p245.f22.Syllabus.Rmd

---
title: |
  ![](images/TAM-PrimaryMarkA.png){width=75%}  
  Syllabus for PBSI 245 (Fall 2022, section 501):
subtitle: |
 Introduction to Psychological Science Methods
knit: (function(input_file, encoding) {
  out_dir <- 'docs';
  rmarkdown::render(input_file,
 encoding=encoding,
 output_file=file.path(dirname(input_file), out_dir, 'index.html'))})
output: 
  rmdformats::robobook:
    use_bookdown: true
---

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<!-- title: | -->

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<!--   Syllabus for PBSI 245 (section 501): -->

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<!--  Introduction to Psychological Science Methods (Fall 2022) -->

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<!-- Setup -->

```{r setup, include=F}
knitr::opts_chunk$set(
	echo = F,
	message = F,
	warning = F,
	comment = NA
)
```

```{r LoadPackages, include=F}
#if(!require(pacman)){install.packages("pacman");require(pacman)} # un-comment and run this if the 'pacman' package is not installed. It's required for the function below.
pacman::p_load(dplyr, knitr, magrittr, kableExtra, readr, tibble, tidyr, pander)
```

# Course Overview {#Overview}

<!-- Course Info -->

```{r}
CourseInfo <- read_tsv("tables/CourseInfo.tsv", na="") 
LeaderInfo <- read_tsv("tables/LeaderInfo.tsv", na="") 
```

## Course Information

```{r CourseInfoHtml, eval=T}

CourseInfo %>%
  kbl(caption = "Section Information for PBSI 245 taught by Dr. Joseph Or") %>%
  row_spec(0:0, bold = T, color = "white", background = "#500000") %>%
  kable_styling(bootstrap_options = "responsive", font_size=14, full_width = F, position = "left")

```

<!-- Only one of the code chunks above and below can be run. The one that is run should have eval=T as a code-chunk option, whereas the other should have eval=F. Above is for html only. Below is for everything else.-->

```{r CourseInfo, results = 'asis', eval = F}

pandoc.table(CourseInfo, style = "multiline", split.table = Inf, missing = "")

```

## People

```{r LeaderInfoHtml}

# Removed headers unnecessary in html table
#PeopleInfo2 <- PeopleInfo %>% select(1,4:7)

LeaderInfo %>% 
  kbl(caption = "People") %>%
  pack_rows("Main Instructor", 1, 1, background = "#D6D3C4") %>%
  pack_rows("Undergraduate Teaching Scholars", 2, 2, background = "#D6D3C4") %>%
  row_spec(0:0, bold = T, color = "white", background = "#500000") %>%
  kable_styling(bootstrap_options = "responsive", font_size=14, full_width = F, position = "left")

```

<!-- Only one of the code chunks above and below can be run. The one that is run should have eval=T as a code-chunk option, whereas the other should have eval=F. Above is for html only. Below is for everything else.-->

```{r LeaderInfo, results = 'asis', eval = F}

pandoc.table(LeaderInfo, style = "multiline", split.table = Inf, missing = "")

```

$\dagger$ [UGTS = Undergraduate Teaching Scholar](https://liberalarts.tamu.edu/psychology/undergraduate/academics/teaching-scholars-program/)

```{r CoursePoints}

# Exam 1
A1 <- 120 # Points per exam
B1 <- 1 # Number of exams

# Exam 2
A2 <- 120 # Points per exam
B2 <- 1 # Number of exams

# Exam 3
A3 <- 130 # Points per exam
B3 <- 1 # Number of exams

# Final Exam
A4 <- 140 # Points per exam
B4 <- 1 # Number of exams

# Exam sum
A_sum <- sum(A1*B1,A2*B2,A3*B3,A4*B4)

# Easier Learning Curves
C <- 10 # Points per easier Learning Curve activity
D <- 19 # Number of easier Learning Curve activities

# Harder Learning Curves
E <- 20 # Points per harder Learning Curve activity
G <- 4 # Number of harder Learning Curve activities

# Easier Post-class quizzes
H <- 10 # Points per easier post-class quiz
I <- 19 # Number of easier post-class quizzes

# Harder Post-class quizzes
J <- 20 # Points per harder post-class quiz
K <- 3 # Number of harder post-class quizzes

# POINT TOTALS
N <- sum((A1*B1),(A2*B2),(A3*B3),(A4*B4),
         (C*D),(E*G),(H*I),(J*K)) # For the entire class
R <- sum((C*D),(E*G),(H*I),(J*K)) # Reading quizzes

