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1 – 10 of 101Li Chen, Dirk Ifenthaler, Jane Yin-Kim Yau and Wenting Sun
The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption…
Abstract
Purpose
The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.
Design/methodology/approach
A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.
Findings
Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.
Originality/value
This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.
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This case study sought to investigate the relationship between pre-service teachers’ participation in designing and delivering one-on-one literacy intervention lessons to…
Abstract
Purpose
This case study sought to investigate the relationship between pre-service teachers’ participation in designing and delivering one-on-one literacy intervention lessons to beginning readers and their own evolving self-efficacy in literacy instruction.
Design/methodology/approach
The study was embedded within a 4000-level course in the elementary education major where pre-service teachers learn to administer, analyze and interpret a variety of literacy assessments. Based on the results of these assessments, pre-service teachers designed and implemented literacy lessons (twice a week, 30-min sessions) that addressed the beginning readers' specific instructional needs. Through collecting pre/post data with their first-grade intervention students, and participating in reflective “check-ins” (surveys, a focus group and end-of-course written reflection), a portrait of increased pre-service teacher self-efficacy in literacy instruction comes into focus.
Findings
The data showed, primarily through the thematic analysis of qualitative data, that the experience of conducting a one-on-one intervention with a striving reader impacted pre-service teachers’ self-efficacy positively.
Research limitations/implications
The methodology of this study was limited by the small sample size and the low participant response rate on the quantitative survey measure.
Practical implications
This paper highlights one aspect in which clinically-rich field experiences can make a difference in the literacy instruction self-efficacy of pre-service teachers.
Originality/value
This study adds to the support for authentic instructional applications of course content in educator preparation programs, specifically in Professional Development School (partner school system) contexts. The aspect of observing and measuring intervention student progress was one lens through which pre-service teachers viewed their efficacy. Further investigations focusing on other assessment-instruction cycles could provide additional insights.
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Vanessa Honson, Thuy Vu, Tich Phuoc Tran and Walter Tejada Estay
Large class sizes are becoming the norm in higher education against concerns of dropping learning qualities. To maintain the standard of learning and add value, one of the common…
Abstract
Purpose
Large class sizes are becoming the norm in higher education against concerns of dropping learning qualities. To maintain the standard of learning and add value, one of the common strategies is for the course convenor to proactively monitor student engagement with learning activities against their assessment outcomes and intervene timely. Learning analytics has been increasingly adopted to provide these insights into student engagement and their performance. This case study explores how learning analytics can be used to meet the convenor’s requirements and help reduce administrative workload in a large health science class at the University of New South Wales.
Design/methodology/approach
This case-based study adopts an “action learning research approach” in assessing ways of using learning analytics for reducing workload in the educator’s own context and critically reflecting on experiences for improvements. This approach emphasises reflexive methodology, where the educator constantly assesses the context, implements an intervention and reflects on the process for in-time adjustments, improvements and future development.
Findings
The results highlighted ease for the teacher towards the early “flagging” of students who may not be active within the learning management system or who have performed poorly on assessment tasks. Coupled with the ability to send emails to the “flagged” students, this has led to a more personal approach while reducing the number of steps normally required. An unanticipated outcome was the potential for additional time saving through improving the scaffolding mechanisms if the learning analytics were customisable for individual courses.
Originality/value
The results provide further benefits for learning analytics to assist the educator in a growing blended learning environment. They also reveal the potential for learning analytics to be an effective adjunct towards promoting personal learning design.
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Kiran Fahd, Shah Jahan Miah and Khandakar Ahmed
Student attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of…
Abstract
Purpose
Student attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale.
Design/methodology/approach
This study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment.
Findings
Identifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods.
Originality/value
The best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.
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Elisei Emili Lubuva, Placidius Ndibalema and Esther Mbwambo
The study aims to assess the effectiveness of engaging tutors in designing and using ICT integrated lesson activities in strengthening their pedagogical use of ICT competences.
Abstract
Purpose
The study aims to assess the effectiveness of engaging tutors in designing and using ICT integrated lesson activities in strengthening their pedagogical use of ICT competences.
Design/methodology/approach
Survey data from an intervention group of 70 tutors from two teachers colleges (TCs) were used to compare their level of ICT competences and domains of professional practice before and after the intervention. Document analysis, lesson observations and feedback from the learning management system (LMS) were used to describe tutors’ experiences from the intervention.
Findings
There was a statistically significant increase in tutors’ level of pedagogical use of ICT competences and domains of professional practice associated with hands-on practice in designing and implementing the intervention.
