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Abstract The South Korean government has been actively promoting an educational-facility improvement program as part of its energy-saving efforts. This research seeks to develop a decision support model for selecting the facility expected to be effective in generating energy savings and making the facility improvement program more effective. In this research, project characteristics and electric-energy consumption data for the year 2009 were collected from 6282 elementary schools located in seven metropolitan cities in South Korea. In this research, the following were carried out: (i) a group of educational facilities was established based on electric-energy consumption, using a decision tree; (ii) a number of similar projects were retrieved from the same group of facilities, using case-based reasoning; and (iii) the accuracy of prediction was improved, using the combination of genetic algorithms, the artificial neural network, and multiple regression analysis. The results of this research can be useful for the following purposes: (i) preliminary research on the systematic and continuous management of educational facilities’ electric-energy consumption; (ii) basic research on electric-energy consumption prediction based on the project characteristics; and (iii) practical research for selecting an optimum facility that can more effectively apply an educational-facility improvement program as a decision support model.

Abstract The South Korean government has been actively promoting an educational-facility improvement program as part of its energy-saving efforts. This research seeks to develop a decision support model for selecting the facility expected to be effective in generating energy savings and making the facility improvement program more effective. In this research, project characteristics and electric-energy consumption data for the year 2009 were collected from 6282 elementary schools located in seven metropolitan cities in South Korea. In this research, the following were carried out: (i) a group of educational facilities was established based on electric-energy consumption, using a decision tree; (ii) a number of similar projects were retrieved from the same group of facilities, using case-based reasoning; and (iii) the accuracy of prediction was improved, using the combination of genetic algorithms, the artificial neural network, and multiple regression analysis. The results of this research can be useful for the following purposes: (i) preliminary research on the systematic and continuous management of educational facilities’ electric-energy consumption; (ii) basic research on electric-energy consumption prediction based on the project characteristics; and (iii) practical research for selecting an optimum facility that can more effectively apply an educational-facility improvement program as a decision support model.

As a way to support teachers’ professional development activities and build communities of practice for teachers, education researchers and practitioners have put considerable effort into building an online learning community for K–12 teachers to create a venue to facilitate teachers’ joint knowledge construction. However, a substantial number of such online communities have failed due to lack of participation of members. Therefore, it is critical to understand how to design a sustainable community that fulfills members’ needs and elicits active participation of members. In this literature review, we adopted a sociological framework to investigate how to create a sustainable online community. This framework suggests that the sustainability of a community comes from individual members’ three types of commitments: instrumental, affective, and moral commitments. Such commitments are results of members’ cognitive, cathectic, and evaluative processes and lead to membership retainment, cohesive relationships, and socially regulated participation. Using this framework, we conducted a systematic literature review on online teacher community articles published from 1990 to 2018. Our findings provide insights on factors associated with teacher members’ instrumental, affective, and moral commitment to an online community. Based on these findings, we further provide design suggestions to build a sustainable community for teachers.

As a way to support teachers’ professional development activities and build communities of practice for teachers, education researchers and practitioners have put considerable effort into building an online learning community for K–12 teachers to create a venue to facilitate teachers’ joint knowledge construction. However, a substantial number of such online communities have failed due to lack of participation of members. Therefore, it is critical to understand how to design a sustainable community that fulfills members’ needs and elicits active participation of members. In this literature review, we adopted a sociological framework to investigate how to create a sustainable online community. This framework suggests that the sustainability of a community comes from individual members’ three types of commitments: instrumental, affective, and moral commitments. Such commitments are results of members’ cognitive, cathectic, and evaluative processes and lead to membership retainment, cohesive relationships, and socially regulated participation. Using this framework, we conducted a systematic literature review on online teacher community articles published from 1990 to 2018. Our findings provide insights on factors associated with teacher members’ instrumental, affective, and moral commitment to an online community. Based on these findings, we further provide design suggestions to build a sustainable community for teachers.

