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ISYS1055 Database Concepts - RMIT University

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.

Learning Outcome 1: Describe various data modelling and database system technologies.
Learning Outcome 2: Explain the main concepts for data modelling and characteristics of database systems.
Learning Outcome 3: Identify issues with and compare, justify relational database design using the functional dependency concepts.
Learning Outcome 4: Apply SQL as a programming language to define database schemas and update database contents.
Learning Outcome 5: Apply SQL as a programming language to extract data from databases for specific users' information needs

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".)

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 for courses (C), or teaching staff (T), indicated in the third column.

Variable	Description	C/T
id	Course identifier. Each row gives the data for a particular course instance.	C
staffid	Identifier for a particular member of teaching staff. One staff member can teach multiple courses in the dataset.	T
age	The age of the staff member.	T
gender	Gander of staff member: female (f), male (m).	T
tenuretrack	Whether the staff member is on the tenure track (working towards a permanent position): yes (1), no (0).	T
nonenglish	Did the staff member complete their undergraduate education in a non-English speaking country: yes (1), no (0).	T
beauty	Rating of the staff member's appearance in a photo, averaged across responses by 6 undergraduate students, and normalised to have a mean score of zero.	T
students	Total number of students in course	C
division	Is the course lower or upper division: lower (L) [usually first- or second-year courses], upper (U) [usually third- or fourth- year courses].	C
courseevaluation	Mean student course evaluation score on a scale from 1 (lowest) to 5 (highest).	C

Note: The data you will be using is a subset of the original data collected by Hamermesh and Parker. Therefore, your results will not be identical to those reported in their paper. The identifier variables (id and staffid) are not necessarily a contiguous sequence. We thank Daniel Hamermesh for supplying the original data.

Data Preparation
Your first task is to load the raw CSV data into the Oracle database, so that you can analyse it.
• You need to design the relation schemas for two appropriate tables (to reflect that the data is at two levels of granularity). Note that there is data redundancy in the provided (starting) CSV data file.
• You can use the "import data" function from SQL Developer to import data from .CSV files to tables.

Analysis
Now that the data is loaded into a database, you can begin to analyse it. The broad goal is to investigate the effect that different variables such as age, gender, and beauty, have on course evaluation scores.

In the following subsections, you will be asked to carry out numerical analysis of a particular variable or variables. For each, you should format your numerical results and present them in a table in your report. You should also briefly comment on your findings, explaining what the numbers show about the variable(s) in question. This commentary should be brief, one or two sentences at most for each specific analysis below.

For each analysis, you should consider carefully whether it is at the course level, or at the teaching staff level. If the analysis is at the teaching staff level, each data point for a staff member may only be included once. If the analysis is at the course level, data points must be included for each course; this also applies if the analysis uses both course and teaching staff variables (unless noted otherwise below).

Note that the table layouts as shown in the following subsections indicate the formatting that should be used in your report document for presentation. You should write SQL queries to obtain data to complete the tables. Your queries do not need to generate tables in the exact format given, and you may sometimes need to write several SQL queries to complete one analysis table.

Course Sizes - Number of Students
Calculate the minimum, mean and maximum number of students in a course. Present the results in your report, in a table similar to the following:

	Minimum	Mean	Maximum
Number of students

Course Sizes - Course Evaluation Score

Analyse the minimum, mean, and maximum course evaluation score for groups of courses, binned into size groups of 18 or less, 19-28, 29-60, 61 or more. (For example, a course size group of 19-28 includes all those courses that had from 19-28 students enrolled, inclusive).

Course size	18 or less	19-28	29-60	61 or more
Number of courses in group
Minimum course evaluation score
Mean course evaluation score
Maximum course evaluation score

Division
Analyse minimum, mean, and maximum course evaluation score by division (course level).

	No. courses in group	Minimum	Mean	Maximum
Upper division
Lower division

Gender - Course Evaluation Score
Analyse minimum, mean and maximum course evaluation score by gender.

	No. courses in group	Minimum	Mean	Maximum
Female
Male

Gender - Beauty
Analyse minimum, mean and maximum beauty by gender.

	No. academics in group	Minimum	Mean	Maximum
Female
Male

Tenure track
Analyse minimum, mean and maximum course evaluation by tenure track status.

