Two noteworthy innovations, automated qualitative feedback and automated grading, are transforming the way educators evaluate student performance.
Qualitative feedback differs from quantitative, also when an AI is involved. Increasingly, students and teachers alike have expressed concern about the lack of time educational institutions have for providing qualitative feedback. Qualitative feedback goes beyond a numerical grade and is designed to provide students with constructive and tangible ways for improvement. We work with the following definition of qualitative automated feedback:
Qualitative automated feedback is a technology-driven process that provides detailed and nuanced assessments of a student’s performance, focusing on specific aspects such as language proficiency, argument strength, tone, and other qualitative elements, to support their learning and skill development.
Qualitative automated feedback is a technology-driven process that provides detailed and nuanced assessments of a student’s performance, focusing on specific aspects such as language proficiency, argument strength, tone, and other qualitative elements, to support their learning and skill development.
We are dedicating our time to a series addressing feedback. In this first post, the focus is on understanding different aspects and potential use cases for both types of automated feedback, whereas we will dive deeper into the much more unexplored topic of qualitative automated feedback in the subsequent entries.
Understanding the Basics
First, let’s explore the two types of feedback in greater detail:
Automated grading, often associated with multiple-choice or objective-type assessments, involves the use of edtech software to assess the correctness of the provided answers. It is an efficient way to quickly evaluate and score assignments, particularly in subjects where answers are binary, such as mathematics.
Automated Qualitative Feedback: Automated feedback, on the other hand, goes beyond assigning scores. It includes providing students with personalized comments and guidance on their work. This feedback is more tailored to individual learning needs and helps students understand not just whether they got an answer right or wrong but why. Areas where Automated Qualitative Feedback is useful are, for example, Speech Analysis, Punctuation and Grammar, Persuasion and Argumentation, Tone of Voice and Clarity and Coherence. In the future, AI will be able to provide this type of feedback for both written and spoken assignments.
While Automated Grading is excellent for straightforward assessments, it may lack the personal touch that students often need to foster academic and personal development.
While Automated Grading is excellent for straightforward assessments, it may lack the personal touch that students often need to foster academic and personal development. Automated feedback, however, can offer a deeper level of personalization, helping students pinpoint their strengths and weaknesses and providing them with a clearer path for improvement. Automated Qualitative Feedback is not designed to replace feedback from teachers, but to fill gaps where not enough feedback is provided.
Automated grading is highly efficient, especially when dealing with a large number of assignments.
Automated grading is highly efficient, especially when dealing with a large number of assignments. It can save educators a significant amount of time. However, relying solely on automated grading may neglect the opportunity for students to learn from their mistakes.
Automated Qualitative Feedback, contributes to learning enhancement. It fosters a growth mindset, as students receive constructive insights on how to improve.
Automated Qualitative Feedback, contributes to learning enhancement. It fosters a growth mindset, as students receive constructive insights on how to improve. The ideal approach often involves a combination of both automated grading and feedback.
The Role of AI in Automated Feedback in the Future of Learning
AI plays a vital role in the generation of automated qualitative feedback, and we have been experimenting in this arena at CanopyLAB for the past two years. We believe in the future AI-driven systems can analyze students’ work and provide specific, actionable comments and feedback for improvement, in fact, we are building a student-centered application for our products that can do just that at present. To shed a bit more light on our areas of focus, it is functionalities that enable a range of qualitative features, such as highlighting errors and suggesting corrections, and offering personalized additional resources for further learning. These approaches must cater to different learning styles and provide a richer feedback experience to be future-proof.
While automated grading is a powerful tool for quick assessment, automated qualitative feedback takes it a step further, offering personalization, constructive insights, and opportunities for growth.
In conclusion, the debate between automated feedback and automated grading is not about choosing one over the other but finding the right balance between efficiency and learning enhancement. While automated grading is a powerful tool for quick assessment, automated qualitative feedback takes it a step further, offering personalization, constructive insights, and opportunities for growth. As technology continues to evolve, it’s important for educators and educational institutions to adapt their assessment strategies to ensure that both aspects are incorporated effectively, ultimately promoting better learning outcomes for students. The key is to embrace the possibilities offered by these technologies and use them to create a more supportive and effective learning environment.