Response scales in auditory perception assessment are critical for capturing the true responses of listeners. Despite its impact on data, response scales have received the least attention in auditory perception assessment. In this study, the usability of visual analogue scales for auditory perception assessment was investigated. Five response scales (a unipolar visual analogue scale–negated to regular, a unipolar visual analogue scale—regular to negated, a bipolar visual analogue scale–positive to negative, a bipolar visual analogue scale—negative to positive, and a unipolar 11-point scale (ISO/TS 15666:2021)) for auditory perception assessment are presented. Music and traffic noise were presented to 60 university students at two different levels, i.e., 45 and 65 dBA, respectively. A web-based experimental design was implemented, and tablet pads were provided to the respondents to record their responses. The unipolar 11-point scale required the longest response time, followed by the two unipolar visual analogue scales and two bipolar visual analogue scales with statistical significance. All response scales used in this study achieved statistical reliability and sensitivity for the auditory perception assessment. Among the five response scales, the bipolar visual analogue scale (negative to positive) ranked first in reliability over repeated measures, exhibited sensitivity in differentiating sound sources, and was preferred by the respondents under the conditions of the present study. None of the respondents preferred the unipolar 11-point scale. The visual analogue scale was favoured over the traditional unipolar 11-point scale by young educated adults in a mobile-based testing environment. Moreover, the bipolar visual analogue scale demonstrated the highest reliability and sensitivity, and it was preferred the most by the respondents. The semantic labelling direction from negated to regular, or from negative to positive, is preferred over its opposite counterpart. Further research is necessary to investigate the use of response scales for the general public including children and the elderly, as well as that of semantic adjectives and their counterparts for auditory perception assessment.

Response scales in auditory perception assessment are critical for capturing the true responses of listeners. Despite its impact on data, response scales have received the least attention in auditory perception assessment. In this study, the usability of visual analogue scales for auditory perception assessment was investigated. Five response scales (a unipolar visual analogue scale–negated to regular, a unipolar visual analogue scale—regular to negated, a bipolar visual analogue scale–positive to negative, a bipolar visual analogue scale—negative to positive, and a unipolar 11-point scale (ISO/TS 15666:2021)) for auditory perception assessment are presented. Music and traffic noise were presented to 60 university students at two different levels, i.e., 45 and 65 dBA, respectively. A web-based experimental design was implemented, and tablet pads were provided to the respondents to record their responses. The unipolar 11-point scale required the longest response time, followed by the two unipolar visual analogue scales and two bipolar visual analogue scales with statistical significance. All response scales used in this study achieved statistical reliability and sensitivity for the auditory perception assessment. Among the five response scales, the bipolar visual analogue scale (negative to positive) ranked first in reliability over repeated measures, exhibited sensitivity in differentiating sound sources, and was preferred by the respondents under the conditions of the present study. None of the respondents preferred the unipolar 11-point scale. The visual analogue scale was favoured over the traditional unipolar 11-point scale by young educated adults in a mobile-based testing environment. Moreover, the bipolar visual analogue scale demonstrated the highest reliability and sensitivity, and it was preferred the most by the respondents. The semantic labelling direction from negated to regular, or from negative to positive, is preferred over its opposite counterpart. Further research is necessary to investigate the use of response scales for the general public including children and the elderly, as well as that of semantic adjectives and their counterparts for auditory perception assessment.

Artificial intelligence (AI) is gradually influencing every aspect of everyday life, including education. AI can also provide special support to learners through academic sustainability or discontinuation predictions. While AI research remains in its early stages, we must examine how it evolves and exerts its potential over time. By utilizing AI in physical education (PE), we can increase its potential use in sports applications, and enact changes upon the nature of PE, its visualization, and repeatability. Based on the concept of AI and related research areas, this study explores its principles and use in PE, and presents a focused, in-depth analysis of the areas of PE technology where AI could be applied—customized PE classes, knowledge provision, learner evaluation, and learner counseling methods. Our findings highlight the expertise required for future PE teachers in applying AI. Regarding practice implications, this study addresses the topic of AI innovations affecting all life domains, including PE; it highlights AI applications’ relevance to PE technology, based on existing research; it proposes that the implications of AI for PE may apply to other educational domains; and finally, it contributes to existing literature and also shares future research prospects regarding AI applications in education and sports.

Artificial intelligence (AI) is gradually influencing every aspect of everyday life, including education. AI can also provide special support to learners through academic sustainability or discontinuation predictions. While AI research remains in its early stages, we must examine how it evolves and exerts its potential over time. By utilizing AI in physical education (PE), we can increase its potential use in sports applications, and enact changes upon the nature of PE, its visualization, and repeatability. Based on the concept of AI and related research areas, this study explores its principles and use in PE, and presents a focused, in-depth analysis of the areas of PE technology where AI could be applied—customized PE classes, knowledge provision, learner evaluation, and learner counseling methods. Our findings highlight the expertise required for future PE teachers in applying AI. Regarding practice implications, this study addresses the topic of AI innovations affecting all life domains, including PE; it highlights AI applications’ relevance to PE technology, based on existing research; it proposes that the implications of AI for PE may apply to other educational domains; and finally, it contributes to existing literature and also shares future research prospects regarding AI applications in education and sports.