Determinants of academic stress amongst engineering students

1 Introduction

Stress embodies the experience of emotions or bodily strain. Stress may be experienced in two ways i.e. emotional or physical. Emotional stress tends to be found there where the situations perceived by people are tough and intriguing. So far, physical stress is concerned, is the reaction of the body due to various triggers. The pain experienced after surgery is an example of physical stress. Physical stress frequently contributes to emotional stress, which leads to physical stress (e.g., cramps in stomach). There is a participation of ‘control’ and ‘tension reduction’ in stress management which takes place in stressful situations by making emotional and physical changes. Stress affected students may resort to take drugs and alcohol making them socially isolated and self-harm [1, 2]. In order to know how much change takes place, it is important to know the level of stress and the dire need for attaining change. These thoughts were reverberated by Bernstein et al. [3] and Auerbach and Grambling [4].

2 Stress

The word “stress” was first introduced in the fields by distinguished endocrinologist, Hans Selye, and was popularly used in the seventeenth century and derived from Latin word to mean “Hardship”, “Strait”, “Adversity” or “Affliction.” Selye suggested that stress is simply the rate of wear and tear in the body [5]. His concept of stress at that time was a physiological one and throughout his life the psychological component of the phenomenon of stress was not so much ignored by him, as placed in a secondary, and to some degree, less important place in the total picture of human stress.

As per to the transactional model, when the person comprehends an external demand as broadening his or her capability to deal with, stress is believed to take place. Thus, whether stress is experienced by an individual is determined by personal valuation of the demand nature, resources available and human skills, and presumed results. Recognition and dealing with individual differences are the goals of this approach [6]. Academic stress has been studied extensively as an important factor in college student adjustment [7, 8]. In general, college-related stress has been found to be inversely related to academic performance among traditional undergraduates [9–11].

Thus, it is important to examine the stress level of engineering students, the reasons for its increase and focus on methods to manage stress. Mechanisms that explains why students perform badly under stress include “hyper vigilance” (excessive alertness to a stressful situation resulting in panic - for example, over studying for an exam) and “premature closure” (quickly choosing a solution to end a stressful situation–for example, rushing through an exam).

Thus, this has attracted the attention of many researchers to examine the factors leading to stress among engineering students.

3 Population and sampling

The entire research activity was segregated into two phases of research:

Phase 1 (Selection of Population)

The engineering students as population were selected as while teaching it was observed that the students in engineering have to undergo heavy academic stress.

Sample size determination

The present study has covered a total of four engineering streams from two different universities. As per calculations the minimum sample size was 246 so 400 sample population was used.

Initial study was conducted on 400 Engineering students (who volunteered), from four streams i.e. Civil, Computer, Electronics and Communication and Mechanical Engineering in the age group of 18 to 22 years. They were selected from two state universities of engineering (one private and other government as mentioned above). The details are shown through Tables 1 and 2. These two universities are rated high in state and national rankings.

Phase 2 (Selection of Sample)

The stratified random sampling technique was used to choose the respondents. The students were short-listed based on their self-observation reported, as well in the interview about exhibiting stress.

The group of 400 students were classified on basis of age <20 years (84),=20 years (134) and > 20 years (182), gender males (296) and females (104), type of family nuclear (300) and joint (100)Religion Hindu (291), Sikh (108) and others (1).

After gaining permission from ethical committee for conducting this study, the respondents from both the groups were also requested to sign a consent form.

For the short-listed students, the Stress level was assessed with the help of a self structured questionnaire. Questionnaire comprised of personal data form, which includes the basic information of the respondents such as age; stream; university; gender; religion; and type of family. There were 34 questions on Academic Stress (Appendix I). Respenose method was through Rating each factor on Likert’s 5 point scale by ticking (√) on one of the five boxes where 1 meant least priority and 5 was highest priority.

Effort was made to compare academic stress based on gender, age, engineering streams, nature of university. Accordingly, the following hypotheses have been framed:

H_1a: There is a significant difference in the academic stress based on gender.

H_text1b: There is a significant difference in the academic stress based on age.

H_1c: There is a significant difference in the academic stress based on engineering streams.

H_1d: There is a significant difference in the academic stress based on nature of university.

6 Results

ANOVA was used to test the hypotheses and for intergroup comparisons post-hoc Tukey was used.

