Emerging adulthood is a key developmental period of identity (including sexual and romantic identity). Sexual self-image, or the intersection between body image and sexual behaviors/sexuality, has implications for mental health and sexual health at this critical developmental stage. Text messages are a ubiquitous method of communication for emerging adults, and sexting (i.e., text messages with sexualized content) has both positive and negative health and relationship effects. We examine how emerging adult couples use text messages to communicate about their sexual self-image. We analyzed 20 emerging adult couples’ text messages in the first six months of their relationship for themes related to how bodies were discussed in the context of sexual activity. We used both content and tonal qualitative analyses. We found six themes: (1) complimenting, (2) affirming, (3) sharing information, (4) expressing boundaries, (5) asserting agency, and (6) expressing desires. Although the majority of research focused on sexting focuses on risk or negative outcomes, we find that sexting as it relates to bodies can be a source of comfort, affirmation, expression of desires, and boundary setting. Our findings point to opportunities for increasing communication skills to improve sexual health and body empowerment among emerging adults.
{"title":"Nudes, just cuddles, and weird questions: Sexual self-image in couples’ text messages","authors":"Jessamyn Moxie , Erika Montanaro , Jasmine Temple , Bridget Jules , Joseph Thompson , Diana Gioia , Sarai Ordonez , Stuti Joshi , Elsa Boehm","doi":"10.1016/j.chbr.2024.100553","DOIUrl":"10.1016/j.chbr.2024.100553","url":null,"abstract":"<div><div>Emerging adulthood is a key developmental period of identity (including sexual and romantic identity). Sexual self-image, or the intersection between body image and sexual behaviors/sexuality, has implications for mental health and sexual health at this critical developmental stage. Text messages are a ubiquitous method of communication for emerging adults, and sexting (i.e., text messages with sexualized content) has both positive and negative health and relationship effects. We examine how emerging adult couples use text messages to communicate about their sexual self-image. We analyzed 20 emerging adult couples’ text messages in the first six months of their relationship for themes related to how bodies were discussed in the context of sexual activity. We used both content and tonal qualitative analyses. We found six themes: (1) complimenting, (2) affirming, (3) sharing information, (4) expressing boundaries, (5) asserting agency, and (6) expressing desires. Although the majority of research focused on sexting focuses on risk or negative outcomes, we find that sexting as it relates to bodies can be a source of comfort, affirmation, expression of desires, and boundary setting. Our findings point to opportunities for increasing communication skills to improve sexual health and body empowerment among emerging adults.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"17 ","pages":"Article 100553"},"PeriodicalIF":4.9,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-07DOI: 10.1016/j.chbr.2024.100539
Amir Reza Rahimi , Mahshad Sheyhkholeslami , Ali Mahmoudi Pour
Currently, chatbots powered by artificial intelligence (AI) have gained considerable attention due to their ability to provide personalized language learning (PLL) for learners. In this regard, recent studies have extensively explored learners' emotional aspects, such as their attitudes and acceptance of personalized language learning in chatbots. It is, however, unclear what factors might determine their cognitive behaviors in such a personalized language learning environment, particularly their self-regulation. To fill the gap, the researchers collected data from 133 Iranian EFL learners who had personalized language learning through ChatGPT in their language learning institute and answered our questionnaire that tapped on their personalized L2 motivational self-system (PEL2MSS) and their personalized self-regulation (PESRL). The researchers analyzed the empirical data using a hybrid SEM-artificial neural network (SEM-ANN), in contrast to previous literature that primarily relied on structural equation modeling (SEM). The results showed that ChatGPT significantly responded to language learners' current L2-self and their ought to L2-self to pass their obligation, and metrics to reach their goals resulted in seeking more assistance from ChatGPT and evaluating their language learning progress with it. Moreover, the sign of digital self-authenticity was also discovered by the researchers, where learners dedicated more motivation to learn language with ChatGPT in comparison with their previous language learning environments, which culminated in having more self-evaluation, goal-setting, and daily academic schedule to learn language with ChatGPT. Additionally, the ANN analysis supported the linear findings of the PLS-SEM by showing that language learners' current L2-self, digital self-authenticity, and ought to L2-self were the most significant motivational factors affecting their PESRL. Based on these findings, a new conceptual framework for the PLL was developed in the literature, and the research view was shifted from covering language learners' emotional aspects to their cognitive aspects in this environment. Thus we recommend that language teachers should avoid seeing ChatGPT as a tool that learners use for cheating; rather, it can be used as a co-teacher outside of the classroom to help students cover their present language learning needs, which might not be covered in the classroom due to the time restriction.
