Comparison of Academic Performance of Students in Computer Science in the Tertiary Institution From 2016-2019
Chapter One
OBJECTIVE OF THE STUDY
The objectives of the study are;
- To identify the behaviors of students in a computer science course
- To ascertain the academic performance of students on computer science
- To recommended improvement strategies to advance the students’ performance in computer science course.
CHAPTER TWO
REVIEW OF RELATED LITERATURE
INTRODUCTION
The rapid growth of information technology has created high demand for skillful programming specialists. Programming skills have become a core competence for engineering and computer science students (Verdú et al., 2012; Hwang, Shadiev, Wang, & Huang, 2012; Fessakis, Gouli, & Mavroudi, 2013). According to (Brooks, 1999; Govender, 2009; Katai & Toth, 2010; Wang, Li, Feng, Jiang, & Liu, 2012; Yeh, Chen, Hung, & Hwang, 2010), learning a computer-programming language involves the understanding of theoretical background and practice of a range of semantic and syntactic knowledge, coding skills, and algorithmic skills, which are usually complex and difficult for most students to master. Researchers have reported that many lecturers have encountered difficulties in teaching programming languages. Moreover, most students and teachers have the same opinion that learning programming is a challenging task that many students struggle with (Govender & Grayson, 2008; Kordaki, 2010). Therefore, it has become an important and challenging issue to develop improvement strategies or tools for teaching computer-programming languages (Emurian, Holden, & Abarbanel, 2008; Hwang, Shadiev, Wang, & Huang, 2012). According to Nwanaka and Amaechule (2011) there are three stages in skills acquisition: theoretical, practical and exposure to challenges. An important factor in skills acquisition process is exposure to practical situations where these skills are displayed and utilized. It is thus essential that at polytechnic students be given the required practical skills, which they need to cope with emerging challenges of the modern world. However, skill is thought of as a quality of performance which does not depend solely upon a person’s fundamental, innate capacities but must be developed through training, practice and experience. In addition, skills represent particular ways of using capacities in relation to environmental demands (Adeyemo, 2009). Nwanaka and Amaehule (2011) emphatically states that it is only with skilled men that materials can be harnessed, manipulated and changed into products. Gumbari (2009) is of the opinion that, there is no issue that should be addressed as a matter of urgency and utmost importance than that of skills acquisition by the youth, considering the failure of our basic education to yield the expected positive results with its attendant consequences such as armed robbery, insurgency, militancy, kidnapping, abduction for ransom and a lot of others. Researchers have argued that when learning programming, continuous practice is compulsory to ensure that the knowledge is sustained (Chen, Chang, & Wang, 2008; Hwang, Wang, Hwang, Huang, & Huang, 2008). Moreover, actively and periodically scheduled learning is important for students to reach high levels of achievement (Hwang & Wang, 2004). Nevertheless, many computer science students cannot grasp the most fundamental concepts of programming and are thus unable to produce even the most basic programs (Eckerdal, 2009). Learning strategy, lack of study, and lack of practice have been identified by researchers as the fundamental attributes of success or failure in a computer programming course (Hawi, 2010; Hwang, Wu, Tseng, & Huang, 2011).
Computer Self-Efficacy
The work of Albert Bandura in the area of social cognitive theory was the first to discuss the concept of computer self-efficacy (Hauser, Paul, & Bradley, 2012). CSE refers to the belief of an individual in his/her ability to use computer skills to a wider range of tasks (Compeau & Higgins, 1995a, 1995b). CSE has been revealed to have a direct impact on students’ academic performance thus the antecedents to CSE might provide a mechanism that can be used to influence it (Hauser et al., 2012). A number of antecedents and consequents of computer self- efficacy have been studied. These antecedents have been grouped into categories such as social influence (encouragement, management support), demographic variables (experience, age, sex, prior performance), and beliefs (self-conceptions of ability, anxiety) (Agarwal, Sambamurthy, & Stair, 2000). Literature evidenced that Internet as an advanced computer tools use platforms such as e-mail, the World Wide Web, and Social Networking Websites are ‘must use’ is applied in the teaching and learning contexts (Ige, 2015). Academics need to enhance their Internet usage skills in order to meet the learning demands of teeming students who have been afforded unhindered Internet access with the application of third generation (3G) mobile communication, which has aided internet development around the world (Zhou, 2014).
