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The purpose of this article is to discuss the “learning nation” concept and examine the characteristics and implications of using the “learning” premodifier in this nation-building program.

This article reviews how the “learning” aspect is inter-related to a series of national information and communication technology masterplans and includes a comparative analysis of the related premodifier “smart” as Singapore sets forth its ambition to become a “smart nation” as part of the digitalization megatrend. A print media indicator and Google Trends form part of the methodology to ascertain the rise of digital technology over a certain period. The former technique involves identifying relevant bibliographic databases and analyzing the volume of publications. The latter technique is a real time index of the volume of queries that users input into Google.

It is suggested that using the term “learning nation” previously and more recently “smart nation” is a consequence of the rise of the digitalization megatrend. The “smart-ness” involves learning about digital technologies, developing digital competencies and harnessing the benefits of these digital capabilities. From a public policy perspective, the article showcases how a city-state can transform itself through technology by riding on megatrends. Also, there is a need to be selective in developing specific areas for the application of digital technologies.

This article contributes to a better understanding on the frequent usage of the word “learning” as a premodifier and Singapore’s nation-building journey through human capital development and digitalization.

The purpose of this paper is to develop a web-based interactive learning object (ILO) of introductory Computer Science (CS) concept on recursion and compare two feedback methods in the learning assessment part.

Test driven development (TDD) approach was used to develop ILO. The authors adapted Multimedia Educational Resource for Learning and Online Teaching (MERLOT) standard instrument to evaluate ILO’s effectiveness as an e-learning tool. Three respondents, from a list of pre-identified prospective evaluators, were randomly chosen and served as raters for MERLOT, while 32 student-respondents coming from first-year Math and CS undergraduate majors were randomly assigned to each ILO version implementing either one of the two feedback methods.

ILO obtained mean ratings above 4 (in scale 1-5) in three MERLOT criteria, namely, potential effectiveness as teaching tool, ease of use, and quality of content, which is rated highest (mean=4.40, SD=0.53). The study also revealed that immediate feedback increases retention while delayed feedback improves generating new knowledge. Respondents who viewed the ILO implementing immediate feedback in their first session had statistically significantly higher scores (mean=8.25, SD=0.80) than those who viewed with delayed feedback (mean=7.63, SD=0.89). In their second session, the same observation was noted although with higher mean scores. These results give evidence that the developed ILO met standards in e-learning material and showed evidence of its effectiveness with preferably implementing immediate feedback.

Although the developed ILO can now be used in school as supplementary learning material in teaching the concept of recursion in an introductory CS subject, a pilot testing of the web-based ILO using a larger sample of respondents to validate its effectiveness for online distance learning educational material can be pursued. Furthermore, in designing and creating an ILO, the provision of feedback during the assessment stage is necessary for effecting learning.

The study was a first to develop ILO for CS topic on recursion. The paper also compared which of two known feedback methods is best to implement in an ILO.

This paper focuses on the orientation of the economy expressed in the communication of the Central Economic Work Conference (CEWC) of China and its relation with the stock market. This study seeks to explore which orientation of the economy may have a stronger impact on the rise of the stock market. It proposes words connoting orientation of the economy (WOE) that is closely related to the stock market, and different WOE has different impacts on the stock market in terms of intensity. The study aims to provide investors with better investment strategies by identifying the stronger developmental WOE.

The paper opted for an exploratory study using the textual analysis approach, based on a corpus of 28 CEWC communications spanning from 1994 to 2021. The raw corpus amounted to 50,754 words in total that are treated with noise reduction method and record an effective corpus of 39,591.

The paper provides empirical insights into the close relationship of the WOE of the CEWC to the stock market, and different WOE has different impacts on the stock market in terms of intensity. It suggests that WOE connoting development may forecast a rising stock market if it is nearly 40% higher than the other two WOEs by impact index.

As WOE is only proven in the CEWC, this paper has its limitations in the scope of samples. It is necessary to apply WOE to more Central Bank communication (CBC) and countries. It is desirable to apply the Gunning–Fog index.

The paper includes implications for investors to read out the orientation of the economy and the degree of different WOEs. Investors are keener to know “what” degree of the CEWC leads to the rise/fall of the stock market. The impact index can be an indicator of a tendency of the stock market, which upgrades the rationality of investment decisions.

This paper fulfills words connoting the orientation of economy as an identified linguistic feature, which the impact of CEWC on stockmarket can be measured.

