Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2
datasets available to search
ShareScore release 0.9.0
Dataset results
2 results for “introductory programming”
SelfCode 2.0: Annotated Corpus of Student Self-Explanations to Introductory JAVA Programs in Computer Science
<p><strong>Dataset Description:</strong> This dataset was collected during a lab study conducted in Spring 2022 for introductory JAVA programming. Students had to provide line by explanations to four JAVA programs in the experimental condition of the study. The JAVA Programs were selected from the examples made available in the <a title="PCEX" href="https://dl.acm.org/doi/abs/10.1145/3279720.3279726" target="_blank" rel="noopener">PCEX Worked Examples interface</a>. The explanations collected were then split by the number of attempts. Students could attempt twice based on the feedback provided using the the PCEX interface and in their third attempt they filled in the blanks to complete an explanation to the particular line of code. In this dataset, we only have the annotated examples of explanations provided by students. The explanations were annotated on their correctness (binary rating 0 or 1), completeness (binary rating 0 or 1) and similarity (rating scale 1 to 5).</p> <p><strong>Correctness:</strong> Given the line of code and context of the line in the program, if the student explanation covers **only** the topics relevant to the line of code</p> <p><strong>Completeness:</strong> Given the line of code and context of the line in the program, if the student explanation covers **all** the topics relevant to the line of code</p> <p><strong>Similarity:</strong> Given the line of code, the context of the line in the program and an expert explanation to the line of code, the metric compares the similarity on a rating scale from 1 to 5, defined in the following manner:</p> <p>1 - expert and student explanations are very different,</p> <p>2 -- expert and student explanations are somewhat alike, but there are major differences in the concepts / topics explained</p> <p>3 -- expert and student explanations are similar but there are differences in the concepts / topics explained</p> <p>4 -- expert and student explanations are similar and have few differences in the concepts / topics explained</p> <p>5 -- expert and student explanations are very similar.</p> <p> </p> <p>Overall 3000 single attempts (corresponding to 40 student explanation submission) were annotated against different various expert explanation pairs.</p> <p> </p> <p><strong>Dataset Summary:</strong></p> <p><strong>Explanation Type N Definition<br></strong>Experts 2 Source Code Line-by-Line Explanations by Experts<strong><br></strong>Students 60 (annotated 40) Source Code Line-by-Line Explanations by Students</p> <table> <tbody> <tr> <td>COUNT of std_sent_count</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>std_sent_count</td> <td>1</td> <td>2</td> <td>3</td> <td>4</td> <td>5</td> <td>6</td> <td>Grand Total</td> </tr> <tr> <td>1</td> <td>1854</td> <td>367</td> <td>245</td> <td>107</td> <td>34</td> <td>33</td> <td>2640</td> </tr> <tr> <td>2</td> <td>222</td> <td>46</td> <td>40</td> <td>12</td> <td>6</td> <td>6</td> <td>332</td> </tr> <tr> <td>3</td> <td>21</td> <td>5</td> <td>5</td> <td>5</td> <td>1</td> <td>2</td> <td>39</td> </tr> <tr> <td>4</td> <td>2</td> <td>1</td> <td>2</td> <td>3</td> <td> </td> <td> </td> <td>8</td> </tr> <tr> <td>Grand Total</td> <td>2099</td> <td>419</td> <td>292</td> <td>127</td> <td>41</td> <td>41</td> <td>3019</td> </tr> </tbody> </table> <p> </p> <p><strong>Sample Data:</strong></p> <p><strong>Program:</strong> PointTester; Line number: 12; Line code: private int y;<br><strong>Expert1: </strong>Every object of the Point class will have its own y-coordinate. Therefore, we<br>need to declare an instance variable for the class to store the y-coordinate of the point.<br>We declare it as int because we want to have integer coordinates for the point. Note<br>that an instance variable is a variable defined in a class, for which each instantiated<br>object of the class has a separate copy, or instance.<br><strong>Expert2:</strong> The instance variables are declared as private to prevent direct access to<br>them from outside the class. In this way, no unexpected modifications to a Point<br>object’s data are possible.<br><strong>Student1: </strong>initialize a private value inside the point class with no value yet<br><strong>Student2:</strong> Declares the private int variable y.<br><strong>Student3: </strong>Creates a private int that can only be accessed by class Point called int y<br>...<br><strong>Student59:</strong> private variable used to store the value entered into the value of the y<br>coordinate</p> <p> </p> <p><strong>Kappa Scores:</strong></p> <div> <table> <tbody> <tr> <td> <p>Round</p> </td> <td> <p>Row Numbers</p> </td> <td> <p>Correctness Rating Agreement %age</p> </td> <td> <p>Correctness Rating Kappa</p> </td> <td> <p>Sufficiency Rating Agreement %age</p> </td> <td> <p>Sufficiency Rating Kappa</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>1000 - 1432 </p> </td> <td> <p>92.9</p> </td> <td> <p>0.365</p> </td> <td> <p>0.708</p> </td> <td> <p>-0.0123</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>1432 - 1864</p> </td> <td> <p>94.2</p> </td> <td> <p>0.263</p> </td> <td> <p>77.6</p> </td> <td> <p>0.329</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>1864 – 1964 </p> </td> <td> <p>75.3</p> </td> <td> <p>0</p> </td> <td> <p>70.3</p> </td> <td> <p>0.299</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>1964 -- 2064 </p> </td> <td> <p>86</p> </td> <td> <p>0.108</p> </td> <td> <p>74.7</p> </td> <td> <p>0.275</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>2064 – 2264</p> </td> <td> <p>95.5</p> </td> <td> <p>-0.0158</p> </td> <td> <p>81.5</p> </td> <td> <p>0.312</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>2264 – 2464</p> </td> <td> <p>83.5</p> </td> <td> <p>0.039</p> </td> <td> <p>86.5</p> </td> <td> <p>0.648</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>2464 – 2864</p> </td> <td> <p>92</p> </td> <td> <p>0.103</p> </td> <td> <p>74.5</p> </td> <td> <p>0.188</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>2864 -- 3005</p> </td> <td> <p>86.5</p> </td> <td> <p>-0.026</p> </td> <td> <p>72.3</p> </td> <td> <p>0.117</p> </td> </tr> </tbody> </table> </div> <p> </p> <p> </p> <p><strong>Citation Format:</strong><br>If using this dataset in your project please cite:</p> <p>Lekshmi-Narayanan, A.-B., Chapagain, J., Brusilovsky, P., & Rus, V. (2023). SelfCode 2.0: Annotated Corpus of Student Self-Explanations to Introductory JAVA Programs in Computer Science [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10912669</p> <p><strong>Acknowledgements:</strong><br>This project was funded as a part of the NSF AWARD # 1822752</p> <p> </p> <p> </p>
Supporting Couples' Perinatal Sexual Well-being: Testing the Introductory Modules of a Novel Prevention Program (STORK)
ClinicalTrials.gov study NCT07036484. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.