	No. academics in group	Minimum	Mean	Maximum
Tenure track
Not tenure track

Education Background
Analyse minimum, mean and maximum course evaluation by education background.

	No. academics in group	Minimum	Mean	Maximum
English education
Non-English education

Interactions between Tenure Track, Gender and Education Background

Analyse course evaluation by gender, tenure track, and education background. Present the results in your report, in a table similar to the following:

Tenure track	Gender	Education	No. academics in group	Mean
Tenure track	Female	English
Tenure track	Female	Non-English
Tenure track	Male	English
Tenure track	Male	Non-English
Not tenure track	Female	English
Not tenure track	Female	Non-English
Not tenure track	Male	English
Not tenure track	Male	Non-English

Correlation Analysis
Age, course size, beauty and course evaluation score are variables that take on many different values (continuous), rather than defining groups (categorical). Therefore, it is useful to analyse the correlation between these variables. Correlation is a measure of association and gives a numerical value to quantify the degree of relationship between two variables. Here, you should use the Spearman rank correlation, which compares the rank ordering of two variables and the extent to which these agree.

Oracle has a built-in Spearman rank correlation aggregate function, CORR_S (Important: NOT the CORR_K function, or the CORR function):

There are two return values of interest that you need to consider from the CORR_S function:
• COEFFICIENT
• TWO_SIDED_SIG

Broadly speaking, COEFFICIENT measures the strength of the association between two variables, in a range from +1 to -1:
• A high positive value indicates that the observations for both variables have a similar rank (i.e. when one variable is high, the other also tends to be high)
• A high negative value indicates that the observations for both variables have an inverse rank (i.e. when one variable is high, the other tends to be low)
• A value close to zero indicates that there is not a strong relationship (i.e. when one variable is high, that doesn't tell us much about the other variable)

TWO_SIDED_SIG is a value returned by a statistical significance test that seeks to establish how confident we can be that our observed correlation coefficient value is different from zero (indicating no correlation, or no relationship between the variables). In other words, based on our data, does it seem reasonable to infer that the correlation coefficient value is showing an actual relationship, beyond random variation or noise in the data?
• As a broad rule of thumb, if this value is smaller than 0.05, you can conclude with some confidence that the relationship indicated by the Spearman rank correlation coefficient is not just due to chance.

Returning to the data of interest, calculate the Spearman rank correlation between the four pairs of variables, as follows:
• Course evaluation score and course size
• Staff age and beauty
• Staff age and mean course evaluation score
• Staff beauty and mean course evaluation score

Note that the first analysis is at the course level, whereas the remaining three should be carried out at the staff level. In particular, this means that for the third and fourth correlations, you should first calculate the mean course evaluation scores for each staff member, by aggregating the course level data. (Hint: you can create Views to complete analysis in several steps if needed.) Report your analysis results in a table similar to the following:

Variables	Correlation Coefficient	Two-sided Significance
Course evaluation score & course size
Staff age & beauty
Staff age and mean course evaluation score
Staff beauty and mean course evaluation score

Report

Report Structure
You should aim to make your report as clear and readable as possible. This involves using clear language that is easy to follow and understand. Your argument should be structured to effectively convey your message.
Include sufficient detail, but avoid adding irrelevant material.

Your report must include the following headings and sub-headings. Details of mark allocation are described in the marking rubric document.
• Data Preparation
o A brief opening paragraph that describes the analysis to be undertaken (what is the high-level question being studied?).
o Give the relation schemas (table definitions) with primary keys and any foreign keys annotated, and the sizes of tables.
o Description of the steps for importing data from the .CSV data file into tables in SQL Developer.
o You must submit all your SQL CREATE TABLE statements as an appendix.

• Analysis
o Include a sub-heading corresponding to each of the previously listed analysis sub-sections.
o For each, include your numerical results, following the given table layouts and a brief (1 to 2 sentences maximum) explanation of what the numbers show about the variable(s) being analysed
o You must submit all your SQL queries, including the queries you used to carry out your analysis and obtain your reported results in the appendix.
• Discussion and Conclusions
o Write a paragraph that brings together your overall findings.
o Write a paragraph that briefly mentions any limitations of the analysis

Attachment:- Database Concepts.rar