CHAPTER THREE
RESEARCH METHODOLOGY
Research design
The researcher used descriptive research survey design in building up this project work the choice of this research design was considered appropriate because of its advantages of identifying attributes of a large population from a group of individuals. The design was suitable for the study as the study sought to comparison of academic performance of students in computer science in the tertiary institution from 2016-2019
Sources of data collection
Data were collected from two main sources namely:
(i)Primary source and
(ii)Secondary source
Primary source:
These are materials of statistical investigation which were collected by the research for a particular purpose. They can be obtained through a survey, observation questionnaire or as experiment; the researcher has adopted the questionnaire method for this study.
Secondary source:
These are data from textbook Journal handset etc. they arise as byproducts of the same other purposes. Example administration, various other unpublished works and write ups were also used.
Population of the study
Population of a study is a group of persons or aggregate items, things the researcher is interested in getting information on comparison of academic performance of students in computer science in the tertiary institution from 2016-2019. 200 staff of University of Ibadan was selected randomly by the researcher as the population of the study.
CHAPTER FOUR
PRESENTATION ANALYSIS INTERPRETATION OF DATA
Introduction
Efforts will be made at this stage to present, analyze and interpret the data collected during the field survey. This presentation will be based on the responses from the completed questionnaires. The result of this exercise will be summarized in tabular forms for easy references and analysis. It will also show answers to questions relating to the research questions for this research study. The researcher employed simple percentage in the analysis.
DATA ANALYSIS
The data collected from the respondents were analyzed in tabular form with simple percentage for easy understanding.
A total of 133(one hundred and thirty three) questionnaires were distributed and 133 questionnaires were returned.
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATION
Introduction
It is important to ascertain that the objective of this study was on comparison of academic performance of students in computer science in the tertiary institution from 2016-2019. In the preceding chapter, the relevant data collected for this study were presented, critically analyzed and appropriate interpretation given. In this chapter, certain recommendations made which in the opinion of the researcher will be of benefits in addressing the challenges of academic performance of students in computer science in the tertiary institution from
Summary
This study was on comparison of academic performance of students in computer science in the tertiary institution from 2016-2019. Three objectives were raised which included: To identify the behaviors of students in computer science course, to ascertain the academic performance of students on computer science and to recommended improvement strategies to advance the students’ performance in computer science course. In line with these objectives, two research hypotheses were formulated and two null hypotheses were posited. The total population for the study is 200 staff of university of Ibadan. The researcher used questionnaires as the instrument for the data collection. Descriptive Survey research design was adopted for this study. A total of 133 respondents made heads of department, senior lecturers, junior lecturers and graduate assistants were used for the study. The data collected were presented in tables and analyzed using simple percentages and frequencies
Conclusion
It cannot be fairly conclusive that the background of student pursuing computer science course is a major influence on performance although there is a significant correlation since majority of students who enrol in computer science in which courses are part and parcel of. The greater percentage of students who pursue computer science related programmes are males as we found out from this research. This presupposes that males are presumed to have better performance. The utilization of computers had motivated students to commit to learning and participate actively in the teaching and learning activities. The findings of the study revealed positive perception of computer utilization and students’ academic performance
Recommendation
Computer science courses should be taught by exemplary lecturers/instructors who have the requisite knowledge to teach the curriculum and who continue to upgrade their technical and teaching skills throughout their careers because of rapid changes and advancement in the world of technology.
References
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- Brooks, R. (1999). Towards a theory of the cognitive processes in computer programming. International Journal of Human Computer Studies, 51, 197–211.
- Butler, A. C., Karpicke, J. D., & Roediger, H. L. (2007). The effect of type and timing of feedback on learning from multiple-choice tests. Journal of Experimental Psychology: Applied, 13(4), 273–281.
- Chen, G. D., Chang, C. K., & Wang, C. Y. (2008). Using adaptive e-news to improve undergraduate programming251.
- Eckerdal, A. (2009). Novice programming students’ learning of concepts and practise (Doctoral dissertation), Retrieved from http://uu.divaportal.org/smash/record.jsf?pid=diva2:173221
- Emurian, H. H., Holden, H. K., & Abarbanel, R. A. (2008). Managing programmed instruction and collaborative peer tutoring in the classroom: Applications in teaching JavaTM. Computers in Human Behavior, 24, 576–614.
- Fessakis, G., Gouli, E., & Mavroudi, E. (2013). Problem solving by 5–6 years old kindergarten children in a computer programming environment: A case study. Computers & Education, 63, 87–97. [8]
