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orientation-code-along-slides-python.html
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orientation-code-along-slides-python.html
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<!DOCTYPE html>
<html lang="en"><head>
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<body class="quarto-light">
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<div class="slides">
<section id="title-slide" class="quarto-title-block center">
<h1 class="title">Workflow & Reproducible Research</h1>
<p class="subtitle">Orientation: Code Along</p>
<div class="quarto-title-authors">
</div>
</section>
<section id="agenda" class="slide level2">
<h2>Agenda</h2>
<h3 id="python-code-along">Python Code-Along</h3>
<div class="columns">
<div class="column" style="width:60%;">
<p>Data-Intensive Research Workflow</p>
<ul>
<li>Prepare</li>
<li>Wrangle</li>
<li>Explore</li>
<li>Model</li>
<li>Communicate</li>
</ul>
</div><div class="column" style="width:40%;">
<p><img data-src="img/laser-cycle.png" class="absolute" style="right: 50px; width: 1000px; "></p>
</div>
</div>
<aside class="notes">
<p>Krumm, A., Means, B., & Bienkowski, M. (2018). <a href="https://www.routledge.com/Learning-Analytics-Goes-to-School-A-Collaborative-Approach-to-Improving/Krumm-Means-Bienkowski/p/book/9781138121836">Learning Analytics Goes to School.</a>. Routledge.</p>
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</section>
<section id="prepare" class="slide level2">
<h2>Prepare</h2>
<div class="columns">
<div class="column" style="width:50%;">
<p><strong>The Study</strong></p>
<p>The setting of this study was a public provider of individual online courses in a Midwestern state. Data was collected from two semesters of five online science courses and aimed to understand students’ motivation.</p>
</div><div class="column" style="width:40%;">
<p><strong>Research Question</strong></p>
<p>Is there a relationship between the time students spend in a learning management system and their final course grade?</p>
</div>
</div>
<aside class="notes">
<p>in our “Prepare” phase, we embark on understanding the setting of our study—a dive into online science courses aiming to unravel the interplay between student motivation and their engagement within a digital learning environment. Here, Python’s prowess comes to the forefront, with its ability to effortlessly ingest and preprocess data from varied sources, thanks to the pandas library. Highlight Python’s role in not just simplifying data importation via pd.read_csv() but also in enabling initial exploratory steps like identifying missing values, understanding data types, and performing simple data summarizations that lay the groundwork for more complex analysis.</p>
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</section>
<section id="the-tools-of-reproducible-research" class="slide level2">
<h2>The Tools of Reproducible Research</h2>
<div class="panel-tabset">
<ul id="tabset-1" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-1-1">Packages</a></li><li><a href="#tabset-1-2">Read in Data</a></li><li><a href="#tabset-1-3">Inspecting Data</a></li><li><a href="#tabset-1-4">Python Syntax</a></li></ul>
<div class="tab-content">
<div id="tabset-1-1">
<p>In Python, packages are equivalent to R’s libraries, containing functions, modules, and documentation. They can be installed using <code>pip</code> and imported into your scripts.</p>
<div id="dd363342" class="cell" data-message="false" data-execution_count="1">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href=""></a><span class="co"># Import pandas for data manipulation and matplotlib for visualization</span></span>
<span id="cb1-2"><a href=""></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb1-3"><a href=""></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
<span id="cb1-4"><a href=""></a></span>
<span id="cb1-5"><a href=""></a><span class="co"># If not installed, you can run in the terminal:</span></span>
<span id="cb1-6"><a href=""></a><span class="co"># python3 -m pip install matplotlib plotly</span></span>
<span id="cb1-7"><a href=""></a><span class="co"># python3 -m pip install pandas</span></span>
<span id="cb1-8"><a href=""></a> </span>
<span id="cb1-9"><a href=""></a></span>
<span id="cb1-10"><a href=""></a><span class="co"># or windows</span></span>
