Knowledge Tracing (KT) has been an established problem in the educational data mining field for decades, and it is commonly assumed that the underlying learning process being modeled remains static. Given the ever-changing landscape of online learning platforms (OLPs), we investigate how concept drift and changing student populations can impact student behavior within an OLP through testing model performance both within a single academic year and across multiple academic years. Four well-studied KT models were applied to five academic years of data to assess how susceptible KT models are to concept drift. Through our analysis, we find that all four families of KT models can exhibit degraded performance, Bayesian Knowledge Tracing (BKT) remains the most stable KT model when applied to newer data, while more complex, attention based models lose predictive power significantly faster.

Secretary of State Marco Rubio said Wednesday that the US plans to "aggressively revoke" the visas of Chinese students, including those working in critical fields or with ties to the Chinese Communist Party. Experts warn the move--along with the Trump administration's broader crackdown on international students--could drain American scientific labs of top STEM talent and upend cutting-edge research in areas like artificial intelligence. "If you were aiming to help China beat the US at AI, the first thing you would do is disrupt the flow of top talent from all around the world into the US," says Helen Toner, director of strategy and foundational research grants at Georgetown University's Center for Security and Emerging Technology. While it has a population only about a quarter the size of China, "the US has had a huge asymmetric advantage in attracting the cream of the global crop," she adds. Several close Trump allies, including Elon Musk, have argued that attracting the best engineers from around the world is essential for the US to maintain its technological dominance.

High-quality professional development for teachers can facilitate the learning of best teaching practices, which in turn can lead to higher levels of student performance (Borko et al., 2010; Didion et al., 2020; Gore et al., 2021; Hubers et al., 2022; Schelling & Rubenstein, 2023). For instance, feedback on actual practices has proven effective in enhancing teaching methods and subsequently improving student outcomes (Allen et al., 2011; Gregory et al., 2017), even among students not directly taught by the teachers receiving the feedback (Opper, 2019). Thus, focusing on the evaluation of teaching practices to facilitate professional development is essential, as it can lead to improved teaching methods and ultimately to higher levels of student outcomes. Despite its acknowledged importance and the pressures from high-stakes accountability systems, most professional development opportunities remain fragmented and insufficient to meet teachers' needs (Borko, 2004; Hsu & Malkin, 2013). The reason for this may be that, although it is known that teaching practices such as cognitive activation, supportive climate, and classroom management, are relevant for enhancing teaching quality and student outcomes (Xie & Derakhshan, 2021), these dimensions may be too abstract or general, which can hinder the implementation of concrete actions to improve teaching quality. In this regard, evidence suggests that targeting more specific factors for intervention, rather than abstract ones, allows teachers to better understand and change their practices (Soderberg et al., 2015).

Engaging messages delivered by teachers are a key aspect of the classroom discourse that influences student outcomes. However, improving this communication is challenging due to difficulties in obtaining observations. This study presents a methodology for efficiently extracting actual observations of engaging messages from audio-recorded lessons. We collected 2,477 audio-recorded lessons from 75 teachers over two academic years. Using automatic transcription and keyword-based filtering analysis, we identified and classified engaging messages. This method reduced the information to be analysed by 90%, optimising the time and resources required compared to traditional manual coding. Subsequent descriptive analysis revealed that the most used messages emphasised the future benefits of participating in school activities. In addition, the use of engaging messages decreased as the academic year progressed. This study offers insights for researchers seeking to extract information from teachers' discourse in naturalistic settings and provides useful information for designing interventions to improve teachers' communication strategies. Keywords: Teacher education; Technology; Discourse; Secondary education; Engagement 1. Introduction Teachers' discourse has the power to shape students' outcomes (Caldarella et al., 2023; Howe & Abedin, 2013; Mercer, 2010).

For three years, our kids and teachers dealt with learning via Zoom. Now, they're facing a new challenge: learning how to navigate artificial intelligence in the classroom. Just as calculators didn't render arithmetic obsolete, and Wikipedia didn't erase the need for critical thinking, AI is poised to enhance--not replace--the rich tapestry of human knowledge. Just like calculators enabled students to engage in complex calculations faster and at an earlier age, generative AI can likewise advance the speed and depth of learning. Every transformative tool, from encyclopedias to calculators and the vast world of the internet, has faced its share of skepticism.

This past spring, as I closed out my 18th year of teaching, I felt anxiety that I'd never before felt at the end of a school year. By the time grades are submitted and signs of summer arrive, teachers are typically able to breathe for the first time in nine months. Instead of the relaxation, joy, and accomplishment that typically awaits the end of an academic year, I was consumed with worry that this might be the last time in a nearly two-decade career that I taught a class without having to worry about AI. I get it–AI has technically been around forever, and natural language processing tools such as OpenAI's ChatGPT are built on decades of research. Anyone who has used spellcheck or language translation apps or heard a spoken text message has used language processing tools driven by AI technology.

Meet Sylvia Christie, our education partnerships manager who's played a leading role in expanding our scholarship programme, which has just celebrated its five-year anniversary. Before DeepMind, I worked for a social purpose startup that increased access to mental healthcare. Then I got a job at a university alongside academics and students. At that point, I realised I was looking for a'Goldilocks' role that brought together everything I loved about these different environments – the speed and excitement of a tech startup, impact-focussed goals, and the fascination of working with brilliant researchers. It seemed impossible to combine all these things.

A brand-new pilot programme about machine learning (ML) and artificial intelligence (AI) will be expanding in the coming academic year in secondary schools - it's free to participate and will be available online. The programme was rolled out to schools all across the country, and reached over 8,000 students - including 25 transition year students in Boherbue Comprehensive School in North Cork. It allowed students to experience the world of self-driving cars, manufacturing robots and learn about jobs of the future. This new expansion will allow new students in the upcoming academic year to participate, for free, and experience the cutting-edge world of technology. The AI module was developed by Joyce Mahon, a PhD student at UCD - with the support of Huawei Ireland - and wanted to roll out the programme over the online learning platform CSLINC for the next school year.

Artificial intelligence offers new opportunities to improve university education. This is demonstrated by the Learning Intelligent System (LIS) project, which has been developed by researchers at the Universitat Oberta de Catalunya (UOC) with backing from the eLearning Innovation Center. The system was created by a transdisciplinary research team at the UOC and has already produced excellent results over the past year. It shows how an automatic system can be used to help students who are at risk of failing or dropping out to improve their academic performance. In 2021, a team from the UOC's Faculty of Computer Science, Multimedia and Telecommunications published a study in the International Journal of Educational Technology in Higher Education (ETHE) on the ability of LIS to successfully identify students at risk of failing a course.

The events of the past year have left different marks on everyone's lives. For some of us in Texas, these marks are especially deep. We have not only experienced the heartache of the COVID-19 pandemic but also the worst winter storm in almost a century. To say the least, the 2020–21 academic year was a very memorable one for all of us. Both the pandemic and big freeze -- and its subsequent statewide power and water outages -- disrupted our lives, but we adapted, both on and off campus.