 Open Access
 Total Downloads : 445
 Authors : Godwin, Harold Chukwuemeka And Okere Chinedu, J
 Paper ID : IJERTV2IS90158
 Volume & Issue : Volume 02, Issue 09 (September 2013)
 Published (First Online): 26092013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Critical Analysis of the Recent Poor Performance of Engineering Students in Engineering Mathematics: A Case of Nnamdi Azikiwe University, Awka, Nigeria
Critical Analysis of the Recent Poor Performance of Engineering Students in Engineering Mathematics: A Case of Nnamdi Azikiwe University, Awka, Nigeria.
Godwin, Harold Chukwuemeka and Okere Chinedu, J. . Department of Industrial/Production Engineering,Nnamdi Azikiwe University, Awka,
Nigeria.
Abstract
The everincreasing rate of failure of engineering students in engineering mathematics is one which the school management and entire stakeholders need not wink at any longer. Engineering mathematics is a prerequisite for all engineering discipline. It equips the student with problem solving skills and cognitive ability needed for higher thinking. The research was conducted to investigate and critically analyze the causes of the recent poor performance of engineering students in the past four academic sessions. Descriptive and Correlation research design were used. The study was designed to capture the entire population of students and some lecturers using questionnaires, interviews, work study, work sampling, and analysis of past results using correlation and regression analysis, chisquare and ANOVA. The findings revealed that students have positive attitude towards engineering mathematics. Nevertheless, poor learning environment and present coordination are major factors contributing to poor performance of students in engineering mathematics. Splitting the lecture schedule into two or more groups, and fair assessment and marking of examination scripts are highly recommended.
Keywords: Engineering Mathematics, Poor Performance, Coordination, Chisquare
INTRODUCTION
Engineering and technology drive the world. The quality and quantity of engineers in any nation directly affect its growth and development. The tertiary institutions are where potential engineers are trained. Performance of students in engineering mathematics is a measure of their cognitive and intuitive abilities, problem identification and solving abilities, abilities of students in carrying out engineering analysis, and abilities to make justifiable decisions.
Mathematics is a key element in engineering studies and serves as language of expressing physical, chemical and engineering laws (Sazhin, 1998). Mathematics is the heart of engineering, being both a language for the expression of ideas and the means of communicating results. Engineering mathematics has always been the fundamental and important courses in engineering curriculum. Engineering students are required to understand the fundamental of mathematics and apply this knowledge to solve real world problem. The requirement for engineering mathematics for the different branches of engineering is more or less the same at the first and second year levels, but tends to be more specific and complicated at the later years. The understanding of
fundamental concepts and ideas in engineering mathematic is very crucial for mastering engineering discipline. It is a subject that is related to other engineering courses such as: basic mechanics, mechanics of materials, mechanics of machines, engineering statistics, design, and engineering economics. It equips engineering students with the basic skills and techniques for handling engineering design problems. Having strong foundation in mathematics for an engineering student is very important to gauge success in engineering. The objective of teaching mathematics to engineering students is to find the right balance between practical applications of mathematical equations and indepth understanding of living situation (Sazhin, 1998).
In recent years, performance of engineering students in engineering mathematics has never been impressive. The recent poor performance of students in engineering mathematics in Nnamdi Azikiwe University, Awka Nigeria is not the first of its kind. Poor performance of students in mathematics has been a global issue among stakeholders in engineering mathematics learning. A lot of researches have attributed this poor performance to some factors. Such factors include: lack of lecture hall, capacity and conduciveness of lecture halls, lack of teaching aids like public address system, poor attendance of students to lectures, poor background in mathematics, anxiety, offcampus settlement, lack of cognitive and computational skills, bastardized admission process (Bell, 1993; Canobi, 2005; Cardella, 2008; Vasudha, 2012; Ernest, 2004, Ainley et al., 2005; Townsend & Wilton, 2003). Aremu and Sokan (2003) submitted that the search for the causes of poor academic achievement in Mathematics is unending. Mason and Spence (1999), Canobi (2005) showed that students conceptions of understanding mathematics are important in their success in mathematics learning. Baloglu and Kocak (2006) stated that the causes of mathematics anxiety fall within three major factors: dispositional, situational, and environmental. The dispositional factors deal with psychological and emotional features such as attitudes towards mathematics, selfconcept, and learning styles.