{"title":"Uncovering personalized L2 motivation and self-regulation in ChatGPT-assisted language learning: A hybrid PLS-SEM-ANN approach","authors":"Amir Reza Rahimi , Mahshad Sheyhkholeslami , Ali Mahmoudi Pour","doi":"10.1016/j.chbr.2024.100539","DOIUrl":"10.1016/j.chbr.2024.100539","url":null,"abstract":"<div><div>Currently, chatbots powered by artificial intelligence (AI) have gained considerable attention due to their ability to provide personalized language learning (PLL) for learners. In this regard, recent studies have extensively explored learners' emotional aspects, such as their attitudes and acceptance of personalized language learning in chatbots. It is, however, unclear what factors might determine their cognitive behaviors in such a personalized language learning environment, particularly their self-regulation. To fill the gap, the researchers collected data from 133 Iranian EFL learners who had personalized language learning through ChatGPT in their language learning institute and answered our questionnaire that tapped on their personalized L2 motivational self-system (PEL2MSS) and their personalized self-regulation (PESRL). The researchers analyzed the empirical data using a hybrid SEM-artificial neural network (SEM-ANN), in contrast to previous literature that primarily relied on structural equation modeling (SEM). The results showed that ChatGPT significantly responded to language learners' current L2-self and their ought to L2-self to pass their obligation, and metrics to reach their goals resulted in seeking more assistance from ChatGPT and evaluating their language learning progress with it. Moreover, the sign of digital self-authenticity was also discovered by the researchers, where learners dedicated more motivation to learn language with ChatGPT in comparison with their previous language learning environments, which culminated in having more self-evaluation, goal-setting, and daily academic schedule to learn language with ChatGPT. Additionally, the ANN analysis supported the linear findings of the PLS-SEM by showing that language learners' current L2-self, digital self-authenticity, and ought to L2-self were the most significant motivational factors affecting their PESRL. Based on these findings, a new conceptual framework for the PLL was developed in the literature, and the research view was shifted from covering language learners' emotional aspects to their cognitive aspects in this environment. Thus we recommend that language teachers should avoid seeing ChatGPT as a tool that learners use for cheating; rather, it can be used as a co-teacher outside of the classroom to help students cover their present language learning needs, which might not be covered in the classroom due to the time restriction.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"17 ","pages":"Article 100539"},"PeriodicalIF":4.9,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-07DOI: 10.1016/j.chbr.2024.100552
Abu Bakar Bin Hamdan, Nor Afiah Binti Mohd Zulkefli, Fatimah Binti Ahmad Fauzi
Background
Phantom Vibration Syndrome (PVS) is a unique and interesting phenomenon caused by the widespread use of smart devices where individuals perceive vibrations from their smart devices when none are occurring. It is classified as a tactile hallucination because the brain interprets an absent sensation. This study highlights the importance of urgency in dealing with this issue in our technologically advanced society by providing useful information for both scholarly discussion and real-life use in the digital age.
Objectives
This study was conducted to determine the prevalence of PVS and its associated factors, which include sociodemographic characteristics, device characteristics, device usage patterns, smartphone addiction, and mental health.
Methodology
A cross-sectional study was conducted using a validated questionnaire among undergraduate students in a public university in Malaysia, who were identified using proportionate stratified random sampling. The dependent variable measured through the questionnaire was the prevalence of PVS, while the independent variables were socio-demographic factors (age, gender, ethnic), device characteristics (types of devices, device location, notification alert in vibration mode and regularity in using vibration mode), device usage patterns (frequency, duration and purpose of using devices), smartphone addiction, and mental health (perceived stress, anxiety and depression). Data were analyzed using IBM SPSS application version 29.
