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Data storytelling courses position students as agents in creating stories interpreted from data about a social problem or social justice issue. The purpose of this study is to explore two research questions: What themes characterized students’ iterative development of data story topics? Looking back at six years of iterative feedback, what categories of data literacy pedagogy did instructors engage for these themes?.

This project examines six years of data storytelling final projects using thematic analysis and three years of instructor feedback. Ten themes in final projects align with patterns in feedback. Reflections on pedagogical approaches to students’ topic development suggest extending data literacy pedagogy categories – formal, personal and folk (Pangrazio and Sefton-Green, 2020).

Data storytelling can develop students’ abilities to move from being consumers to creators of data and interpretations. The specific topic of personal data exposure or risk has presented some challenges for data literacy instruction (Bowler et al., 2017). What “personal” means in terms of data should be defined more broadly. Extending the data literacy pedagogy categories of formal, personal and folk (Pangrazio and Sefton-Green, 2020) could more effectively center social justice in data literacy instruction.

Implications for practice include positioning students as producers of data interpretation, such as role-playing data analysis or decision-making scenarios.

Data storytelling has the potential to address current challenges in data literacy pedagogy and in teaching critical data literacy.

Course descriptions provide a template for future data literacy pedagogy involving data storytelling, and findings suggest implications for expanding definitions and applications of personal and folk data literacies.

The author employed a five-step approach: Data (e.g., qualitative primary and secondary data) collection (about a major project at the examined organisation), Critical thinking (in order to determine the dilemma), Setting learning objectives (e.g., with respect to the Bloom's taxonomy), Testing (in order to confirm the teaching plan) (e.g., with research assistants and doctoral candidates), and Ensuring clarity (e.g., especially for the case narrative).

The site manager at a UNESCO World Heritage Site by the name Ephesus in Türkiye (Turkey) was considering who would update the site management plan. UNESCO was regularly asking for updates. Would site management outsource the management plan from a firm? For example, the site management had had an outside firm develop the management plan and Ephesus had become a UNESCO World Heritage Site. Otherwise, would the site management rely on their own experience this time? Was there another way?

The educators could use the case study to introduce graduate students to “the value conception” in “marketing management” courses and to “the social exchange school of thought” in “marketing theory” courses. The learning objectives develop over the tension between owning and outsourcing main responsibilities of a scientific field as well as the tension between claims and objective evaluations. “The value conception” in “the social exchange school of thought” could improve planning in favour of humanity in a way that the United Nations could recognise (e.g., “value-based planning”). Corresponding discussions motivate a main question about the future: What is marketing for?

This study aims to examine the impact of two dimensions of agile work practices (i.e. agile taskwork and agile teamwork) on team efficacy and creativity. Further, it examines the mediating effect of team efficacy in the relationship between two dimensions of agile work practices and team creativity.

The data were collected from 563 professionals working in 290 information technology (IT) companies in India using a self-reporting structured questionnaire. Partial least squares-structural equation modeling (PLS-SEM) was used to test the hypothesized model.

The results demonstrate that agile taskwork and agile teamwork positively impact team creativity and team efficacy, and team efficacy positively impacts team creativity. Furthermore, team efficacy partially mediates the impact of agile taskwork and agile teamwork on team creativity.

This study shows the importance of agile work practices and team efficacy to enhance team creativity. The research offers managers strategies to boost team creativity.

There is a dearth of research examining the distinct effects of agile taskwork and agile teamwork on team efficacy and team creativity. Also, this study is one of its kind that examines the mediating mechanisms that explain the effect of agile taskwork and agile teamwork on team creativity.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

This paper aims to find the best method to predict the friction coefficient of textured 45# steel by comparing different machine learning algorithms and analytical calculations.

Five machine learning algorithms, including K-nearest neighbor, random forest, support vector machine (SVM), gradient boosting decision tree (GBDT) and artificial neural network (ANN), are applied to predict friction coefficient of textured 45# steel surface under oil lubrication. The superiority of machine learning is verified by comparing it with analytical calculations and experimental results.

The results show that machine learning methods can accurately predict friction coefficient between interfaces compared to analytical calculations, in which SVM, GBDT and ANN methods show close prediction performance. When texture and working parameters both change, sliding speed plays the most important role, indicating that working parameters have more significant influence on friction coefficient than texture parameters.

This study can reduce the experimental cost and time of textured 45# steel, and provide a reference for the widespread application of machine learning in the friction field in the future.

The purpose of this paper is to describe this new evaluation experience with portfolio in economics, not only from the teacher’s point of view but from the student perspective, and all the learning from its implementation; to provide ideas of evaluation practices in virtual and face-to-face modality in international business education; to motivate the rethinking of assessment practices in higher education to combine the best of each modality in the future.

The present work is a case study based on a qualitative description of the implementation of a portfolio as an assessment practice, supported by a reflection questionnaire with students’ perceptions and some elements of metacognition. The first section summarizes the literature used as a theoretical framework of this work. The second section describes the portfolio implementation by analyzing teachers and students reflections with a qualitative approach. The third section presents the findings. The fourth section is a discussion of findings, practical implications, limitations and future research directions. Finally, the conclusions of the work are shared.