Previous academic research studies mostly focus on the impact on stock market from the language features of CBC, rather than that from the more influential body, CEWC communication. This study seeks to provide the relationship of CEWC communication and the time length of the impact on the stock prices.

This work presents a system based on an ensemble of Convolutional Neural Networks (CNNs) and descriptors for bioimage classification that has been validated on different datasets of color images. The proposed system represents a very simple yet effective way of boosting the performance of trained CNNs by composing multiple CNNs into an ensemble and combining scores by sum rule. Several types of ensembles are considered, with different CNN topologies along with different learning parameter sets. The proposed system not only exhibits strong discriminative power but also generalizes well over multiple datasets thanks to the combination of multiple descriptors based on different feature types, both learned and handcrafted. Separate classifiers are trained for each descriptor, and the entire set of classifiers is combined by sum rule. Results show that the proposed system obtains state-of-the-art performance across four different bioimage and medical datasets. The MATLAB code of the descriptors will be available at https://github.com/LorisNanni.

Due to the important role of orthography in society, the project called IDeRBlog presented in this paper created a web-based tool to motivate pupils to write text as well as to read and to comment on texts written by fellow students. In addition, IDeRBlog aims to improve student’s German orthography skills and supports teachers and parents with training materials for their students. The paper aims to discuss these issues.

With the aid of learning analytics, the submitted text is analyzed and special feedback is given to the students so that they can try to correct the misspelled words themselves. The teachers as well as the parents are benefiting from the analysis and exercises suggested by the system.

A recent study showed the efficiency of the system in form of an improvement of the students’ orthographic skills. Over a period of four months 70 percent of the students achieved a significant reduction of their spelling mistakes.

IDeRBlog is an innovative approach to improving orthography skills combining blogging and new media with writing and practice.

This paper aims to introduce an information and communication technology (ICT) artifact that uses text mining to support the innovative and standardized assessment of professional competences within the validation of prior learning (VPL). Assessment means comparing identified and documented professional competences against a standard or reference point. The designed artifact is evaluated by matching a set of curriculum vitae (CV) scraped from LinkedIn against a comprehensive model of professional competence.

A design science approach informed the development and evaluation of the ICT artifact presented in this paper.

A proof of concept shows that the ICT artifact can support assessors within the validation of prior learning procedure. Rather the output of such an ICT artifact can be used to structure documentation in the validation process.

Evaluating the artifact shows that ICT support to assess documented learning outcomes is a promising endeavor but remains a challenge. Further research should work on standardized ways to document professional competences, ICT artifacts capture the semantic content of documents, and refine ontologies of theoretical models of professional competences.

Text mining methods to assess professional competences rely on large bodies of textual data, and thus a thoroughly built and large portfolio is necessary as input for this ICT artifact.

Following the recent call of European policymakers to develop standardized and ICT-based approaches for the assessment of professional competences, an ICT artifact that supports the automatized assessment of professional competences within the validation of prior learning is designed and evaluated.

This study aims to explore the use of technology-based strategies by Yemeni undergraduate students to develop their English as a foreign language skills in informal learning settings.

A mixed methods research design was used to collect quantitative and qualitative data from 110 undergraduate students enrolled at the English Departments in two universities in Yemen. In the first phase of the study, 10 students were interviewed to obtain information about their use of technology to develop their English language skills and subsystems in informal settings. Following the analysis of the interview data, a questionnaire was built to collect quantitative data, and the second phase of the study was carried out with 100 undergraduate students.

The findings revealed that students developed four technology-based strategies that they used in informal settings. These strategies included using social media, being inspired by someone, accessing social networks and websites. Students reported that these strategies helped them develop their listening, speaking and reading skills, while they also reported that their vocabulary was enhanced over grammar and pronunciation.

The study findings can be of benefit not only for helping to raise students’ awareness of informal learning strategies to develop their English skills outside the classroom but also for teachers to rethink the importance of integrating technology tools and digital resources in their teaching practice. The results could also guide curriculum designers to augment textbook materials by integrating technology-based informal learning strategies.

This is the first study on this topic conducted in the context of higher education in Yemen and offers unique insights into informal learning practices of Yemeni students. In addition, the findings of the study open new dimensions for rethinking the classification of language learning strategies to include those that are technology-based and mainly useful for independent informal learning.