Results
A total of 381 responses were obtained, with response rate of 92.7%. The mean age of respondents was 21.96 ± 1.64 years. The prevalence of PVS was 49.3%, which was predicted by age (AOR: 0.55; 95%CI: 0.38–0.95), location of device carried in the front pocket of pants (AOR: 0.58; 95%CI: 0.36–0.95), location of device carried in sling bag (AOR: 0.49; 95%CI: 0.32–0.77), notification alert in vibration mode (AOR: 2.33; 95%CI: 1.33–4.09) and regularity using vibration mode (AOR: 2.91; 95%CI: 1.84–4.61).
Conclusion
Five factors predicted PVS in this study, comprising one sociodemographic variable and 4 device characteristics variables. Based on the results, health education should teach undergraduate students to recognize PVS symptoms and implement healthy technology practices such as optimizing device placement, decreasing vibration mode usage, and regulating device usage behaviors. Practical advice on setting limits and taking breaks can also reduce PVS risk.
{"title":"Prevalence of phantom vibration syndrome and its associated factors among undergraduate students in a public university","authors":"Abu Bakar Bin Hamdan, Nor Afiah Binti Mohd Zulkefli, Fatimah Binti Ahmad Fauzi","doi":"10.1016/j.chbr.2024.100552","DOIUrl":"10.1016/j.chbr.2024.100552","url":null,"abstract":"<div><h3>Background</h3><div>Phantom Vibration Syndrome (PVS) is a unique and interesting phenomenon caused by the widespread use of smart devices where individuals perceive vibrations from their smart devices when none are occurring. It is classified as a tactile hallucination because the brain interprets an absent sensation. This study highlights the importance of urgency in dealing with this issue in our technologically advanced society by providing useful information for both scholarly discussion and real-life use in the digital age.</div></div><div><h3>Objectives</h3><div>This study was conducted to determine the prevalence of PVS and its associated factors, which include sociodemographic characteristics, device characteristics, device usage patterns, smartphone addiction, and mental health.</div></div><div><h3>Methodology</h3><div>A cross-sectional study was conducted using a validated questionnaire among undergraduate students in a public university in Malaysia, who were identified using proportionate stratified random sampling. The dependent variable measured through the questionnaire was the prevalence of PVS, while the independent variables were socio-demographic factors (age, gender, ethnic), device characteristics (types of devices, device location, notification alert in vibration mode and regularity in using vibration mode), device usage patterns (frequency, duration and purpose of using devices), smartphone addiction, and mental health (perceived stress, anxiety and depression). Data were analyzed using IBM SPSS application version 29.</div></div><div><h3>Results</h3><div>A total of 381 responses were obtained, with response rate of 92.7%. The mean age of respondents was 21.96 ± 1.64 years. The prevalence of PVS was 49.3%, which was predicted by age (AOR: 0.55; 95%CI: 0.38–0.95), location of device carried in the front pocket of pants (AOR: 0.58; 95%CI: 0.36–0.95), location of device carried in sling bag (AOR: 0.49; 95%CI: 0.32–0.77), notification alert in vibration mode (AOR: 2.33; 95%CI: 1.33–4.09) and regularity using vibration mode (AOR: 2.91; 95%CI: 1.84–4.61).</div></div><div><h3>Conclusion</h3><div>Five factors predicted PVS in this study, comprising one sociodemographic variable and 4 device characteristics variables. Based on the results, health education should teach undergraduate students to recognize PVS symptoms and implement healthy technology practices such as optimizing device placement, decreasing vibration mode usage, and regulating device usage behaviors. Practical advice on setting limits and taking breaks can also reduce PVS risk.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"17 ","pages":"Article 100552"},"PeriodicalIF":4.9,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid expansion of social media and digital device use has created a new phenomenon: Problematic Internet Use (PIU). Numerous studies have explored the potential impact of digital use on the self-esteem and well-being of users. This systematic review examines and synthesizes the findings of studies that have investigated the possible outcomes of self-esteem, anxiety, depression, and loneliness. A search on PsycINFO, PubMed, and Scopus identified 37 eligible studies that investigated these variables in a sample of adults (18 years and above). The findings from these studies were categorized based on their impact on self-esteem, anxiety, depression, and loneliness. They reported the relationship of these variables with other interesting factors such as Fear of Missing out (FoMo), social anxiety, emotional investment, and differences between young adults and older adults and the differences based on gender. However, the studies included in the review have limitations, such as small sample sizes, cross-sectional design, and self-reporting to evaluate the possible consequences. To gain a deeper understanding of the mechanisms underlying the relationship between digital use and its impact on users, future studies must investigate this relationship through qualitative and longitudinal studies.