In 2011/2012 academic session in the Faculty of Engineering of Nnamdi Azikiwe University, Awka Nigeria there was a restructure in the coordination of engineering mathematics courses because of the pervasive complaints that post graduate students lack the knowledge of basic engineering mathematic tools and techniques. The originally decentralized coordination was centralized for more control and supervision. More lecturers were involved to lecture various topics of expertise. Hence, the course content was diversified to equip the students with computational and analytical tools. Contrary to expectation, the performance of students soared tremendously, with far much higher failure rates when compared with previous sessions. A careful study of 2011/2012 session results revealed that in the department of Mechanical Engineering 95% of the students scored below average, and 68% failed FEG 404; in the department of Industrial/Production Engineering, about 91% of the students scored below average and 28% failed FEG 303. Results obtained from other departments do not significantly portray otherwise, as no department produced more than two students who made distinctions.
Even though the effect of teaching method employed in teaching engineering students on their performance in mathematics have long been established, the effect of a change in coordination
has not been fully investigated. Hence, it is pertinent that methodologies employed in coordinating engineering mathematics should be properly analyzed so as to achieve continuous improvement and higher productivity. Moreover, analysis of students performance in engineering mathematics is a novel research whose crucial importance has been relegated to the background in the faculty of engineering, Nnamdi Azikiwe University, Awka. Apt research studies are never conducted to criticize existing academic structure in order to effect positive improvement.
This study employs interviews, work study, questionnaires, and interactive sessions with students, lecturers, and various stakeholders in engineering mathematics learning to establish the root causes of students poor performance in engineering mathematics. Employing an informal approach will facilitate the revelation of the hidden things associated with students performances. This is effective, especially with respect to engineering students, and will ensure that all possible factors are identified for detailed analysis. The correlation and regressin analysis, control chart, performance chart, chisquare and ANOVA are utilized in the analysis of data collected.
The results obtained in this study will help Nnamdi Azikiwe University, Awka and other Universities in understanding the effect of coordination and other factors on students performances, hence improving the performances of students in engineering mathematics.
RESEARCH METHODOLOGY
Methods of Data Collection
The research survey covered the entire population of engineering students, some lecturers and the coordinators of FEG courses in the faculty. Four years past results (20082012) from selected courses were collected from all the departments in the faculty of engineering. Other information was gathered from engineering students, some lecturers and the Coordinators of FEG courses using: interviews, well constructed questionnaire and work study.
Work studies were carried out to evaluate the efficiency of the learning process adopted in engineering mathematics, identify more factors that could be responsible for the poor performance as well as to investigate and rate the utilization of the learning resources. The following were critically examined; the learning environment, seating arrangement and capacity, lecture period (total credit hours, conduciveness of the lecture time), Students and lecturers attendance to lectures, lecture method, and course content.
The questionnaire was structured to capture the level of agreement of the students to the identified factors affecting performance. The questionnaire covered student information (Dept, sex, age, high school); attitude towards mathematics, mathematics anxiety, seating position lecture methods, availability of lecture materials, nature of examination questions; and school related factors (availability of library resources and adequacy lecture halls).
Methods of Data Analysis
Descriptive Statistics: Descriptive statistics was used for organizing, summarizing and classifying the performance scores of students in engineering mathematics. The mean and standard deviation were used as the measures of central tendency and variability respectively.
Correlation and Regression analysis: The correlation coefficient was used to determine the strength of such relationship; whereas, the regression analysis was used to predict the future outcomes.
Control Chart: The proportion control chart was used to monitor the variation of the failure percentages of students in various engineering mathematics courses for the four sessions. This tool was used to determine if the performance of students were out of the anticipated performance range (control limit). Consequently, predictions could be made on the process capability of the system.
Performance Chart: This gives a birdeye view of the performance achievements of engineering students in engineering mathematics. The point average method was used to weigh the grades scored by students.
Progress Chart: The Progress chart was used to monitor the average performance of final year students who have completed all courses in engineering mathematics. Identification of the variation of the students performance can be done, and inferences can be made on possible factors affecting such variations.
One Way Analysis Of Variance (ANOVA): ANOVA was used to test the existence of a significant difference in the grade distribution of the 500 level students across the various engineering mathematics courses.
ChiSquare Analysis: The Chisquare, 2 was used in testing the hypothesis concerning the existence of a significant difference between the number of passes and failures in engineering mathematics against expected or theoretical frequencies.
RESULTS AND DISCUSSION OF FINDINGS
Descriptive Statistics
Table 1: Mean and standard deviation of scores of Industrial & Production engineering students.
DESCRIPTIVE STATISTICS 

