A comparative study of mental health literacy in university students in Czechia and China

This study compares the mental health literacy (MHL) of university students in China and the Czech Republic. MHL refers to the ability to recognize mental health problems, adjust one’s mental state and seek professional assistance. The study recruited 358 Chinese university students (244 female and 114 male) and 282 Czech university students (247 female and 35 male) and collected data through online questionnaires using the O’Connor’s MHL Scale. The results indicated that Czech students had a significantly higher level of MHL compared with Chinese students based on the total score and other subscales. The findings of this study emphasize the importance of MHL on a global scale and the potential of cross-cultural comparisons to promote MHL and improve mental health outcomes. The disparity in MHL between the two countries highlights the need for increased mental health education and resources in China. Further research is needed to explore the cultural and educational factors contributing to the difference in MHL between China and the Czech Republic

Modelling student online behaviour in a virtual learning environment

In recent years, distance education has enjoyed a major boom. Much work at The Open University (OU) has focused on improving retention rates in these modules by providing timely support to students who are at risk of failing the module. In this paper we explore methods for analysing student activity in online virtual learning environment (VLE) - General Unary Hypotheses Automaton (GUHA) and Markov chain-based analysis - and we explain how this analysis can be relevant for module tutors and other student support staff. We show that both methods are a valid approach to modelling student activities. An advantage of the Markov chain-based approach is in its graphical output and in the possibility to model time dependencies of the student activities. Drahomira Herrmannova,Lucie Vachova,Jakub Kuzilek,Zdenek Zdrahal,Annika Wolf

Modelling student online behaviour in a virtual learning environment

In recent years, distance education has enjoyed a major boom. Much work at The Open University (OU) has focused on improving retention rates in these modules by providing timely support to students who are at risk of failing the module. In this paper we explore methods for analysing student activity in online virtual learning environment (VLE) -- General Unary Hypotheses Automaton (GUHA) and Markov chain-based analysis -- and we explain how this analysis can be relevant for module tutors and other student support staff. We show that both methods are a valid approach to modelling student activities. An advantage of the Markov chain-based approach is in its graphical output and in the possibility to model time dependencies of the student activities.Comment: In Proceedings of the 2014 Workshop on Learning Analytics and Machine Learning at the 2014 International Conference on Learning Analytics and Knowledge (LAK 2014