<span id="cb1-11"><a href=""></a><span class="co"># py -m pip install matplotlib plotly</span></span>
<span id="cb1-12"><a href=""></a><span class="co"># py -m pip install pandas</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
<div id="tabset-1-2">
<p>The pandas library in Python is used for data manipulation and analysis. Similar to the {readr} package in R the function like <code>pd.read_csv()</code> is for importing rectangular data from delimited text files such as comma-separated values (CSV), a preferred file format for reproducible research.</p>
<div id="cf8ef8e9" class="cell" data-message="false" data-execution_count="2">
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href=""></a><span class="co"># Load data into the Python environment from a CSV file</span></span>
<span id="cb2-2"><a href=""></a>sci_data <span class="op">=</span> pd.read_csv(<span class="st">"data/sci-online-classes.csv"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
<div id="tabset-1-3">
<p>Inspecting the dataset in Python can be done by displaying the first few rows of the DataFrame.</p>
<div id="6a2ad0d7" class="cell" data-message="false" data-execution_count="3">
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href=""></a><span class="co"># Display the first five rows of the data</span></span>
<span id="cb3-2"><a href=""></a><span class="bu">print</span>(sci_data.head())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> student_id course_id total_points_possible total_points_earned \
0 43146 FrScA-S216-02 3280 2220
1 44638 OcnA-S116-01 3531 2672
2 47448 FrScA-S216-01 2870 1897
3 47979 OcnA-S216-01 4562 3090
4 48797 PhysA-S116-01 2207 1910
percentage_earned subject semester section \
0 0.676829 FrScA S216 2
1 0.756726 OcnA S116 1
2 0.660976 FrScA S216 1
3 0.677335 OcnA S216 1
4 0.865428 PhysA S116 1
Gradebook_Item Grade_Category ... q7 q8 q9 \
0 POINTS EARNED & TOTAL COURSE POINTS NaN ... 5.0 5.0 4.0
1 ATTEMPTED NaN ... 4.0 5.0 4.0
2 POINTS EARNED & TOTAL COURSE POINTS NaN ... 4.0 5.0 3.0
3 POINTS EARNED & TOTAL COURSE POINTS NaN ... 4.0 5.0 5.0
4 POINTS EARNED & TOTAL COURSE POINTS NaN ... 4.0 4.0 NaN
q10 TimeSpent TimeSpent_hours TimeSpent_std int pc uv
0 5.0 1555.1667 25.919445 -0.180515 5.0 4.5 4.333333
1 4.0 1382.7001 23.045002 -0.307803 4.2 3.5 4.000000
2 5.0 860.4335 14.340558 -0.693260 5.0 4.0 3.666667
3 5.0 1598.6166 26.643610 -0.148447 5.0 3.5 5.000000
4 3.0 1481.8000 24.696667 -0.234663 3.8 3.5 3.500000
[5 rows x 30 columns]</code></pre>
</div>
</div>
<p>What variables do you think might help us answer our research question?</p>
</div>
<div id="tabset-1-4">
<p>Python syntax for reading and inspecting data can be intuitive and powerful. For example:</p>
<p><code>sci_data = pd.read_csv("data/sci-online-classes.csv")</code></p>
<ul>
<li><strong>Functions</strong> are like verbs: pd.read_csv() is the function used to read a CSV file into a pandas DataFrame.</li>
<li><strong>Objects</strong> are the nouns: In this case, sci_data becomes the object that stores the DataFrame created by pd.read_csv(“data/sci-online-classes.csv”).</li>
<li><strong>Arguments</strong> are like adverbs: “data/sci-online-classes.csv” is the argument to pd.read_csv(), specifying the path to the CSV file. Unlike R’s read_csv, the default behavior in pandas is to infer column names from the first row in the file, so there’s no need for a col_names argument.</li>
<li><strong>Operators</strong> are like “punctuation”: = is the assignment operator in Python, used to assign the DataFrame returned by pd.read_csv(“data/sci-online-classes.csv”) to the object sci_data.</li>
</ul>
</div>
</div>
</div>
<aside class="notes">
<p>This segment underscores the significance of Python’s ecosystem in fostering reproducible research. Delve into how Python, with its rich library ecosystem—pandas for data wrangling, matplotlib and seaborn for visualization—supports the creation of transparent and replicable research workflows. Emphasize the critical role of virtual environments (using venv or conda) and dependency management tools (pip, pipenv) in encapsulating the research environment, ensuring that findings can be recreated and validated by others, a cornerstone of scientific integrity.</p>
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</section>
<section id="wrangle" class="slide level2">
