2d:["$","title",null,{"children":"IAT 355 Final Project | hanabarazandeh | Observable"}] 32:T671,md`In this interactive visualization I intend to explore the change in the population of international students for each region as well as the STEM vs BHASE ratio for each dimension.
**Interaction:**

If you slide the time range (starting from 2010-21011) to 2017-2018, you can dynamically observe:

On the right, how is the gender ratio (for total region groups) changing over time, and how is the STEM vs BHASE ratio changing for each gender
On the left, howing results for total genders, which regions are increasing, and which ones on a faster rate, and how the STEM vs BHASE is changing for each region group

For a given time range, if you would like to compare the gender ratio for each region group, you can brush over the bar chart on the left to filter the results on the right.
**Observations:**

Working with the interactive tools, I realized:

that Eastern Asia and Southern Asia have the fastet rate of increasing over the years, while Central Asia and Middle East is the only region with a decrease.
Central Asia and Middle East has the highest STEM to BHASE ratio
Overal in all years, STEM to BHASE ratio seems to be lower for female gender.
it seems that Central Asia and Middle East has the highest STEM to BHASE ratio in female gender: For Middle East almost half of female students are in STEM.
It seems that Americas, Eastern Asia and Europe have the highest female to male ratio among international students, but more precise comparison is not possible due to the trade of dynamic vs static visualization

`33:T78a,VegaLite({ data: {values: uni_enrollment_parsed.filter(function(c){return c.Status_of_student_in_Canada == "International students "})}, hconcat: [ // First Plot { transform: [ {filter: {field: "REF_DATE", lte: range[1]}}, {filter: {field: "REF_DATE", gte: range[0]}} ], mark: "bar", width: 350, height: 250, selection: { brush: { type: "interval", on: "[mousedown, window:mouseup] > window:mousemove!", encodings: ["x", "y"], translate: "[mousedown, window:mouseup] > window:mousemove!", zoom: "wheel!", mark: {"fill": "#333", "fillOpacity": 0.125, "stroke": "white"}, resolve: "global" } }, encoding: { x: {field: "Country_of_citizenship", type: "nominal"}, y: {aggregate: "sum",field: "VALUE", type: "quantitative", scale: {domain: [0,200000]}}, color: {field: "Classification_of_Instructional_Programs_STEM_and_BHASE_groupings", type:"nominal"} } }, { transform: [{"filter": {"selection": "brush"}}, {filter: {field: "REF_DATE", lte: range[1]}}, {filter: {field: "REF_DATE", gte: range[0]}} ], selection: { bar: { type: "single", encodings: ["x"], on: "click", resolve: "global", empty: "all" } }, height: 250, width: 100, mark: "bar", encoding: { x: {field: "Gender", type: "nominal"}, y: {aggregate:"sum",field: "VALUE", type: "quantitative"}, color: { condition: { selection: "bar", field: "Classification_of_Instructional_Programs_STEM_and_BHASE_groupings", type:"nominal", scale: {range: ["#55B5C8", "#EC9F6E"]}, }, value: "lightgrey" } } } ] })34:T8dc,md`Visualization 2 observations raised some questions, which I could not explore with the original dataset, because it did not include the populations for each region. So I created my own dataset gathering the population for each year and region, and also made a column that calculates the percentage value using the populations and values from the university enrolment dataset.
**Interaction:**

Again, I am using a slider:

Showing results for total genders, we can see how fast the population of each region growing over time (height of the bar)
Showing results for total genders, we can see how fast the percentange of number of international students of each region growing over time (color)

**Questions:**

Questions emerged from previous visualization

What percentage of the population(home country) is the number of international students for each region? And is the change seen in previous visualization proportional to the change of this percentage?
Since Eastern Asia and Southern Asia have the fastet rate of increasing over the years, are these values proportional to the population of those regions? And since Middle East and central Asia have had a decline in number of international students, is that proportional to the population of their region?