```

<br/>

------------------------------------------------------------------------

<br/>

# Course Details

## Description

This course is the **first** of three courses in the *Psychological & Brain Sciences* major that explain how psychological science is carried out. Psychological science is an extraordinarily diverse, academic field, where researchers collect data ranging from reaction times, to brainwaves, to eye movements, to survey/polling data, to biochemical reactions, to the sheer counting of behaviors or even words on the internet (and more!).

To get a broad-based education in psychological science then, you need to get used to thinking critically about data. The main focus in this introductory course, therefore, is an overview of the general data-collection and data-analysis processes in the social sciences and psychology, but without emphasizing very much math, per se (though there will be a little bit of math).

As such, it may be one of the most useful general-purpose classes that you take at the university. This is because you are bombarded every day by data and simple statistics, be it on the news, in print media, or via social media. Much of this data is summarized or presented very poorly (often deliberately so), yet there is no time to analyze it mathematically; rather, you need to digest it quickly and critically.

Thus, the goal here is to advance your capabilities as critical consumers of data in this class, resulting in students ultimately who not only can "defend themselves" against quantitative chicanery, but also are ready to start analyzing data in a more formal way (via mathematics) in *PBSI 301* (*Elementary Statistics for Psychology*), as well as its subsequent counterpart, *PBSI 302* (*Experimental Psychology*).

## Meetings (TR)

### Section 501

-   2:20 to 3:35 pm in Psychology 338

**Make sure you attend the correct section. This is section 501. The other section may cover different material and/or use a different schedule.**

There is no credit for attendance in this class, nor are there any points for any in-class activities. That said, my lecture slides will only give you an outline for the course and will not serve as a substitute for attendance. I will be recording lectures, but these should only serve as a study aid or in the case of absences. I will not guarantee that recordings will available for all lecture or capture the material with sufficient quality.

Exams will be taken outside of class. We will be using Respondus browser lockdown software; it is your responsibility to make sure you can run this without issues. If you do not have a reliable computer you should take exams in a computer lab. Deadlines will **not** be extended due to computer issues. The professor and teaching staff cannot provide technical support with respondus, canvas, or other software.

## Prerequisites

None

## Course Catalog Description

The [description from the Undergraduate Course Catalog](https://catalog.tamu.edu/undergraduate/course-descriptions/psyc/) is quoted below.

> *Introduction to Psychological Science Methods*. (3-0). Credit 3. Basic elements of statistics and research methods in the psychological and brain sciences; topics include ethics in psychological research, measurement, research design and descriptive and inferential statistics.

## Learning outcomes at the course-content level

The following fifteen learning outcomes reflect the knowledge that student will have by the end of the course. These outcomes will be measured on not only exams but also both pre- and post-class reading quizzes.

### Part 1: Data integrity

This will include an understanding of the following:

-   `LO1`: ***Measurement*** (constructs, operationalization, reliability, validity)\
-   `LO2`: ***Inferencing*** (i.e., from samples to populations)\
-   `LO3`: ***Research Design*** (i.e., observations, experiments)

### Part 2: Summarizing data

This will include an understanding of the following:

-   `LO4`: ***Data visualization*** (histograms, densities, boxplots, scatterplots)\
-   `LO5`: ***Mean and variance*** (averages, variance)\
-   `LO6`: ***Theoretical distributions***

### Part 3: Probability

This will include an understanding of the following:

-   `LO7`: ***Probability*** (personal probability, likelihood, odds)\
-   `LO8`: ***Risk***\
-   `LO9`: ***Sampling distributions***\
-   `L10`: ***Simulation***\
-   `L11`: ***Expected values / estimation***