<h2>Wrangle</h2>
<p>Data wrangling is the process of cleaning, <a href="https://r4ds.had.co.nz/tidy-data.html?q=tidy%20data#tidy-data-1">“tidying”</a>, and transforming data. In Learning Analytics, it often involves merging (or joining) data from multiple sources.</p>
<ul>
<li>Data wrangling in Python is primarily done using pandas, allowing for cleaning, filtering, and transforming data.</li>
</ul>
<p>Since we are interested the relationship between time spent in an online course and final grades, let’s <code>select()</code> the <code>FinalGradeCEMS</code> and <code>TimeSpent</code> from <code>sci_data</code>.</p>
<div id="9e37f1c6" class="cell" data-message="false" data-execution_count="4">
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href=""></a><span class="co"># Selecting specific columns and creating a new DataFrame</span></span>
<span id="cb5-2"><a href=""></a>sci_data_selected <span class="op">=</span> sci_data[[<span class="st">'FinalGradeCEMS'</span>, <span class="st">'TimeSpent'</span>]]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<aside class="notes">
<p>Data wrangling represents the transformative process of refining our dataset into a format ripe for analysis. Here, spotlight pandas’ capability to perform sophisticated data manipulations—filtering rows, selecting columns of interest, handling missing data, and merging datasets. This step is pivotal in distilling our raw data into a structured form that precisely addresses our research questions. Discuss practical examples, like using .dropna() to clean data or .merge() to combine datasets, illustrating Python’s efficiency in handling typical data preparation challenges.</p>
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</section>
<section id="explore" class="slide level2">
<h2>Explore</h2>
<div class="panel-tabset">
<ul id="tabset-2" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-2-1">EDA</a></li><li><a href="#tabset-2-2">Graph-template</a></li><li><a href="#tabset-2-3">Our first graph</a></li></ul>
<div class="tab-content">
<div id="tabset-2-1">
<p>Exploratory data analysis in Python involves processes of <strong>describing</strong> your data numerically or graphically, which often includes:</p>
<ul>
<li><p><strong>calculating</strong> summary statistics like frequency, means, and standard deviations</p></li>
<li><p><strong>visualizing</strong> your data through charts and graphs</p></li>
</ul>
<p>EDA can be used to help answer research questions, generate new questions about your data, discover relationships between and among variables, and create new variables (i.e., feature engineering) for data modeling.</p>
</div>
<div id="tabset-2-2">
<div class="columns">
<div class="column" style="width:50%;">
<p>The workflow for making a graph typically involves:</p>
<ol type="1">
<li><p>Choosing the type of plot or visualization you want to create.</p></li>
<li><p>Using the plotting function directly with your data.</p></li>
<li><p>Optionally customizing the plot with titles, labels, and other aesthetic features.</p></li>
</ol>
</div><div class="column" style="width:50%;">
<p>A simple template for creating a scatter plot could look like this:</p>
<div id="74e85d0a" class="cell" data-message="false" data-execution_count="5">
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href=""></a><span class="co">#import packages for visualization</span></span>
<span id="cb6-2"><a href=""></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
<span id="cb6-3"><a href=""></a><span class="im">import</span> seaborn <span class="im">as</span> sns</span>
<span id="cb6-4"><a href=""></a></span>
<span id="cb6-5"><a href=""></a><span class="co"># Basic scatter plot template</span></span>
<span id="cb6-6"><a href=""></a><span class="co"># sns.scatterplot(x=<VARIABLE1>, y=<VARIABLE2>, data=<DATA>)</span></span>
<span id="cb6-7"><a href=""></a><span class="co"># plt.show()</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
</div>
</div>
<div id="tabset-2-3">
<div class="columns">
<div class="column" style="width:50%;">
<p>Scatter plots are useful for visualizing the relationship between two continuous variables. Here’s how to create one with seaborn.</p>
</div><div class="column" style="width:50%;">
<div id="e2b87d0b" class="cell" data-message="false" data-execution_count="6">
