EdTech | Artificial Intelligence | Machinelearning
One of the unexplored areas in EdTech is grading. Grading is expected to be the basis for some of the Key AI based transformational usecases. One of the recent survey states that $6 Billion is the estimated total market value of AI in education by 2024. That is not too far, and AI is becoming increasingly part of education industry. Educational institutions are in a race to catch up the trend and welcome the change AI is bringing to entire education industry.
According to Forbes.com, not only is education being transformed as far as science, technology, engineering, and math (STEM) curricula, but the education industry as a whole is being transformed by AI. Increasingly, educational institutions from elementary to higher education as well as adult and professional learning are being transformed by intelligent systems that are helping humans learn better and achieve their learning objectives.
What is hodgepodge Grading all about? Educational institutions today have aligned themselves towards standardized curriculums and assessments. What about grading practices? A question that is being asked time to time. Though grading is a key responsibility there are challenges of determining which academic vs non-academic factors representing student’s achievement.
According to Wikibooks.org, Teachers often use “hodgepodge grading,” i.e., a combination of achievement, effort, growth, attitude or class conduct, homework, and class participation. A survey of over 8500 middle and high school students in Virginia supported the hodgepodge practices commonly used by their teachers (Cross & Frary, 1999). According to Virginia Commonwealth University (VCU) 2011 grading related theses, measurement theory experts recommend that achievement factors should be the only factors that determine student grades, the results of the study conducted indicates that teachers use a mixture of variables in determining student grades, known as hodgepodge grading.
The purpose of this article is to leave the readers with thoughts of goodness that AI and Machinelearning will bring to education industry and includes grading practices as well, which calls for transforming traditional way of grading for sure. This is an initiative towards visualizing a future where innovative ways are being used for grading and AI/ML is one among them…
- AI based grading will clearly communicate the process, method in which grading is being done to student and teachers’ community. This is one of eight recommendations made by measurement experts in terms of grading practices that were found to be consistent in various survey.
- AI based grading does not consider students attitude, levels of interest in the subject and personalities. This is again one of the eight recommendations made by measurement experts as well.
- AI based grading considers written form of tests as primary means of measuring achievement and However it also provides flexibility to configure measurement parameters which can be academic and non-academic aspects to certain levels.
- AI based grading system provides teachers with the huge insights and very useful feedback to them which is possible with the machinelearning techniques.
- AI based grading system provides teachers on the focus areas per class/per student/per subject without much effort using the data where they get meaningful analytics which can be used for upskilling teachers themselves in certain areas and when followed will provide a different level of high-end results.
- Students and teacher’s performance assessments can include AI based grading results.
- AI based system does not consider the fact that achievement should be the sole ingredient in determining grades. The system does help and provide useful configurable parameters or measurements which can be used to get overall achievement factors and not just grades.
- The grading systems can adopt to new policies.
- Using the AI based system one can be rest assured that amount of grading data gathered over time will be a great asset to reflect enough evidence on performance of each student.
Finally, there are in total 3 key recommendations which we found interesting in the VCU theses, which we refer here for the readers benefits.
Recommendation A: States that research is needed to determine why a disparity exists between grading recommendations and practitioners and to determine how to narrow the gap. Teachers do not solely rely on student achievement grades and they also rely on a variety of factors. It may be seen that grading recommendations followed today do not relate to the practical use in the classroom.
Recommendation B: States that further research is needed in the area of how to report grades that serve as a multipurpose tool which includes both academic and non-academic factors. Developing a student reporting method that produces a comprehensive picture about student performance, ranging from achievement grades to student behaviours, such as effort, motivation, and responsibility, may alleviate the teachers’ need to average student achievement grades with varying non-achievement factors. This may provide big picture to students, parents, and teachers about what a student grades represent.
Recommendation C: States that future research to determine the effectiveness of teacher training or professional development opportunities to help teachers meet the challenge of grading.
In conclusion quoting from Zoeckler, 2007 For grades to be interpreted with accurate understanding, the grade requires an understanding from both the student receiving the grade and the teacher assigning the grade. AI based grading can further provide the solution to the above recommendations and transform Hodgepodge grading defining a new age of grading.
In the not too distant future, you can expect that AI and machine learning will be a core part of all educational experiences. AI is starting to show its benefits and application to a wide range of educational needs, and the hope is that it will greatly improve overall learning outcomes for all – Forbes.com