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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....

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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
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
evised 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
• 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

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.
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• 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., S XXXXXXXXXXpdf) 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
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 e
ors (e.g.
if one was to enter them exactly as presented in your appendix).

● It is your responsibility to co
ectly submit your file. Please verify that your submission is co
ectly submitted by
downloading what you have submitted to see if your .pdf file includes the co
ect content.
● Never leave submission to the last minute -- you may have difficulty uploading files.
● You can submit multiple times – a new submission will ove
ide 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 a
anged by the course coordinator).
● More information on special consideration is available at
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. Ru
ic/assessment criteria for marking
The detailed ru
ic and assessment criteria are available online via Canvas→Assignments→Assignment 3.

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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 ca
y 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
eferences 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
oom 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 ca
y 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 refe
ed 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”.)
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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
ow of the file co
esponds 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 co
esponds 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
e repeated for each course that they teach.

The meaning of the variables is explained in the following table. Each variable can be
Answered Same Day Jun 03, 2021 ISYS1055


Shikha answered on Jun 07 2021
139 Votes
ISYS1055/1057 Database Concepts
(Student Name)
At most colleges, teaching can be assessed through a procedure by the students' complete course surveys rating courses in light of questions with respect to the content, material clarity, and many other variables. These questions are normally refined into a single score that should reflect by and large instructing quality. In most Australian colleges including RMIT, this is the Good Teaching Score (or GTS). Earlier research has demonstrated that numerous components can impact students’ criticism, and these may incorporate things that are legitimately solicited as part from the surveys.
Daniel Hamermesh and Amy Parker gathered university data to perform the teaching evaluation that is impacted by the engaging quality of teaching staff. The main objective of this assessment is to make an analysis on the basis of criteria. Two analysts have collected data at the University of Texas at Austin, USA, and incorporates data around 455 courses, instructed by teaching staff from various departments. Courses are categorized on the basis of number of students. The evaluation of courses are done on the basis of student surveys by answering the question about the course they were enrolled. This analysis was characterized by 0-5 on a scale. 5 means the excellent experience in that course whereas 0 is at minimum level on the scale. The students were surveys about every staff member. Also, they saved some other details like gender, their tenure track, their minority and so on.
Data Preparation
This data analysis is to evaluate the effect of age, gender and beauty on course evaluation scores. There are many criteria given which needs to be evaluated on the basis of using some SQL functions. Before querying data, we have to create these tables in SQL developer and then import the data in these tables.
Relational Schema
The relational schema of the given tables will be as following:
Staff (StaffID, Age, Gender, tenuretrack, Division, NonEnglish)
Course (ID, StaffID*, Students, CourseEvaluation, Beauty)
Staff is the master table, therefore, StaffID will be primary key that will uniquely identify a row. Now, some modification has been done in original data file. This modification is we have to create staff table with unique staffid value. Therefore, repetitive values will be deleted.
The second table is Course which will consist of all values from original data file. This is transaction table and will...

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