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.
I’m fascinated by how artificial intelligence (AI) revolutionises various aspects of life, and educational assessment is no exception. By incorporating AI into assessment processes, educational institutions can potentially benefit from enhanced accuracy and efficiency in evaluating and selecting candidates. With the integration of AI, assessment tasks can be generated, and student work automatically scored, significantly offloading tasks from the teacher to AI. But is this a good thing? Let’s look at this in more detail.
AI has made significant advancements in recent years, and its applications in education are continually expanding. One of the areas where AI can make an impact is student assessment. AI-based assessment systems utilise machine learning algorithms and natural language processing to evaluate student performance. Two primary AI-based assessment methods are rules-based and machine learning-based assessments. Rules-based AI follows pre-defined criteria to evaluate performance, while machine learning-based AI adapts dynamically, learning from data and improving its accuracy over time. These systems can assess various skills, from knowledge recall to problem-solving and critical thinking abilities. By automating the evaluation process, AI can save time for educators and provide real-time feedback to students, allowing them to adjust their learning strategies accordingly.
AI in assessment offers numerous potential benefits, including:
Adaptive testing: AI can adjust the difficulty of assessment items based on a student’s performance, providing a tailored testing experience that accurately measures their abilities.
Automatic grading: Machine learning algorithms can evaluate open-ended responses, such as essays and short answers, reducing the workload for educators.
Objective scoring: AI helps maintain a consistent scoring standard, eliminating human bias and subjectivity from the evaluation process.
Data-driven insights: Assessment data collected by AI systems can be used to identify patterns and trends, allowing educators to make more informed decisions about their instruction.
Assessment for learning (AfL) is a teaching approach that focuses on generating feedback for students to improve their performance. Rather than solely concentrating on results, AfL emphasises assessment processes that actively help students make progress in their learning journey. This methodology encourages learners to become more involved in the learning process, resulting in increased confidence in their understanding of the material and the expected achievement standards.
By prioritising assessment for learning, educators can initiate improvements in teaching and learning experiences, fostering an environment where students and instructors work together to facilitate the growth and transformation of knowledge and skills.