### Part 4: Inference & Ethics

This will include an understanding of the following:

-   `LO12`: ***Null hypothesis significance testing***\
-   `LO13`: ***p-values***\
-   `LO14`: ***confidence intervals***\
-   `L015`: ***Research ethics***

## Learning outcomes at the university level

Texas A&M University has identified [student learning outcomes](https://catalog.tamu.edu/undergraduate/general-information/student-learning-outcomes/) that describe our institutional commitment to your educational goals. They are listed below (in shortened form), along with the dimension of that learning outcome that applies most closely to this class (in boldface italics), and an explanation of how this class meets that learning outcome.

Note however, that most of these six university outcomes are not directly measurable outcomes in this particular course.

### Knowledge of Degree

Both psychological and brain sciences have become very, very quantitative over the years, to the point where it is rare to find qualitative studies without statistics (other than pure reviews). For this reason, it is critical to orient you to this way of thinking. This course is the first step in that direction. It will be very hard for you to succeed in the Psychology or Neuroscience major without a **strong** foundational understanding of statistics.

Thus, we have our first learning outcome, `LO16`: ***Knowledge of the importance of quantification in psychology***. By the end of the course, you should have a good idea about the pervasiveness and nature of numbers as you encounter them in primary research in psychological and brain sciences. This outcome is measurable, but it is a summary of the learning outcomes in the section above.

### Critical Thinking

There is a great deal of data that you encounter on an informal, daily basis through visual/aural and print media. Likewise, there are many ways to deceive unwary consumers of this data.

This brings us to the next learning outcome, `LO17`: ***Critical thinking***. In this class, you will learn to defend yourself against any such deception. You will learn the basics of data collection, data summaries, probability, and inferential statistics to become confident in evaluating almost any kind of "everyday" data.

### Personal/Social Responsibility

The next learning outcome, `LO18`, is ***choosing ethical courses of action in research and practice***.

You will consider how data should be collected, presented, and interpreted in an ethical manner. By the end of this course, you will learn about the importance of transparency in conducting and reporting statistical analyses.

### Social/Cultural/Global competence

The modern world is very diverse, but it is important that we communicate with each other using tools that we mutually understand. Science is the one of the most important such tools (if not **the** most important tool). And engaging in science is almost impossible without some quantitative background.

This brings us to the next learning outcome, `LO19`: **Live and work effectively in a diverse and global society**. By the end of this course, you should be able to "converse" at a basic level in one of the common "languages" that allow us to communicate about information, despite our diversity: statistics.

### Lifelong learning

One way to facilitate lifelong learning is to learn the foundations of statistics. As stated above, we are inundated with quantitative information on a daily basis through visual/aural and print media. Having an understanding of how that quantitative data works (or should work) will make further learning easier, and therefore more attractive in the long run.

Thus, the next learning outcome, `LO20` is **exhibit the skills necessary to acquire, organize, reorganize, and interpret new knowledge**. By the end of this course, you should also have a keener interest in analyzing quantitative information when you encounter it (which is always *often* in the industrialized world).

### Collaboration

Many of the issues covered in this textbook (and therefore in this class) are current events in the social sciences. People will have different opinions. Statistics does not usually give a "final" answer. Critical thinking and interpretation are always involved. Since this is the case, your classmates will inevitably generate different ideas from the same data.

This brings us to the final learning outcome, `LO21`: **Consider different points of view**. Working together with your fellow students and understanding their point of view will be an integral part of this course By the end of it, you should have a stronger grasp of the validity of differing opinions and how to agree or even agree to disagree politely.

<br/>

------------------------------------------------------------------------

<br/>

# Course Tasks

## Exams

There will be `r sum(B1,B2,B3,B4)` exams. They increase in points as the semester progresses. *Exam 1* is worth `r A1` points; *Exam 2* is worth `r A2` points; *Exam 3* is worth `r A3` points, and the *Final Exam* is worth `r A4` points. These sum to `r A_sum` points, or `r paste0(round((A_sum/N)*100,0),"%")` of your total grade).