Because the portfolio has had overwhelming results to assess what students have learned during the pandemic, it has become the learning and assessment tool after the pandemic, as it transforms the classes experience by shifting the focus from traditional examinations to more comprehensive, personalized and reflective ones. It also empowers students to take ownership of their learning, develop essential skills and cultivate a deeper understanding. Among other benefits, the portfolio means the creation of a safe and supportive environment for honest reflection, the development and design of strategic directions to improve learning and lead students toward metacognitive autonomy. Reflection pieces, a critical component of the portfolio, are a vital tool in the proactive learning process, as through reflection students learn to examine their own performance and discuss strategies to enhance their success in future work.

This work began as an educational experience per se, not for research purposes, which caused it to be systematized and reconstructed in a descriptive way, not to measure quantitative results. In this way, the present work describes that the portfolio helps to achieve better results on students’ learning than traditional examinations but, as another limitation, it does not measure them nor the process. One more limitation of this work is that it was written in a postpandemic context but was implemented during the pandemic; therefore, the circumstances of writing are not the same as those of implementation, and this could also entail a certain margin of decontextualization. At the same time, this is an experience that is still in process and continually being adapted to this changed and changing educational postpandemic context.

One of the main implications of the portfolio experience, transferable to all educational contexts, is that it transforms the final exam into a metacognitive one, letting students be aware of their own process of learning and results – objectives and competences – acquired. In this way, it lets teachers witness a part of the learning process that is not so evident in the traditional assessment practices – focused on some aspect of the learning – as it makes visible the way in which students receive, process and apply content, that is to say, how they make it their own.

The portfolio promotes reflective learning and metacognition, vital skills that benefit students beyond the classroom. This can have a positive impact on societal attitudes toward education and the quality of learning. Of the students, 82% felt the portfolio creation was helpful in their personal and professional lives, suggesting a broader societal impact. The paper’s findings contribute to the body of knowledge about the effectiveness of portfolio-based assessment in higher education, especially in the worldwide transition from online education to postpandemic education. This could guide future studies in similar educational contexts or with different pedagogical innovative tools.

In light of the 2020 pandemic lockdown, this work delves into the pressing need for educators to adapt and modify their teaching approaches. The relevance of this study is accentuated by the worldwide transition from online education to postpandemic education. This paper bridges the gap between theory and practice because the research can be applied to the educational practice of any international business education context, as well as lay the foundations for future research in the field that contributes to increasing evidence of the effectiveness of the use of the portfolio to achieve significant and deep learning in higher education.

Many practical control problems require achieving multiple objectives, and these objectives often conflict with each other. The existing multi-objective evolutionary reinforcement learning algorithms cannot achieve good search results when solving such problems. It is necessary to design a new multi-objective evolutionary reinforcement learning algorithm with a stronger searchability.

The multi-objective reinforcement learning algorithm proposed in this paper is based on the evolutionary computation framework. In each generation, this study uses the long-short-term selection method to select parent policies. The long-term selection is based on the improvement of policy along the predefined optimization direction in the previous generation. The short-term selection uses a prediction model to predict the optimization direction that may have the greatest improvement on overall population performance. In the evolutionary stage, the penalty-based nonlinear scalarization method is used to scalarize the multi-dimensional advantage functions, and the nonlinear multi-objective policy gradient is designed to optimize the parent policies along the predefined directions.

The penalty-based nonlinear scalarization method can force policies to improve along the predefined optimization directions. The long-short-term optimization method can alleviate the exploration-exploitation problem, enabling the algorithm to explore unknown regions while ensuring that potential policies are fully optimized. The combination of these designs can effectively improve the performance of the final population.

A multi-objective evolutionary reinforcement learning algorithm with stronger searchability has been proposed. This algorithm can find a Pareto policy set with better convergence, diversity and density.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Using the social constructivist perspective of learning, this study aims to examine the patterns and the key areas of entrepreneurial learning based on a case study of 16 participants who were the incubatees of two technology-based business incubators in China. The key research question is: how do novice entrepreneurs, focusing on technology-based business incubators, learn from a social constructivist perspective?

The researchers applied a qualitative methodology in this study as they wanted to understand better the complexity of the learning process that is hard to achieve quantitatively. The qualitative data was collected through in-depth interviews with the incubatees, who were the managers and owners of their businesses. The interviews with the entrepreneurs were mainly focused on the learning patterns and the factors influencing learning through the use of the critical incident technique.

This will allow incubator managers to better evaluate the extent of effective entrepreneurial learning within the incubator's eco-system. The results show that the participants learn through socially constructivist systems that are structured around the support provided by the incubators. Learning in this context takes place in an extended spectrum, and participants are more interested in learning from networking with experienced entrepreneurs rather than from other incubatees or formal courses. Findings of this study help incubator managers and novice entrepreneurs to better shape learning and teamwork in an effort to improve the learning process. Policy makers should consider introducing schemes that encourage novice entrepreneurs to exhibit the creativity and innovation behaviour reported by experienced entrepreneurs.

The focus of this study is primarily on incubators as the context of learning, whereas the macro-environmental factors, such as the socio-cultural and regulatory environments in China, were considered as playing a subtle role and would affect the incubatees' learning indirectly. The paper is based on a relatively small sample size and is geographically located in Ningbo, China. As such, the authors call for further research for comparative studies with a larger sample size so that a possible theory of entrepreneurial learning in the context of incubators might emerge in the future.