README File For Success Factors of E-Assessment in Computer Science for Higher Education: Perspectives from Instructors

Dataset DOI: https://doi.org/10.5258/SOTON/D2607
Date that the file was created: March, 2025

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GENERAL INFORMATION
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ReadMe Authors: Safiah Alamr, Manuel Leon Urrutia, and Leslie Carr
University of Southampton

Date of data collection: April 2024 - July 2024

Information about geographic location of data collection:
United Kingdom and Saudi Arabia (higher education institutions, computer science departments).

Related projects:
Doctoral thesis: Success Factors of E-Assessment in Computer Science for Higher Education: Perspectives from Instructors

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SHARING/ACCESS INFORMATION
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Licenses/restrictions placed on the data, or limitations of reuse:
Data are anonymised and made available under a Creative Commons Attribution (CC-BY) licence.

Recommended citation for the data:
Safiah Alamr, Manuel Leon Urrutia, and Leslie Carr (2025). Success Factors of E-Assessment in Computer Science for Higher Education: Perspectives from Instructors [Dataset]. University of Southampton. DOI: https://doi.org/10.5258/SOTON/D2607

This dataset supports the publication:
Safiah Alamr, Manuel Leon Urrutia, and Leslie Carr (2025). Success Factors of E-Assessment in Computer Science for Higher Education: Perspectives from Instructors. Frontiers in Education. [Accepted] DOI: [to be added once available].


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DATA & FILE OVERVIEW
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This dataset contains:
E-assessment_Survey_year_3.csv Ð anonymised survey responses from computer science instructors.
Pure_interviews_data.pdf Ð anonymised transcripts of semi-structured interviews with computer science instructors.
Readme File.txt Ð this documentation file.

Relationship between files:
The survey responses provide quantitative insights at scale, while the interview transcripts provide detailed qualitative perspectives. Together they triangulate findings on e-assessment adoption.

Additional related data not included in this package:
Raw non-anonymised transcripts and identifiable metadata have been excluded to protect participant confidentiality.

If data derived from another source:
Not applicable.

Multiple versions of the dataset:
This is the first public release.

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METHODOLOGICAL INFORMATION
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Description of methods used for collection/generation of data:
Survey: Online questionnaire distributed to computer science instructors in the UK and Saudi universities.
Interviews: Semi-structured interviews conducted with computer science instructors.

Methods for processing the data:
Survey data exported to CSV format.
Interview recordings transcribed, anonymised, and compiled into a single PDF document.

Software or instruments needed to interpret the data:
Survey data are provided in CSV format and can be opened with any spreadsheet software (Excel) or statistical tools (SPSS, R, Python). Data in this project were processed and analysed primarily using Python.
Interview transcripts viewable with any PDF reader.


Quality assurance procedures:
Manual anonymisation of transcripts (removal of names, institutions, and identifiable details).
Consistency checks across survey dataset for missing or inconsistent responses.

People involved:
Safiah Alamr, Manuel Leon Urrutia, Leslie Carr ,University of Southampton, data collection, transcription, anonymisation, and analysis.

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DATA-SPECIFIC INFORMATION
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For Survey:
Number of variables: 26 columns
Number of rows: 44

For Interviews:
Format: Text transcripts (English) anonymised.
Coding: Interviewees identified with pseudonyms (P1, P2, É).