GPT Making higher education affordable, personalised, inclusive, and reflective
In this post, we discuss how prompts and GPT could play a role in education coupled with metacognition and the inverse bloom’s taxonomy. The views are based on my teaching at the University of Oxford but are my individual / personal perspective. Based on this approach, we could potentially achieve goals of personalization and scale in learning and assessment. More interestingly, they take us back to a much older, reflective style of teaching and learning characterised by the Oxford tutorial. Comments welcome - especially from other educators. I am also developing this idea at our start-up Salooki. Other major universities such as Harvard University are also adopting GPT/LLMs in the classroom - such Harvard for their flagship coding course - Computer Science 50: Introduction to Computer Science. I am keen to discuss how other educators are adopting Generative AI in higher education.
Exploring Responsible Data Analytics - Empowering Students for a Data-Driven Future
In today's data-driven world, organizations rely heavily on big data for crucial insights and business solutions. With the advancements in machine learning and artificial intelligence, data analytics has become an essential competency for organizations' viability. However, the use of big data and data analytics also raises ethical questions regarding privacy, surveillance, and responsible decision-making. Recognizing the significance of these issues, Radboud University has revamped its Business Administration Master’s elective course, "Data-driven analytics for responsible business solutions (DARBS)," to equip students with the necessary knowledge and skills to navigate the complexities of the data-driven landscape.Cross the Hurdles: Innovate in the Classroom with Power Platform
More and more we see teachers implementing the Power Platform tools to teach students future-ready skills, innovate the classroom, and decrease the gap between the university and the workforce. But in our endeavor to teach low-code skills, there are some hurdles on the track on the way to the finish. How can we identify and cross those?Learn how to develop Java applications and services with Microsoft technologies
Learn how to develop Java applications and services with Microsoft technologies. Join the developer Java Webinar on 23rd Nov 2022 and learn all about Microsoft resources at Microsoft for Java Developers | Microsoft LearnMicrosoft Learn Catalog API - Blended Learning and LMS Integration
Microsoft Learn is the primary entry point for training on all Microsoft technologies. Free, guided micro-learning, by product, service, job-role, or title Role-based learning paths to prepare you for certification. Free accounts allow you to easily test-drive products and services within a protected sandbox
Students viewing feedback before teacher hits "return"
Hi I have just tested the functionality and as some other people have identified, students can view feedback as and when teachers add comments or highlight the attached documents in an assignment. As a teacher, there are times when this is okay, but other times when it would make Teams unsuitable. When students submit exams or coursework teachers need the ability to edit and provide feedback within the document (by adding comments, or highlighting) in draft mode, before it is visible to the students. Since this has been an identified issue since 2018, as far as I can see from previous comments, I was hoping that there is a workaround or a setting that can be applied to achieve this. Please confirm. Best wishes, GarethIntroduction to Auto Assessment and Grading with PyBryt for Python
We continuously interact with computerized systems to achieve goals and perform tasks in our personal and professional lives. Therefore, the ability to program such systems is a skill needed by everyone. Consequently, computational thinking skills are essential for everyone, which creates a challenge for the educational system to teach these skills at scale and allow students to practice these skills. To address this challenge, we present a novel approach to providing formative feedback to students on programming assignments. Our approach uses dynamic evaluation to trace intermediate results generated by student’s code and compares them to the reference implementation provided by their teachers. We have implemented this method as a Python library and demonstrate its use to give students relevant feedback on their work while allowing teachers to challenge their students’ computational thinking skills