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href=""></a><span class="co"># Create a scatter plot showing the relationship between time spent and final grades</span></span>
<span id="cb7-2"><a href=""></a>sns.scatterplot(x<span class="op">=</span><span class="st">'TimeSpent'</span>, y<span class="op">=</span><span class="st">'FinalGradeCEMS'</span>, data<span class="op">=</span>sci_data)</span>
<span id="cb7-3"><a href=""></a>plt.xlabel(<span class="st">'Time Spent'</span>)</span>
<span id="cb7-4"><a href=""></a>plt.ylabel(<span class="st">'Final Grade CEMS'</span>)</span>
<span id="cb7-5"><a href=""></a>plt.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-display">
<div>
<figure>
<p><img data-src="orientation-code-along-slides-python_files/figure-revealjs/cell-7-output-1.png" width="816" height="429"></p>
</figure>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<aside class="notes">
<p>Our journey through Exploratory Data Analysis (EDA) with Python equips us with the tools to uncover hidden patterns and relationships. Utilize this segment to showcase how seaborn and matplotlib enable us to visualize data in ways that reveal these underlying structures, whether through histograms, scatter plots, or box plots. EDA is our investigative tool, prompting hypotheses and guiding the direction of our modeling efforts. Explicitly demonstrate creating a scatter plot with sns.scatterplot() to examine the relationship between time spent and final grades, highlighting the direct feedback loop between visual exploration and analytical insight.</p>
<p><strong>Components of the Scatter Plot Command:</strong> sns.scatterplot(): This is the function from the seaborn library (often imported as sns) used to create scatter plots. Seaborn is a statistical data visualization library built on top of matplotlib, offering a higher-level interface for drawing attractive and informative statistical graphics.</p>
<p>x=<variable1>: This argument specifies the data for the x-axis. <variable1> should be replaced with the name of the column from your dataset that you want to display on the x-axis.</variable1></variable1></p>
<p>y=<variable2>: Similarly, this argument specifies the data for the y-axis. <variable2> should be replaced with the name of another column from your dataset that you want to display on the y-axis.</variable2></variable2></p>
<p>data=<data>: This argument is where you specify the dataset containing <variable1> and <variable2>. <data> should be replaced with the variable name of your dataset, which is typically a pandas DataFrame.</data></variable2></variable1></data></p>
<p>plt.show(): After creating the scatter plot with sns.scatterplot(), this command from matplotlib’s pyplot interface (often imported as plt) is used to display the plot. Without this command, the plot may not be visible (depending on your environment, like Jupyter Notebooks might automatically show plots even without explicitly calling plt.show()).</p>
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</section>
<section id="model" class="slide level2">
<h2>Model</h2>
<div class="panel-tabset">
<ul id="tabset-3" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-3-1">Dealing with missing data (NaN)</a></li><li><a href="#tabset-3-2">A Simple Model</a></li><li><a href="#tabset-3-3">Interpret</a></li></ul>
<div class="tab-content">
<div id="tabset-3-1">
<p>Before adding a constant or fitting the model, ensure the data doesn’t contain NaNs (Not a Number) or infinite values. In a pandas DataFrame, you can use a combination of methods provided by <code>pandas</code> and <code>NumPy</code>.</p>
<div id="8db1e644" class="cell" data-message="false" data-execution_count="7">
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href=""></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
<span id="cb8-2"><a href=""></a></span>
<span id="cb8-3"><a href=""></a><span class="co"># Drop rows with NaNs in 'TimeSpent' or 'FinalGradeCEMS'</span></span>
<span id="cb8-4"><a href=""></a>sci_data_clean <span class="op">=</span> sci_data.dropna(subset<span class="op">=</span>[<span class="st">'TimeSpent'</span>, <span class="st">'FinalGradeCEMS'</span>])</span>
<span id="cb8-5"><a href=""></a></span>
<span id="cb8-6"><a href=""></a><span class="co"># Replace infinite values with NaN and then drop those rows (if any)</span></span>
