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grading.Rmd
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# Assignments and Grading
This section provides general details on the different types of assignments for this course. It also contains policies for submitting work, reciving feedback, and late work.
## Assignments
Your grade for this course will consist of a number of different assignments on which points may be earned. Each category of assignment is described below.
### Attendance and Participation
```{block, type = "rmdtip"}
Attendance and participation are worth **10%** of your final grade.
```
Both attendance and participation are critically important aspects of this class. The class participation grade will be based on (a) attendance, (b) level of engagement during lectures and labs, (c) level of engagement on Slack, and (d) the completion of other exercises including "entry" and "exit" tickets, the student information sheet, a pre-test, and an end of the semester course evaluation.
Each of these elements is assigned a point value and assessed using a scale that awards full, partial, or no credit. Your participation grade will be split, with 50 points (5% of your final grade) for the first half of the semester (through Lecture-08) and another 50 points (5%) for the second half. Since the number of points awarded for participation are variable, the total number of points earned for each half will be converted to a 0 to 50 scale.
I provide the final number of points earned for each half of the course. If you would like a more detailed breakdown of your participation grade, please reach out and I will provide one.
### Lecture Preps
```{block, type = "rmdtip"}
Lecture preps are worth **6%** of your final grade.
```
Before each course meeting, you will need to complete all assigned readings. For a part of these readings, you will also need to complete a short exercise. These prep exercises are designed to get you ready for the week's material by exposing you to basic, guided examples before class begins. Instructions for the lecture preps will be posted in the lecture repositories on [**GitHub**](https://github.com/slu-soc5050) and will be linked to from the lecture pages on the [**course website**](https://slu-soc5050.github.io/). The instructions will also detail the deliverables to be submitted to demonstrate completion of each assignment.
For many of the lecture preps, I will post a YouTube video of me completing the exercise and narrating the process. These videos will be embedded in the lecture pages on the [**course website**](https://slu-soc5050.github.io/). You should follow along with the video and use it as a guide for completing the exercise yourself. I will also post replication files that detail the process and, if relevant, the code for completing the lecture prep. Like the instructions, these will be posted in the lecture repositories on [**GitHub**](https://github.com/slu-soc5050).
There will be a total of fifteen lecture preps over the course of the semester, each of which is worth 4 points (0.4% of your final grade). Lecture preps are graded using the “check” grading system. Since replication files are posted, feedback for lecture preps is not generally returned and I will only respond with the number of points awarded if you do not earn full credit.
### Lab Exercises
```{block, type = "rmdtip"}
Labs are worth **15%** of your final grade.
```
Each course meeting (except the first) will include time dedicated to practicing the techniques and applying the theories described during the day's lecture. These exercises will give you an opportunity to practice skills that correspond with the first four course objectives. Instructions for the labs will be posted in the lecture repositories on [**GitHub**](https://github.com/slu-soc5050) and will be linked to from the lecture pages on the [**course website**](https://slu-soc5050.github.io/). The instructions will also detail the deliverables to be submitted to demonstrate completion of each assignment. Replication files are also provided in the lecture repositories on [**GitHub**](https://github.com/slu-soc5050).
Lab exercises will be completed in small workgroups, though each student is expected to turn in the required deliverables. We will assign students to workgroups and may shuffle their composition over the course of the semester. Completing a lab entails not just successfully submitting the required deliverables but also actively contributing to the group discussions that help to produce them.
There will be a total of fifteen lab exercises over the course of the semester, each of which is worth 10 points (1.5% of your final grade). Lab exercises are graded using the “check” grading system. Since replication files are posted, feedback for labs is not generally returned and I will only respond with the number of points awarded if you do not earn full credit.
### Problem Sets
```{block, type = "rmdtip"}
Problem sets are worth **28%** of your final grade.
```
Problem sets will require students to draw on a variety of skills, including cleaning data, performing statistical analyses, producing plots, and reporting results. They are designed to assess your progress with the first four course objectives. Instructions for the problem sets will be posted in the lecture repositories on [**GitHub**](https://github.com/slu-soc5050) and will be linked to from the lecture pages on the [**course website**](https://slu-soc5050.github.io/). The instructions will also detail the deliverables to be submitted to demonstrate completion of each assignment. Replication files that illustrate my approach to each problem set will be posted on [**GitHub**](https://github.com/slu-soc5050) in the [`Replications`](https://github.com/slu-soc5050/Replications) repository once all students have submitted their problem sets.
There will be a total of eight problem sets over the course of the semester, each of which is worth 35 points (3.5% of your final grade). Each Problem Set will include a simple rubric describing how each problem set is evaluated. A key aspect of these assignments is not only demonstrating comfort with a particular set of statistics skills, but also demonstrating and evolving in your analysis development, programming, and analytical skills as well. The weight given to quality of your process and code will increase as the semester progresses.
