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2010 Assignment 3 Page 1 of 9 School of Science ISYS1055/1057 Database Concepts Assignment 3 Assessment Type: Individual assignment; no group work. Submit online via Canvas→Assignments→Assignment 3. Marks are awarded for meeting requirements as closely as possible. Clarifications/updates may be made via announcements/relevant discussion forums. Due date: Tuesday 09 June 2020, 23:59. Please check Canvas→Syllabus or via Canvas→Assignments→Assignment 3 for the most up to date information. As this is a major assignment in which you demonstrate your understanding, a late penalty of 10% of full available marks per day or part day applies for up to 5 days late. After 5 days, 0 marks will be awarded. Weighting: 30 marks 1. Overview Database systems are a key technology for the storage, management, manipulation, and retrieval of structured data. In this assignment you will apply the skills and concepts that you have learned about database systems in the course so far to analyse data, and then write a report based on your findings. 2. Assessment Criteria This assessment will determine your ability to: 1. Follow coding, convention and behavioural requirements provided in this document and in the lessons. 2. Independently solve problems by using database concepts taught in the course. 3. Understand the relational model. 4. Write and understand SQL queries. 5. Meet deadlines. Seek clarification from your instructor, when needed, via discussion forums. This assignment is worth thirty points in total, which accounts for 30% of the overall assessment for the course. The revised assessment components and weights for the course are: Assignment 1 Assignment 2 Assignment 3 20% 50% 30% 3. Learning Outcomes This assessment is relevant to the following Course Learning Outcomes: • CLO 1: Describe various data modelling and database system technologies. • CLO 2: Explain the main concepts for data modelling and characteristics of database systems. • CLO 3: Identify issues with and compare, justify relational database design using the functional dependency concepts. • CLO 4: Apply SQL as a programming language to define database schemas and update database contents. • CLO 5: Apply SQL as a programming language to extract data from databases for specific users’ information needs It also supports the following Graduate Learning Outcomes: • Enabling Knowledge: You will gain skills as you apply data modelling knowledge effectively in diverse contexts. • Critical Analysis: Analyse and model requirements and data to understand underlying issues. Page 2 of 9 • Problem solving: Design and implement database solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification. 4. Submission Submit your assignment via Canvas→Assignments→Assignment 3. Your submission must be a single .pdf file, with the filename being your student number (e.g., S1234567.pdf) that contains the following sections: 1. A report that presents your analysis and findings. Before submitting, check carefully that your report follows all of the formatting requirements, and includes all of the section and subsection headings, as detailed in Section 8 below. 2. An appendix that contains the SQL code that you developed to analyse the provided data. The SQL must be able to be run on the RMIT Oracle database server using the SQL Developer interface without producing errors (e.g. if one was to enter them exactly as presented in your appendix). ● It is your responsibility to correctly submit your file. Please verify that your submission is correctly submitted by downloading what you have submitted to see if your .pdf file includes the correct content. ● Never leave submission to the last minute -- you may have difficulty uploading files. ● You can submit multiple times – a new submission will override any earlier submissions. However, if your final submission is after the due time, late penalties apply. ● If unexpected circumstances affect your ability to complete the assignment, you can apply for special consideration. An outcome of special consideration may be an equivalent assessment, assessing the same knowledge and skills of the assignment (time to be arranged by the course coordinator). ● More information on special consideration is available at https://www.rmit.edu.au/students/student-essentials/assessment-and-exams/assessment/special-consideration 5. Academic integrity and plagiarism (standard warning) Academic integrity is about honest presentation of your academic work. It means acknowledging the work of others while developing your own insights, knowledge and ideas. You should take extreme care that you have: • Acknowledged words, data, diagrams, models, frameworks and/or ideas of others you have quoted (i.e. directly copied), summarised, paraphrased, discussed or mentioned in your assessment through the appropriate referencing methods, • Provided a reference list of the publication details so your reader can locate the source if necessary. This includes material taken from Internet sites. If you do not acknowledge the sources of your material, you may be accused of plagiarism because you have passed off the work and ideas of another person without appropriate referencing, as if they were your own. RMIT University treats plagiarism as a very serious offence constituting misconduct. Plagiarism covers a variety of inappropriate behaviours, including: • Failure to properly document a source • Copyright material from the internet or databases • Collusion between students For further information on our policies and procedures, please refer to the University website. 