Exams will consist of multiple-choice questions, but sometimes calculations are necessary.

These exams will all be taken online through the [Respondus Lockdown Browser](https://web.respondus.com/he/lockdownbrowser/).

The *Respondus Lockdown Browser* is a browser that shuts down all your other applications on that computer while you take exams. You need to download it. You will need to do the following during exams:

-   verify your student ID$^{\dagger\dagger}$\
-   record the exam with your webcam

The first three exams will be due within 1 class meeting of the end of the respective unit. The final exam window spans the entire period set out for final exams by the university.

Unless you have an accommodation, the first three exams will have a 1-hour, 15-minute limit (like a TTh class), once started in the *Lockdown Browser*. The *Final Exam* will have a 2-hour limit.

You get three attempts for each exam. But you have to be careful: There is a 12-hour waiting period between attempts. If you are in the middle of an attempt when the time period expires you will not be given a chance to complete that attempt and your progress will be recorded.

$\dagger\dagger$ If you do not have access to your student ID for whatever reason, you can log in to the *Howdy Portal* and click on the ***My Profile*** tile. In the top right corner, you will see you ***Aggie Card photo*** along with your full ***name*** and ***UIN***. You can take a screenshot of this display with your smart phone or print it out in order to present for online classes and exams.

## *Learning Curve* quizzes

There will be `r D+G` *Learning Curve* quizzes.

The Learning Curves for the first `r D` (Chapters 1-15 & 17-20)$^{\dagger\dagger}$ are relatively easy, each being worth `r C` points. This included Chapter 7 which is discussed at the end of the term.

The last `r G` (Chapters 21-24), however, are a bit harder conceptually and are worth `r E` points.

All together, these sum to `r (C*D)+(E*G)` points, or `r paste0(round((sum((C*D),(E*G))/N)*100,0),"%")` of your total grade). The lowest 4 scores will be dropped. 

*Learning Curve* quizzes must be completed **before the relevant class starts**. See the schedule in *Canvas* for details.

*Learning Curve* quizzes are scored in an interesting way. If you provide incorrect answers, *Achieve* just makes you answer more questions, so it takes longer. But once you get to the target, you get full credit. If you don't reach the full target, you get no credit. It's low-stakes, but it's all or nothing.

There is no time limit to completing these other than the deadline.

$\dagger\dagger$ We are not covering Chapter 16 (which concerns the *Consumer Price Index* because it is too unrelated psychological and brain sciences). We also cover Chapter 7 at the end of the semester.

## Post-Class quizzes

There will be `r I+K` Post-Class quizzes.

*Almost* exactly parallel to the *Learning Curve* quizzes, the first `r I` (Chapters 1-15 & 17-20)$^{\dagger\dagger}$ are relatively easy, each being worth `r H` points.

The last `r K` (Chapters 21-23),$^{\dagger\dagger\dagger}$ however, are conceptually a bit harder and are worth `r J`.  Due to schedule constraints, there is no assigned Post-Class Quiz for Ch 24 (It will be available if you'd like to use it for exam preparation - no extra credit for completing it).

Together, these sum to `r (H*I)+(J*K)` points, or `r paste0(round((sum((H*I),(J*K))/N)*100,0),"%")` of your total grade). The lowest 3 scores will be dropped. 

*Post-Class quizzes* must be completed **within one week** of the end of the relevant class (i.e., Post-class quiz for Ch. 1 is due within a week of the class in which Ch. 1 was discussed). See the schedule for specific dates.

The post-class quizzes are pretty different from the *Learning Curve* quizzes. Specifically, they are not "all or nothing." You can get answers wrong. However, you get up to 3 attempts at each quiz (within the 1-week window it's available), and we take your highest score of your attempts. On most of the post-class quizzes, there are around 10-15 questions.

There is no time limit to completing each attempt other than the deadline.

$\dagger\dagger$ We are not covering Chapter 16 (which concerns the *Consumer Price Index* because it is too far afield from psychological and brain sciences).