{"title":"Social media in the adult population: Potential outcomes and its relationship with self-esteem and well-being - A systematic literature review","authors":"Giorgia Rossi , Caterina Fiorilli , Giacomo Angelini , Teresa Grimaldi Capitello","doi":"10.1016/j.chbr.2024.100555","DOIUrl":"10.1016/j.chbr.2024.100555","url":null,"abstract":"<div><div>The rapid expansion of social media and digital device use has created a new phenomenon: Problematic Internet Use (PIU). Numerous studies have explored the potential impact of digital use on the self-esteem and well-being of users. This systematic review examines and synthesizes the findings of studies that have investigated the possible outcomes of self-esteem, anxiety, depression, and loneliness. A search on PsycINFO, PubMed, and Scopus identified 37 eligible studies that investigated these variables in a sample of adults (18 years and above). The findings from these studies were categorized based on their impact on self-esteem, anxiety, depression, and loneliness. They reported the relationship of these variables with other interesting factors such as Fear of Missing out (FoMo), social anxiety, emotional investment, and differences between young adults and older adults and the differences based on gender. However, the studies included in the review have limitations, such as small sample sizes, cross-sectional design, and self-reporting to evaluate the possible consequences. To gain a deeper understanding of the mechanisms underlying the relationship between digital use and its impact on users, future studies must investigate this relationship through qualitative and longitudinal studies.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"17 ","pages":"Article 100555"},"PeriodicalIF":4.9,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-06DOI: 10.1016/j.chbr.2024.100550
Yi-Ning Kelly Huang , Mei-Chen Chang , Shiang-Yao Liu
The profound impact of AI technology on human life, from daily routines to professional settings, necessitates a corresponding evolution in education, where the content must adapt to the advancements in AI technology. Given high school students' status as digital natives, understanding their perspectives is invaluable for planning suitable lessons and informing policy formulation in education. This study therefore aimed to investigate high school students' knowledge, perceptions, and acceptance of the application of AI in medicine and agriculture. Questionnaires and interviews were used to explore students' perspectives. Data were collected from 373 students (aged 15–17), and the results indicated that their understanding of AI was limited: approximately 64 percent of participants could not identify the meaning of ‘AI’. Moreover, students were more reluctant to accept the use of AI in medicine. However, students' acceptance improved significantly when the context of the questions involved humans in AI decision-making. Nonetheless, the analysis showed a positive correlation between students' perceptions of the benefits and acceptance of AI applications in both the agricultural and medical domains. These findings underscore the importance of incorporating discussions on emerging AI technologies into high school science curricula, helping cultivate students' fundamental understanding of emerging technological advancements and knowledge, thereby facilitating societal progress.
{"title":"Taiwanese high school students’ perspectives on artificial intelligence and its applications","authors":"Yi-Ning Kelly Huang , Mei-Chen Chang , Shiang-Yao Liu","doi":"10.1016/j.chbr.2024.100550","DOIUrl":"10.1016/j.chbr.2024.100550","url":null,"abstract":"<div><div>The profound impact of AI technology on human life, from daily routines to professional settings, necessitates a corresponding evolution in education, where the content must adapt to the advancements in AI technology. Given high school students' status as digital natives, understanding their perspectives is invaluable for planning suitable lessons and informing policy formulation in education. This study therefore aimed to investigate high school students' knowledge, perceptions, and acceptance of the application of AI in medicine and agriculture. Questionnaires and interviews were used to explore students' perspectives. Data were collected from 373 students (aged 15–17), and the results indicated that their understanding of AI was limited: approximately 64 percent of participants could not identify the meaning of ‘AI’. Moreover, students were more reluctant to accept the use of AI in medicine. However, students' acceptance improved significantly when the context of the questions involved humans in AI decision-making. Nonetheless, the analysis showed a positive correlation between students' perceptions of the benefits and acceptance of AI applications in both the agricultural and medical domains. These findings underscore the importance of incorporating discussions on emerging AI technologies into high school science curricula, helping cultivate students' fundamental understanding of emerging technological advancements and knowledge, thereby facilitating societal progress.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"17 ","pages":"Article 100550"},"PeriodicalIF":4.9,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-06DOI: 10.1016/j.chbr.2024.100557
