Posted in 2024
Exploring Personal Experience and Value Creation in Postdigital Education: Insights from a Large-Scale MOOC Survey.
Article written by Ali Soleymani, Maarten De Laat and Marcus Specht. This is a preprint (version 1); it has not been peer reviewed by a journal.
Abstract
This study investigates students’ online learning experiences based on their perception of the value this course creates for them through a comprehensive analysis of responses from 1227 participants in MOOCs offered by the Extension School of the Technical University of Delft. Utilizing the value creation framework by Wenger, Trayner, and De Laat (2011) , the research explores the immediate, potential, applied, realized, and transformative value creation cycles. Our findings reveal significant insights into the multifaceted impacts of study behavior on learners’ perceptions. Participants reported benefits such as skill acquisition, professional development, and enhanced confidence while highlighting areas needing improvement, such as practical application opportunities and course relevance. This study highlights the importance of aligning MOOC content with learner needs and providing ongoing support tomaximize the educational value online courses can offer them. These insights contribute to understanding educational value in the postdigital age, advocating for the development and support of MOOCs to foster continued personal and professional growth.
Keywords
Value Creation Framework, Postdigital Education, MOOCs, Online Assessment
Reference
Soleymani, A., De Laat, M., & Specht, M. M. (2024) EXPLORING PERSONAL EXPERIENCE AND VALUE CREATION IN POSTDIGITAL EDUCATION: INSIGHTS FROM A LARGE-SCALE MOOC SURVEY., Preprint (Version 1) available at Research Square
https://doi.org/10.21203/rs.3.rs-5043440/v1
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License
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International licence (https://creativecommons.org/licenses/by/4.0/), which permits reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
Debugging the Divide: Exploring Men’s and Women’s Motivations and Engagement in Computer Science MOOCs
Master thesis written by Casper Hildebrand at the faculty Electrical Engineering, Mathematics and Computer Science to obtain the degree of Master of Science at the Delft University of Technology.
Summary
Within the field of computer science (CS), women are under-represented in the workforce and education settings. As Massive Open Online Courses (MOOCs) grow in popularity, understanding the gender differences in reasons for enrollment and engagement remains crucial to improving learner outcomes. This study investigates why men and women enroll in introductory CS MOOCs and how they interact with these courses. This is done with data from four MOOCs offered by TU Delft between 2015 and 2022.
Using survey data for the learners’ reasons for enrolment and clickstream data for their behavioural engagement, we applied k-means clustering to identify engagement patterns. Our analysis reveals that the three most important reasons for men and women are career-related, interest-related, and degree-related, in that order. Women are more likely to enrol for career-related reasons than men, while men are more driven by interest in the topic than women. Women also tend to show lower engagement levels compared to men, who are more likely to complete the courses. We found no significant association between reasons for enrollment and engagement for men and women.
These findings highlight the need for gender-sensitive course design strategies to enhance engagement and completion rates. Providing mentorship opportunities, fostering peer interaction platforms, and highlighting role models in the field could also help create a more inclusive learning environment. Future research should explore specific learner challenges and incorporate a more comprehensive engagement model.
Keywords
Gender diversity, online learning, computer science, MOOCS, continuing education, motivation, engagement
Reference
Hildebrand, C.W.R. (2024) DEBUGGING THE DIVIDE: EXPLORING MEN’S AND WOMEN’S MOTIVATIONS AND ENGAGEMENT IN COMPUTER SCIENCE MOOCS Master thesis at the TU Delft faculty Electrical Engineering, Mathematics and Computer Science
http://resolver.tudelft.nl/uuid:f4aceeec-5947-4578-834c-4bb43288c91a
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Data for Learning in Engineering Education
White paper ‘100 DAYS OF… Data for Learning’
Written by Marcus Specht, Selma van Esveld, Jacopo De Stefani, Ted Adrichem and Andra Gherghiceanu to combine the insights and findings from the 2022/2023 event series 100 DAYS OF… Data for Learning.
