Articles 
 

The impact of changing learning environment on students’ learning in marketing 

education: A case-study applied in higher education in Egypt 

Samia Adly Hanna El Sheikh, Reda Youssef Assaad 

October University for Modern Sciences and Arts (MSA), Egypt, New Cairo Higher Institute of 

Management Sciences and Computer, New Cairo, Egypt 

 

 

Abstract: 

As per Hall et al., (2002), well-planned sessions, interactive teaching methods and appropriate 

assessment could encourage a deep approach to learning. Moreover, problem-based learning 

in the form of real-life case study exercises also promotes deep learning.  

The purpose of this study is to change the learning environment in an introductory course of 

Principles of Marketing (MKT 201), so as to foster a deep and productive approach to learning 

rather than mere surface learning. This change in environment is achieved through presenting 

two teaching/learning strategies:  

1. Pointing out learning outcomes (LOs) of each session to the students at the 

beginning of each lecture while making sure they understand them; 

2. Consistent use of in-class real-life exercises in the form of case studies to check that 

students have digested the main concepts and are able to apply them. 

This study measures the impact of this change in learning environment on students’ overall 

achievement (results). This study was conducted on a cohort of students in semester fall 2016 

and the grades (results) of the students of this cohort are compared against the grades (results) 

of two previous cohorts in the same course (Principles of Marketing) at October University for 

Modern Sciences and Arts (MSA) Egypt.   

Keywords:  

- learning environment  

- deep approach to learning versus surface approach to learning  

- outcome-based approach in teaching, learning and assessment 

- problem-based learning (PBL)  

- experiential learning (EL)  

- least significant differences (LSD)/multiple comparisons 

 

Introduction  

Marketing educators have always been encouraged to reflect on the methods that are employed 

to educate marketing students.  



  
Compass: Journal of Learning and Teaching, Vol 11, No 2, 2018 

 

The understanding and application of the concepts of the marketing discipline are of vital 

importance to marketing graduates. Yet, at the younger levels, when students are just being 

introduced to the main concepts in a Principles of Marketing module, the concepts are 

numerous and could be confusing if we don’t both alert them to what it is that we want them to 

learn and provide them with sufficient applications/activities for them to check their own learning 

and assure themselves that they have indeed digested the concepts. In other words, when 

students focus upon specific learning outcomes and when the activities and exercises are 

designed to fit those learning outcomes, students learn well. 

This study aims to present two simple teaching strategies employed in teaching an introductory 

Principles of Marketing module and to show the impact of using both these strategies on 

changing the learning environment – and thus on positively influencing the final overall grade of 

the students. This grade comprises students’ performance in a coursework application team 

project (requiring cognitive and application skills) and in mid-term and final exams (each 

including a variety of questions as mini cases that also require application skills); it therefore 

fully reflects how well each student has performed against the learning outcomes of the whole 

module.  

A logical assumption has been made here that students who achieved higher grades are the 

ones who took a deeper approach to learning – viz. characterised by interest in the subject and 

an intention to understand what is studied. Such students seek to create meaning and thus 

interpret knowledge in the light of previous experiences; they therefore tend to achieve better 

retention and, consequently, such deeper skills as critical thinking. In contrast, a surface 

approach to learning derives from rote memorisation, poor understanding and low retention 

(Marton and Saljo, 1976; Ramsden, 1992, Biggs, 1999, and Entwistle,1987, cited in Atherton, 

2003). 

According to Atherton (op.cit.), a student may use both approaches but not with the same task. 

For example, students may learn by memorising, but in an organised way, with consequently 

productive effect. According to Biggs (op.cit.), students do not adopt deep or surface learning 

because they have respectively deep or surface personalities, but because the learning 

environment triggers their approach to learning.  

Deep learning is discussed in the work of Marton and Saljo (op.cit.), as cited in Bacon and 

Stewart (2006). Marton and Saljo regard deep learning as happening when students work hard 

to comprehend the concepts and are highly engaged in finding meaning; they relate meaning to 

previous experiences and go beyond the surface learning of rote memorisation; they are more 

likely to score better in assignments, tests and exams that measure comprehension by means 

of case studies and projects, not just questions that ask students to repeat what they have 

learned.  