2008/2009 
2009/2010 
2010/2011 
2011/2012 

COURSE 
MEAN 
S.D 
MEAN 
S.D 
MEAN 
S.D 
MEAN 
S.D 
FEG 101 
49.5 
19.4894 
50.5918 
17.3428 
38.8472 
15.8497 
52.5556 
17.7992 
FEG 102 
48.6732 
10.5789 
52.3 
12.5988 
43.7143 
14.5152 
47.2064 
10.2495 
FEG 303 
50.8367 
9.28538 
50.6415 
11.6183 
56.0732 
10.7871 
37.0465 
12.3796 
FEG 404 
47.3571 
14.5059 
55.66 
9.62624 
51.2069 
10.9714 
36.8864 
14.1425 
Table 1 shows the mean and standard deviation of the performance of Industrial and Production engineering students. The mean score in FEG 404 and FEG 303 2011/2012 session were significantly low. The mean scores in bold format shows the average score rating of students admitted in 2008/2009 session. Following the mean score improvement in FEG 303 (56.0732), there was a performance drop in FEG 404 (36.8864); of which change in coordination was the only significant event that took place between the courses.
Correlation and Regression analysis
The performance of Mechanical, and Industrial/Production engineering students in FEG 102 was correlated with their performance in MEC 372 and FEG 103. At 0.95 confidence level, there was a significant linear relationship between the performance of students in FEG 102 and their performance in FEG 103. There was no conclusive linear relation between the performance of students in FEG 303, and their performance in MEC 372.
Table 2: linear correlation coefficient of the performance of students in FEG 303 and MEC 372
Correlation Coefficient 

2008/2009 
2009/2010 
2010/2011 
2011/2012 

FEG 303 
FEG 303 
FEG 303 
FEG 303 

MEC 372 
0.106 
0.291 
0.311 
0.151 
No of Students 
45 
49 
67 
67 
Table3: linear correlation coefficient of students performance in FEG 101, FEG 102 and FEG 103
Correlation Coefficient 