### Final Project
```{block, type = "rmdtip"}
The final project is worth, in total, **41%** of your final grade. Depending on your section, it will be broken down into a variety of assignments, each of which has their own point value. See below for details.
```
The final project corresponds with the fourth learning outcome. It will be organized slightly differently depending on which section you are enrolled in. Specific instructions will be provided in the [**final project guide**](https://slu-soc5050.github.io/finalGuide), and updates will be posted on the [**course website's**](https://slu-soc5050.github.io/) [**final project page**](https://slu-soc5050.github.io/final-project).
In brief, all students will select a topic and submit their topic by Lecture-03 (**September 10^th^**) as an "Issue" in their individual [**GitHub**](https://github.com/slu-soc5650) assignments repository. Groups will be formed based on topic area. These groups will be used for support throughout the semester as well as peer review of particular pieces of the project itself.
As work progresses, there will be a number of **waypoints** where students will need to submit updates on their progress. Waypoints beyond the memo submission are as follows:
- Lecture-05 (**September 24^th^**) - Progress report from each student due as a GitHub issue in each student's final project repository
- Lecture-08 (**October 15^th^**) - Progress report from each student due as a GitHub issue in each student's final project repository
- Lecture-11 (**November 5^th^**) - Draft materials due in each student's final project repository
- Lecture-12 (**November 12^th^**) - Peer reviews due to group members as a GitHub issue in each student's final project repository
- Lecture-15 (**December 3^rd^**) - Progress report from each student due as a GitHub issue in each student's final project repository
- Final Presentations (**December 17^th^**) - Response to reviewer due in the GitHub issue opened by the reviewer
Deliverables for each waypoint are described in the [**final project guide**](https://slu-soc5050.github.io/finalGuide). All waypoints are graded using the "check" grading system. Final materials will be due on **December 17^th^** (during Finals Week), when we will hold a "research conference" in Morrissey Hall. During our conference, each student will present their results using PowerPoint (or similar). Final deliverables differ by course section.
#### SOC 4015
If you are enrolled in SOC 4015, you will need to pick a continuous variable from the 2012 General Social Survey to use as your main study variable. You will then clean the data and conduct an analysis of this variable using a variety of statistical tests covered this semester. Your final results will be presented as a PowerPoint presentation during finals week.
```{r, echo=FALSE}
final4015 <- data.frame(
Assignment = c("Memo", "Waypoints", "Draft Code & Docs", "Draft Slides", "Final Code & Docs", "Final Slides", "Final Presentation"),
Points = c("20 pts", "20 pts", "20 pts", "20 pts", "100 pts", "100 pts", "30 pts"),
Quantity = c("x1", "x6", "x1", "x1", "x1", "x1", "x1"),
Total = c("20 pts", "120 pts", "20 pts", "20 pts", "100 pts", "100 pts", "30 pts"),
stringsAsFactors = FALSE
)
knitr::kable(
final4015, booktabs = TRUE,
caption = "SOC 4015 Final Project Breakdown"
)
```
#### SOC 5050
If you are enrolled in SOC 5050, you will need to identify an appropriate data set that contains a continuous variable that you can use as your main study variable. You will then clean the data and conduct an analysis of this variable using a variety of statistical tests covered this semester. Your final results will be presented as a PowerPoint presentation during finals week.
You will also have to produce a 5,000 word final journal article manuscript that places your project in the relevant social science literature, presents your data and methods, and provides a summary and discussion of your results. An annotated bibliography will be due at Lecture-07 (**October 8^th^**) and the draft paper will be due at Lecture-12 (**Novemeber 12^th^**; note that this is one week *after* the other draft materials). Peer reviews of papers will be due at Lecture-13 (**Novemeber 19^th^**).
```{r, echo=FALSE}
final5050 <- data.frame(
Assignment = c("Memo", "Waypoints", "Annotated Bibliography", "Draft Code & Docs", "Draft Slides", "Draft Paper", "Final Code & Docs", "Final Slides", "Final Presentation", "Final Paper"),
Points = c("10 pts", "10 pts", "15 pts", "15 pts", "15 pts", "15 pts", "35 pts", "100 pts", "35 pts", "100 pts"),
Quantity = c("x1", "x7", "x1", "x1", "x1", "x1", "x1", "x1", "x1", "x1"),
Total = c("10 pts", "70 pts", "15 pts", "15 pts", "15 pts", "15 pts", "35 pts", "100 pts", "35 pts", "100 pts"),
stringsAsFactors = FALSE
)
knitr::kable(
final5050, booktabs = TRUE,
caption = "SOC 5050 Final Project Breakdown"
)
```
## Submission and Late Work
### Assignment Submission
Copies of all assignment requested deliverables should be uploaded to your private assignments repository on [GitHub](https://github.com/slu-soc5650) before class on the day that the assignments are due. All assignments will contain details on required deliverables.
