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orientation-code-along.html
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<!DOCTYPE html>
<html lang="en"><head>
<script src="orientation-code-along_files/libs/clipboard/clipboard.min.js"></script>
<script src="orientation-code-along_files/libs/quarto-html/tabby.min.js"></script>
<script src="orientation-code-along_files/libs/quarto-html/popper.min.js"></script>
<script src="orientation-code-along_files/libs/quarto-html/tippy.umd.min.js"></script>
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<link href="orientation-code-along_files/libs/quarto-html/quarto-html.min.css" rel="stylesheet" data-mode="light">
<link href="orientation-code-along_files/libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
<meta name="generator" content="quarto-1.4.550">
<title>The Data-Intensive Research Workflow</title>
<meta name="apple-mobile-web-app-capable" content="yes">
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<link rel="stylesheet" href="orientation-code-along_files/libs/revealjs/dist/reveal.css">
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code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
div.columns{display: flex; gap: min(4vw, 1.5em);}
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div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
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color: #aaaaaa;
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pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
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@media screen {
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}
code span { color: #003b4f; } /* Normal */
code span.al { color: #ad0000; } /* Alert */
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div.csl-indent {
margin-left: 2em;
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margin-bottom: 1em;
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<section id="title-slide" class="quarto-title-block center">
<h1 class="title">The Data-Intensive Research Workflow</h1>
<p class="subtitle">Orientation Module: Code-Along</p>
<div class="quarto-title-authors">
</div>
</section>
<section id="overview" class="slide level2">
<h2>Overview</h2>
<img data-src="img/laser-cycle.png" class="quarto-figure quarto-figure-center r-stretch" style="width:60.0%"><p class="caption">Data-Intensive Research Workflow <span class="citation" data-cites="krumm2018">(<a href="#/references" role="doc-biblioref" onclick="">Krumm, Means, and Bienkowski 2018</a>)</span></p><aside class="notes">
<p><strong>Speaker Notes:</strong></p>
<p><strong>Slide Overview:</strong> This slide illustrates the data-intensive research workflow, which is a cyclical process and which we use to frame all of the case studies for each module. Each stage is interconnected, emphasizing the iterative nature of the research process. Let’s briefly walk through each stage.</p>
<p><strong>Prepare:</strong> The preparation phase involves defining and refining the research questions, ensuring they are clear and relevant to the data at hand. This stage sets the foundation for the entire research process, establishing the goals and objectives that guide the subsequent stages.</p>
<p><strong>Wrangle:</strong> Data wrangling is the process of cleaning, transforming, and merging raw data into a structured format suitable for analysis. This stage is crucial as it ensures the data is accurate, complete, and ready for exploration and modeling.</p>
<p><strong>Explore:</strong> In the exploration phase, researchers perform exploratory data analysis (EDA) to uncover patterns, trends, and insights within the data. Visualization tools and statistical methods are commonly used to gain a deeper understanding of the dataset, which helps inform the modeling stage.</p>
<p><strong>Model:</strong> The modeling phase involves building and evaluating predictive models based on the prepared and explored data. Various statistical and machine learning techniques are applied to predict outcomes or classify data, with a focus on accuracy and reliability.</p>
<p><strong>Communicate:</strong> Effective communication of findings is essential. In this stage, researchers present their results through polished data products, such as reports, visualizations, and presentations, tailored to their intended audience. The goal is to ensure that the insights are clear and actionable.</p>
<p><strong>Iterative Nature:</strong> The dashed lines in the diagram indicate the iterative nature of this workflow. New analyses often lead to refinements in the research questions or data, prompting a return to the preparation or wrangling stages. Additionally, communicating findings can generate new ideas for further analysis, creating a continuous cycle of improvement and discovery.</p>
