Recent research on the learning process has shown that students tend to learn in different ways and that they prefer to use different teaching resources as well. Many researchers agree on the fact that learning materials shouldnpsilat just reflect of the teacherpsilas style, but should be designed for all kinds of students and all kind of learning styles. Even though they agree on the importance of applying these learning styles to different learning systems, various problems still need to be solved, such as matching teaching contents with the student's learning style. In this paper, we describe the design of a personalized teaching environment that is based on an adaptive taxonomy using Felder and Silverman's learning styles and which is combined with the selection of the appropriate teaching strategy and the appropriate electronic media. Students are able to learn and to efficiently improve their learning process with such method.

Nowadays, CEOs from most of the industries investigate digitalization opportunities. Customer preferences and behavior are driving enterprise technology choices, and internal organizational change is essential to maintain focus on the customer in today's digital world. To the enterprise architect, such initiatives sound like a perfect application of Enterprise Architecture (EA). Despite the ongoing research in academia, the benefits and the role of EA management in digital context are still a topic of lively discussions, and there is a gap in research on how to leverage EA for digital transformation. This paper explores the concepts of digital transformation and EA based on selected publications drawn from the literature and discusses the potential role of EA in supporting digital transformation. We also sketch an approach based on the TOGAF framework to position EA as a problem-solving tool for digital transformation initiatives.

(2010). ICT and education: A critical role in human and social development. Information Technology for Development: Vol. 16, Special Issue: ICT Education in Development, pp. 151-158.

Purpose: The authors examine cryptocurrency market behavior using a hidden Markov model (HMM). Under the assumption that the cryptocurrency market has unobserved heterogeneity, an HMM allows us to study (1) the extent to which cryptocurrency markets shift due to interactions with social sentiment during a bull or bear market and (2) the heterogeneous pattern of cryptocurrency market behavior under these two market conditions.Design/methodology/approach: The authors advance the HMM model based on two six-month datasets (from November 2017 to April 2018 for a bull market and from December 2018 to May 2019 for a bear market) collected from Google, Twitter, the stock market and cryptocurrency trading platforms in South Korea. Social sentiment data were collected by crawling Bitcoin-related posts on Twitter.Findings: The authors highlight the reaction of the cryptocurrency market to social sentiment under a bull and a bear market and in two hidden states (an upward and a downward trend). They find: (1) social sentiment is relatively relevant during a bull compared to a bear market. (2) The cryptocurrency market in a downward state, that is, with a local decreasing trend, tends to be more responsive to positive social sentiment. (3) The market in an upward state, that is, with a local increasing trend, tends to better interact with negative social sentiment.Originality/value: The proposed HMM model contributes to a theoretically grounded understanding of how cryptocurrency markets respond to social sentiment in bull and bear markets through varied sequences adjusted for cryptocurrency market heterogeneity.