## Final word on quizzes

***IMPORTANT!!!***

This class is set up so that getting high scores on the quizzes (both *Learning Curve* and *Post-class*) is fairly easy. In the case of the former, you just keep at it until you get full credit. In the case of the latter, you just repeat it (up to 3 times) until you get your maximum score.

***But you must keep on top of them by doing them on time!!!***

This is the key to doing well in this class. The reason is that the exams can be fairly difficult, so the quizzes act as an anchor to passing the class.

<br/>

------------------------------------------------------------------------

<br/>

# Course Grading
Extra credit opportunities will *not* be available. It is unethical to offer extra credit opportunities that are not available to the entire class. Further, extra credit assignments would distract you from the assigned work. You can earn points in this course in a variety of ways described below.

## Breakdown by Assignment

Final grades will be assigned at the end of the semester on the basis of the total number of points earned out of a possible `r N` points, based on the following components:

|                                         Component | Points per Component | Number of Components | Total Points |                    \%                    |
|-------------:|:--------------:|:--------------:|:------------:|:------------:|
|                       Exam 1 (Part I, less Ch. 7) |        `r A1`        |        `r B1`        |  `r A1*B1`   | `r paste0(round(((A1*B1)/N)*100,0),"%")` |
|                                  Exam 2 (Part II) |        `r A2`        |        `r B2`        |  `r A2*B2`   | `r paste0(round(((A2*B2)/N)*100,0),"%")` |
|                                 Exam 3 (Part III) |        `r A3`        |        `r B3`        |  `r A3*B3`   | `r paste0(round(((A3*B3)/N)*100,0),"%")` |
|                  Final Exam (Part IV, plus Ch. 7) |        `r A4`        |        `r B4`        |  `r A4*B4`   | `r paste0(round(((A4*B4)/N)*100,0),"%")` |
| Easier *Learning Curve* Quizzes (Chs 1-15; 17-20) |        `r C`         |        `r D`         |   `r C*D`    |        `r round(((C*D)/N)*100,0)`        |
|      Harder *Learning Curve* Quizzes (Chs. 21-24) |        `r E`         |        `r G`         |   `r E*G`    |        `r round(((E*G)/N)*100,0)`        |
|  Easier Post-class Quizzes (Chs 1-6; 8-15; 17-20) |        `r H`         |        `r I`         |   `r H*I`    |         `r round((H*I/N)*100,0)`         |
|            Harder Post-class Quizzes (Chs. 21-23) |        `r J`         |        `r K`         |   `r J*K`    |         `r round((J*K/N)*100,0)`         |
|                                                   |                      |       TOTALS:        |    `r N`     |                   100%                   |

## Final Letter Grades

**Below,** $y$ represents any particular student's total percentage/points for the course. Letter grades (with strict cutoffs) will be assigned as follows:

| Grade   |         A          |                 B                 |                 C                 |                 D                 |         F          |
|------------|:----------:|:----------:|:----------:|:----------:|:----------:|
| Percent |    89.5 $\le y$    |       79.5 $\le y \lt$ 89.5       |       69.5 $\le y \lt$ 79.5       |       59.5 $\le y \lt$ 69.5       |    $y \lt$ 59.5    |
| Points  | `r N*.895` $\le y$ | `r N*.795` $\le y \lt$ `r N*.895` | `r N*.695` $\le y \lt$ `r N*.795` | `r N*.595` $\le y \lt$ `r N*.695` | $y \lt$ `r N*.595` |

<br/>

------------------------------------------------------------------------

<br/>

# Course Schedule

```{r}
Schedule <- read_tsv("tables/Schedule.tsv", na="") 
```

The schedule below is very general. Exact assignments are listed in*Canvas*.

```{r ScheduleNonHtml, results = 'asis', eval = F}

pandoc.table(Schedule, 
             style = "multiline", 
             split.table = Inf,
             split.cells=c(4,7,10,20,15,13),
             missing = "")

