Manage Information of Northampton Business School - Essay Example

MANAGING INFORMATION OF NORTHHAMPTON BUSINESS SCHOOL Table of Contents Table of Contents 2 Introduction: 4 Task 4 Database making: 4 Identified entities: 8 Primary and Foreign Keys: 8 Task 2 9 Report 1: Course enrolment: 9 Report 2: Attendance of the Students 9 Report 3: Reasons for Absence 10 Report 4: Master Student Database 10 Task 3 11 Website Page Development for Student Database: 11 Task 4 15 4.1 Data Collected from the University Database: 15 Conclusion and Recommendations: 18 Reference List: 19 Bibliography: 21 Appendix: 23 Introduction: The practices of managing information have evolved with the growth of technological support of the business houses. As technology is growing rapidly, the process of managing the information and activities of business have transformed from paper files to e-files reducing the space and expenses of data management. In the words of Ramakrishnan, Gehrke and Gehrke (2003), database management process not only reflects effective utilisation of information, but also measures the efficacy of a business houses in terms of technological resource utilisation and adaptations to the changes in technology. The influence of data base management has not only improved the synchronisation between technology and business management but also contributed towards the strategic decision making process by enabling accelerated decision making process (Sumathi and Esakkirajan, 2007); (Piedade and Santos, 2008); (Rosca and Wild, 2002); (Kardasis and Loucopoulos, 2004). Considering the high influence of database management system (DBMS) in managerial activities, this report will focus on developing a suitable database for observing and identifying reasons behind the decreasing student attendance of the Northampton Business School. Task 1 Database making: The database making process has been developed considering not only the problem of reduced attendance of the students but will also provide an overall view of the factors that is contributing to the reduced rate of attendance in Northampton Business School. The database management process will be based on the relationships and attributes reflected in the Entity Relationship Model. The relationship model of the Department reflects the overall scenario that leads to the students and their attributes whereas the relationship model of students specifically focuses on the information required by the University for assessing the factors leading to the growing numbers of absentees among the students. A broader relationship model of the University has been designed for understanding the process of management of the operations of the University and the departments involved in managing the students (Please refer to Appendix 1). The databases presented below include the student identification characteristics and their attendance perspective. Student ID Name Contact Details Date of Birth Gender Course Enrolled 064 Jack Pearson +44 7716 551659 12-05-1996 Male MBA-Marketing 065 Miranda Newman +44 7345 646541 21-08-1996 Female MBA- Marketing 066 Leonard Dias +44 7243 346842 04-03-1995 Male MBA-Finance Table 1: Master Database for Students (Source: Created by Author) Figure 1: Entity Relationship Model with Department View (Source: Created by Author) Course ID Name Address Attendance % Reason for absence 012 Jack Pearson Lady’s Lane, Northampton NN1, UK, 57% Day Job 012 Miranda Newman 62, Charles Street, Northampton 81% Sick Leave 014 Leonard Dias 165 Ashburnham Rd, Northampton, NN1 4RB, UK 65% Distance from University Total Table 2: Database for monitoring student attendance (Source: Created by Author) Figure 2: Entity Relationship Model with Student View (Source: Created by Author) Identified entities: The entities identified in both the relationship models are having direct or indirect relationships with the students monitoring process. Considering the overall view as provided in the relationship model with department view, the entities are department, faculty, students and course whereas the specific student relationship model reflects only two entities namely, students and courses. The relationship between the entities is mostly defined by the purpose served by each entity (Bell, Kindhal and Thalmann, 2010); (Ram and Khatri, 2005). For instance, the relationship between the departments and faculty is that of employment as the department employs the faculties and similarly the relationship between department and students is that of admission (Adzic, Fiore and Spelta, 2001). It can be gathered that the primary entity with direct relationship for attendance is course as it will help in observing the individual attendance of the students in context of the courses. Moreover, the attribute “Reason for absence” also helps the University to assess the factors contributing to the growing absenteeism among the student base. Primary and Foreign Keys: The primary and foreign keys in the created models have been marked with multicoloured attributes to reflect them clearly. The primary keys in both the models are the id numbers of the courses and the students. On the other hand, the foreign key is the name attribute which will be common in case of both course and student entity. As it can be observed from the database, the name column reflects similar results while the IDs are changed. However, when a query is put with the name or the course all the details regarding the students can be evaluated. Task 2 Report 1: Course enrolment: The first report will reflect the number of students enrolling for the courses offered by the Northampton University. This will enable the University to assess the effectiveness of the studying process of each course while the organisation can assess the gaps in the education