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</section>
<section id="prepare" class="slide level2">
<h2>Prepare</h2>
<div class="panel-tabset">
<ul id="tabset-1" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-1-1">Research Context</a></li><li><a href="#tabset-1-2">Question</a></li><li><a href="#tabset-1-3">Data</a></li><li><a href="#tabset-1-4">Packages</a></li></ul>
<div class="tab-content">
<div id="tabset-1-1">
<ul>
<li><p>The setting of this study was a public provider of individual online courses in a Midwestern state.</p></li>
<li><p>Data was collected from two semesters of five online science courses and aimed to understand students motivation.</p></li>
</ul>
</div>
<div id="tabset-1-2">
<p><br></p>
<blockquote>
<p>Is there a relationship between the time students spend in a learning management system and their final course grade?</p>
</blockquote>
</div>
<div id="tabset-1-3">
<p>The data used in this case study has already been “wrangled” quite a bit, but the original datasets included:</p>
<ol type="1">
<li><p>A self-report <strong>survey</strong> assessing three aspects of students’ motivation</p></li>
<li><p><strong>Log-trace data</strong> from the learning management system (LMS) including discussion board posts</p></li>
<li><p><strong>Administrative</strong> <strong>records</strong> including academic achievement and student demographic data</p></li>
</ol>
</div>
<div id="tabset-1-4">
<p>Sometimes referred to as “libraries”, packages are shareable collections of R code that can contain functions, data, and/or documentation.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href=""></a><span class="co"># Load the {tidyverse} package</span></span>
<span id="cb1-2"><a href=""></a></span>
<span id="cb1-3"><a href=""></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb1-4"><a href=""></a></span>
<span id="cb1-5"><a href=""></a><span class="co"># How to install the package if not already installed</span></span>
<span id="cb1-6"><a href=""></a><span class="co"># install.packages("tidyverse")</span></span>
<span id="cb1-7"><a href=""></a><span class="co"># Note this code is commented out so it will not run</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
</div>
</div>
</section>
<section id="wrangle" class="slide level2">
<h2>Wrangle</h2>
<div class="panel-tabset">
<ul id="tabset-2" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-2-1">Define</a></li><li><a href="#tabset-2-2">“Read”</a></li><li><a href="#tabset-2-3">Inspect</a></li><li><a href="#tabset-2-4">Select</a></li><li><a href="#tabset-2-5">Discuss</a></li></ul>
<div class="tab-content">
<div id="tabset-2-1">
<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>
</div>
<div id="tabset-2-2">
<p>The {readr} tidyverse package has several functions like <code>read_csv()</code> for importing rectangular data from delimited text files such as comma-separated values (CSV), a preferred file format for reproducible research.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href=""></a><span class="co"># "read" into your R environment the sci-online-classes.csv file </span></span>
<span id="cb2-2"><a href=""></a><span class="co"># stored in the data folder of your R project and</span></span>
<span id="cb2-3"><a href=""></a><span class="co"># save as a new data "object" named sci_data </span></span>
<span id="cb2-4"><a href=""></a></span>
<span id="cb2-5"><a href=""></a>sci_data <span class="ot"><-</span> <span class="fu">read_csv</span>(<span class="st">"data/sci-online-classes.csv"</span>, </span>
<span id="cb2-6"><a href=""></a> <span class="at">col_names =</span> <span class="cn">TRUE</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>R has also packages for proprietary data formats like .xls, .sas, and .dat.</p>
</div>
<div id="tabset-2-3">
<p>You should now see a new data “object” named <code>sci_data</code> saved in your Environment pane. Try clicking on it and see what happens!</p>
<p>Now let’s “print” the output to our Console to view another way:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href=""></a><span class="co"># print data to console to view</span></span>
<span id="cb3-2"><a href=""></a></span>
<span id="cb3-3"><a href=""></a>sci_data</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>What variables do you think might help us answer our research question?</p>
</div>
<div id="tabset-2-4">
<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 class="cell">
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href=""></a><span class="co"># select student_id and course_id and save as </span></span>
<span id="cb4-2"><a href=""></a></span>
<span id="cb4-3"><a href=""></a>sci_data <span class="sc">|></span> </span>