**Observations:**

Working with the interactive tools, I realized:

In 2010, percentage of international students (100=population of their home countries) highest for Eastern Asia, Europe and Middle East and central Asia and lowest for Southern Asia.
From 2010 to 2018, this percentage increases for all regions, while for Middle East the increase is very small and it is the only region with a decrease at the end, which explains the decrease in number of international students of Middle East and central Asia(observation in the previous visualization)
It seems that Southern Asia, Eastern Asia and Europe have the fastest rate in increasing this proportion, which explains the rapid increase in number of Eastern Asian international students (from previous visualization) and is interesting for Europe, because the length of the bar is not increasing at the same rate

`35:T422,md`visualization 2 revealed the low STEM to BHASE ratio for female gender throughout the years for international students. Working with the slider, we could see how that ratio is changing over the years for total countries. And working with brush, we could see how in one year that ratio varies for different countries. But we coild not see how number of female students is changing in STEM in different countries, due to the tradeoff between dynamic and static. And that is what I intend to explore in this visualization.
**Interaction:**

Hovering over each chart, and different areas, we can get details on demand.

**Questions:**

Questions emerged from visualization 2

In which region are female students increasing in STEM? and which ones on a faster rate?

**Observations:**

Female Southern Asians have the fastest growth in STEM among female students in STEM of different regions
Female Southern Asians in STEM are increasing at a faster rate than their male counterparts.

`36:T6c1,md`Analysing the 3rd observation in the visualization 1(low STEM to BHASE ratio for female gender throughout the years for international students), I intend to investigate the possible causes for that. One factor in choosing STEM could be the academic performance in high school (PISA scores). (On the left results are for both gender, over the years of 2009-2015, and and by subjects and countries)
**Interactions:**

dragging the mouse horizantally across a subject filters the gender ratio on the right for that subject, across different countries
dragging the mouse vertically across different subjects and one country filters the gender ratio on the right for that country

**Questions:**

Questions emerged from previous visualization

Is there a correlation between academic performance in mathematics and science (PISA scores) and choosing STEM for female students for Eastern Asians coming to Canada?

**Observations:**

on the first glance, we can see that for both gender and across 3 subjects, all Eastern Asian countries have scores significantly higher than the global average
dragging mouse horizantally over science, we can see that on the average for different countries, the female vs male scores are very close to equal, while for math females have a less score(visible yet very small amount). And the difference is the largest for reading, with females having a higher score.
We can see that there is no correlation, and it raises the question, why do very few female students choose STEM, given the very slight difference in their scores in mathematics and science with to their male counterparts