process and develop them. The Course enrolment database will include the course id, course name, ID of the students enrolled, attendance percent of each student and the reasons for absence. The database will also include other aspects such as when the user will move the cursor over the student ID, the user will be able to see the name of the student in the preview box. The database can be generated by selecting course entity and the subsequent course details that will be given in the master database. The higher level of management can compare the results of course enrolment with the practical scenario of the education and the job industry and improve their service structure while the faculties can assess the factors that can repel the students from attending the class and reduce them. Report 2: Attendance of the Students Designing, managing and observing the attendance process of the students is a key function of the educational insitutions. The attendance report also needs to be updated and monitored regularly for assessing the trend of absenteeism among the students. The database developed will reflect the attendance of the students on a daily process and will generate the overall results fortnightly. However, this can also be altered while creating the report and set for the necessary time period by the user of the report (Blaha, 1997); (Alhajj, 2002); (Wan-Kadir and Loucopoulos, 2004). The report will be developed by selecting the attribute “Attendance %” and will include Student name, Student ID, contacts, Courses enrolled, attendance % and the reason of absence. The report provides an informative outlook that can be used for measuring the skills of the students and also understands the problems faced by the students in attending the concerned course (Gregersen and Jensen, 1999); (Zhou, 2012). On the other hand, the concerned faculties can also contact the guardians of the students with lower attendance % and communicate for assessing the authenticity of the reasons selected by the students. For the higher management, this report provides the scope of communicating with the guardians of the students and enhances stakeholder engagement. Report 3: Reasons for Absence Observing the reasons behind or the factors influencing absenteeism will enhance the significance of the student monitoring process. The faculties can focus on the individual students who need support and can design their teaching process accordingly. The concerned report will be generated by selecting the attribute “Reason for Absence”. The report will be generated with the attributes of Student ID, Student Name, Attendance %, Course Enrolled and the reason for absence. However, as a single student can be absent for multiple reasons, the “reason for absence” will consider the most selected option from the individual student and then reflect the final report (Davis, 2001); (Tan and Zhao, 2003). The users can identify the factors creating barriers for the students to attend their classes and reduce them. This process will require contributions from the managerial levels and hence is significant for all. Report 4: Master Student Database Finally, the database can help the users generate a master database for monitoring the student activities. The master database will include the attributes under the student entity as given in the relationship model with department view and also include additional attributes such as core strengths, performance, and weaknesses of the students in context of their sports, academic courses, and cultural activities. The master database is also an effective process of monitoring the service structure of the University. This is a benefit for the higher management and the administration department for improving the competitiveness of their organisation in the industry. On the other hand, the faculty can identify the weaker students and focus on them in order to improve their performance. Task 3 Website Page Development for Student Database: In order to develop the online form, the researcher has selected extension of the University website and develops a separate student database in it. The primary reason for selecting this process is because survey pages can be neglected by students while attaining membership in the University website and can be highlighted as a mandatory process and will also seem appealing to the students. The researcher will try to create a user friendly webpage by implementing easy navigation system and focus on the specifications and details. The process will begin by considering the framework of the existing website of Northampton University. Figure 3: Existing Home page of Northampton University (Source: Created by Author) Figure 4: New Home page of Northampton University (Source: Created by Author) As can be seen from the above graphical representations, the existing home page of Northampton University has kept the student page in the top right hand corner. But, the back ground colour of the page, the font and style of the words are not effectively highlighting the necessary text blocks. Based on these issues some changes have been made in the new home page, the colour texture of the text boxes have been matched with the colour of the Northampton University logo and hence enhances the visibility of the text. Also, the side panel has been created that has brought the top-right hand corner blocks to the page centre. Similarly, the below given framework for the log in page of the students also reflects some changes (Please refer to Appendix 2). In comparison to the existing webpage, where there was no option for new users to make their account, the new web page has added a “Sign up” button. Also the existing login page does not show the headings which have been kept in new website, so that in case