Research limitations/implications
The intervention focus on hands-on practice, actual teaching and learning needs, and the use of active learning strategies like flipped classroom and the LMS, were useful means for tutors to make sense of pedagogical use of ICT competences.
Practical implications
The results offer useful insights to teacher education institutions and policymakers on how to prepare professional learning and supportive policies to enhance teaching and learning with ICT for addressing the learning needs of the subject matter.
Originality/value
Creating 16 ICT integrated lesson activities helped tutors to learn pedagogical use of ICT competences by doing. Use of such intervention could be a useful strategy in teacher education institutions to reposition ICT competence development from reproducing technological competences toward developing knowledge creators who could innovate their pedagogical practice with support from mentors, digital learning resources and networks.
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Isuru Udayangani Hewapathirana
This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.
Abstract
Purpose
This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.
Design/methodology/approach
Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.
Findings
The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.
Practical implications
The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.
Originality/value
This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.
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Abdelhadi Ifleh and Mounime El Kabbouri
The prediction of stock market (SM) indices is a fascinating task. An in-depth analysis in this field can provide valuable information to investors, traders and policy makers in…
Abstract
Purpose
The prediction of stock market (SM) indices is a fascinating task. An in-depth analysis in this field can provide valuable information to investors, traders and policy makers in attractive SMs. This article aims to apply a correlation feature selection model to identify important technical indicators (TIs), which are combined with multiple deep learning (DL) algorithms for forecasting SM indices.
Design/methodology/approach
The methodology involves using a correlation feature selection model to select the most relevant features. These features are then used to predict the fluctuations of six markets using various DL algorithms, and the results are compared with predictions made using all features by using a range of performance measures.
Findings
The experimental results show that the combination of TIs selected through correlation and Artificial Neural Network (ANN) provides good results in the MADEX market. The combination of selected indicators and Convolutional Neural Network (CNN) in the NASDAQ 100 market outperforms all other combinations of variables and models. In other markets, the combination of all variables with ANN provides the best results.
Originality/value
This article makes several significant contributions, including the use of a correlation feature selection model to select pertinent variables, comparison between multiple DL algorithms (ANN, CNN and Long-Short-Term Memory (LSTM)), combining selected variables with algorithms to improve predictions, evaluation of the suggested model on six datasets (MASI, MADEX, FTSE 100, SP500, NASDAQ 100 and EGX 30) and application of various performance measures (Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error(RMSE), Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE)).
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Rasha Abdullah Alshaye, Amr Selim Wannas and Mohamed Saeed Bakr
The search for new techniques to teach English nowadays has been more than ever. These techniques have to be interesting and enjoyable in order to lower the anxiety levels of…
Abstract
Purpose
The search for new techniques to teach English nowadays has been more than ever. These techniques have to be interesting and enjoyable in order to lower the anxiety levels of students when learning English (Bakhsh, 2016). That is why many scholars and teachers look forward to integrating technology into language teaching. Social media platforms (SMPs) are among these techniques since millions of people around the world utilize them for daily interaction. Yet, teaching English for specific purposes (ESPs) relies on learners’ needs and employs an eclectic approach in delivering its course content. For this reason, the current study reviewed articles that tackled the topic of teaching or learning ESP from SMPs so as to uncover their effect and the attitude or motivation of learners.
Design/methodology/approach
The researchers used the PRISMA flowchart model in order to identify, screen and include articles in the study.
Findings
The results revealed that SMPs are effective in teaching and learning ESP writing, speaking and vocabulary. Yet, the included studies showed that learners’ attitude toward SMPs is positive as they believe that they are motivating and interesting.
Research limitations/implications
Some aspects of social media have turned out to be beneficial in the learning process and they need further investigation from ESP practitioners and scholars.
Originality/value
According to the study, it is crystal clear that the various social networks and platforms are beneficial and helpful for improving ESP productive skills.
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Habeeb Balogun, Hafiz Alaka and Christian Nnaemeka Egwim
This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO2 prediction models. The purpose of this paper is to…
Abstract
Purpose
This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO2 prediction models. The purpose of this paper is to pre-process a relatively large data of NO2 from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO2 prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison.
Design/methodology/approach
This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist.
Findings
The results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO2 pollution.
Practical implications
This paper's hybrid model provides a basis for giving an informed decision on the NO2 pollutant avoidance system.
Originality/value
This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration.
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Leila Ismail and Huned Materwala
Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine…
Abstract
Purpose
Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.
Design/methodology/approach
Health professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.
Findings
The authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.
Originality/value
This paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.
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