<span id="cb8-7"><a href=""></a>sci_data_clean.replace([np.inf, <span class="op">-</span>np.inf], np.nan, inplace<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb8-8"><a href=""></a>sci_data_clean.dropna(subset<span class="op">=</span>[<span class="st">'TimeSpent'</span>, <span class="st">'FinalGradeCEMS'</span>], inplace<span class="op">=</span><span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
<div id="tabset-3-2">
<p>You can use libraries such as <code>statsmodels</code> or <code>scikit-learn</code>. We’ll dive much deeper into modeling in subsequent learning labs, but for now let’s see if there is a statistically significant relationship between students’ final grades, <code>FinaGradeCEMS</code>, and the <code>TimeSpent</code> in the LMS:</p>
<div id="d347a1d6" class="cell" data-message="false" data-execution_count="8">
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href=""></a><span class="co"># add the statsmodels</span></span>
<span id="cb9-2"><a href=""></a><span class="im">import</span> statsmodels.api <span class="im">as</span> sm</span>
<span id="cb9-3"><a href=""></a></span>
<span id="cb9-4"><a href=""></a><span class="co"># Add a constant term for the intercept to the independent variable</span></span>
<span id="cb9-5"><a href=""></a>X <span class="op">=</span> sm.add_constant(sci_data_clean[<span class="st">'TimeSpent'</span>]) <span class="co"># Independent variable</span></span>
<span id="cb9-6"><a href=""></a>y <span class="op">=</span> sci_data_clean[<span class="st">'FinalGradeCEMS'</span>] <span class="co"># Dependent variable</span></span>
<span id="cb9-7"><a href=""></a></span>
<span id="cb9-8"><a href=""></a><span class="co"># Fit the model</span></span>
<span id="cb9-9"><a href=""></a>model <span class="op">=</span> sm.OLS(y, X).fit()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
<div id="tabset-3-3">
<div id="968a18c4" class="cell" data-message="false" data-execution_count="9">
<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href=""></a><span class="co"># Print the summary of the model</span></span>
<span id="cb10-2"><a href=""></a><span class="bu">print</span>(model.summary())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> OLS Regression Results
==============================================================================
Dep. Variable: FinalGradeCEMS R-squared: 0.134
Model: OLS Adj. R-squared: 0.132
Method: Least Squares F-statistic: 87.99
Date: Sun, 14 Jul 2024 Prob (F-statistic): 1.53e-19
Time: 15:22:54 Log-Likelihood: -2548.5
No. Observations: 573 AIC: 5101.
Df Residuals: 571 BIC: 5110.
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 65.8085 1.491 44.131 0.000 62.880 68.737
TimeSpent 0.0061 0.001 9.380 0.000 0.005 0.007
==============================================================================
Omnibus: 136.292 Durbin-Watson: 1.537
Prob(Omnibus): 0.000 Jarque-Bera (JB): 252.021
Skew: -1.381 Prob(JB): 1.88e-55
Kurtosis: 4.711 Cond. No. 3.97e+03
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 3.97e+03. This might indicate that there are
strong multicollinearity or other numerical problems.</code></pre>
</div>
</div>
</div>
</div>
</div>
<aside class="notes">
<p><strong>Overview Modeling</strong> Modeling is where we apply statistical techniques to interpret our data’s story. With Python’s statsmodels, we can fit a linear regression model to quantify the relationship between variables of interest. This section is an opportunity to detail the process of model selection, fitting, and evaluation within Python’s framework. Discuss the interpretation of regression outputs from statsmodels, such as coefficients for understanding variable impact, p-values for statistical significance, and R-squared values for model fit. This not only aids in hypothesis testing but also in predicting outcomes based on observed data.</p>
<p><strong>Dealing with Missing Data (NaN)</strong> Importance: Handling missing data is a crucial preprocessing step to ensure the quality and reliability of your statistical models. Missing values (NaNs) or infinite values in your dataset can lead to errors or biased results when fitting models. Thus, cleaning your data is essential.</p>
<p>Process: Drop NaNs: This step removes any rows in your dataset that contain NaN values in specific columns of interest (TimeSpent or FinalGradeCEMS). This ensures that the model only uses complete cases without missing values, which could distort the analysis.</p>
