Since the COVID-19 pandemic, tech-assisted learning has expanded from online education to every type of learning. Educators are using a variety of tools to aid their teaching workflow, enhancing efficiency and the efficacy of learning.
With technology assisting learning both in and out of the classroom in the age of AI, and students being exposed to various tools that can do everything from explaining concepts to fully generating homework, there arises a need for a fair grading system.
This system must be accurate in its evaluation of students and keep in check any issues that arise due to the active role of technology in learning. Here’s how you can create fair grading systems in tech-assisted learning.
What Makes Fair Grading in Tech-Assisted Learning Crucial?
At various levels of education, Learning Management Systems(LMS), adaptive learning platforms, and AI tools are being used to make the teaching and learning process easier and more efficient.
Tech-assisted learning can be more effective because it can help teachers provide more personalized learning and provide instant feedback to students.
For teachers, these tools can help reduce their generally overwhelmed schedule, enabling them to automate the processes that can be automated, leaving them more time to innovate their teaching methods.
However, when we delegate more teaching-related tasks to algorithms, being ethical and fair with grading becomes a critical challenge. We can no longer do without this technology to keep up with advancements and to provide teachers with the help they need.
This is why a fair grading system can ensure that educators can rely on this technology while ensuring that students are learning properly and receiving an impartial learning experience.
A Clear, Comprehensive, and Transparent Rubric For Assessment
In a fair grading system, a rubric is needed to ensure that the evaluation expectations are visible, and the same standards apply to every student. A clear rubric is crucial to ensure that it can guide both the teacher and any AI-assisted assessment tools properly
A transparent rubric can ensure that the judgment is not subjective, as the teachers have provided defined standards. It can improve consistency of grading. It can also help make grading more accountable while helping students understand the expectations of the assessment.
Metrics for Crafting the Rubric
A well-designed rubric can ensure that students are evaluated according to understandable, consistent, and relevant standards. The rubric should clearly explain the learning outcomes being assessed and the criteria used to judge the student’s work.
For each criterion, the performance level needed and the marks assigned should be clearly stated. There should also be examples of strong, satisfactory, and weak performance.
Other than instructions for how to assess, it should also state how and to what extent any technology used will contribute to the grading and feedback. The rubric should state whether students can request human review or appeal for a grade change.
The metrics for assessing any work can include things like content accuracy, analysis depth, quantity and quality of evidence provided, organization, and articulation.
Each criterion should include observable descriptions rather than vague terms. This clear rubric should be shared with the students and guardians to ensure every stakeholder is aligned.
The Role of Technology in Creating a Fair Grading System
In a tech-assisted learning environment, technology will naturally play a crucial role. One of the biggest threats to assessments is the use of AI by students, especially when the assessment output, like the assignment, exists in a digital environment.
AI has made it possible for students to generate ideas, answers, and whole assignments. AI use is limited to below 30% of the output.
To ensure that, fair grading should have an AI writing detector in the mix to discern whether the student output is authentic and a true representation of a student’s knowledge and effort.
Teachers can also use various grading tools that are available, but they must ensure two things. Firstly, they must provide a clear rubric to the grading tool. Secondly, they must ensure that they review the feedback and grade that comes from the AI system to evaluate whether the tool did a good job.
Students should be given the opportunity to challenge the grade if they feel like the assessment was not accurate.
Many of these tools come with an analytics dashboard that can help educators understand patterns of unusual grading or whether the same students are persistently getting a higher AI report, so that they can intervene with the situation and get to the root of the problem.
Dealing with Algorithmic Bias
Every AI-assisted tool learns from historical data, and hidden in it are biases that can be difficult to find. However, they do end up affecting the output we receive from them.
In automated grading systems, such biases can unintentionally end up favoring particular language styles, writing patterns, and cultural expressions.
To reduce this risk, educators need to test these grading tools by feeding them diverse samples coming from students with different linguistic, socioeconomic, cultural, and learning backgrounds.
The grading score received from the tool needs to be compared against that of a seasoned human teacher to investigate the efficacy of the tool and find any significant differences or unusual patterns.
To deal with algorithmic bias, systems will need to be continuously monitored and improved. They should be evaluated constantly, as bias can suddenly appear due to changes in data, rubric, and even software.
This is why human oversight is indispensable here, and so are clear documentation and transparent criteria.
Create a Safe Grading Workflow
A safe grading workflow should work like this. The student submits work, which is first checked on an AI detector. Once the work passes the detection, it is transferred to the grading tool and checked against the provided rubric. AI produces deep feedback and a provisional score.
The teacher reviews evidence and the AI reasoning, then confirms the grade or changes it as needed. The student receives feedback, and if the rationale doesn’t support the score, they can appeal for a change. Then the teacher can do a full review and either reject the grade or change it.
Such a grading workflow should safely support fair assessment.
Final Thoughts
Both AI tools and human evaluation should be part of a fair grading system in a tech-assisted environment. Teachers should be clear about the rubric, and careful with hidden biases. Student should try to stay authentic with their work, since it will be checked with a detection tool.
Ultimately, the system must support successful learning outcomes, while still ensuring that students receive the most accurate grade.