William Cope
Helen Repp.png "Rita van Haren")
Rita van Haren
Nadia Ayub
Micheal M Van Wyk
Clifford Blizard
Mellissa Rosal EspirituUbiquitous Learning and Instructional Technologies MOOC’s Updates
Opportunities and challenges in evaluating programs, platforms, or teaching approaches to enhance understanding of how computation works in relation to learning computer programming.
It is given as the coding and computational thinking so now the assessment in programming education has many ways that have a number of opportunities but also with some challenges to improve students understanding. In this context, adopting creative assessment techniques that emphasize project-based learning and act on issues of fairness and access in programming education will serve to strengthen teaching effectiveness. Assessment approaches should keep up with the pace of change in coding skills and equip students for a new era of global challenges driven by technology.
Evaluating programs and platforms designed to enhance computational understanding in programming education presents both opportunities and challenges. On the one hand, adaptive learning platforms like Code.org or Replit provide personalized feedback and scaffolded exercises, making programming more accessible to diverse learners. Additionally, block-based programming tools (e.g., Scratch) help beginners grasp computational thinking before transitioning to text-based coding.
However, challenges arise in assessing true conceptual understanding versus rote memorization of syntax. Metrics such as completion rates or quiz scores may not fully capture problem-solving skills or creativity in coding. Another challenge is ensuring equitable access, as some students may lack the resources or prior exposure to digital tools.
What evaluation methods do you think are most effective in measuring deep computational understanding in programming education?
This project focuses on developing a custom Convolutional Neural Network (CNN) to accurately identify and classify objects in satellite images. The system leverages deep learning techniques to process high-resolution imagery, enabling applications such as land use analysis, urban planning, disaster management, and environmental monitoring. The model is trained on labeled satellite datasets to detect features like buildings, roads, water bodies, vegetation, and other objects with high precision and efficiency.
Suggested Changes in Teaching Methods
Interactive Teaching Strategies: Educators should employ a variety of teaching methods that include not just traditional lectures but also hands-on activities, pair programming, and project-based learning. These strategies help students connect theoretical concepts with practical applications.
Emphasis on Real-World Applications: Teachers can enhance learning by incorporating real-world coding projects that resonate with students’ interests. For example, building a simple mobile app or website can demonstrate the relevance of coding skills in everyday life and can motivate students to dive deeper into the subject matter.
Comprehensive Assessment Approaches: Assessment methods need to shift away from standardized testing and towards portfolios that showcase a student’s coding projects and collaborative efforts. Rubrics that assess creativity, problem-solving skills, and the ability to work collaboratively can provide a more holistic view of a student’s abilities.