```

<!-- For html, the code chunk below should have eval = T. For non-html (e.g., Word, pdf), the code chunk above should have eval = T. Only one -->

```{r ScheduleHtml, results = 'asis', eval = T}

options(knitr.kable.NA = '')
kable(Schedule,
caption = 'Schedule by Unit/Week/Day') %>%
pack_rows("Overview", 1, 1, background = "#D6D3C4") %>%
pack_rows("Part 1: Producing Data", 2, 8, background = "#D6D3C4") %>%
pack_rows("Part 2: Organizing Data", 9, 16, background = "#D6D3C4") %>%
pack_rows("Part 3: Chance", 17, 22, background = "#D6D3C4") %>%
pack_rows("Part 4: Inference, Ethics, & Methods", 23, 30, background = "#D6D3C4") %>%
pack_rows("Final Exam Week", 33, 34, background = "#D6D3C4") %>%
row_spec(0:0, bold = T, color = "white", background = "#500000") %>%
kable_styling(font_size=14, full_width = F, position = "left") %>%
scroll_box(width = "100%", height = "600px")

```

$*$ The *Learning Curve Quiz* due date for a chapter is the first day that chapter appears in this column\
$\dagger$ The window to take the exam opens at 3:35 pm on this day.\
$\ddagger$ The window to take the exam closes at 11:59 pm on this day\
$**$ The final-exam window begins on Dec 9 at 7:30 am and closes Dec 14 at 8:00 pm.

<br/>

------------------------------------------------------------------------

<br/>

# Course Materials

There is only one required purchase for this class, and it is online through *Macmillan* (their *Achieve* platform). Your actual reading experience will be through *VitalSource* via *Macmillan* (**Do not buy the textbook through VitalSource!!**)

## Textbook

-   Moore, D. S., & Notz, W. I. (2020). *Statistics: Concepts and controversies*, 10$^{th}$ ed. Austin, TX: Macmillan Learning.

Purchase this directly through *Canvas* if at all possible. The only good reason to purchase the code from the bookstore is that, apparently, some people have to purchase from the bookstore in order to satisfy scholarship and financial-aid requirements.

## Hardware

### Computer

The university now requires you to personally possess the following:

-   a computer with access to the internet (*if you do not have reliable internet at home you should take exams on campus*)
-   a webcam

[click here to see Texas A&M's full computer requirements](https://it.tamu.edu/services/academics-and-research/teaching-and-learning-tools/computer-requirements/)

## Software

### Online test-taking technology

This main class requires the use of the [Respondus Lockdown Browser](https://web.respondus.com/he/lockdownbrowser/) for online exams. The webcam can be the type that's built into your computer or one that plugs in with a USB cable.

Watch this [brief video](https://www.respondus.com/products/lockdown-browser/student-movie.shtml) to get a basic understanding of the *Lockdown Browser* and the webcam feature.

-   Download and install *Lockdown Browser* from [this link](https://download.respondus.com/lockdown/download.php?id=961615468).

-   Once Installed

    -   Log into *Canvas* with your regular browser (e.g., Chrome, Safari, etc.)\
    -   Navigate to the exam\
    -   The exam will start the *Lockdown Browser*

### Video Overview

Watch this [brief video](https://www.respondus.com/products/lockdown-browser/student-movie.shtml) to get a basic understanding of *Lockdown Browser* and the webcam feature.

### Download (if taking the exam on your own computer)

-   Download and install *Lockdown Browser* from [this link](https://download.respondus.com/lockdown/download.php?id=961615468)
    -   Note that there is no *Respondus Lockdown Browser* for Chromebook\
    -   Please let me know if all you have is a Chromebook without access to any other kind of computer

### Guidelines

-   When taking an online test, follow these guidelines:
    -   Ensure you're in a location where you won't be interrupted\
    -   Turn off all other devices (e.g. tablets, phones, second computers) and place them outside of your reach\
    -   Before starting the test, know how much time is available for it, and also that you've allotted sufficient time to complete it\
    -   Clear your desk or workspace of all external materials not permitted - books, papers, other devices\
    -   Remain at your computer for the duration of the test\
    -   If the computer, Wi-Fi, or location is different than what was used previously with the "Webcam Check" and "System & Network Check" in Lockdown Browser, run the checks again prior to the exam\
    -   To produce a good webcam video, do the following:
        -   Avoid wearing baseball caps or hats with brims\
        -   Ensure your computer or device is on a firm surface (a desk or table)\
        -   Do NOT have the computer on your lap, a bed, or other surface where the device (or you) are likely to move\
        -   If using a built-in webcam, avoid readjusting the tilt of the screen after the webcam setup is complete\
        -   Take the exam in a well-lit room, but avoid backlighting (such as sitting with your back to a window)\
-   Remember that *Lockdown Browser* will prevent you from accessing other websites or applications
    -   You will be unable to exit the test until all questions are completed and submitted