2009/2010 
2010/2011 
2011/2012 

FEG 101 
FEG 102 
FEG 101 
FEG 102 
FEG 101 
FEG 102 

FEG 103 
0.172929 
0.585235 
0.369446 
0.483298 
0.462193 
0.564533 
No of Students 
43 
43 
56 
52 
40 
40 
Control Chart
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
SAMPLES
SAMPLES
0.3
0.3
0.25
0.2
0.25
0.2
SAMPLES
SAMPLES
0.15
0.15
Percentage failure
Percntage failure
Percentage failure
Percentage failure
The Pbar chart was used to monitor the performance of engineering students in engineering mathematics. The failure percentages in each session were plotted as samples while the grand mean was plotted as the centre line. From the chart, it was observed that the process is out of statistical control. While the performance of students in FEG 101 and FEG 102 were adversely affected in 2010/2011, the performance in FEG 303 and FEG 404 soared in 2011/2012. It became evident that there is a need to critically investigate the assignable causes that resulted in the performance drop of engineering students.
LCL
CL UCL
1
LCL
CL UCL
1
2
2
3
3
4
4
Samples
Samples
LCL
CL
LCL
CL
0.1
0.1
UCL
UCL
0.05
0
0.05
0
1
1
2 Sample 3
2 Sample 3
4
4
Fig. 1: Pchart showing mean percentage failure in FEG 101
Fig. 2: Pchart showing mean percentage failure in FEG 102
0.05
0
0.05
0
0.05
0
0.05
0
0.35
0.35
0.3
0.25
0.2
0.15
0.1
0.3
0.25
0.2
0.15
0.1
S
A M P L E S
S
A M P L E S
1
1
2
2
3
3
4
4
0.3
0.3
0.25
0.2
0.15
0.1
0.25
0.2
0.15
0.1
SA
M PL ES
SA
M PL ES
1
1
2Samples3
2Samples3
4
4
Percentage failure
Percentage failure
Percentage failure
Percentage failure
Fig. 3: Pchart showing mean percentage failure in FEG 103
Samples
Samples
Fig. 4: Pchart showing mean percentage failure in FEG 104
Performance Chart
3
3
2
RÂ² = 1
2
RÂ² = 1
The Point average of the grades scored by students in various engineering mathematics courses in the past four sessions were plotted using excel spreadsheet. The performances are summarized in Figures 5 and 6.
4
4
Point Average Score
5
Point Average
Point Average
4
3
2
1
0
3y.=50.275x + 1.493x – 2.149x + 3.328
3y.=50.275x + 1.493x – 2.149x + 3.328
3
2.5
2
1.5
1
0.5
0
3
2.5
2
1.5
1
0.5
0
FEG
y = 0.137×3 – 0.432×2 – 0.602x + 3.922 101
RÂ² = 1
FEG 102
FEG 303
FEG
y = 0.137×3 – 0.432×2 – 0.602x + 3.922 101
RÂ² = 1
FEG 102
FEG 303
Point Average
Point Average
FEG 101
FEG 102
y = 0.486×3 + 3.149×2 – 6.291x + 6.495
RÂ² = 1
y = 0.018×3 – 0.433×2 + 1.510x + 1.263 RÂ² = 1
y = 0.486×3 + 3.149×2 – 6.291x + 6.495
RÂ² = 1
y = 0.018×3 – 0.433×2 + 1.510x + 1.263 RÂ² = 1
FEG
404
FEG
404
FEG 303
FEG 404
Academic sessions
Fig. 5a: Point Average Score of Mechanical Engineering Mathematics
0 2 4 6
0 2 4 6
Fig. 5b: Point Average Score of Mechanical Engineering Mathematics
FEG
101
FEG
102
FEG
303
2 y = 0.585×3 + 3.843×2 – 7.362x + 6.554 RÂ² = 1
FEG
303
FEG
101
FEG
102
FEG
303
2 y = 0.585×3 + 3.843×2 – 7.362x + 6.554 RÂ² = 1
FEG
303
0
0
Academic sessions
0
1 Acade2mic se3ssions 4
5
Academic sessions
0
1 Acade2mic se3ssions 4
5
4
4
y = 0.339×3 + 1.863×2 – 2.564x + 3.374
y = 0.339×3 + 1.863×2 – 2.564x + 3.374
3
3
RÂ² = 1
RÂ² = 1
1
1
FEG
404
FEG
404
4.5
4.5
5 Point Average Score
4
5 Point Average Score
4
3.5
3
2.5
2
1.5
1
0.5
0
3.5
3
2.5
2
1.5
1
0.5
0
Point Average
Point Average
Point Average
Point Average
Fig. 6a: Point Ave Score of Industrial & Production Engineering Students in Engineering Mathematics
Progress Chart
Fig. 6b: Point Average Score of Industrial & Production Engineering Students in Engineering Mathematics
The Point Average rating of the performance of the final year students was plotted to ascertain the students progress across the various engineering mathematics courses. Fig. 7 shows that there is a significant drop in the students performance in 2011/2012. The most significant factor that contributed to the omen was the change in coordination and academic restructuring which took place in 2011/2012 session.
Progress Chart of 500l
ME3C.02&5 IPE stude3n.2t4s4
2.896
2.332
100
Cummulative
frequency chart
Progress Chart of 500l
ME3C.02&5 IPE stude3n.2t4s4
2.896
2.332
100
Cummulative
frequency chart
50
50
3.5
3
2.5
2
1.5
1
0.5
0
3.5
3
2.5
2
1.5
1
0.5
0
2.358
2.358
2.359
2.359
M
E
M
E
Point Average
Point Average
Percentage distribution
Percentage distribution
1.182
MEC 500l
IPE 500l
1.182
MEC 500l
IPE 500l
0.563
FEG 101
0
FEG 101 FEG 1C0o2urFsEeGs 303 FEG 404
0.563
FEG 101
0
FEG 101 FEG 1C0o2urFsEeGs 303 FEG 404
FEG 1C0o2ursFeEsG 303
FEG 1C0o2ursFeEsG 303
FEG 404
FEG 404
Fig. 7: Progress Chart of 500L Students of Mechanical and Industrial/Prod Engineering
One Way Analysis Of Variance
Fig. 8: Cumulative frequency distribution of the progress chart of MEC and IPE students
ANOVA was used to test the existence of a significant difference in the grade distribution of the 500 level students across the various engineering mathematics courses.
Table 4: Grade distribution of 500L IPE students performance in engineering mathematics
GRADE DISTRIBUTION 

