As part of my PhD research, I investigate the factors of student success and the influence of the use of social media by first year students in higher education. For this I use the insights provided by the highly influential and leading integration theory of Tinto and diminished the amount of variables by only using the best predictive ones. Hereby, avoiding the capitalization of chance and establishing a more easy to use model for teachers and management. Furthermore, I enriched the model with the use of social media, in particular Facebook, to better suit students’ contemporary society in the developed world. Principal component analysis on Facebook usage provided different integration/engagement components, which I coined peer-engagement and knowledge engagement. Both consisted of various purposes of Facebook use (information, education, social and leisure) and the use of different pages amongst students. To uncover if these latent variables play a significant role in student success or if Facebook is a multi-distracting platform, two models were compared using structural equation modeling with SPSS AMOS; one with and one without the peer-, and knowledge engagement variables. The fit of both models are compared using the normed fit index (NFI), the comparative fit index (CFI), the Tucker-Lewis Index (TLI) and the root mean square error of approximation (RMSEA). In addition, the direct influence and indirect influence of all variables are compared to provide a better insight into what kind of influence social media can have upon student success.
Flipping the Classroom is hot in onderwijsland, iedereen praat erover en veel docenten zijn er al mee aan de slag gegaan. Maar wat is Flipping the Classroom nu eigenlijk ? Wat is de relatie met de taxonomie van Bloom? En waar moet je allemaal aan denken als je als docent aan de slag wil met Flipping the Classroom?
In the past decades, we have faced an increase in the digitization, digitalization, and digital transformation of our work and daily life. Breakthroughs of digital technologies in fields such as artificial intelligence, telecommunications, and data science bring solutions for large societal questions but also pose a new challenge: how to equip our (future)workforce with the necessary digital skills, knowledge, and mindset to respond to and drive digital transformation?Developing and supporting our human capital is paramount and failure to do so may leave us behind on individual (digital divide), organizational (economic disadvantages), and societal level (failure in addressing grand societal challenges). Digital transformation necessitates continuous learning approaches and scaffolding of interdisciplinary collaboration and innovation practices that match complex real-world problems. Research and industry have advocated for setting up learning communities as a space in which (future) professionals of different backgrounds can work, learn, and innovate together. However, insights into how and under which circumstances learning communities contribute to accelerated learning and innovation for digital transformation are lacking. In this project, we will study 13 existing and developing learning communities that work on challenges related to digital transformation to understand their working mechanisms. We will develop a wide variety of methods and tools to support learning communities and integrate these in a Learning Communities Incubator. These insights, methods and tools will result in more effective learning communities that will eventually (a) increase the potential of human capital to innovate and (b) accelerate the innovation for digital transformation