`37:T5d3,VegaLite ({ data: {values:PISA_scores_eastern_asia_parsed}, hconcat: [{ width: 450, height: 200, selection: { brush: { type: "interval", on: "[mousedown, window:mouseup] > window:mousemove!", encodings: ["x", "y"], translate: "[mousedown, window:mouseup] > window:mousemove!", zoom: "wheel!", mark: {"fill": "#333", "fillOpacity": 0.125, "stroke": "white"}, resolve: "global" } }, mark: "bar", encoding: { x: {field: "LOCATION", type: "nominal"}, y: {field: "Subject", type: "nominal", "title": "subjects"}, color: {aggregate:"mean",field: "VALUE", type: "quantitative", scale: {range: ["#edeec9", "#77bfa3"]} } } }, { transform: [{"filter": {"selection": "brush"}}], width: 60, selection: { bar: { type: "single", encodings: ["x"], on: "click", resolve: "global", empty: "all" } }, mark: "bar", encoding: { x: {field: "Gender", type: "nominal"}, y: {aggregate:"mean",field: "VALUE", type: "quantitative", "title": "scores"}, color: { condition: { selection: "bar", field: "Gender", type: "ordinal", scale: {range: ["#55B5C8", "#EC9F6E"]}, }, value: "lightgrey" } } } ] })38:[{"id":"621deb1c5f4d0d269e3ff7df6fac5eeba452430da53c7d5fb7f32143fc64301979199bce00ed8762cf900e104f7495128b97db37e2fa214b268fdd22d0e674db","href":"https://static.observableusercontent.com/files/621deb1c5f4d0d269e3ff7df6fac5eeba452430da53c7d5fb7f32143fc64301979199bce00ed8762cf900e104f7495128b97db37e2fa214b268fdd22d0e674db","name":"PISA_scores_eastern_Asia.csv","type":"text/csv","lastModified":1618809720431.162,"size":2275},{"id":"e65e75b560ae22ff6992e67f942212bc406a8790fee50c7da28548140be41feb4ecf7ffdc54868662f4b78ed89e88e80bc2ede4491460d1c0c0dee178a1b780e","href":"https://static.observableusercontent.com/files/e65e75b560ae22ff6992e67f942212bc406a8790fee50c7da28548140be41feb4ecf7ffdc54868662f4b78ed89e88e80bc2ede4491460d1c0c0dee178a1b780e","name":"uni_enrolment_finall@1.csv","type":"text/csv","lastModified":1618869172606.936,"size":60467},{"id":"34f01b590d8be57f925fb9a773d97b9f49645deac80862d333da33c63a94ee3495bc228acb09d0c7d7618681ff45adddac883f9e47c62ccf518884e024c7f112","href":"https://static.observableusercontent.com/files/34f01b590d8be57f925fb9a773d97b9f49645deac80862d333da33c63a94ee3495bc228acb09d0c7d7618681ff45adddac883f9e47c62ccf518884e024c7f112","name":"value_vs_population_2.csv","type":"text/csv","lastModified":1618870255485.51,"size":4365},{"id":"7f99a1eb7e70011f8bbcf10ec11298df1abd5950cf6278e9326992db607e23395214bb5853f479f3180e2ae8509780a1a1b35ce9e2a799f1dcbae473917fcafc","href":"https://static.observableusercontent.com/files/7f99a1eb7e70011f8bbcf10ec11298df1abd5950cf6278e9326992db607e23395214bb5853f479f3180e2ae8509780a1a1b35ce9e2a799f1dcbae473917fcafc","name":"canadian_enrolment_vs_population@1.csv","type":"text/csv","lastModified":1619060914540.655,"size":1614}] 30:{"id":"f956ad8163f31ebb","slug":"iat-355-final-project","title":"IAT 355 Final Project","thumbnail":"8ca8df182a50ae6b0bded490948520130f8eaa517a7d9f4cc4472909b2a44d7c","access_level":"unlisted","create_time":"$D2021-04-16T09:01:30.805Z","update_time":"$D2021-08-07T19:32:15.664Z","view_time":null,"first_public_time":"$D2021-04-22T05:31:39.768Z","has_unpublished_edits":null,"fork":false,"stars":1,"starred":false,"owner":{"type":"team","id":"343b01640bb8b760","login":"hanabarazandeh","name":"","avatar_url":"https://avatars-next.observableusercontent.com/53002cf166be0651fe2e353b8bbe3704c818867d7baee16c0927ebdadd0bf19d","tier":"starter_2024"},"version":1061,"publish_version":1061,"role":null,"authored":false,"authors":[{"type":"individual","id":"a531bed43ace0215","login":"hanabarazandeh","name":"","avatar_url":"https://avatars-next.observableusercontent.com/53002cf166be0651fe2e353b8bbe3704c818867d7baee16c0927ebdadd0bf19d","tier":null}],"members":[{"type":"individual","id":"a531bed43ace0215","login":"hanabarazandeh","name":"","avatar_url":"https://avatars-next.observableusercontent.com/53002cf166be0651fe2e353b8bbe3704c818867d7baee16c0927ebdadd0bf19d","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"IAT 355 Final Project","cells":[{"id":0,"value":"md`# IAT 355 Final Project`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":312,"value":"md`## Hanieh Barazandeh`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":314,"value":"md`## Spring 2021`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":275,"value":"md` **Questions I explore in this project: **\n

**How is the international student population changing in Canada?**

How is international student population changing across different countries of citizenship?

How is the gender ratio changing among students of different citizenships?

How is STEM vs BHASE across different countries of citizenship, and how is it changing over time?

**How is the international student population changing in Canada? **

Is there a correlation between academic performance in high school (PISA scores) and choosing field of study in university for different genders?

Is there a correlation between academic performance in high school (PISA scores) and future career expectations for different genders?

What makes female students choose STEM?

We can see that for both genders, international students are increasing and Canadian students for both genders are almost a straight line.
For domestic enrolments, female students have a noticeably bigger share, while it is the opposite for international enrolments.

We can see that for both genders, there was a slight decrease in enrolments in 2016
It is interesting that even though male population 15 to 24 years old is higher than their female counterparts, they have lower post secondary enrolment.