Moreover, Ausubel (2000) adds that, if learners attach new concepts to previous relevant 

knowledge already possessed, this would make the concepts more meaningful and the level of 

retention higher, as emphasised by Neisser (1984), who stated that concepts tied to previous 

experiences are more “resistant to forgetting”.  



  
Compass: Journal of Learning and Teaching, Vol 11, No 2, 2018 

 

Bacon and Stewart (op.cit.) also state that knowledge tested more than once during the course 

is at a deeper level of learning and thus more likely to be retained. This could, for example, be 

achieved through in-class formative assessments, to ensure that students distinguish one 

concept in marketing from similar concepts, so as to avoid confusion – “a stable trace” (Ausubel, 

op.cit.). Halpern and Hakel (2003) similarly state that repeated exposure to concepts creates 

deeper understanding.  

Hall et al., (2002) argue that well-designed sessions, interactive teaching methods and 

appropriate assessment could encourage a deep approach and suggest that problem-based 

learning, in the form of real-life exercises, promotes deep learning.  

The purpose of this study is to show the impact of the changing of learning environment so as to 

promote a deep and productive approach to learning, as compared to surface learning. This 

change in environment is achieved by presenting two teaching/learning techniques which are:  

1. Pointing out to students, at the beginning of each lecture, the session’s learning 

outcomes (LOs), while making sure they understand them; thus, learning 

outcomes are made immediately transparent to learners;  

 

2. Intensive use in class of real-life exercises, in the form of case-studies, to check 

that students have digested the main concepts and are able to apply them. 

This study subsequently measures the impact of this change in learning environment on 

students’ overall results.  

Literature review  

A. Outcomes-based approach: 

Outcomes-based approaches to any form of lesson planning, course planning or curriculum 

development are promoted by quality-assurance bodies and higher-education providers: 

instructors, lecturers and programme leaders are expected to outline their sessions and their 

curriculum in this format.  

Moon (2002) defined a LO as a statement of what a learner is expected to know, understand 

and be able to do at the end of a period of learning – which could be a session, a term, a course 

or a programme – and how that learning is to be demonstrated in the assessment. 

LOs help students to understand what is expected of them, to identify their own targets and to 

work systematically towards demonstrating them. LOs are now an essential element of 

curriculum documentation, to enable external examiners and other evaluators of new or existing 

courses or programmes to judge their quality (Biggs and Tang, 2011). 

Not that LO approach to learning design is without criticism: in fact, many writers have the 

notion that the misuse of LO can lead to distortion and harm to education (Hussey and Smith, 

2002; Biggs and Tang, 2011). 



  
Compass: Journal of Learning and Teaching, Vol 11, No 2, 2018 

 

The main theoretical underpinning of a LO approach is ‘constructive alignment’, which is about: 

making sure that both teaching and feedback to students work smoothly to help student learning 

in relation to LOs; so designing assessment that successful students can demonstrate their 

ability to meet the LOs. 

Constructive alignment is based on constructivist theory, in which learners actively construct 

their knowledge and the lecturer creates and builds a learning environment where learning 

activities fit well with the LO; thus, students are motivated to take a deep learning approach and 

better achieve the learning outcomes, leading to better grades in both exams and application 

projects.  

When students are aware of the LOs of each session, they are focused upon what is expected 

of them. If, additionally, the in-class assessment exercises fit well with the teaching and LOs, 

the resulting environment will allow for deep learning and consequently ensure better results in 

marketing courses. 

B. Hybrid problem-based learning: 

Problem-based learning (PBL) is built on the premise that using realistic problems and real-life 

scenarios as exercises offers opportunities to widen students’ understanding and challenges 

them to solve these problems; they thus develop their problem-solving skills, as required of 

university graduates in general, but especially so of graduates of business schools.  

Vernon and Blake (1993) argue that PBL is better than traditional teaching at improving long-

term retention of knowledge. Major and Palmer (2001), furthermore, emphasize that PBL 

increases both library use and class attendance and encourages a deeper approach to learning.  

PBL lies under the wider umbrella of the constructivist theory of learning. In his research, Lebow 

(1993) ‘identified, amongst other positive features, collaboration, personal autonomy and 

reflective engagement of students in learning as key benefits of the constructivist approach. 

Constructive learning theory has its roots in experiential approaches to learning, based mainly 

on the work of Dewey.  