of clicking on the login page by mistake they can easily direct back to their desired web page. A new link has been added “Forgot Password” that will guide the students in case they forget their password or user id. In order to ensure that the website is not being manipulated by any harmful programs such as malwares and confirm that the user is a human the captcha enabling has been included. These additional aspects will secure the website from any kind of cyber attacks or attempts to hack the University website. The web page developed presents an opportunity for the University to collect the necessary student information and develop their student database (Please refer to appendix 3). The entire student database reflected above in the entity relationship models can be fulfilled with the help of this webpage. The basic layout of the page has been kept same with ash background and sky-blue theme for the text boxes. The side panel and the top panel options will be reflected in the data entry page for user convenience in context of navigation. The “welcome user” notion will create a sense of belongingness among the students. In order to ensure and enhance the validity of data provided by the students, the researcher has provided drop boxes that will create run-time validity check. The drop boxes will include all the possible results that can be put in answer box. For instance, any authorised user cannot fulfil this form as the student ID given will not be accepted or all the courses enrolled against the student may not tally or the roll numbers for each course differs or their attendance % is not confirming with the University records of the student. The web pages specifically focus on the attendance and absence of the students. The researcher has created separate options for reasons considering the two most probable options that can be selected by the students. This will also increase the scope of database analysis for the University in context of assessing the factors behind the increasing absenteeism among the students of Northampton Business School. The webpage will also present the opportunity to gather the contact details of the students so that the faculties can communicate with the guardians of the students if necessary. Task 4 4.1 Data Collected from the University Database: The data collected from the new page of Northampton University for observing and identifying the reasons behind the increasing absenteeism of the students has been presented in below tables and graphs: Q.1 Total Number of students enrolled in MBA Course: Figure 7: Number of Students enrolled in MBA Course (Source: Created by Author) (Please refer to Appendix 4) Based on the information collected from the data base, it can be observed that the total number of students attending MBA courses is 1006. MBA marketing has the highest student population with 135 male and 129 female students, where as MBA Operations has the lowest with 89 male and 77 female students. Apart from these, MBA Finance has 114 male and 69 female students, MBA HRM has 89 male and 121 female students and finally MBA Information Systems has 109 male and 74 female students. Q.2 Attendance %age of students on weekly basis: Figure 8: Attendance %age of students on weekly basis: (Source: Created by Author) (Please refer to Appendix 5) The above figure reflects the trend of absenteeism from the various departments of MBA in Northampton Business School. The assumption made by researcher for evaluating the given results is that, classes of all the courses are held 5 days a week. From the given results, it can be observed that highest percentage of attendance can be observed in MBA operations (86.14%), whereas MBA marketing has the lowest attendance (78.41%). However, all the streams of MBA have reflected moderately high attendance rate such as MBA Finance has 82.78%, 80.48% in MBA HRM and 79.23% in MBA Information Systems. But the faculties of the University confirmed that there has been a definite fall in the attendance rate. Q.3 Reasons for fall in Attendance: Figure 9: Reasons for fall in Attendance (Source: Created by Author) (Please refer to Appendix 5) The collected data reflected that majority of the students were absent because of health issues (26.84%). From all the students who have created their database in the University webpage, 31.7% of males and 22.57% of females considered health issues for their absence. On the other hand, the lowest number of students (7.75%) considered the teaching process as one of the reasons for their absence. This reflects that the teaching process is not the primary factor or it can also be further deduced that the learning process is quite satisfactory for majority of the students. However, the two highest factors apart from the health issues were distance from university (23.06%) and day jobs (24.95%). However, 17.4% of the students suggested that the environment of the college often demoralises them for attending the college classes. Conclusion and Recommendations: The absenteeism among the students of Northampton Business School is not because of the teaching process. The students mentioned that the campus environment can be improved in order to encourage the students for attending classes and college. For tackling health issues, the college can arrange for monthly health check-up campaigns that will help the students maintain their health. On the other hand, the college should develop their hostel conditions for reducing the distance factors. As a result, the college authorities can monitor the student activities and ensure an increased attendance report. However, reducing the absenteeism because of day jobs can be a major issue as financial support is not possible for such a huge student base. The college can create an exam or