FEG 101 
FEG 102 
FEG 303 
FEG 404 

A 
14 
2 
7 
1 
B 
9 
7 
12 
1 
C 
11 
14 
12 
5 
D 
2 
11 
5 
5 
E 
15 
14 
4 
18 
F 
16 
4 
1 
14 
Total 
67 
52 
41 
44 
Table5: ANOVA Computation Table
Sum of Squares 
degree of freedom 
Mean Square 
F ratio 

Between Group 
91.51 
3 
30.5 
13.24 
within group 
460.74 
200 
2.3037 

Total 
552.25 
203 
F0.95,3,200= 2.60
Since F*0.95, 3,200= 13.24 > 2.60, we reject the null hypothesis of equal treatmentof means, and conclude that on the average, the grade distribution in the four sessions is significantly different.
Comparing the grade distribution in FEG 303 with that of FEG 404, at 0.95 confidence level, the confidence interval obtained using the tscore analysis was 1.3186 Âµ2 Âµ3 2.8074.Since this interval does not include zero, it can be concluded that the performance in FEG 303 has a grade distribution that is significantly different with that of FEG 404.
Further investigation was carried out using Scheffe multiple comparison. The grade distribution of the students in FEG 101, FEG 102 and FEG 303 were compared with that of FEG 404. Applying Scheffe Critical Value (S) at 0.95 confidence level, the confidence interval obtained was 0.7379 L 2.1873. The confidence interval does not include zero, we can conclude that the performance in FEG 101, FEG 102 and FEG 303 have a grade distribution that is significantly different from that of FEG 404.
ChiSquare Analysis
Chisquare was used to investigate if there was a significant difference in the number of passes and failures recorded in FEG 101, FEG 303, and FEG 404. The analysis carried out is summarized below;
Null Hypothesis H01: There is no significant difference in the performance of students in FEG 101 across the various academic sessions.
Table 6: FEG 101 Chisquare result for 2008/2009 to 2011/2012 Academic sessions
Performance 
2008/2009 
2009/2010 
2010/2011 
2011/2012 
Total 
Pass 
51 (50.3) 
42 (36.8) 
43 (54.1) 
60 (54.8) 
196 
Failure 
16 (16.7) 
07 (12.2) 
29 (17.9) 
13 (18.2) 
65 
Total 
67 
49 
72 
73 
261 
2 = 14.13 