Experiential learning (EL), according to Hmelo-Silver (2004), is built mainly around investigation 

and the solving of problems; by such means, students become more active learners because 

they are placed in real-life situations where they construct knowledge and develop strategies for 

facing and solving problems.  

EL is a growing trend in marketing education and it does help to engage students in the learning 

process. EL could be considered as an effective pedagogy for teaching the broad body of 

marketing principles and concepts. The use of EL in marketing courses has been discussed in 

such articles as those by Schwartz and Fontenot (2007) and Linrud and Hall (1999). 

Furthermore, Gremler et al. (2000) state that EL exercises can be used in service marketing 

courses which tend to facilitate the integration of course concepts and build critical-thinking and 

problem-solving skills, together with such other teamwork-enhancing skills as communication 



  
Compass: Journal of Learning and Teaching, Vol 11, No 2, 2018 

 

and listening. In addition, EL assignments further engage students in active learning, thereby 

encouraging them to take more responsible roles and to contribute more actively to their 

education.  

Brennan (2014) also adds that EL methods have become more and more attractive to marketing 

education instructors, who use them widely, being convinced of the logic of applying practical 

case studies and real-life experience to what is a very practical field.  

Another reason for the growing trend of applying EL to marketing education is that it enhances 

employability skills, graduate attributes now expected by most universities in Egypt, and so 

responds to the requirements of their students (customers). However, according to Brennan 

(2014), although applying EL to marketing education is generally beneficial, its methodology 

should be used thoughtfully and not totally freely.  

Thus, to encourage deep learning (and simultaneously engage students and expose them to 

real-life problems), marketing educators should create an appropriate learning environment – 

one that seeks to incorporate a wider range of classroom teaching strategies to stimulate deep 

learning, including face-to-face lecturing and activities / open-ended exercises using ‘real-life’ 

topics that require integration and manipulation of information and so build the required 

problem-solving skills.  

To create this required learning environment, the nearest pedagogical approach is to adopt the 

‘hybrid PBL’ which is not a strict PBL but a more flexible approach, as outlined by Besant et al, 

(2013).The hybrid PBL offers students a variety of teaching/learning techniques and a mixed 

range of in-class approaches: new information can be provided by lectures and seminars, but 

these have to be supported by, and regularly interspersed with, in-class activities and case 

studies derived from real marketing problems; they must certainly include a group-based 

assignment to encourage students to apply their constructed knowledge.  

Methodology  

On completion of the Post Graduate Certificate for Higher Education (PG Cert) the lecturer who 

is also the researcher (the author) decided, before the start of the fall semester (semester 61) of 

the academic year 2016/2017, to re-prepare and re-engineer the module of Principles of 

Marketing , so as to change the learning environment and encourage a deep approach to 

learning by applying two strategies:  

1. To point out clearly to the students the LOs of each session.  

2. To build in-class activities with real-life case studies to suit each session’s LOs.  

The grades of 310 students of the fall semester (semester 61) of the academic year 

(2016/2017), in which the lecturer re-designed the learning environment of the students, were 

compared to the grades of the students of two previous cohorts (semester 58 and 59). 

The following frequency table reports the grades of marketing students in course MKT (201) in 

semesters 58, 59 and 61 and the relative frequency of each class of grades: 



  
Compass: Journal of Learning and Teaching, Vol 11, No 2, 2018 

 

Table 1: Frequency table of the grades of the students in three semesters 58, 59 and 

61(MSA university). 

Classes 

Semester 58 Semester 59 Semester 61 

frequency 
Relative 

frequency 
frequency 

Relative 

frequency 
frequency 

Relative 

frequency 

Less than 50 20 0.071 11 0.081 15 0.048 

50upto53 11 0.039 7 0.052 10 0.032 

53upto56 5 0.018 2 0.015 6 0.019 

56upto60 22 0.078 11 0.081 10 0.033 

60upto65 25 0.089 16 0.119 15 0.048 

65upto70 36 0.128 7 0.052 17 0.055 

70upto75 26 0.092 17 0.126 26 0.084 

75upto80 41 0.145 17 0.126 37 0.119 

80upto85 28 0.099 15 0.111 47 0.152 

85upto90 27 0.096 13 0.096 39 0.126 

90or more 41 0.145 19 0.141 88 0.284 

Total 282 1.00 135 1 310 1 

(Statistics of grades of students in course MKT 201 MSA University in three semesters.) 

