environment. However, thetype of course was not significantly related to satisfaction in any of the instructional modes.This finding suggests that, by adapting the combination of blending methods according to thespecific requirements (complex explanations, hands-on laboratories, etc.) of every course, blendedenvironments can be used for any course.

A reduction in class size improved satisfaction in both the blended and the traditional mode.Specifically, students in groups with a maximum of 20 students perceived the highest level ofsatisfaction. Hence, a priori, it seems that a reduced class size is important to improve satisfaction.However, it is important to highlight that small courses are not a guarantee of satisfaction per se.In any given course, the remaining influential variables analysed, as well as other factors such asthe teaching style or the resources used, could, in some circumstances, make smaller class sizesmore uncomfortable for students. This issue should be addressed in future studies.

Class attendance, motivation, access to teacher and collaboration with classmates had an influ-ence on satisfaction in both instructional modes. However, students reported higher levels of classattendance, motivation and collaboration with classmates in blended learning than in classroominstruction. There are some reasonable explanations for this finding. For example, online collab-orative activities appear specifically to increase the level of student–peer interaction. In addition,


in an online discussion, each student must be engaged as compared with the traditional classroomwhere some percentage of the students may revert to a passive role, observing other students. Anasynchronous online discussion may also enable students to consider their contributions morethoughtfully, without the time constraints of the traditional classroom discussion (Webb et al.2005). These activities may increase student engagement and motivation. According to Forcadaet al. (2007), the drop-out rate is lower for the blended learning modality because of greatermotivation and communication with other students, suggesting an interrelation between the threevariables.

With regard to access to teacher, no significant differences were found across instructionalmodes. Hence, the idea that, in online environments, the opportunities for teacher–student inter-actions may be or are often limited because of physical separations may not be valid for blendedenvironments.

When the simultaneous effects of the influential variables on satisfaction were analysed, classattendance, access to teacher, collaboration with classmates and motivation were found to bepredictors of satisfaction in blended environments, with access to teacher being the strongestpredictor of all. Therefore, the role of teachers is a very important issue for a satisfactory blendedlearning experience. Class size and course type were not significant. It was expected that coursetype did not influence satisfaction because, as explained earlier, an adequate combination ofmethods makes blended learning suited for any course, regardless of its character. Previously,class size was found to influence satisfaction, but when the various influential variables werecombined, class size had no significant influence. This finding may be because of the fact that thedisadvantages and difficulties of large groups may be compensated with appropriate levels of theremaining influential variables: class attendance; access to teacher; collaboration with classmates;motivation. In addition, the results reveal that variables act at different levels. Following Benbunan-Fich and Hiltz (2003), class size and course type could be defined as moderator variables, whichinfluence the strength of the relationship between the other variables (class attendance, access toteacher, collaboration with classmates and motivation) and satisfaction. Future studies need tolook deep into the relationship between the influential variables to achieve a better understandingof what combination of factors produce the best results.

Finally, this study is not lacking in limitations. The novelty of taking part in blended coursesmay have produced a Hawthorne effect in the students, which would have an influence on theirperceptions. Replication of this study in samples with previous experience with blended learningenvironments would be a good way to address this limitation of the current study. In addition, inthis study it has been assumed that the participants were typical students and that the teachers,the design and the methodology used in the analysed courses are representative. Nonetheless, thisassumption may not be totally valid, which would have an influence on the generalisation of theresults. It would be pertinent to consider other universities, course contents, design, methodology,teaching staff and student characteristics.

5. Conclusions

The study reported here was designed to compare student satisfaction in traditional and blendedlearning environments and to identify the leading factors that influence satisfaction in blendedcourses. According to the results and findings in this study, the present authors believe that theirresearch contributes to blended learning in engineering education in three different ways. First,this research contributes to the existing literature by demonstrating that student satisfaction wasgreater in blended courses than in face-to-face courses in a large sample of courses and students.Most of the previous studies look at one or a few courses with a limited number or students. Thisstudy included 21 courses and 796 students and, thus, has good statistical power.


The second contribution of this research is to test the suitability of blended learning in engineer-ing. In this study no differences were found with regard to the level of teacher–student interactionsor the course type across the delivery modes. In addition, levels of class attendance, motivationand collaboration with classmates were found to be higher in blended learning than in the class-room. This implies that, as blended learning seems to be suited for any course – regardless ofits technical character, or the physical or time distances – topics across the broad spectrum ofengineering disciplines should be available online, allowing blended engineering education to bebroadly accepted and utilised.

Finally, the third contribution is to determine the leading predictors of satisfaction in blendedenvironments. Class attendance, access to teacher, collaboration with classmates and motivationwere found to be the main factors associated with satisfaction, with access to teacher beingthe strongest predictor of satisfaction. The direct implication of these findings is that teachersmust adopt strategies to not only promote teacher–student interactions, but also to enhance classattendance, student–student interaction and motivation. Such strategies can include being availableonline to interact with students, encouraging cooperative group work or designing activities topromote student engagement and motivation.

In summary, this study can encourage engineering colleges and schools to explore, implementand extend blended learning. The findings of this research can help colleges to learn more aboutthe methods of enhancing students’ perceived satisfaction by engaging in blended environments.

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About the authors

Eva Martínez-Caro is an assistant professor of operation management in the School of Industrial Engineering, TechnicalUniversity of Cartagena (Spain). She received her degree in industrial automatic and electronic engineering in 2000 andher PhD degree in business management in 2005. Her current research interests include virtual learning environments,knowledge management, and educational technology management.

Francisco Campuzano-Bolarín is an assistant professor at the Business Management Department at Technical Universityof Cartagena. Graduated in 2000 in Management Engineering, in 2006 he got the PhD degree in Management at TechnicalUniversity of Valencia (Spain). His Doctoral Thesis was awarded by the Spanish Logistics Centre (CEL) in 2007. Hismain fields of research are focused on modelling and simulation of Supply Chain Systems using System Dynamicsmethodology.

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