20.95,3 = 7.815 
Since 2 = 14.13 > 7.815, we reject the null hypothesis, and conclude that there is a significant difference in the performance of students in FEG 101 across the various academic sessions.
Table 7: Result obtained when 2010/2011session which has the greatest contribution to Chi square value was omitted.
Performance 
2008/2009 
2009/2010 
2011/2012 
Total 
Pass 
51 (54.2) 
42 (36.7) 
60 (59.1) 
153 
Failure 
16 (12.8) 
07 (9.3) 
13 (13.9) 
36 
Total 
67 
49 
73 
189 
2 = 2.39517 20.95,2 = 5.991 
Since 2 = 2.39517 < 5.991, we accept the null hypothesis, and conclude that there is no significant difference in the performance of students in FEG 101 across the various academic sessions.
Table 8: Comparing the performance in 2010/2011 sessions with that of other sessions
Performance 
2010/2011 
Others 
Total 
Pass 
43 (54.1) 
153 (141.9) 
196 
Failure 
29 (17.9) 
36 (47.1) 
65 
Total 
72 
189 
261 
2 = 12.6449 20.95,1 = 3.841 
Since 2 = 12.6449 > 3.841, we reject the null hypothesis, and conclude that there is a significant difference in the performance of students in FEG 101 across the various academic sessions.
Ipso facto, we conclude that the difference in the performance of students in FEG 101 in the four sessions is primarily due to the relatively poor performance in 2010/2011 session.
Null Hypothesis H02: There is no significant difference in the performance of students in FEG 303 across the various academic sessions.
Performance 
2008/2009 
2009/2010 
2010/2011 
2011/2012 
Total 
Pass 
43 (42.1) 
50 (35.2) 
40 (35.2) 
31 (36.9) 
164 
Failure 
06 (6.9) 
08 (8.2) 
01 (5.8) 
12 (6.1) 
27 
Total 
49 
58 
41 
43 
191 
2 = 11.41918 

20.95,3 = 7.815 
Performance 
2008/2009 
2009/2010 
2010/2011 
2011/2012 
Total 
Pass 
43 (42.1) 
50 (35.2) 
40 (35.2) 
31 (36.9) 
164 
Failure 
06 (6.9) 
08 (8.2) 
01 (5.8) 
12 (6.1) 
27 
Total 
49 
58 
41 
43 
191 
2 = 11.41918 

20.95,3 = 7.815 
Table 9: FEG 303 Chisquare result for 2008/2009 to 2011/2012 Academic Sessions
Since 2 = 11.4192 > 7.815, we reject the null hypothesis, and conclude that there is a significant difference in the performance of students in FEG 303 across the various academic sessions.
Table 10: Result obtained when 2011/2012 session which has the greatest contribution to Chi square value was omitted.
Performance 
2008/2009 
2009/2010 
2010/2011 
Total 
Pass 
43(44) 
50(52.1) 
40(36.8) 
133 
Failure 
06(5.0) 
08(5.9) 
01(4.2) 
15 
Total 
49 
58 
41 
148 
2 = 3.77119 

20.95,2 = 5.991 
Since 2 = 3.377119 < 5.991, we accept the null hypothesis, and conclude that there is no significant difference in the performance of students in FEG 303 across the various academic sessions.
Table 11: Comparing the performance in 2011/2012 sessions with that of other sessions
Performance 
2011/2012 
Others 
Total 
Pass 
31 (36.9) 
133 (127.1) 
164 
Failure 
12 (6.1) 
15 (20.9) 
27 
Total 
43 
148 
191 
2 = 8.58935 

20.95,1 = 3.841 
Since 2 = 8.58935 > 3.841, we reject the null hypothesis, and conclude that there is a significant difference in the performance of students in FEG 303 across the various academic sessions.
Ipso facto, we conclude that the difference in the performance of students in FEG 303 in the four sessions is primarily due to the relatively poor performance in 2011/2012 session.
Null Hypothesis H03: There is no significant difference in the performance of students in FEG 404 across the various acadmic sessions.
Table 12: FEG 404 Chisquare Result for 2008/2009 to 2011/2012
Performance 
2008/2009 
2009/2010 
2010/2011 
2011/2012 
Total 
Pass 
33 (35.5) 
47 (42.3) 
54 (49.9) 
31 (37.2) 
165 
Failure 
09 (6.5) 
03 (7.7) 
05 (9.1) 
13 (6.8) 
30 
Total 
42 
50 
59 
44 
195 
2 = 13.39904 