     

In this research, the researcher who is also the lecturer tested the following hypotheses: 

H0:  there is no statistically significant difference among the mean values of the students’ 

grades in the three semesters (fall 2015 ‘58’, spring 2016 ‘59’ and fall 2016 ‘61’). 

 In other words, there is no impact of changing the learning environment through the use 

of the new teaching strategies on the mean values of the students’ grades in the three 

semesters (fall 2015 ‘58’, spring 2016 ‘59’ and fall 2016 ‘61’). 

against:  

H1:  there is an impact of changing the learning environment through the use of the new 

teaching strategies on the mean values of the students’ grades in the three semesters 

(fall 2015 ‘58’, spring 2016 ‘59’ and fall 2016 ‘61’). 

Let µ58, µ59, µ61 represent the mean values of the students’ grades in the three semesters (fall 

2015, spring 2016 and fall 2016). 

Thus H0:     µ58 = µ59 = µ61 

against H1: The mean grades are not all equal. 

The researcher employed the analysis of variance technique (ANOVA) to test the above-

mentioned hypotheses to indicate whether or not there is a statistically-significant impact of 

changing the learning environment of the students on the grades of the students. However, 

before applying the ANOVA, the researcher applied the test of Homogeneity. 



  
Compass: Journal of Learning and Teaching, Vol 11, No 2, 2018 

 

The researcher firstly applied the test of Homogeneity of variances on the students’ grades of 

three semesters, which is a condition that is required to apply ANOVA and that the results are 

correct.  

Results, Analysis and Discussion 

Table 2:  Descriptive statistics of grades of marketing students in a course MKT 

(201)   (MSA University) 

 N Mean 
Std. 

Deviation 

Std. 

Error 

95% Confidence 

Interval for Mean 
Minimum Maximum 

Lower 

Bound 

Upper 

Bound 

semester (58) 

Fall 2015 
282 71.7642 17.90893 1.06646 69.6649 73.8635 25.00 95.00 

semester (59) 

Spring 2016 
135 71.0000 18.68963 1.60855 67.8186 74.1814 25.00 95.00 

semester (61) 

Fall 2016 
310 78.3371 17.31238 .98328 76.4023 80.2719 25.00 95.00 

Total 727 74.4250 18.10092 .67133 73.1071 75.7430 25.00 95.00 

(Results of the analysis using SPSS) 

1. From the above table, it can be seen that the mean grade value (in the semester where the 

new approach was applied) is 78.3371, which is more than the two previous semesters – 

semesters 58 (fall 2015) 71.7642 and 59 (spring 2016) 71.0000. 

2. The above table also shows the estimation of the true mean value in each semester by 

constructing a confidence interval (lower and upper limit) of the average of grades with 

probability of 95%. 

3. Using the ANOVA to test significance in the differences among the three mean values of the 

three semesters 58, 59 and 61. 

H0: µ58 = µ59 = µ61 

against  

H1: The mean grades are not all equal. 

where:  

- H1 actually means that when the lecturer changed the learning environment (changing 

the strategy by clearly stating to the learners the LOs of each session and using 

engaging in-class activities that fitted with the LOs), this led to a deeper approach in 

learning, as reflected in student achievement and grades.  

- Ho actually means there is no impact of changing the learning environment on the 

achievements of the students. 

The ANOVA will test this, and either accept H0 or H1 is accepted. 

  



  
Compass: Journal of Learning and Teaching, Vol 11, No 2, 2018 

 

 

Homogeneity test:  

Before applying the ANOVA, the test of homogeneity of variances has to be conducted, which is 

an essential condition to ensure that the ANOVA test gives correct results, and this has been 

done by using Levene statistic (SPSS). In the homogeneity test, the variances have to be equal.  

H0: the variances are equal (i.e. there is no significance difference between variances) 

H1: the variances are not equal (i.e. there is at least one which is not equal) 

Table 3: Test of Homogeneity of Variances 

Levene Statistic df1 df2 Sig. 

0.810 2 724 0.445 

(Results of research analysis using SPSS) 

 

From the above table, P-value equals 0.445 > 0.05  

Therefore H0, is accepted, so it is accepted that there is homogeneity in variances of the grades 

in the three semesters and thus the ANOVA can be safely used. 