<span id="cb4-4"><a href=""></a> <span class="fu">select</span>(FinalGradeCEMS, TimeSpent)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Note the use of a the powerful <code>|></code> operator called a <strong>pipe</strong>, which are used for combining a sequence of functions or processes.</p>
</div>
<div id="tabset-2-5">
<p>The R language can express complex ideas like in this simple “sentence”:</p>
<p><code>sci_data <- read_csv("data/sci-online-classes.csv", col_names = TRUE)</code></p>
<ul>
<li><em>Functions</em> are like verbs: <code>read_csv()</code></li>
<li><em>Objects</em> are the nouns: <code>read_csv("data/sci-online-classes.csv")</code></li>
<li><em>Arguments</em> are like adverbs: <code>read_csv("data/sci-online-classes.csv", col_names = TRUE)</code></li>
<li><em>Operators</em> are like “punctuation” <code>sci_data <- read_csv("data/sci-online-classes.csv", col_names = TRUE)</code></li>
</ul>
</div>
</div>
</div>
</section>
<section id="explore" class="slide level2">
<h2>Explore</h2>
<div class="panel-tabset">
<ul id="tabset-3" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-3-1">EDA</a></li><li><a href="#tabset-3-2">Graph Template</a></li><li><a href="#tabset-3-3">Our First Plot!</a></li><li><a href="#tabset-3-4">Interpret Graph</a></li></ul>
<div class="tab-content">
<div id="tabset-3-1">
<p>Exploratory data analysis involves processes of <strong>describing</strong> your data numerically or graphically and often involves:</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 or generate new questions about your data, discover relationships between and among variables, and create new variables for data modeling.</p>
</div>
<div id="tabset-3-2">
<p>The <a href="https://ggplot2.tidyverse.org">{ggplot2}</a> package follows a common <a href="https://r4ds.had.co.nz/data-visualisation.html?q=template#a-graphing-template">graphing workflow</a> for making graphs. To make a graph, you simply:</p>
<ol type="1">
<li>Start the graph with <code>ggplot()</code> function and include your data as an argument;</li>
<li>“Add” elements to the graph using the <code>+</code> operator <code>a geom_()</code> function;</li>
<li>Select variables to graph on each axis with the <code>aes()</code> argument.</li>
</ol>
<p>A common graphing template is as simple as two lines of code:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href=""></a><span class="fu">ggplot</span>(<span class="sc"><</span>DATA<span class="sc">></span>) <span class="sc">+</span> </span>
<span id="cb5-2"><a href=""></a> <span class="er"><</span>GEOM_FUNCTION<span class="sc">></span>(<span class="fu">aes</span>(<span class="at">x =</span> <span class="sc"><</span>VARIABLE1<span class="sc">></span>, <span class="at">y =</span> <span class="sc"><</span>VARIABLE2<span class="sc">></span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
<div id="tabset-3-3">
<p>Scatterplots use the point <strong>geom</strong>, i.e., the <code>geom_point()</code> function, and are most useful for displaying the relationship between two continuous variables.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href=""></a><span class="co"># make a scatter plot showing time spent and final grades</span></span>
<span id="cb6-2"><a href=""></a><span class="fu">ggplot</span>(sci_data) <span class="sc">+</span></span>
<span id="cb6-3"><a href=""></a> <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">x =</span> TimeSpent, <span class="at">y =</span> FinalGradeCEMS))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
<div id="tabset-3-4">
<div class="columns">
<div class="column" style="width:50%;">
<p>Hopefully your scatterplot looks like something like the one to the right.</p>
<p><br></p>
<p><em>How would you interpret this graph?</em></p>
</div><div class="column" style="width:50%;">
<div class="cell">
<div class="cell-output-display">
<div>
<figure>
<p><img data-src="orientation-code-along_files/figure-revealjs/unnamed-chunk-7-1.png" width="672"></p>
</figure>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section id="model" class="slide level2">
<h2>Model</h2>
<div class="panel-tabset">
<ul id="tabset-4" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-4-1">Goal</a></li><li><a href="#tabset-4-2">A Simple Model</a></li><li><a href="#tabset-4-3">Interpret</a></li><li><a href="#tabset-4-4">Discuss</a></li></ul>
<div class="tab-content">
<div id="tabset-4-1">
<p>According to <span class="citation" data-cites="krumm2018">Krumm, Means, and Bienkowski (<a href="#/references" role="doc-biblioref" onclick="">2018</a>)</span>, there are two general types of approaches:</p>
<ul>
<li><p><strong>Unsupervised</strong> learning algorithms can be used to understand the structure of one’s dataset.</p></li>