20.95,3 = 7.815 
Since 2 = 13.39904 > 7.815, we reject the null hypothesis, and conclude that there is a significant difference in the performance of students in FEG 404 across the various academic sessions.
Table 13: Result obtained when 2011/2012 session which has the greatest contribution to Chi square value was omitted.
Performance 
2008/2009 
2009/2010 
2010/2011 
Total 
Pass 
33(37.3) 
47 (44.4) 
54 (52.4) 
134 
Failure 
09 (4.7) 
03 (5.6) 
05 (6.4) 
17 
Total 
42 
50 
59 
151 
2 = 5.59744 

20.95,2 = 5.991 
Since 2 = 5.59744 < 5.991, we accept the null hypothesis, and conclude that there is no significant difference in the performance of students in FEG 404 across the various academic sessions.
Table 14: Comparing the performance in 2011/2012 sessions with that of other sessions
Performance 
2011/2012 
Others 
Total 
Pass 
31 (37.2) 
134 (127.8) 
165 
Failure 
13 (6.8) 
17 (23.2) 
30 
Total 
44 
151 
195 
2 = 8.64595 

20.95,1 = 3.841 
Since 2 = 8.64595 > 3.841, we reject the null hypothesis, and conclude that there is a significant difference in the performance of students in FEG 404 across the various academic sessions.
Ipso facto, we conclude that the difference in the performance of students in FEG 404 in the four sessions is primarily due to the relatively poor performance in 2011/2012 session.
Questionnaire
The response of students on the rating of the various factors affecting engineering mathematics learning and students performance is shown in fig. 9. The higher the point score, the better the contribution to the overall performance in engineering mathematics. The figure shows that factors such as Interest and Perception have relatively higher point rating. This implies that engineering students generally have positive attitude towards engineering mathematics. However, factors such as learning environment and Coordination have lower point rating. It is vivid that the learning environment is not conducive for engineering mathematics learning. Noise and seating capacity are the major area of concern. Also, the students opinion suggest a review in the coordination of the courses in aspects of timing of the lecture, and fair and standard marking of examination scripts. The important discoveries in the questionnaire responses are given thus;

There was a nonnegative response on lecturer attendance and class participation. Audibility and solving ample examples during lectures were critical areas for possible improvements

There were negative responses towards the timing of lectures and the marking of script.

Most engineering students like studying engineering mathematics (A=50%, SA=16%)

Current seating capacity is not adequate, and learning environment not conducive (A=33%, SA=48%)

Lecturers attend lectures (A=67.4%, SA=7%)

Engineering mathematics is important and related to other engineering courses (A=50%, SA 30%)

Conduciveness of the Lecture halls (Fair = 39%, Poor = 41%)

Only 17% of respondents agreed that the marking of examination scripts is fair and standard.

Only 20% of respondents disagreed that the examination questions are more difficult than expected.
Factors affecting Engineering Mathematics
5
Learning
4 3.5579
2.8763 3.1018 2.9694 3.0032
3.3269
Point rating
Point rating
2.722
3
2
2.06805
1
0
Fig. 9: Factors affecting Engineering mathematics learning
CONCLUSION
From the findings, there is a significant relationship between the performances of students in engineering mathematics with their performance in other related courses. It is obvious that the effect of the recent poor performance in engineering mathematics is grave, and cannot be neglected. Hence, there is need for diligence in handling engineering mathematics courses. It is highly recommended that a timely intervention should be made on improving the learning environment. There is no gainsaying that the high StudentLecturer ratio is an important factor affecting learning and performance. Moreover, the seating capacity is far from being adequate for all engineering students offering engineering mathematics. Therefore, it is recommended that the students should be split, according to their departments into two or three classes to enhance learning. A careful review should be made on setting and marking examination scripts, so that unwarranted disparity in the performance of students across various departments can be minimized. From the control chart, if the recent performance trend continues, there could be 94.2% failure in engineering mathematics by 2014/2015 academic session. A further research should be conducted to investigate the effects of some demographic factors such as; mode of entry, sex, age, type of high school attended, achievement scores in SSCE and UTME, and place of residence on students performance in engineering mathematics.
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