Summary
For the past decades, the role of data has been ever growing in almost all fields – learning (and education) not excluded. But what is actually meant by ‘Data for Learning’? What data is available at TU Delft to support teaching and learning? In what way is it being used? For what purpose – and with which impact? And what are the challenges involved? These have been the questions which motivated the team of ‘100 DAYS OF… DATA FOR LEARNING’ to shape the program.
This paper outlines the findings of our 100+ days’ exploration. The aim of this paper is to raise awareness, understand conditions and needs, and discuss concerns and opportunities of (further) development of using ‘Data for Learning’ within the context of TU Delft Education.
In the ‘100 DAYS OF… DATA FOR LEARNING’ we have organised peer exchange among scientific staff and educational support, students, and lecturers. We have held journal clubs, invited science speaker sessions, and a hackathon event to understand the role and potential of data to support teaching and learning. In detail this included twelve Science speaker sessions in which a variety of topics has been presented and discussed about current applications of data in teaching and learning support as also fields of tension and challenges. In the 2022 CEL (Centre for Education and Learning) annual meeting, experts and interested participants convened for three keynotes and eight workshops on the topics of Learning Analytics and Data for Teaching and Learning.
In the hackathon initiative a dataset of real student data from a higher education institution in the Netherlands was collected and presented to different stakeholders for discussion and analysis. On one hand, students had the possibility to analyse the data to extract insights that could support the work of educational advisors. On the other hand, practitioners reflected on the current status of learning data and discussed improvements or potential future projects.
Overall, a variety of stakeholders from the TU Delft, on a national and international level, have been involved and contributed to the 100 Days. This has created awareness and new initiatives about the potential and challenges of ‘Data for Learning’. This brochure gives background, details, and starting points for further exploration of this important topic for the future of education.
Keywords
Data for learning, learning analytics, Data for teaching and learning, white paper, engineering education
Reference
Specht, M., van Esveld, S. L., De Stefani, J., Gherghiceanu, A., Adrichem, T. (2023). DATA FOR LEARNING IN ENGINEERING EDUCATION. WHITE PAPER ‘100 DAYS OF… DATA FOR LEARNING’.
Delft University of Technology.
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Data Driven Course Improvement – Pitch Presentation LCDA
Abstract of pitch presentation of the TU Delft workgroup that won first place with a process-mining case study in the Leadership Challenge with Data Analytics (LCDA) organized by Erasmus Centre for Data Analytics in collaboration with SURF.
Abstract
Online evaluation surveys increasingly suffer from declining and selective response rates. Hence, MOOC Learning developers/ Lecturers have limited and insufficient insights to enable fact-based course improvement. In order to provide more accurate insights in actual learner behaviour we performed pattern analysis on event-log data in two TU Delft Extension School MOOCs. We applied Process Mining as an explorative method combined with clustering techniques to compare intended vs. actual paths followed in the MOOCs. We find that higher performers show patterns of an iterative learning strategy compared to more linear learning paths of lower performers and non-passing learners. This corroborates the theories of Self-regulated Learning and Metacognition. Implications for data driven course improvement such as learning path analysis and other applications for Process Mining are discussed for TU Delft.
Keywords
Learning Analytics, MOOCs, course evaluation, process mining, learning paths.
Reference
Gherghiceanu, A., van Huik, B., Hunte, Z., Vriend, A., de Vries, N. (2024) DATA DRIVEN COURSE IMPROVEMENT., Abstract of pitch presentation on team findings during the 2024 Leadership Challenge with Data Analytics (LCDA) organized by Erasmus Centre for Data Analytics in collaboration with SURF.
Interested?
You can contact us for more information on research-es@tudelft.nl
License
This is an Open Access abstract, distributed under the terms of the Creative Commons Attribution 4.0 International licence (https://creativecommons.org/licenses/by-nc-sa/4.0/), which permits reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator, the purposes are non-commercial and distribution of remixed, adapted or build upon material should be released under the same license.