Extracting Core ICT Indicators Using Entropy Method

In this article a methodology is presented to extract indicators that appropriately measure the information society and the digital divide between countries and the relevant statistics that the majority of countries can collect. With the help of content analysis, the entropy method, and consideration of the diffusion aspects of digitalization, 37 reputable information society and digital divide models are analyzed to indentify “core information and communication technology (ICT) indicators.” To overcome the limitation of the nonexistence of data, the information and the knowledge embedded in information society and digital divide models are employed as proxies for experts' opinions for extracting the core ICT indicators. Comparison of the prior indicators and the proposed ones reveals that the former ignore three important dimensions: e-learning, e-government, and networked world enablers.     

Teaching algorithmic problem solving or conceptual understanding: Role of developmental level,mental capacity,and cognitive style

It has been shown previously that many students solve chemistry problems using only algorithmic strategies and do not understand the chemical concepts on which the problems are based. It is plausible to suggest that if the information is presented in differing formats, the cognitive demand of a problem changes. The main objective of this study is to investigate the degree to which cognitive variables, such as developmental level, mental capacity, and disembedding ability explain student performance on problems which: (1) could be addressed by algorithms or (2) require conceptual understanding. All conceptual problems used in this study were based on a figurative format. The results obtained show that in all four problems requiring algorithmic strategies, developmental level of the students is the best predictor of success. This could be attributed to the fact that these are basically computational problems, requiring mathematical transformations. Although all three problems requiring conceptual understanding had an important aspect in common (the figurative format), in all three the best predictor of success is a different cognitive variable. It was concluded that: (1) the ability to solve computational problems (based on algorithms) is not the major factor in predicting success in solving problems that require conceptual understanding; (2) solving problems based on algorithmic strategies requires formal operational reasoning to a certain degree; and (3) student difficulty in solving problems that require conceptual understanding could be attributed to different cognitive variables.     

Induction of alternative fate other than default neuronal fate of embryonic stem cells in a membrane-based two-chambered microbioreactor by cell-secreted BMP4

Cell-secreted soluble factor signaling in a diffusion dominant microenvironment plays an important role on early stage differentiation of pluripotent stem cells invivo. In this study, we utilized a membrane-based two-chambered microbioreactor (MB) to differentiate mouse embryonic stem cells (mESCs) in a diffusion dominant microenvironment of the top chamber while providing enough nutrient through the bottom chamber. Speculating that accumulated FGF4 in the small top chamber will augment neuronal differentiation in the MB culture, we first differentiated mESCs for 8 days by using a chemically optimized culture medium for neuronal induction. However, comparison of cellular morphology and expression of neuronal markers in the MB with that in the 6-well plate (6WP) indicated relatively lower neuronal differentiation in the MB culture. Therefore, to investigate whether microenvironment in the MB facilitates non-neuronal differentiation, we differentiated mESCs for 8 days by using chemically defined basal medium. In this case, differentiated cell morphology differed markedly between the MB and 6WP cultures: epithelial sheet-like morphology in the MB, whereas rosette morphology in the 6WP. Expression of markers from the three germ layers indicated lower neuronal but higher meso- and endo-dermal differentiation of mESCs in the MB than the 6WP culture. Moreover, among various cell-secreted soluble factors, BMP4 expression was remarkably upregulated in the MB culture. Inhibition of BMP4 signaling demonstrated that enhanced effect of upregulated BMP4 was responsible for the prominent meso- and endo-dermal differentiation in the MB. However, in the 6WP, downregulated BMP4 had a minimal influence on the differentiation behavior. Our study demonstrated utilization of a microbioreactor to modulate the effect of cell-secreted soluble factors by autoregulation and thereby inducing alternative self-capability of mESCs. Understanding and implementation of autoregulation of soluble factors similar to this study will lead to the development of robust culture systems to control ESC behavior.     

Robust simultaneous fault detection and control for a class of nonlinear stochastic switched delay systems under asynchronous switching

This paper concerns the simultaneous fault detection and control (SFDC) problem for a class of nonlinear stochastic switched systems with time-varying state delay and parameter uncertainties. The switching signal of detector/controller unit (DCU) is assumed to be with switching delay, which results in the asynchronous switching between the subsystems and DCU. By constructing a switching strategy depending on the state and switching delays, new sufficient conditions expressed by a set of linear matrix inequalities (LMIs) is derived to design DCU gains. This problem is formulated as an H_∞ optimization problem and both mean square exponential stability and fault detection of augmented system are considered. A numerical example is finally exploited to verify the effectiveness and potential of the achieved scheme.     

Understanding privacy and data protection issues in learning analytics using a systematic review

The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross-regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence-based, thereby increasing the trustworthiness of learning analytics and its usefulness.

Practitioner notes

What is already known about this topic

• Research on privacy and data protection in learning analytics has become a recognised challenge that hinders the further expansion of learning analytics.
• Proposals to counter the privacy and data protection issues in learning analytics are blurry; there is a lack of a summary of previously proposed solutions.

What this study contributes

• Establishment of what privacy and data protection issues exist at different phases of the learning analytics cycle.
• Identification of how different stakeholders view privacy, similarities and differences, and what factors influence their views.
• Evaluation and comparison of previously proposed solutions that attempt to address privacy and data protection in learning analytics.

Implications for practice and/or policy

• Privacy and data protection issues need to be viewed in the context of the entire cycle of learning analytics.
• Stakeholder views on privacy and data protection in learning analytics have commonalities across contexts and differences that can arise within the same context. Before implementing learning analytics, targeted research should be conducted with stakeholders.
• Solutions that attempt to address privacy and data protection issues in learning analytics should be put into practice as far as possible to better test their usefulness.