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1 – 10 of over 22000Yen-Ning Su, Chia-Cheng Hsu, Hsin-Chin Chen, Kuo-Kuang Huang and Yueh-Min Huang
This study aims to use sensing technology to observe the learning status of learners in a teaching and learning environment. In a general instruction environment, teachers often…
Abstract
Purpose
This study aims to use sensing technology to observe the learning status of learners in a teaching and learning environment. In a general instruction environment, teachers often encounter some teaching problems. These are frequently related to the fact that the teacher cannot clearly know the learning status of students, such as their degree of learning concentration and capacity to absorb knowledge. In order to deal with this situation, this study uses a learning concentration detection system (LCDS), combining sensor technology and an artificial intelligence method, to better understand the learning concentration of students in a learning environment.
Design/methodology/approach
The proposed system uses sensing technology to collect information about the learning behavior of the students, analyzes their concentration levels, and applies an artificial intelligence method to combine this information for use by the teacher. This system includes a pressure detection sensor and facial detection sensor to detect facial expressions, eye activities and body movements. The system utilizes an artificial bee colony (ABC) algorithm to optimize the system performance to help teachers immediately understand the degree of concentration and learning status of their students. Based on this, instructors can give appropriate guidance to several unfocused students at the same time.
Findings
The fitness value and computation time were used to evaluate the LCDS. Comparing the results of the proposed ABC algorithm with those from the random search method, the algorithm was found to obtain better solutions. The experimental results demonstrate that the ABC algorithm can quickly obtain near optimal solutions within a reasonable time.
Originality/value
A learning concentration detection method of integrating context-aware technologies and an ABC algorithm is presented in this paper. Using this learning concentration detection method, teachers can keep abreast of their students' learning status in a teaching environment and thus provide more appropriate instruction.
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Madhavan Maya, V.M. Anjana and G.K. Mini
The study explores the perspectives of college students on the pedagogical shift as well as frequent transitions between online and offline learning modes during the COVID-19…
Abstract
Purpose
The study explores the perspectives of college students on the pedagogical shift as well as frequent transitions between online and offline learning modes during the COVID-19 pandemic in Kerala, the most literate state in India.
Design/methodology/approach
A descriptive cross-sectional study was conducted among 1,366 college students in Kerala during December 2021. A pre-tested questionnaire was sent using Google Forms to students of arts and science colleges. The authors analyzed quantitative data using descriptive statistics and qualitative data using thematic content analysis.
Findings
The reported advantages of online learning were increased technical skill, flexibility in study time, effectiveness in bridging the gap of the missed academic period and provision of attending more educational webinars. Students expressed concerns of increased workload, difficulty in concentration due to family circumstances, academic incompetency, uncleared doubts and addiction to mobile phones and social media during the online classes. The main advantages reported for switching to an offline learning mode were enhanced social interaction, effective learning, better concentration and reduced stress. The reported challenges of offline classes were fear of getting the disease, concern of maintaining social distancing and difficulty in wearing masks during the classes. The shift in offline to online learning and vice versa was perceived as a difficult process for the students as it took a considerable time for them to adjust to the switching process of learning.
Originality/value
Students' concerns regarding transition between different learning modes provide important information to educators to better understand and support the needs of students during the pandemic situations.
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Chih-Hsien Hsia, Chin-Feng Lai and Yu-Sheng Su
The purpose of this study, we present a robot used in education. Influenced by the epoch of revolutionary digital technology, the methodology of education has gone boundless. The…
Abstract
Purpose
The purpose of this study, we present a robot used in education. Influenced by the epoch of revolutionary digital technology, the methodology of education has gone boundless. The robot programming sustainability and ability to solve problems is one an important skill that coding students require to learn programming. This educational have been integrated into curriculum instruction in clubs.
Design/methodology/approach
Robotics education has been regarded as a potential approach to enhance students' Science, technology, engineering, and mathematics learning competencies. The popular platform of robots diversifies educational practices by its advantages of reorganizational and logical forms. In this paper, we focus on the effects of applying blended instructional approaches to robot education on students' programming sustainability and ability.
Findings
The students of department of mechanical engineering at the University in Taipei city, who participate elective educational robot courses, prove through surveys that the problem-based leaning method with robot programming can effectively enhance students' interests and learning motivations in learning new knowledge and promote students' designing skills for a sustainable society.
Originality/value
In this paper, the authors focus on the effects of applying blended instructional approaches to robot education on students' programming sustainability and ability.
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Ewa McGrail, J. Patrick McGrail and Alicja Rieger
To explore the potential of conversations with an authentic audience through blogging for enriching in young writers the understanding of the communicative function of writing…
Abstract
Purpose
To explore the potential of conversations with an authentic audience through blogging for enriching in young writers the understanding of the communicative function of writing, specifically language and vocabulary use.
Design/methodology/approach
We situate our work in the language acquisition model of language learning, in which learners develop linguistic competence in the process of speaking and using language (Krashen, 1988; Tomasello, 2005). We also believe that language learning benefits from formal instruction (Krashen, 1988). As such, in our work, we likened engaging in blogging to learning a language (here, more broadly conceived as learning to write) through both natural communication (acquisition) and prescription (instruction), and we looked at these forms of learning in our study.
We were interested in the communicative function of language learning (Halliday, 1973; 1975; Penrod, 2005) among young blog writers, because we see language learning as socially constructed through interaction with other speakers of a language (Tomasello, 2005; Vygotsky, 1978).
Findings
The readers and commenters in this study supported young writers in their language study by modeling good writing and effective language use in their communication with these writers. Young writers also benefited from direct instruction through interactions with adults beyond classroom teachers, in our case some of the readers and commenters.