<p>Handle Infinite Values: Infinite values, which can result from divisions by zero or other operations, are not suitable for most statistical models. Replacing these with NaN (np.inf or -np.inf to np.nan) and then dropping them is a way to ensure that your dataset is finite and can be processed by statistical modeling tools.</p>
<p><strong>A Simple Model</strong> Importance: Building a statistical model allows us to quantify the relationship between variables in our dataset. In this case, you’re interested in the relationship between TimeSpent in the learning management system (LMS) and students’ final grades (FinalGradeCEMS). A simple linear regression model can provide insights into whether there is a statistically significant association between these variables.</p>
<p>Process: Add a Constant Term: Many statistical models, including linear regression, assume that your equation will have an intercept term. Adding a constant to your independent variables (using sm.add_constant()) accommodates this intercept in the model.</p>
<p>Fit the Model: Using statsmodels’ OLS function (Ordinary Least Squares), the model is fitted to the data. This process involves finding the parameters (intercept and slope) that minimize the sum of squared residuals, providing the best linear approximation of the relationship between the independent and dependent variables.</p>
<p>Interpret Importance: After fitting the model, interpreting the output is crucial for understanding the relationship between the variables. The model summary provides several key pieces of information, including the coefficients of the model, their statistical significance, and the overall fit of the model.</p>
<p>Process: Coefficients: Indicate the expected change in the dependent variable (e.g., FinalGradeCEMS) for a one-unit increase in the independent variable (e.g., TimeSpent), assuming all other variables in the model are held constant.</p>
<p>P-values: Help determine the statistical significance of each coefficient. A low p-value (typically <0.05) suggests that the effect of the independent variable on the dependent variable is statistically significant and not due to chance.</p>
<p>Model Fit: Indicators like R-squared value give an idea of how well the model explains the variability in the dependent variable. A higher R-squared value suggests a better fit, although it’s important to consider other factors and diagnostics to evaluate model performance fully.</p>
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</section>
<section id="communicate" class="slide level2">
<h2>Communicate</h2>
<div class="panel-tabset">
<ul id="tabset-4" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-4-1">Data Products</a></li><li><a href="#tabset-4-2">Dashboards</a></li><li><a href="#tabset-4-3">Websites</a></li><li><a href="#tabset-4-4">Books</a></li><li><a href="#tabset-4-5">…and more</a></li></ul>
<div class="tab-content">
<div id="tabset-4-1">
<p>Krumm et al. (2018) have outlined the following 3-step process for communicating finding with education stakeholders:</p>
<ol type="1">
<li><p><strong>Select.</strong> Selecting analyses that are most important and useful to an intended audience, as well as selecting a format for displaying that info (e.g. chart, table).</p></li>
<li><p><strong>Polish.</strong> Refining or polishing data products, by adding or editing titles, labels, and notations and by working with colors and shapes to highlight key points.</p></li>
<li><p><strong>Narrate.</strong> Writing a narrative pairing a data product with its related research question and describing how best to interpret and use the data product.</p></li>
</ol>
</div>
<div id="tabset-4-2">
<p><img src="img/dashboards.png" height="350px"></p>
<p><a href="https://quarto.org/docs/dashboards/" class="uri">https://quarto.org/docs/dashboards/</a></p>
</div>
<div id="tabset-4-3">
<p><img src="img/websites.png" height="350px"></p>
<p><a href="https://quarto.org/docs/websites/" class="uri">https://quarto.org/docs/websites/</a></p>
</div>
<div id="tabset-4-4">
<p><img src="img/books.png" height="350px"> ]</p>
<p><a href="https://quarto.org/docs/books/" class="uri">https://quarto.org/docs/books/</a></p>
</div>
<div id="tabset-4-5">
<ul>