<li><p><strong>Supervised</strong> models, on the other hand, help to quantify relationships between features and a known outcome.</p></li>
</ul>
<p>Ideally, a good model will separate true “signals” (i.e. patterns generated by the phenomenon of interest) from the “noise” (i.e. random variation that you’re not interested in).</p>
</div>
<div id="tabset-4-2">
<p>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 class="cell">
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href=""></a><span class="co"># use a simple linear regression model to predict final course grades</span></span>
<span id="cb7-2"><a href=""></a>m1 <span class="ot"><-</span> <span class="fu">lm</span>(FinalGradeCEMS <span class="sc">~</span> TimeSpent, <span class="at">data =</span> sci_data)</span>
<span id="cb7-3"><a href=""></a></span>
<span id="cb7-4"><a href=""></a><span class="fu">summary</span>(m1)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
<div id="tabset-4-3">
<div class="cell">
<div class="cell-output cell-output-stdout">
<pre><code>
Call:
lm(formula = FinalGradeCEMS ~ TimeSpent, data = sci_data)
Residuals:
Min 1Q Median 3Q Max
-67.136 -7.805 4.723 14.471 30.317
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.581e+01 1.491e+00 44.13 <2e-16 ***
TimeSpent 6.081e-03 6.482e-04 9.38 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 20.71 on 571 degrees of freedom
(30 observations deleted due to missingness)
Multiple R-squared: 0.1335, Adjusted R-squared: 0.132
F-statistic: 87.99 on 1 and 571 DF, p-value: < 2.2e-16</code></pre>
</div>
</div>
</div>
<div id="tabset-4-4">
<p>Our simple model suggests that there is a statistically significant, though relatively small, positive association between the time spent in the LMS and a student’s final grade. However, many other factors not included in the model may be influencing final grades.</p>
</div>
</div>
</div>
</section>
<section id="communicate" class="slide level2">
<h2>Communicate</h2>
<div class="panel-tabset">
<ul id="tabset-5" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-5-1">Data Products</a></li><li><a href="#tabset-5-2">Dashboards</a></li><li><a href="#tabset-5-3">Websites</a></li><li><a href="#tabset-5-4">Books</a></li><li><a href="#tabset-5-5">And More!</a></li></ul>
<div class="tab-content">
<div id="tabset-5-1">
<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-5-2">
<div class="quarto-figure quarto-figure-center">
<figure>
<p><a href="https://sbkellogg.quarto.pub/final-grades-and-hours-logged/"><img data-src="img/data-dashboard.png" class="quarto-figure quarto-figure-center" style="width:60.0%"></a></p>
</figure>
</div>
</div>
<div id="tabset-5-3">
<div class="quarto-figure quarto-figure-center">
<figure>
<p><a href="https://laser-institute.github.io/laser-website/"><img data-src="img/laser-website.png" class="quarto-figure quarto-figure-center" style="width:60.0%"></a></p>
</figure>
</div>
</div>
<div id="tabset-5-4">
<div class="quarto-figure quarto-figure-center">
<figure>
<p><a href="https://datascienceineducation.com"><img data-src="img/books.png" class="quarto-figure quarto-figure-center" style="width:60.0%"></a></p>
</figure>
</div>
</div>
<div id="tabset-5-5">
<iframe width="2000" height="800" src="https://quarto.org/docs/guide/" title="Webpage example"></iframe>
</div>
</div>
</div>
</section>
<section id="whats-next" class="slide level2">
<h2>What’s Next</h2>
<p>Next you will complete an interactive “case study” which is a key component to each LASER Module.</p>
<p><br></p>
<p>Navigate to the Files tab and open the following file:</p>
<p><code>orientation-case-study-R.qmd</code></p>
</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>
</figure>
</div>
</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>
</div>
</div>
</section>
<section id="references" class="slide level2 smaller scrollable">
<h2>References</h2>
<div class="quarto-auto-generated-content">
<p><img src="img/LASERLogoB.png" class="slide-logo"></p>
<div class="footer footer-default">
<p><a href="https://www.go.ncsu.edu/laser-institute">go.ncsu.edu/laser-institute</a></p><a href="https://www.go.ncsu.edu/laser-institute">
</a></div><a href="https://www.go.ncsu.edu/laser-institute">
</a></div><a href="https://www.go.ncsu.edu/laser-institute">
</a><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list"><a href="https://www.go.ncsu.edu/laser-institute">
</a><div id="ref-krumm2018" class="csl-entry" role="listitem"><a href="https://www.go.ncsu.edu/laser-institute">
Krumm, Andrew, Barbara Means, and Marie Bienkowski. 2018. <em>Learning Analytics Goes to School</em>. Routledge. </a><a href="https://doi.org/10.4324/9781315650722">https://doi.org/10.4324/9781315650722</a>.
</div>
</div>
</section>
</div>
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