Table 4: The ANOVA test  

 

 Sum of Squares df Mean Square F Sig. 

Between 

Groups 
8324.573 2 4162.286 13.128 .000 

Within Groups 229544.592 724 317.051   

Total 237869.164 726    

(Results of this research analysis using SPSS) 

From the ANOVA table, the P-value of related F statistic = 0.00 which is less than 0.05.  

Then H0 can be refused with 5% level of significance; this indicates that there is a significant 

difference between the grades of the students across the semesters.  

4. A significant difference having been detected between the grades of the students across the 

semesters, the researcher compared the grades of each two semesters together, using a 

multiple comparison* table applying Fisher’s Least Significant Differences (LSD), as per 

Williams and Abdi (2010). 

  



  
Compass: Journal of Learning and Teaching, Vol 11, No 2, 2018 

 

Table 5 : Multiple comparisons 

 

(I) factor (J) factor 
Mean 

Difference (I-J) 

Std. 

Error 
Sig. 

95% Confidence Interval 

Lower 

Bound 
Upper Bound 

semester 

(58) 

semester (59) 0.76418 1.86355 0.682 -2.8944 4.4228 

semester (61) -6.57291* 1.46528 0.000 -9.4496 -3.6962 

semester 

(59) 

semester (58) -.76418 1.86355 0.682 -4.4228 2.8944 

semester (61) -7.33710* 1.83610 0.000 -10.9418 -3.7324 

semester 

(61) 

semester (58) 6.57291* 1.46528 0.000 3.6962 9.4496 

semester (59) 7.33710* 1.83610 0.000 3.7324 10.9418 

*. The mean difference is significant at the 0.05 level. 

(Results of the research analysis using SPSS) 

By using LSD method: 

First, in a comparison of the grades of semesters 58 and 59,                                                                     

H0: µ58 = µ59 

H1: µ58 ≠ µ59 

From the above table, H0 is accepted and thus there is no significant difference between the 

grades of the two semesters 58 and 59 as P-value = 0.76418 > 0.05. 

Second, in a comparison of the grades of semesters 61 and 58, 

H0: µ61 ≤ µ58 

H1: µ61 > µ58 

from the results in the table of multiple comparison.  

Therefore H1 is accepted, where H1: µ61 > µ58 as the P - value = 0.0 < 0.05.  

H0 (null hypothesis) is refused with level of significance 5% and it is accepted that µ61 > µ58. 

Third, in a comparison of the grades of semesters 61 and 59, 

H0: µ61 ≤ µ59 

H1: µ61 > µ59 

from the table of multiple comparison.  

P – value = 0.00 < 0.05. Thus, H0 is refused with 5% level of significance and it is accepted that 

µ61 > µ59. 

 



  
Compass: Journal of Learning and Teaching, Vol 11, No 2, 2018 

 

To sum up: 

From the above, we can indicate that in semesters 58 and 59 there was no significant difference 

in grades, as within these two semesters we had not applied the new teaching strategies which 

would change the learning environment. Yet the grades of students in semester 61 (in which we 

started to change the learning environment to encourage deep learning characterized by better 

understanding of concepts and higher retention of knowledge) are higher than those of the 

previous cohorts in both exams and projects.  

Fourth, from Table 1, it can be seen that the percentage of students who achieved distinction 

level in semester 58 represented 24.1% of the students, as compared to 23.7% of the students 

gaining distinctions in semester 59.  

However, in semester 61, the percentage of distinctions (above 85 out of 100) represented 

about 41%. Thus, the new approach did improve the percentage of students who gained 

distinction. 

To be sure, this was tested by a Z test (test of the differences between two sample proportions). 

The percentage of distinctions in semester 58 = 0.241 

The percentage of distinctions in semester 59 = 0.237 

The percentage of distinction in semester 61= 0. 41 

A. (Z test of the differences between π58 and π59) 

H0: π58 = π59    against, 

H1: π58 ≠ π59     

H0 is accepted that is there is no significance difference between 𝜋58 and 𝜋59 because the 

computed Z is within the acceptance region of H0 (± 1.96)*. 