6. Assessment declaration When you submit work electronically, you agree to the assessment declaration. 7. Rubric/assessment criteria for marking The detailed rubric and assessment criteria are available online via Canvas→Assignments→Assignment 3. Page 3 of 9 8. Assignment Questions Assignment Overview For this assignment, you will be applying your SQL skills to analyse research data, and write a report that details your investigations into the question of whether particular variables such as class size and the perceived attractiveness of teaching staff influence course evaluations. As part of this assignment, you are likely to need to carry out some research and refer to additional information beyond what was covered in the course. This is an important skill. Keep a note of any external references that you use, as these will need to be detailed in your report. Analysing Variables That Influence University Course Evaluations At most universities, teaching is evaluated through a process whereby students complete course experience surveys, rating courses in response to questions regarding the content, clarity of material, presentation, and other factors. These questions are typically distilled into a single score that is supposed to reflect overall teaching quality. In most Australian universities including RMIT, this is the Good Teaching Score (or GTS). Prior research has indicated that many factors can influence student feedback, and these may include things that are directly asked as part of the surveys (Were the teaching staff good at explaining things? Did the staff work hard to make the course interesting?) and other factors that are not explicitly asked (Was the lecture room too crowded and noisy? Did an unexpected event occur part-way through the semester that required fundamental changes in teaching delivery? Are the teaching staff attractive?). Daniel Hamermesh and Amy Parker, two researchers form the USA, collected data to investigate the question of whether teaching evaluations are influenced by the attractiveness of teaching staff [1]. In this assignment, you will be analysing their data to carry out a preliminary investigation into answering this question. The data was collected at the University of Texas at Austin, USA, and includes information about 455 courses, taught by teaching staff in various departments (note that some staff taught multiple courses included in the data set). Courses were of various sizes in terms of the number of enrolled students. Each course was evaluated using student surveys, with responses to the question “Overall, this course was…?” being collected on a 5-level ordinal scale with a minimum score of (1) “very unsatisfactory” and a maximum score of (5) “excellent”. Information was obtained on each faculty member, based on characteristics including their gender, whether they are on a tenure track (roughly speaking, working towards being offered a permanent position at their university), whether they are part of a minority group, and whether they received their education in an English-speaking country. Separately, a picture of each teaching staff member was rated by 6 undergraduate students. Hamermesh and Parker describe the rating process as follows: “The raters were told to use a 1 (lowest) to 10 (highest) rating scale, to concentrate on the physiognomy of the instructor in the picture, to make their ratings independent of age, and to keep 5 in mind as an average” [1]. The ratings – subsequently referred to as “beauty” scores – were then normalised to have a mean score of zero. (This means that someone with a rating greater than zero was judged to be more “beautiful” than the average, while someone with a negative score was judged to be less “beautiful” than the average”.) Page 4 of 9 Data File The raw research data that you need to analyse is in the file profEvaluations.csv, available from the Course Canvas as part of the Assignment 3 specification. The file is in comma-separated value or “CSV” format. This is a format for representing table data, where each row of the file corresponds to a single record (row, or tuple); and the individual data items (attributes/cells) are separated by the comma (“,”) symbol. The first row gives the column headings (schema). To explore the file, you can open it in a text editor, or in a spreadsheet program (e.g. MS Excel, Numbers). Notice that each row of the original file corresponds to observations about a single course, and includes details such as number of students, and course evaluation score. It also includes information about the teaching staff member who taught the course, including a staffid, their age, and their educational background. Notice that a particular teaching staff member can teach more than one course – that is, their individual information may be repeated for each course that they teach. The meaning of the variables is explained in the following table. Each variable can be