November 2024 - EduTec Science

Nov222024 by Daniele Di MitriNo Comments

New Pub: Impact of Artificial Intelligence Based Systems to Improve Mathematics Achievement in Rural Schools

Artificial Intelligence, Journal, New Pub, School

Poor mathematics achievement, especially in rural areas, remains a persistent challenge. Schools in rural areas often struggle to attract and retain highly qualified mathematics teachers, and the teacher shortage across the United States further amplifies this issue. Numerous studies indicate that AI-based systems can enhance mathematics achievement and improve test scores on both standardized and non-standardized tests in K-12 and higher education. These systems offer adaptive and personalized education tailored to the unique needs of each student. By creating learning pathways based on students' current knowledge and offering real-time feedback and support, AI-based systems have the potential to improve learning outcomes across the P-20 educational spectrum. Despite the increasing adoption of AI-based tools, there is limited research on its impact on K-12 classrooms within rural contexts, especially among socioeconomically disadvantaged…

Nov182024 by Heather WehnerNo Comments

New Pub: Understanding Learning Engagement in Asynchronous Online Settings

Higher Education, Journal, Learning Analytics, Publication

A newly published study illustrates the complexities of learning engagement (LE) in asynchronous online settings (AOSs) for university students. For university students it can be difficult to learn in such environments since these lack real-time interactions. This also makes it difficult for teachers to measure how engaged students actually are with their study materials. Through trace data, learning analytics can be used as a foundation to analyze students’ learning methods and LE. The study investigates whether LE can be characterized by the sub-dimensions: effort, attention and content interest. The study also explores the question of which trace data from student behavior within AOSs can best represent these factors of LE in self-reports. The research involved 764 university students and utilized best-subset regression analysis to determine which indicators most reliably represent…

Nov82024 by Heather WehnerNo Comments

PhD Defense on Software Infrastructure for Contextualized Learning Analytics in Online Education

Learning Analytics, PhD defense

We warmly congratulate our esteemed alumni George-Petru Ciordas-Hertel on the successful defense of his PhD thesis this past Tuesday, 05.11.2024! Way to go, Dr. George! We are proud of you for achieving this milestone! At universities we have seen a big shift to online education in the past ten years, as universities have continuously integrated technology into educational environments. This development has brought students and educators new possibilities, but also challenges. A promising method is Learning Analytics (LA), which uses data to gain insights into learning behaviors and enhance educational outcomes. In his dissertation, George highlights a critical limitation in traditional learning analytics: they often overlook significant aspects of learners’ digital and physical environments. His research proposes that integrating contextual information from these environments could make LA even more effective.…

Nov72024 by Daniele Di MitriNo Comments

Guest Talk at Monash University

Feedback, Invited talk

During his recent research visit to Australia, Daniele Di Mitri had the privilege of presenting at Monash University, specifically at the Centre for Learning Analytics Monash (COLAM). His talk, titled “The Quest for Automated Feedback,” explored the evolving role of AI in education, particularly in providing meaningful feedback to students. As generative AI tools like ChatGPT become increasingly popular among students seeking feedback on their essays, a critical question arises: How effective is this feedback? Daniele's research delves into the complexities of generating automated feedback, emphasising that it is not a one-size-fits-all solution. Feedback is inherently context-dependent, influenced by various factors, including the learner’s needs, the task at hand, and the feedback modality. The literature on feedback is vast and nuanced. Daniele referenced several foundational models, including Hattie & Timperley (2007) and…