Review of CSEDM Data and Introduction of Two Public CS1 Keystroke Datasets
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| Title: | Review of CSEDM Data and Introduction of Two Public CS1 Keystroke Datasets |
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| Language: | English |
| Authors: | Edwards, John, Hart, Kaden, Shrestha, Raj |
| Source: | Journal of Educational Data Mining. 2023 15(1):1-31. |
| Availability: | International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: https://jedm.educationaldatamining.org/index.php/JEDM |
| Peer Reviewed: | Y |
| Page Count: | 31 |
| Publication Date: | 2023 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education High Schools Secondary Education |
| Descriptors: | Data Analysis, Computer Science Education, Learning Analytics, Research Methodology, Keyboarding (Data Entry), Barriers, Data Collection, Metadata, Computer Software, Grades (Scholastic), Programming, Undergraduate Students, Majors (Students), College Entrance Examinations, High School Students, Teaching Methods, Student Behavior, Outcomes of Education, Intelligent Tutoring Systems, Physical Characteristics, Computer Security, Plagiarism, Identification, Assignments, State Universities |
| Geographic Terms: | Utah |
| Assessment and Survey Identifiers: | ACT Assessment |
| ISSN: | 2157-2100 |
| Abstract: | Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as submission, compilation, edit, and keystroke events, with keystroke-level logs being the most fine-grained of commonly used dataset types. However, the lack of open datasets, especially at the keystroke level, is notable. There are several reasons for this failing, with the most prominent being the challenges of deidentification that are peculiar to keystroke log data. In this paper, we present the public release of two fully deidentified keystroke datasets that are the first of their kind in terms of both event and metadata richness. We describe our collection technique and properties of the data along with deidentification techniques that, while not fully relieving researchers of significant effort, at least reduce and streamline manual work in hopes that researchers will release similar datasets in the future. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| Accession Number: | EJ1383373 |
| Database: | ERIC |
| Abstract: | Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as submission, compilation, edit, and keystroke events, with keystroke-level logs being the most fine-grained of commonly used dataset types. However, the lack of open datasets, especially at the keystroke level, is notable. There are several reasons for this failing, with the most prominent being the challenges of deidentification that are peculiar to keystroke log data. In this paper, we present the public release of two fully deidentified keystroke datasets that are the first of their kind in terms of both event and metadata richness. We describe our collection technique and properties of the data along with deidentification techniques that, while not fully relieving researchers of significant effort, at least reduce and streamline manual work in hopes that researchers will release similar datasets in the future. |
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| ISSN: | 2157-2100 |