survey process and select few needy students who will be given a job in the college to pay off their educational expenses. The database made from the relationship model of the University has helped in identifying the contributing factors of absenteeism. The webpage developed is based on the attributes and entities identified in the relationship model and the overall process reflects some of the key benefits of appropriate database management. Reference List: Adzic, J., Fiore, V. and Spelta, S., 2001. Data warehouse population platform: Databases in telecommunications II, Springer: Heidelberg. Alhajj, R., 2002. Extracting the extended entity-relationship model from a legacy relational database. Information Systems. Elsevier Science Ltd. Bell, C., Kindhal, M. and Thalmann, L., 2010. MySQL high availability: tools for building robust data centers. Sebastopol, USA: Oreilly. Blaha, M., 1997. Dimensions of relational database reverse engineering, Proceedings of the International Working Conference on Reverse Engineering, Amsterdam, October pp. 176–183. Davis, K.H., 2001. Lessons Learned in Data Reverse Engineering. Proceedings of the Eighth Working Conference on Reverse Engineering, October. pp. 323–327. Gregersen, H. and Jensen, C.S., 1999. Temporal entity–relationship models—a survey, IEEE Transaction on Knowledge and Data Engineering, 11 (3), pp. 464–497 Kardasis, P. and Loucopoulos, P., 2004. Expressing and organizing business rules. Information and Software Technology, 46(11), pp. 701–718. Piedade M.B. and Santos M.Y., 2008. Student Relationship Management: Concept, Practice and Technological Support, International Engineering Management Conference, 5, pp.1-5. Ram, S. and Khatri, V., 2005. A comprehensive framework for modeling set-based business rules during conceptual database design, Information Systems, 30 (2), pp. 89–118. Ramakrishnan, R., Gehrke, J. and Gehrke, J., 2003. Database management systems. New York: McGraw-Hill. Rosca, D. and Wild, C., 2002. Towards a flexible deployment of business rules, Expert Systems with Applications, 23 (4), pp. 385–394. Sumathi, S. and Esakkirajan, S., 2007. Entity–Relationship Model.Fundamentals of Relational Database Management Systems, pp.31-63. Tan, H. B. and Zhao, Y., 2003. Automated elicitation of inclusion dependencies from the source code for database transactions. Journal of Software Maintenance and Evolution: Research and Practice 15 (6), pp.79–392. Wan-Kadir, W.M.N. and Loucopoulos, P., 2004. Relating evolving business rules to software design, Journal of Systems Architecture 50 (7), pp. 367–382. Zhou, H., 2012. Design of Student Information Management Database Application System for Office and Departmental Target Responsibility System, 2012 International Conference on Solid State Devices and Materials Science, 25, pp. 1660-1665. Bibliography: Chiang, R.H.L., Barron, T. and Storey, V., 1997. A framework for the evaluation of database reverse engineering methods for relational databases, Data & Knowledge Eng. 21, pp. 57–77. Ghannouchi, S.A., Ghezala, H.H.B. and Kamoun, F., 1998. A generic approach for data reverse engineering taking into account application domain knowledge. Proceedings of the Second Euromicro Conference on Software Maintenance and Reengineering, March. Palazzodegli Affari, pp. 21–28. Huhtala, Y. et al., 1998. Efficient discovery of functional and approximate dependencies using partitions, Proceedings of the IEEE International Conference on Data Engineering, pp. 392–401. J. Skyt, J., Jensen, C.S. and Mark, L., 2003. A foundation for vacuuming temporal databases, Data and Knowledge Engineering. 4 (1), pp. 1–29. Mfourga, N., 1997. Extracting entity-relationship schemas from relational databases: a form-driven approach. Proceedings of the Fourth Working Conference on Reverse Engineering (WCRE’97). IEEE Rajan, S.N., Sinha, A.K. and Singh, J.B., 2012. Efficient Utilization of DBMS Potential in Spatial Data Mining Applications – Neighborhood Relation Modeling Approach, International Journal of Information and Communication Technology Research, 2(5), pp. 465-470. Ruiz, A. N., Maldonado, C. and Martinez, S., 2003. Development of a Relational Database Management System. JCS&T, 3(2), pp. 33-37. Wang, M., Yuan, J., Miao, H. and Gemiao Tan., 2008. A Static Analysis Approach for Automatic Generating Test Cases for Web Applications, 2008. International Conference on Computer Science and Software Engineering, 2, pp.751-754, Zhang, C., Naughton, J., DeWitt, D., Luo, Q., and Lohman, G., 2001. On supporting containment queries in relational database management systems, In ACM SIGMOD Record, 30(2), pp. 425-436. Appendix: Appendix 1: Appendix 2: Figure 5: New Student Login Page of Northampton University (Source: Created by Author) Appendix 3 Figure 6: New Student Data Collection Page of Northampton University (Source: Created by Author) Appendix 4: Particulars Male Female Total MBA Finance 114 69 183 MBA HRM 89 121 210 MBA Marketing 135 129 264 MBA Operations 89 77 166 MBA Information Systems 109 74 183 Total 536 470 1006 Table 3: Number of Students enrolled in MBA Course (Source: Created by Author) Appendix 5 Particulars Male (average per day) Female`(average per day) Total attendance per week %age Total MBA Finance 94 55 745 81.42 MBA HRM 65 104 845 80.48 MBA Marketing 110 97 1035 78.41 MBA Operations 81 62 715 86.14 MBA Information Systems 94 51 725 79.23 Total 444 369 4065 80.82 Table 4: Attendance %age of students on weekly basis: (Source: Created by Author) Appendix 6 Reasons for Absence Male Female Total Distance from University 98 134 232 Day Job 164 87 251 Health Issues 121 149 270 Dissatisfied with teaching process 67 11 78 Poor Campus Environment 86 89 175 Total 536 470 1006 Table 5: Reasons for fall in Attendance (Source: Created by Author) Read More