Practical implications
Blogging can extend conversations to audiences far beyond the classroom and make writing a more authentic endeavor for young writers. Teachers should take advantage of such a powerful tool in their writing classrooms to support their students’ language study and vocabulary development.
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At the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC…
Abstract
Purpose
At the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).
Design/methodology/approach
The method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.
Findings
The discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO2) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.
Originality/value
Finally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO2 based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.
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Yalalem Assefa, Bekalu Tadesse Moges and Shouket Ahmad Tilwani
Lifelong learning has become one of the most interesting areas of research. Hence, the current study was aimed at developing and validating a tool that helps to study how well…
Abstract
Purpose
Lifelong learning has become one of the most interesting areas of research. Hence, the current study was aimed at developing and validating a tool that helps to study how well people working in higher education institutions are engaged in lifelong learning.
Design/methodology/approach
A review of theories in the literature and experts' consultation were used to develop a pool of items and validate the self-assessment instrument for measuring lifelong learning. The study employed factor analytic methodologies such as principal component analysis, varimax rotation and exploratory factor analyses.
Findings
The study yielded a reliable and valid lifelong learning measurement scale made up of 18 items and four underlying factors that are theoretically supported.
Originality/value
The significant information is that, the current study aimed at developing a tool that could help to measure the engagement in lifelong learning of higher education institutions workers. The study found this tool to be important because lifelong learning is considered essential for personal and professional growth, and having a sound way to measure it can help individuals and organizations identify areas for improvement.
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Yu-Sheng Su, Wen-Ling Tseng, Hung-Wei Cheng and Chin-Feng Lai
To support achieving sustainable development goals (SDGs), we integrated science, technology, engineering and math (STEM) and extended reality technologies into an artificial…
Abstract
Purpose
To support achieving sustainable development goals (SDGs), we integrated science, technology, engineering and math (STEM) and extended reality technologies into an artificial intelligence (AI) learning activity. We developed Feature City to facilitate students' learning of AI concepts. This study aimed to explore students' learning outcomes and behaviors when using Feature City.
Design/methodology/approach
Junior high school students were the subjects who used Feature City in an AI learning activity. The learning activity consisted of 90-min sessions once per week for five weeks. Before the learning activity, the teacher clarified the learning objectives and administered a pretest. The teacher then instructed the students on the features, supervised learning and unsupervised learning units. After the learning activity, the teacher conducted a posttest. We analyzed the students' prior knowledge and learning performance by evaluating their pretest and posttest results and observing their learning behaviors in the AI learning activity.
Findings
(1) Students used Feature City to learn AI concepts to improve their learning outcomes. (2) Female students learned more effectively with Feature City than male students. (3) Male students were more likely than female students to complete the learning tasks in Feature City the first time they used it.
Originality/value
Within SDGs, this study used STEM and extended reality technologies to develop Feature City to engage students in learning about AI. The study examined how much Feature City improved students' learning outcomes and explored the differences in their learning outcomes and behaviors. The results showed that students' use of Feature City helped to improve their learning outcomes. Female students achieved better learning outcomes than their male counterparts. Male students initially exhibited a behavioral pattern of seeking clarification and error analysis when learning AI education, more so than their female counterparts. The findings can help teachers adjust AI education appropriately to match the tutorial content with students' AI learning needs.
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Christina Morfaki and Apostolos Skotis
The purpose of this paper is to provide an overview of the literature regarding the academic online learning experience under the lens of broad personality traits, in the…
Abstract
Purpose
The purpose of this paper is to provide an overview of the literature regarding the academic online learning experience under the lens of broad personality traits, in the transition from traditional to online learning due to global coronavirus pandemic (COVID-19).
Design/methodology/approach
The systematic literature review is based on preferred reporting items for systematic reviews and meta-analyses (PRISMA) method and includes indexed empirical studies in academic institutes during the period of COVID-19 outbreak.
Findings
Electronic sources identified 103 references; while after the elimination of duplicates and irrelevant titles, 42 papers were forwarded for abstract screening and later full-text assessment. Of these, 14 met the eligibility criteria. Finally, nine studies were included in the literature review profiling and in the qualitative analysis.
Originality/value
The research insights provided in this study are useful in terms of enhancing the view that link broad personality traits and various learning outcomes, during the necessitated transition to online learning by the public health emergency of the COVID-19 pandemic.
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Four active‐like (A‐like) and six passive‐like (P‐like) business teaching/learning techniques are described. It is proposed that students enrolled and faculty teaching in the…
Abstract
Four active‐like (A‐like) and six passive‐like (P‐like) business teaching/learning techniques are described. It is proposed that students enrolled and faculty teaching in the international business (INTB), marketing (MKT), and management (MAN) business concentrations would rate the A‐like techniques higher than students enrolled and faculty teaching in the management information systems (MIS), finance (FIN), and accounting (ACC) business concentrations. And that students enrolled and faculty teaching in the MIS, FIN, and ACC concentrations would rate the P‐like techniques higher than the students and faculty in the INTB, MKT, and MAN concentrations. Using a survey questionnaire, upper undergraduate and MBA university business students and faculty were asked to indicate the importance level for each technique. Students' ratings do not support the proposition in nine techniques and the faculty ratings do not support it in eight. The conclusion is that the study at least provides a framework that can aid instructors in understanding that different students prefer and different situations require different instructional techniques.
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Nehal Elshaboury, Eslam Mohammed Abdelkader and Abobakr Al-Sakkaf
Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up…
Abstract
Purpose
Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.
Design/methodology/approach
Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO2) and particulate matter 10 µm in diameter (PM10). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.
Findings
The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO2 and PM10 are expected to reach 10 and 127 µg/m3, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.
Originality/value
This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.
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