<li><p><a href="https://quarto.org/docs/websites/website-blog.html"><strong>BLOGS</strong> - https://quarto.org/docs/websites/website-blog.html</a></p></li>
<li><p><a href="https://quarto.org/docs/manuscripts/"><strong>Manuscripts</strong> - https://quarto.org/docs/manuscripts/</a></p></li>
<li><p><a href="https://quarto.org/docs/presentations/revealjs/"><strong>REVEAL JS</strong> - https://quarto.org/docs/presentations/revealjs/</a></p></li>
<li><p><a href="https://quarto.org/docs/presentations/powerpoint.html"><strong>Powerpoint</strong> - https://quarto.org/docs/presentations/powerpoint.html</a></p></li>
</ul>
</div>
</div>
</div>
<aside class="notes">
<p>In this section, we highlight the essence of communicating research findings effectively. Krumm et al. (2018) advocate for a targeted approach: select key findings that resonate with your audience, polish your data products for clarity and engagement, and narrate your insights to ensure they are actionable. Quarto enhances this process by supporting diverse formats for data products, from interactive dashboards and websites to traditional publications like books and manuscripts. Its flexibility allows for tailored communication strategies, ensuring your research not only reaches but also impacts your intended audience. By leveraging Quarto, we can create compelling narratives that are accessible across various platforms, fostering broader understanding and application of our findings.</p>
<p>Quarto stands out in the communication phase due to its inherent design for reproducibility and collaboration. It’s a powerful tool that allows researchers to seamlessly integrate Python analysis with narrative text, creating a cohesive document that not only presents findings but also the code and data behind those conclusions. This integration promotes transparency and facilitates peer review, ensuring that the research can be easily validated and reproduced by others.</p>
<p>Moreover, Quarto’s ability to produce a wide array of output formats—from interactive web pages and dashboards to PDFs and slides—ensures that our communication is not just wide-reaching but also adaptable to the preferences and needs of diverse audiences. Whether stakeholders prefer a static report, an interactive web application, or a formal presentation, Quarto enables us to cater to these varied requirements without needing to alter the underlying content. This flexibility, combined with the platform’s emphasis on reproducibility, positions Quarto as an ideal choice for modern data-driven research communication.</p>
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<section>
<section id="whats-next" class="title-slide slide level1 center">
<h1>What’s Next?</h1>
<div class="columns">
<div class="column" style="width:50%;">
<p><strong>Our First LASER Badge!</strong> Next you will complete an interactive “case study” which is a key component to each learning lab.</p>
<p>Navigate to the Files tab and open the following file:</p>
<p><code>laser-lab-case-study.RMD</code> <strong>Change this to the new one</strong></p>
</div><div class="column" style="width:50%;">
<p><strong>Essential Readings</strong></p>
<ul>
<li><p><a href="https://github.com/christophergandrud/Rep-Res-Book">Reproducible Research with R and RStudio</a> (chapters 1 & 2)</p></li>
<li><p><a href="https://www.routledge.com/Learning-Analytics-Goes-to-School-A-Collaborative-Approach-to-Improving/Krumm-Means-Bienkowski/p/book/9781138121836">Learning Analytics Goes to School</a> (pages 28 - 58)</p></li>
<li><p><a href="https://datascienceineducation.com">Data Science in Education Using R</a></p></li>
<li><p><a href="https://r4ds.had.co.nz/index.html">R for Data Science</a></p></li>
</ul>
</div>
</div>
</section>
<section id="acknowledgements" class="slide level2">
<h2>Acknowledgements</h2>
<div class="columns">
<div class="column" style="width:20%;">
<div class="quarto-figure quarto-figure-center">
<figure>
<p><img data-src="img/nsf.jpg" class="quarto-figure quarto-figure-center" style="width:80.0%"></p>
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</div><div class="column" style="width:80%;">
<p>This work was supported by the National Science Foundation grants DRL-2025090 and DRL-2321128 (ECR:BCSER). Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.</p>
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