The results of factor analysis as shown through Table 4 which highlighted that based on factor analysis seven factors emerged. These seven factors explained 51.126 % of variation. The first factor viz. AS-1 Examination Stressors had Eigen Value 9.881 and explained 9.881 % of variation. The first two items, viz. i.) AS19: Concern about results after examinations and ii.) AS17: Examination syllabus is too heavy in some subjects loaded heavily on first factor signifying higher importance than other factors. The composite reliability is 0.845 and Average variance extracted is 0.501. This suggests high construct reliability for this factor.

The second factor AS-2 Inadequate Teaching Interaction explained 8.839 % of variation. i.) AS10: Lack of opportunity to meet teachers and ii.) AS11: Slow in getting along with the curriculum loaded high on this factor. Average variance extracted is 0.529 and composite reliability is 0.769, well above acceptable range.

The next factor viz. AS-3 Lack of Outreach opportunities explained 8.751 % of Variation. The first two items, viz. i.) AS29: Inadequate lab space and ii.) AS27: Not able to grasp the subject matter loaded high on this factor. The Composite reliability is 0.774 and Average variance extracted is 0.546.

The fourth factor is AS-4 Inadequate Learning Orientation. It had four items and item i) AS2: Lack of concentration during study hours and Item ii) AS3: Difficulty in memorizing and retention of lectures are important. The composite reliability is 0.730 and average variance extracted is 0.514 are both in acceptable range.

The fifth factor is AS-5 Lack of Interest. It has three items. The First item viz. i.) AS24: Enjoyment of loafing (spending time ideally), ‘bull- seasons’ (amusing and illogical use of words) interferes with my study had highest loading of 0.773 followed by ii.) AS23: Too tired, sleepy and restless to study efficiently with loading of 0.693. This factor explains 6.812 % of Variance. The average variance extracted is 0.501 and Composite reliability is 0.716.

AS-6: Problems of Interaction had two items only viz. i.) AS22: Missing important points while taking notes in class with item loading of 0.622; and ii.AS15: Lack of interactive sessions in class. The average variance extracted is 0.403 and Composite reliability is.508. The values are comparatively low, but are important and hence included for further examination.

AS-7: Poor Time Management also had two items, viz. i.) AS12: Unable to complete the assignment in time has loading of 0.703 and ii.) AS6: Personal hesitation to ask questions in class having a lower loading of 0.544. The average variance extracted is 0.405 and composite reliability is 0.562. The values are comparatively low, but are important for further examination, hence retained.

ANOVA results (Table 5) reflect that there is a significant difference in perception of respondents in only one of the seven factors influencing academic stress based on gender groups (male and female) considered for analysis. This is: AS-4: Inadequate learning orientation but for other six factors i.e. AS-1: Examination stressors, AS-2: Inadequate teaching interaction, AS-3: Lack of outreach opportunities, AS-5: Lack of interest, AS-6: Problems of interaction, AS-7: Poor time management, the results didn’t reflect significant difference on academic stress with respect to gender grouping. This shows there is a similarity of perception of the respondents on the academic stress based on gender grouping.

Thus, the hypothesis H_1a: there is a significant difference in the academic stress with respect to the different gender groups has been rejected.

ANOVA results shown through Table 6 reflect that there is a significant difference in only one of the seven factors influencing academic stress based on age groups considered for analysis. This is: AS-1: Examination stressors, but for AS-2: Inadequate teaching interaction, AS-3: Lack of outreach opportunities, AS-4: Inadequate learning orientation, AS-5: Lack of interest, AS-6: Problems of interaction and AS-7: Poor time management, the results didn’t reflect significant difference on academic stress with respect to age grouping. This shows there is a similarity of perception of the respondents on the academic stress based on age grouping.

Post-hoc Tukey was conducted to have clarity on the differences. The results are shown through Table 7. In case of AS-1: Examination Stressors, there was a significant difference between all 3 age groups i.e.<20 and = 20 age group and >20 and <20 age group.

Thus, the hypothesis H_1b: there is a significant difference in the academic stress with respect to the different age groups has been rejected.

ANOVA results in Table 8 reflect that there is a significant difference in perception of respondents in four of the seven factors influencing academic stress based on engineering streams have been considered for analysis. This is: AS-1: Examination stressors, AS-2: Inadequate teaching interaction, AS-4: Inadequate learning orientation, AS-6: Problems of interaction, but for other three factors i.e. AS-3: Lack of outreach opportunities, AS-5: Lack of interest and AS-7: Poor time management, the results didn’t reflect significant difference on academic stress with respect to engineering stream grouping. This shows for these three factors AS-3: Lack of outreach opportunities, AS-5: Lack of interest and AS-7: Poor time management, there is a similarity of perception of the respondents on the academic stress based on engineering streams grouping.

