The security of online assessments is a major concern due to widespread cheating. One common form of cheating is impersonation, where students invite unauthorized persons to take assessments on their behalf. Several techniques exist to handle impersonation. Some researchers recommend use of integrity policy, but communicating the policy effectively to the students is a challenge. Others propose authentication methods like, password and fingerprint; they offer initial authentication but are vulnerable thereafter. Face recognition offers post-login authentication but necessitates additional hardware. Keystroke Dynamics (KD) has been used to provide post-login authentication without any additional hardware, but its use is limited to subjective assessment. In this work, we address impersonation in assessments with Multiple Choice Questions (MCQ). Our approach combines two key strategies: reinforcement of integrity policy for prevention, and keystroke-based random authentication for detection of impersonation. To the best of our knowledge, it is the first attempt to use keystroke dynamics for post-login authentication in the context of MCQ. We improve an online quiz tool for the data collection suited to our needs and use feature engineering to address the challenge of high-dimensional keystroke datasets. Using machine learning classifiers, we identify the best-performing model for authenticating the students. The results indicate that the highest accuracy (83%) is achieved by the Isolation Forest classifier. Furthermore, to validate the results, the approach is applied to Carnegie Mellon University (CMU) benchmark dataset, thereby achieving an improved accuracy of 94%. Though we also used mouse dynamics for authentication, but its subpar performance leads us to not consider it for our approach.
One of the most important societal trends affecting our workplace and workforce in the following decade concerns the combination of a smaller number of younger workers relative to their older counterparts, and the current ‘early exit’ culture in Europe. Because of the staff shortages and possible knowledge loss (e.g., Calo 2008; Joe et al. 2013) that may accompany these demographic changes, there is a strong financial reason to retain and sustain ageing employees at work (Kooij et al. 2014; Truxillo and Fraccaroli 2013). In order to respond to today’s labour market needs, many governments have chosen to increase the official retirement age to 66 or even higher. In the Netherlands, for example, retirement age will be gradually raised to 66 years in 2019 and to 67 years in 2023. Other European Union countries have similar plans to steadily raise their retirement ages to 67 years in 2023 (France), 2027 (Spain), or 2031 (Germany). In the UK and Ireland, the retirement age will increase to 68 in 2028 (Ireland) and in 2046 (the UK). However, the reality of older workers’ current employment does not yet match these political ambitions. According to figures collected by the European Union Labour Force in the European Union Labour Force Survey (Eurostat 2014), the EU-28 (i.e., average of the 28 European Union countries) employment rate for persons aged 15–64 was 64.1 per cent in 2013. However, when looking more closely at the country level or when differentiating between age categories, the active labor participation of older European employees does not appear to be as high. The EU employment rate of older workers—calculated by dividing the number of persons in employment and aged 55–64 by the total population of the same age group—was 49.5 per cent in 2013 (OECD 2014), whereas the OECD average was 54.9 per cent in the same year. In the USA and Korea, for example, employment rates of workers of 55–64 years old were, respectively, 60.9 per cent and 64.3 per cent in 2013.
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