Remote Learning and Digital Assessments 

The COVID-19 pandemic abruptly pushed education into the digital realm, making remote learning the new normal and accelerating the adoption of digital assessments. But what does this digital shift mean for teachers, students, and the educational landscape? This article delves into the transformative power of digital assessments in shaping the future of remote learning.

Remote learning is far from a new concept; however, the COVID-19 pandemic acted as a catalyst, forcing educational institutions to adapt and optimize digital platforms quickly. From being a choice, remote learning has evolved into a necessity, birthing innovative teaching methods like blended learning that marry traditional classrooms with online instruction.

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Using AI for Auto-Marking of Assessment

Artificial intelligence (AI) has become a key player in various aspects of our lives, including the education industry. One intriguing application of AI is in the auto-marking of assessments. By leveraging the capabilities of AI, educators can save time and effort while ensuring that students receive accurate and precise feedback on their performance.

Auto-marking through AI provides a reliable, efficient, and impartial way to evaluate student submissions. This approach eliminates human error and bias from grading, resulting in objective and consistent student evaluation. Additionally, AI-assisted marking enables teachers to spend less time on administrative tasks and more on personalised teaching and student interaction.

With the continuous advancement in AI research, the potential for more sophisticated auto-marking algorithms is limitless. As educators increasingly explore these technologies, they lay the foundation for a more efficient and equitable learning environment.

AI-assisted grading – is this the future?

Understanding AI’s Role in Assessment

AI-powered systems can learn from data, meaning that they can be trained to understand specific marking criteria and effectively grade student work based on this understanding. Some popular applications of AI in assessment include:

  • Natural Language Processing (NLP): Utilised for evaluating language-based assignments such as essays or tests, AI can analyse the structure, coherence, and quality of a student’s response.
  • Multiple-choice question grading: AI can auto-mark multiple-choice tests, increasing the efficiency of the grading process and potentially reducing human error.
  • Plagiarism detection: By identifying patterns and text similarities, AI can effectively screen submissions for potential instances of plagiarism.

The benefits of using AI in assessment are numerous:

  • Increased efficiency: AI-powered systems can process large amounts of data much quicker than a human marker, providing students with faster feedback.
  • Reduced workload for educators: By automating the marking process, teachers can dedicate more time and energy to other aspects of teaching and learning.
  • Consistent and unbiased grading: AI can help to eliminate the subjectivity sometimes associated with human markers, ensuring that each student’s work is assessed fairly and consistently.

However, AI’s role in assessment also comes with some potential drawbacks:

  • Lack of human touch: While AI is effective at assessing responses by following pre-defined criteria, it cannot truly understand the nuances and complexities of human creative work, nor can it provide personalised feedback.
  • Potential biases in data: AI algorithms are only as unbiased and effective as the data they are trained on. As such, care must be taken to ensure the training data is accurate and representative of the desired assessment criteria.
  • Technical challenges: Implementing AI-assisted assessment may require a significant investment in infrastructure, training, and support for educators and students.

Integrating AI into the assessment process is a promising avenue for enhancing efficiency and reducing educator workload. However, attention must be given to addressing its limitations and ensuring that systems are accurate and fair.

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Assessment in Foundation Phase Coding and Robotics

The CAPS Coding and Robotics curriculum was released a while back by the DBE. While still waiting for the final version to be signed off by Umalusi, many schools have already implemented it as a subject. I took a bash at explaining the assessment requirements below, providing some examples from material I’ve developed.

CAPS Foundation Phase Assessment Principles 

Let’s take one step back and look at the principles behind assessment at the Foundation Phase. According to the CAPS curriculum document, assessment is a continuous planned process involving four steps:

  • Generating and collecting evidence of achievement
  • Evaluating the evidence
  • Recording the findings
  • Using the information to understand and assist learner development

The CAPS curriculum emphasizes that assessment should include both informal (Assessment for Learning) and formal (Assessment of Learning) methods. Regular feedback should be provided to learners to enhance their learning experience.

Assessment in Foundation Phase Coding and Robotics

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