Post-hoc Tukey was conducted to have clarity on the differences. The results are shown through Table 9. In case of AS-1: Examination stressors, significant difference was found between civil and computer, civil and electronics and communication, civil and mechanical. In AS-2: Inadequate teaching interaction, the difference was significant between civil and electronics and communication, computer and electronics and communication In AS-4: Inadequate learning orientation, there was a significant difference between civil and electronics and communication, mechanical and electronics and communication. In AS-6: Problems of Interaction, significant difference was found between civil and computer. Thus, the hypothesis H_1c: there is a significant difference in the academic stress with respect to the different engineering stream groups has been partially accepted.

ANOVA results (Table 10) reflect that there is a significant difference in perception of respondents in three of the seven factors influencing academic stress based on university groups considered for analysis. This is: AS-2: Inadequate teaching interaction, AS-3: Lack of outreach opportunities and AS-5: Lack of interest but for other four factors i.e. AS-1: Examination stressors, AS-4: Inadequate learning orientation, AS-6: Problems of interaction and AS-7: Poor time management, the results didn’t reflect significant difference on academic stress with respect to universities. This shows there is a similarity of perception of the respondents on the academic stress based on university grouping. Thus, the hypothesis H_1d: there is a significant difference in the academic stress with respect to the different university groups have been partially accepted.

The researchers have investigated the effects of academic stress and students’ Mental Health management. [20–22]. Also the research was done on Stress monitoring through non-invasive instrumental analysis [23–25]. This study indicates that there is a significant difference in the factors influencing Academic stress based on different streams of engineering. Also there is a significant difference in case of the factors of Academic Stress based on the nature of university. It consisted of 34 items on Academic Stress describing the stress information from their institution/personal life as well as from the other various sources. There were 34 questions on Academic Stress. Academic Stress was compared based on gender, age, engineering streams and nature of university. The results of factor analysis as shown through Table 4 which highlights that based on factor analysis seven factors emerged. These seven factors explained 51.126 % of variation.

ANOVA results through Table 5 reflect that there is a significant difference in perception of respondents in one of the seven factors influencing academic stress based on gender groups (male and female) considered for analysis to reject hypothesis H_1a . 

ANOVA results through Table 6 and 7 showed that there is a significant difference in the academic stress with respect to the different age groups to reject hypothesis H_1b

ANOVA results through Table 8 and 9 reflect that there is a significant difference in perception of respondents in four of the seven factors influencing academic stress based on engineering streams considered for analysis. Thus, the hypothesis H_1c: there is a significant difference in the academic stress with respect to the different engineering stream groups has been partially accepted.

ANOVA results (Table 10) reflect that there is a significant difference in perception of respondents in three of the seven factors influencing academic stress based on nature of university groups considered for analysis. Thus, the hypothesis H_1d: there is a significant difference in the academic stress with respect to the nature of university groups have been partially accepted.

8 Summary

This is a detailed Indian study done via a questionnaire study about what determines academic success among engineering students from two different universities. Four hypotheses were examined with results highlighting engineering streams and the nature of the universities most affecting the students.

9 Limitations of the study

Like any research study, the present study also has some limitations. In addition to theoretical analysis, physiological parameters like breathing rate, pulse rate could also be included for analysis. It is time to think intensely about managing and controlling students’ stress as it is leading to depression and tackling it, is an urgent concern.

10 Significance of the study

It is time to think intensely about managing and controlling academic stress as it is deep-rooted malaise and tackling it, is the immediate concern. As engineering students have to work in industries, so for better quality of life free from stress and improved employability skills, such innovative activities should be developed to ascertain the sustainable academic’s abilities of students. To control the outcome of stress properly, there are certain precautions and methods that should be taken that will boost productivity. This may boost the efficiency of students and assist in dealing with the critical issue of suicides and many other crimes, thus contributing to healthy social system.

11 Implications of the study

Academic stress can severally hinder academic performance as well as adjustment in life in several ways. The suggested research will help to reduce stress, improve efficiency and productivity of the engineering students. This attempt may help in plunging stress that emerges from drug addiction, smoking and drinking. Their physical and mental health will improve. This study can be extended to students of other colleges and public enterprises. The study will also be extremely useful for students of Medical and para-medical sciences. The suggested techniques can be adopted as for managing stress in all educational institutions.

Conflict of interest

None to report.