Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Questions with open ansrs are rarely used as e-learning assessment tools because of the resulting high workload for the teacher/tutor that should grade them. This can be mitigated by having students grade each other's ansrs, but the uncertainty on the quality of the resulting grades could be high. In our OpenAnsr system  have modeled peer-assessment as a Bayesian network connecting a set of sub-networks (each representing a participating student) to the corresponding ansrs of her graded peers. The model has shown good ability to predict (without further info from the teacher) the exact teacher mark and a very good ability to predict it within 1 mark from the right one (ground truth). From the available datasets  noticed that different teachers sometimes disagree in their assessment of the same ansr. For this reason in this paper  explore how the model can be tailored to the specific teacher to improve its prediction ability. To this aim,  parametrically define the CPTs (Conditional Probability Tables) describing the probabilistic dependence of a Bayesian variable from others in the modeled network, and  optimize the parameters generating the CPTs to obtain the smallest average difference beten the predicted grades and the teacher's marks (ground truth). The optimization is carried out separately with respect to each teacher available in our datasets, or respect to the whole datasets. The paper discusses the results and shows that the prediction performance of our model, when optimized separately for each teacher, improves against the case in which our model is globally optimized respect to the whole dataset, which in turn improves against the predictions of the raw peer-assessment. The improved prediction would allow us to use OpenAnsr, without teacher intervention, as a class monitoring and diagnostic tool.
['Maria De Marsico', 'Andrea Sterbini', 'Marco Temperini']
I am writing a research paper on a idea Modeling peer assessment as a personalized predictor of teacher's grades: The case of OpenAnswer
[{"from": "human", "value": "I am writing a research paper on a idea Modeling peer assessment as a personalized predictor of teacher's grades: The case of OpenAnswer"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Questions with open ansrs are rarely used as e-learning assessment tools because of the resulting high workload for the teacher/tutor that should grade them. This can be mitigated by having students grade each other's ansrs, but the uncertainty on the quality of the resulting grades could be high. In our OpenAnsr system  have modeled peer-assessment as a Bayesian network connecting a set of sub-networks (each representing a participating student) to the corresponding ansrs of her graded peers. The model has shown good ability to predict (without further info from the teacher) the exact teacher mark and a very good ability to predict it within 1 mark from the right one (ground truth). From the available datasets  noticed that different teachers sometimes disagree in their assessment of the same ansr. For this reason in this paper  explore how the model can be tailored to the specific teacher to improve its prediction ability. To this aim,  parametrically define the CPTs (Conditional Probability Tables) describing the probabilistic dependence of a Bayesian variable from others in the modeled network, and  optimize the parameters generating the CPTs to obtain the smallest average difference beten the predicted grades and the teacher's marks (ground truth). The optimization is carried out separately with respect to each teacher available in our datasets, or respect to the whole datasets. The paper discusses the results and shows that the prediction performance of our model, when optimized separately for each teacher, improves against the case in which our model is globally optimized respect to the whole dataset, which in turn improves against the predictions of the raw peer-assessment. The improved prediction would allow us to use OpenAnsr, without teacher intervention, as a class monitoring and diagnostic tool."}]