The GitHub submission policy is in place because it facilitates clear, easy grading that can be turned around to you quickly. Submitting assignments in ways that deviate from this policy will result in a late grade (see below) being applied in the first instance and a zero grade for each subsequent instance.
### Licensing of Student Work
All assignment repositories are licensed under a [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License](https://creativecommons.org/licenses/by-nc-nd/4.0/). This license explicitly gives you copyright to all work you create for this course. The license gives Chris permission to copy your work (such as for grading) and to re-use your work later for non-commercial purposes (such as in-class examples) so long as you are given credit for it. However, your work cannot be used for monetary gain (such as in a textbook) and derivative works based on your work are prohibited.
The syllabus agreement at the end of the Student Information Sheet includes an acknowledgement of this licensing arrangement. If you have questions about this, please contact Chris before submitting the form.
### Late Work
Once the class begins, any assignments submitted will be treated as late. Assignments handed in within 24-hours of the beginning of class will have 15% deducted from the grade. I will deduct 15% per day for the next two 24-hour periods that assignments are late. After 72 hours, I will not accept late work. If you cannot attend class because of personal illness, a family issue, jury duty, an athletic match, or a religious observance, you must contact me beforehand to discuss alternate submission of work.
## Extra Credit
From time to time I may offer extra credit to be applied to your final grade. I will only offer extra credit if it is open to the entire class (typically for something like attending a lecture or event on-campus). If I offer extra credit, I will typically require you to submit a short written summary of the activity within a week of the event to obtain the credit. When offered, extra credit opportunities cannot be made-up or substituted if you are unable to attend the event.
## Grading
Grades will be included with assignment feedback, which will be disseminated through Github’s **Issues** tool. At midterms, Lecture 15, and finals, I will upload a summary of all assignment grades to a new **Issue** on GitHub.
All grades that use a “check” system (the lecture preps, labs, and some aspects of the final project) will be calculated using the following approach. A “check-plus” represents excellent work and will get full credit. A “check” represents satisfactory work and will get 85% of the points available for that assignment. A “check-minus” represents work that needs substantial improvement and will get 75% of the points available for that assignment.
I use a point system for calculating grades. The following table gives the weighting and final point totals for all assignments for this course:
```{r, echo=FALSE}
points <- data.frame(
Assignment = c("Participation", "Lecture Preps", "Labs", "Problem Sets", "Final Project"),
Points = c("50 pts", "4 pts", "10 pts", "35 pts", "410 pts"),
Quantity = c("x2", "x15", "x5", "x8", "x1"),
Total = c("100 pts", "60 pts", "150 pts", "280 pts", "410 pts"),
Percent = c("10%", "6%", "15%", "28%", "41%"),
stringsAsFactors = FALSE
)
knitr::kable(
points, booktabs = TRUE,
caption = "SOC 4015 and 5050 Points Breakdown"
)
```
All feedback will include grades that represent number of points earned. If you want to know your percentage on a particular assignment, divide the number of points earned by the number of points possible and then multiply it by 100.
Final grades will be calculated by taking the sum of all points earned and dividing it by the total number of points possible (1,000). This will be multiplied by 100 and then converted to a letter grade using the
following table:
```{r, echo=FALSE}
grades1 <- data.frame(
GPA = c("4.0", "3.7", "3.3", "3.0", "2.7"),
Letter = c("A", "A-", "B+", "B", "B-"),
Percent = c("93.0% - 100%", "90.0% - 92.9%", "87.0% - 89.9%", "83.0% - 86.9%", "80.0% - 82.9%"),
stringsAsFactors = FALSE
)
grades2 <- data.frame(
GPA = c("2.3", "2.0", "1.7", "1.0", "0.0"),
Letter = c("C+", "C", "C-", "D", "F"),
Percent = c("77.0% - 79.9%", "73.0% - 76.9%", "70.0% - 72.9%", "63.0% - 69.9%", "< 63.0%"),
stringsAsFactors = FALSE
)
knitr::kable(
list(
grades1,
grades2
),
caption = "Course Grading Scale", booktabs = TRUE
)
```
Borderline grades (i.e. a grade within half a percentage point of the next highest letter grade) *will* be rounded up before final grade submission at the end of the semester. A grade of 89.6% would therefore be submitted to SLU as an "A-" while a grade of 89.4% would be submitted to SLU as a "B+". The final grade report will include both the original letter grade and the rounded letter grade if applicable.
```{block, type = "rmdwarning"}
No chances will be given for revisions of poor grades. Incomplete grades will be given upon request only if you have a "C" average and have completed at least two-thirds of the assignments. You should note that incomplete grades must be rectified by the specified deadline or they convert to an "F".
```