Martin Hlosta
Biographic Data
| ID | 9493975 |
|---|---|
| NAME | Martin Hlosta |
| GIVEN NAMES | Martin |
| FAMILY NAME | Hlosta |
| SIGNATURE | HLOSTA M |
| AFFILIATIONS | Swiss Distance University of Applied Sciences |
| ORCID | 0000-0002-7053-7052 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Learning Analytics to Uncover Ethnic Bias in Educational Texts
Online learning platforms have expanded access to education but also raise concerns about biased content, particularly in text-based learning materials such as textbooks, lesson plans, and course excerpts. Such biases can perpetuate discrimination, can harm student outcomes, and can often be difficult to detect, as identification typically relies on time-consuming human review. Learning analytics (LA) can enhance this process by supporting human …
From hype to evidence
Inter-group biases can diminish student achievements in several ways. Yet, manually identifying those biases in vast learning texts is challenging because of their subtle nature. In the light of processing nuanced language, approaches based on large language models (LLMs) have emerged as promising mechanisms (e.g. ChatGPT). However, their potential for classifying bias in learning text seems under-explored. This study examines the ability of thre…
Co‐creating an equality diversity and inclusion learning analytics dashboard for addressing awarding gaps in higher education
Educational outcomes from traditionally underrepresented groups are generally worse than for their more advantaged peers. This problem is typically known as the awarding gap (we use the term awarding gap over ‘attainment gap’ as attainment places the responsibility on students to attain at equal levels) and continues to pose a challenge for educational systems across the world. While Learning Analytics (LA) dashboards help identify patterns contr…
Using Survival Analysis to Identify Populations of Learners at Risk of Withdrawal
High dropout rates constitute a major concern for higher education institutions, due to their economic and academic impact. The problem is particularly relevant for institutions offering online courses, where withdrawal ratios are reported to be higher. Both the impact and these high rates motivate the implementation of interventions oriented to reduce course withdrawal and overall institutional dropout. In this paper, we address the identificati…
The engagement of university teachers with predictive learning analytics
The scalable implementation of predictive learning analytics at a distance learning university
No prominent works on this page.
The scalable implementation of predictive learning analytics at a distance learning university
The engagement of university teachers with predictive learning analytics
Using Survival Analysis to Identify Populations of Learners at Risk of Withdrawal
High dropout rates constitute a major concern for higher education institutions, due to their economic and academic impact. The problem is particularly relevant for institutions offering online courses, where withdrawal ratios are reported to be higher. Both the impact and these high rates motivate the implementation of interventions oriented to reduce course withdrawal and overall institutional dropout. In this paper, we address the identificati…
Co‐creating an equality diversity and inclusion learning analytics dashboard for addressing awarding gaps in higher education
Educational outcomes from traditionally underrepresented groups are generally worse than for their more advantaged peers. This problem is typically known as the awarding gap (we use the term awarding gap over ‘attainment gap’ as attainment places the responsibility on students to attain at equal levels) and continues to pose a challenge for educational systems across the world. While Learning Analytics (LA) dashboards help identify patterns contr…
From hype to evidence
Inter-group biases can diminish student achievements in several ways. Yet, manually identifying those biases in vast learning texts is challenging because of their subtle nature. In the light of processing nuanced language, approaches based on large language models (LLMs) have emerged as promising mechanisms (e.g. ChatGPT). However, their potential for classifying bias in learning text seems under-explored. This study examines the ability of thre…
Learning Analytics to Uncover Ethnic Bias in Educational Texts
Online learning platforms have expanded access to education but also raise concerns about biased content, particularly in text-based learning materials such as textbooks, lesson plans, and course excerpts. Such biases can perpetuate discrimination, can harm student outcomes, and can often be difficult to detect, as identification typically relies on time-consuming human review. Learning analytics (LA) can enhance this process by supporting human …
Computer Science (5 works) · Psychology (5 works) · Analytics (4 works) · Data science (4 works) · Learning analytics (4 works) · Mathematics education (4 works) · Online Learning and Analytics (4 works) · E-Learning and Knowledge Management (3 works) · Medical education (3 works) · Hate Speech and Cyberbullying Detection (2 works)