Developing Novel Machine Learning Techniques to Improve Comparative Judgements for e-Learning and e-Assessment

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There is a wide selection of aims we could examine under this project:1) To understand how machine learning (ML) can function as a support for an educator and not a strait jacket. Key questions to ask:Are different measures relevant to different teachers? How do they interpret the data and use it to guide their teaching?2) To understand how we can gather large volumes of quantifiable data about qualitative assessments allowing machine learning (ML) approaches to work on the data.3) What design methods are suitable to use in order to engage with students and teachers in this complex design process. Key questions are:How do we express possibilities for different insights that we can gather from the data?How do we capture ideas about more nebulous elements of ML like privacy and ensure ongoing consent for data collection?4) To identify appropriate ML methods and develop a complete ML framework for analysing qualitative assessments.5) To actively query a small subset of all submissions to learn what attributes constitute excellent quality, taking an interactive approach with the educators to improve our proposedframework's performance.6) To validate and make the developed tool useable in the real-world.We aim to redress the imbalance caused by automated marking tools promoting specific approaches to assessment. We will develop a decision support tool for educators assessing qualitative work that will make such assessments more attractive by increasing the educators understanding of the student cohorts' work and, potentially, reducing the amount of time they need to spend marking it. A comparative judgement framework [1] will be developed to allow educators to understand how changes in practice between different cohorts' impact on assessments. If the comparative judgement framework can show reliable performance at the cohort level, we will explore how it can be applied to individual qualitative assignments to support educators' assessments of them. Year One - Identifying assessments: Qualitative assessments are subjective and learning about good and bad practices autonomously challenging. To address this, the student will survey teaching practitioners to identify the most important types of qualitative assessments, and key learning outcomes in those assessments. We will also work with educators and students to understand their attitudes to automated assessment of their work, their concerns about the tool and the ways in which they can be reassured of the validity of such assessments following Value Sensitive Design approaches [2]. This will narrow down thescope of the tool and be used to create a proof-of-concept.Year One and Two - Learning knowns and identifying gaps in knowledge: CDSM has awealth of data on submissions and their respective grades. At this stage, we aim to learn from this data to identify key features within the scope identified in the previous step. Here, we will first focus on finding meaningful structures in the dataset from a semantic perspective [3], potentially utilising a supervised approach (using existing grades) and some form of contextual embeddings [4]. These will help derive insight into what makes a submission address specificlearning outcomes. Given we use a probabilistic model, we can deduce which submissions our model has low confidence (or high uncertainty in predictions) about: this shows the gaps in our knowledge, which only a human practitioner can help fill.Year Three - Interactively improving models: develop an interactive visual system to display insights into cohort data and individual submissions and allow educations practitioners to respond to two key questions about a submission:Were our model predictions correct? How would they rate a given assignment?These submissions will be carefully picked via an active learning strategy where we select submission that we are unsure about or that are likely to improve our model to provide better predictions.