The objective of the article is to identify the reference group of countries with similar Covid strategies and other groups with their performance success, and to construct a composite Covid Mitigation Index for comparative purposes, thus, implying how to redesign the strategic policies. Research Design & Methods: Gaussian Mixture Modelling and Factor Analysis: the main design is quantitative, using Gaussian Mixture Modelling to find the optimal number of country clusters, and Factor Analysis with Principal Axis Factoring (FA-PAF) to build a composite index of governmental policies. Data includes eight mitigation policy variables and three supporting economic policy variables. Data are aggregated to form three periods and the cluster changes are identified by Gaussian Mixture Modelling. Then, the Covid Mitigation Index (CMI) is constructed by FA-PAF to obtain a comparative measure over the periods and the country clusters. The results were obtained by means of R studio and SPSS. Findings: The dynamic clustering leads to a decreasing number of clusters from nine clusters in the first period (January-February 2020), four clusters in the second period (March-April 2020), and two clusters in the third period (May-June 2020). In the first period, China (with CMI=48) took serious actions forming its own cluster, while 11 other countries (with CMI>10), e.g., early affected European countries such as Italy and Spain and large Asian countries such India and Indonesia, took moderate actions. In the second period all cluster averages were greater than China's in the first period, i.e., most world countries were dedicated to fight Covid-19. In Europe, Italy, San Marino and France showed the highest CMI values, similarly to Iraq and Palestine in the Middle East, Peru and Honduras in the Latin America, and China, India and Indonesia in Asia. In the third period, cluster averages showed even tighter policies even though 42 countries had lower CMI values than previously. Implications & Recommendations:The approach provided a big picture for decision makers both in business and in governments. The key idea was to reveal reference groups of countries which help governmental actors to design and adapt their strategies over time by learning by their own experience and the results of the better performing clusters. It was suggested that a multi-criteria approach accounting for individual government's preferences over health and economy is used along with the presented approach. Contribution & Value Added: Clustering with Gaussian Mixture Models and factor analysis based on Principal Axis Factoring for composite-index building were used. The methods are well-established, but they were applied in a novel way dynamically over time and for the composite CMI. CMI was built on two factors which identified the structure of mitigation policies and economic policies. The development of governmental polices over the first cycle of Covid-19 pandemic was described. Article type:research article

The aim of this case study was to better understand how TPACK can be developed through constructionist activities. Accordingly, a course was designed and a purposively sampled group was selected for collecting in-depth data. The findings of the study demonstrated that teachers' knowledge and conception of using technology for teaching developed in three levels. With each level, there was an improvement of teachers' TPACK and its components as a result of performing constructionist activities. First level was limited to usage of technology for exhibiting curriculum information. In the second level, the participants focused on using technology to present content and materials. The results indicated that in this level although two components of the participants' TPACK, viz. TCK and PCK developed, considering technology as a learning tool (TPK) appeared to be missing. However, in the third level they developed the ability to use technology for enhancing teaching and learning. The result of the study highlighted inter and intra group interactions and learning-through-making as two aspects of constructionist activities that were more influential in the development of TPACK.

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With the extraordinary growth of both mis- and disinformation, it has become even more vital to prepare content to provide accurate information so that users receive concepts correctly. With the spread of the Internet and the Web 2.0, websites are responsible for publishing information. While most attention is on the advanced technology and graphics to attract users, many studies have indicated that users are not satisfied with the content of many websites. Hence, this paper introduces Technological Pedagogical Content Design (TPCD) model for explaining the concept of integration of pedagogy into content and technology. Further, practical instruction of using the TPCD model for designing digital services, especially websites is suggested. TPCD has fundamentally employed the concept of the Technological Pedagogical Content Knowledge (TPACK), which is a well-known framework in the field of teacher education to increase the teachers’ knowledge for integrating technology into teaching and learning. However, TPCD transforms TPC Knowledge into TPC Design as practice by integrating pedagogy into technology design in a broader context. TPCD uses human science findings to design a user-centered website with a pedagogical perspective for creating, selecting, and organizing content. Given the speed and variety of technology capabilities and the increasing content volume, TPCD helps designers identify target objectives, based on true users’ demands, and select appropriate content and suitable technological tools to increase desirability, usability, accessibility, and findability of information. Further, technical research on TPCD may open perspectives for developing a learning content management system for educational and other digital services.

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