# Modeling E-Behaviour, Personality and Academic Performance with Machine Learning
###### Abstract  
The analysis of student performance involves data modelling that enables the formulation of hypotheses and insights about student behaviour and personality. We extract online behaviours as proxies to Extraversion and Conscientiousness, which have been proven to correlate with academic performance. The proxies of personalities we obtain yield significant (\(p<0.05\)) population correlation coefficients for traits against grade--0.846 for Extraversion and 0.319 for Conscientiousness. Furthermore, we demonstrate that a student's e-behaviour and personality can be used with deep learning (LSTM) to predict and forecast whether a student is at risk of failing the year. Machine learning procedures followed in this report provide a methodology to timeously identify students who are likely to become at risk of poor academic performance. Using engineered online behaviour and personality features, we obtain a Cohen's Kappa Coefficient (\(\kappa\)) of students at risk of 0.51. Lastly, we show that we can design an intervention process using machine learning that supplements the existing performance analysis and intervention methods. The methodology presented in this article provides metrics that measure the factors that affect student performance and complement the existing performance evaluation and intervention systems in education. e-behaviour; big five personality; student performance Article  
Working, Teaching  
Traditional approaches to revealing relationships between student behaviour, personality and performance include questionnaires, surveys and interviews. The reliability of a questionnaire would depend on the contextual framework for the research and the metric construct used for outcomes. However, respondents' biases from the above qualitative methods of data collection can compromise the accuracy of their responses [3; 4; 5]. Furthermore, it has not proven easy to measure the reliability of an opinion [6], especially for each individual in a population. We address the self-reporting problems using unobtrusive and automated approaches that measure how students behave rather than how they think they behave. For instance, instead of asking, 'In how many weekly online discussions do you participate? ', we instead obtain the exact number of discussions from an LMS register. The models developed in this research use quantitative metrics to proxy behaviour and personality traits traditionally obtainable from surveys. These metrics are used to draw correlations with features later used to predict student performance. From e-behaviour and personality, we modelled an intervention framework that supplements current student intervention systems. We extract behavioural insights linked to two of the five personality traits in the Big Five personality model through a quantitative analysis--Conscientiousness and Extraversion. We use statistical metrics to extract forum and login behaviours, respectively. We define the relationships between these metrics and online behaviours, detailing the relationships between a student's expressions of personality traits through their behaviours. Through this research, we:  
1. Define a framework for personality traits and behaviour in the context of student online engagement;
2. Show the relationship between [PERSON]'s Three Forms of Capital and academic performance;
3. Show the relationship between personalities and academic performance through e-behaviours;
4. Show that we can use e-behaviour and machine learning to predict student performance;
5. Highlight the importance of the explainability of modelled personality traits and e-behaviours. This work contributes to the prediction of student outcomes using online behaviours in the following ways:  
* We present a framework and methodology for arriving at predictive models for student performance starting with personality traits. These traits are the drivers of online behaviours that generate features that are predictive of performance.