#### Help

-   Several resources are available if you encounter problems with Lockdown Browser:
    -   The Windows and Mac versions of *Lockdown Browser* have a "Help Center" button located on the toolbar
        -   Use the "System & Network Check" to troubleshoot issues\
        -   If an exam requires you to use a webcam, also run the "Webcam Check" from this area\
    -   You can get support from Texas A&M through [this link](https://ai.tamu.edu/Help-Support/Technical-Support)\
    -   Respondus has a [Knowledge Base](https://support.respondus.com/support/)
        -   Select the "Knowledge Base" link and then select "Respondus Lockdown Browser" as the product
            -   If your problem is with a webcam, select "Respondus Monitor" as your product\
        -   If you're still unable to resolve a technical issue with Lockdown Browser, go to support.respondus.com and select "Submit a Ticket"
            -   Provide detailed information about your problem and what steps you took to resolve it

<br/>

------------------------------------------------------------------------

<br/>

# Course Policies

## Classroom Policies

### Classroom Behavior

*PBSI 245* is a learning community where civility and mutual respect are crucial for success. I will **prepare informative, interesting lectures and do my best to deliver them that way.** I will treat you like mature members of a learning community. Likewise, I expect that you will **attend class meetings, having completed the readings beforehand, and therefore prepared to both learn and contribute to the course in a thoughtful manner.** Let's be nice to each other and have a great semester!

### Grade Disputes

If you wish to dispute a grade on an assignment or exam, you must submit a written rationale (email is fine) to justify the change **within 1 week of receiving your score in Canvas** for that exam or activity, respectively.

### Cheating in this class

(adapted from wording provided by Drs. Duane and Tilly McVay)

#### What constitutes academic misconduct?

-   ***During*** an examination: ...
    -   ... looking at another student's work or using external aids (for example, books, notes, conversation with others, internet resources, etc.) unless specifically allowed in advance by instructor$^{\dagger}$
    -   ... acquiring answers for any assigned work or examination from any unauthorized source, including obtaining information from students who have previously taken the examination or quiz$^{\dagger\dagger}$\
    -   ... knowingly allowing another student to copy your work

$\dagger$ It will be obvious if you are looking at other resources during the exam. *My lab uses eye tracking to predict decisions.* $\dagger\dagger$ I do ***not*** consider it cheating on *Learning Curve* or *Post-Class Quizzes* when you look up outside sources. You can use whatever you can find to answer those questions (though the textbook should suffice).

#### Reporting an academic violation -- What happens?

-   I will report the violation to [AHSO](https://aggiehonor.tamu.edu/), regardless of the magnitude of the violation\
-   The report is submitted online and includes the following:
    1.  the details of the violation$^{\dagger\dagger}$\
    2.  an election to handle autonomously or refer to the Honor Council\
    3.  specification of sanctions, and\
    4.  student acknowledgment of acceptance/ rejection of violation and/or sanction, though you have the right to appeal to the AHSO
-   Importantly, you would then be logged into the AHSO system
    -   If there is a second violation, you will automatically go before the Honor Council\
-   I will treat students ***giving*** unauthorized help the same as students ***receiving*** help
    -   Such cases would entail the same course sanctions and reporting to AHSO\
        $\dagger\dagger$ Note that I can use the records from the *Respondus Lockdown Browser* (or other online proctor) as evidence in my report to AHSO.

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# University Policies & Other Course Policies

Please see my [syllabus addendum](http://bit.ly/OrrSyllabusAddendum).