 

 

*𝜋 = 
282 𝑥 0.241+135 𝑥 0.237 

282+135
= 0.24  

*𝑍 = 
0.241−0.237 

√(0.24)(0.67)(
1

282
+ 

1

135
)

= 0.002            

 

  



  
Compass: Journal of Learning and Teaching, Vol 11, No 2, 2018 

 

B.  (Z test of the differences between π58 and π61) 

H0: π61 ≤ π58    against, 

H1: : π61  > π58 

The null hypothesis is refused with 5%level of significance and thus it is accepted that 

π61 > π58 because the computed Z is out of the acceptance region of H0 (> 1.645). (One tail.)** 

C.  (Z test of the difference between π59 and π61) 

H0: π61 ≤ π59    against, 

H1: π61 >  π59      

The null hypothesis is refused with 5% level of significance and thus it is accepted that 𝜋61 > 𝜋59 

because the computed Z is out of the acceptance region of H0 (> 1.645). (One tail.)*** 

 

 

-------------------------------------------------------------------------------------------- 

**𝜋 = 
310 𝑥 0.41+282 𝑥 0.241 

310+282
= 0.329 

**𝑍 = 
0.41−0.241 

√(0.329)(0.67)(
1

310
+ 

1

282
)

= 2.7 

 

***𝜋 = 
310 𝑥 0.41+135 𝑥 0.237 

310+135
= 0.358 

***𝑍 = 
0.41−0.237 

√(0.358)(0.642)(
1

310
+ 

1

135
)

= 3.4 

 

I.e. in semester 61 (in which the new approach was applied), the percentage of distinction 

grades is significantly higher than the previous two semesters.  

Moreover, there is no significant difference between the percentages of distinction in semesters 

58 and 59, which proves that the new approach did lead to this improvement in the percentage 

of students who achieved distinction; it also indicates that changing the learning environment 

led to a deeper approach to learning by the students, as indicated by better understanding and 

enhancement in problem-solving skills, the cognitive skills reflected by better grades.   

Conclusion  

To conclude on a personal note, my experience of the above application suggests that 

marketing lecturers, through changing the learners’ environment by altering their teaching 

strategies, could bring about the desired deeper learning which leads to better results.  

It is of the utmost importance to note here that the strategies used in this study to help foster a 

deeper learning approach might not be particularly innovative in UK higher education institutions 

(HEIs) or even at postgraduate level in some Egyptian HEIs, but these strategies have not been 

the focus of lecturers in the field of marketing in many Egyptian undergraduate HEIs. This focus 



  
Compass: Journal of Learning and Teaching, Vol 11, No 2, 2018 

 

came about as a result of the enrolment of higher education staff (instructors of my institution, 

MSA) in the Postgraduate Certificate in Higher Education offered by the University of 

Greenwich. This qualification is becoming a staff development requirement at MSA University. 

Being one of the first cohort of lecturers who successfully completed the Higher Education 

PGCert has encouraged me to take a more critical approach to personal teaching strategies and 

to take more time to reflect on lesson plans and review how teaching/learning strategies actually 

serve the learning outcomes of various marketing modules. Moreover, it has offered me the 

chance to read deeply into various learning theories and relate them to my own field, as well as 

to pursue investigation into related pedagogical literature. Finally, my experience has confirmed 

me in the belief that revising previously conventional teaching strategies to a hybrid PBL 

approach is more effective, encouraging a deeper approach to learning characterized by deeper 

understanding of concepts, better retention of knowledge and improved problem-solving skills. 

I’m convinced that these PBL activities also allow instructors to test concepts more than once, in 

different ways and with different scenarios, all resulting in higher retention, as required in 

introductory modules with basic concepts. 

Furthermore, these in-class activities enhance student engagement, another feature of a deeper 

learning environment and fundamental to increased student acquisition of distinction-level 

grades.  

Future development  

The building of a multivariate model to measure the impact on student achievement of each 

suggested independent variable is a logical next step from having confirmed the positive 

benefits of changing the learning environment to one which encourages deeper learning; it 

should help to maximize those benefits for the students. 

Moreover, future research could study the significance of the model, using such an advanced 

statistical technique as structural equation modeling (path analysis) or another multivariate 

technique. This would also require building a measuring tool or administering a questionnaire to 

ask the students themselves about the various variables suggested to have an impact on 

creating an environment that encourages a deep approach to learning.   

  

 

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