Algae Kit Yee Au , Sophie Kai Lam Cheng , Wesley Chi Hang Wu , David H.K. Shum , John B. Nezlek , Bryant Pui Hung Hui
Prosocial behaviors (PB), referring to voluntary acts intended to benefit others, have become increasingly prevalent online due to advancements in Internet and technology, providing opportunities to benefit people globally. Moreover, previous research suggests that age is a crucial determinant of PB, although the findings are mixed. This study explored the types of online prosocial behaviors (OPB) preferred by different age groups among a sample of 31 Hong Kong Chinese aged 20–70. The participants included a roughly equal number of females and males, recruited through social media platforms. Participants engaged in four focus group discussions, sharing their experiences and thoughts on OPB. The thematic analysis was guided by a recently developed classification of prosociality, distinguishing between interpersonal prosociality (direct PB with immediate feedback) and ideological prosociality (indirect benefits toward collectives without immediate outcomes). Inductive codes that could not be allocated to either type were grouped as a new theme. Three themes emerged: (i) interpersonal OPB (e.g., helping others online for specific goals), (ii) ideological OPB (e.g., concern about injustice and environmental issues), and (iii) mixed OPB (e.g., saving animals, updating COVID-19 information). We found that attention to interpersonal prosociality was highest among older adults (aged 60+), while younger adults (aged 18–29) exhibited greater concern for ideological OPB compared to their older counterparts. Our findings contribute to the conceptual framework of prosociality and underscore the importance of age-related factors in future quantitative research on OPB and on the design of online charity campaigns.
{"title":"Understanding age-related differences in online prosocial behavior: A qualitative thematic analysis of interpersonal, ideological, and mixed patterns","authors":"Algae Kit Yee Au , Sophie Kai Lam Cheng , Wesley Chi Hang Wu , David H.K. Shum , John B. Nezlek , Bryant Pui Hung Hui","doi":"10.1016/j.chbr.2024.100557","DOIUrl":"10.1016/j.chbr.2024.100557","url":null,"abstract":"<div><div>Prosocial behaviors (PB), referring to voluntary acts intended to benefit others, have become increasingly prevalent online due to advancements in Internet and technology, providing opportunities to benefit people globally. Moreover, previous research suggests that age is a crucial determinant of PB, although the findings are mixed. This study explored the types of online prosocial behaviors (OPB) preferred by different age groups among a sample of 31 Hong Kong Chinese aged 20–70. The participants included a roughly equal number of females and males, recruited through social media platforms. Participants engaged in four focus group discussions, sharing their experiences and thoughts on OPB. The thematic analysis was guided by a recently developed classification of prosociality, distinguishing between interpersonal prosociality (direct PB with immediate feedback) and ideological prosociality (indirect benefits toward collectives without immediate outcomes). Inductive codes that could not be allocated to either type were grouped as a new theme. Three themes emerged: (i) interpersonal OPB (e.g., helping others online for specific goals), (ii) ideological OPB (e.g., concern about injustice and environmental issues), and (iii) mixed OPB (e.g., saving animals, updating COVID-19 information). We found that attention to interpersonal prosociality was highest among older adults (aged 60+), while younger adults (aged 18–29) exhibited greater concern for ideological OPB compared to their older counterparts. Our findings contribute to the conceptual framework of prosociality and underscore the importance of age-related factors in future quantitative research on OPB and on the design of online charity campaigns.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"17 ","pages":"Article 100557"},"PeriodicalIF":4.9,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-04DOI: 10.1016/j.chbr.2024.100554
Ebrahim Mohammadpour , Yahya Maroofi
The concept of technological, pedagogical, and content knowledge is influenced by various factors and can be assessed using self-assessment scales. However, relying solely on these scales raises concerns. This study comparing two teacher recruitment and training methods found that longer-term training resulted in better pedagogical and technological knowledge, while the short-term training group excelled in content knowledge. However, there were no significant differences in other TPACK components. The study also emphasized that teachers often overestimate their knowledge, underscoring the importance of comprehensive training programs and multiple assessment methods to evaluate teacher skills and knowledge.
{"title":"The disparity between performance-based and self-reported measures of TPACK: Implications for teacher education and professional development","authors":"Ebrahim Mohammadpour , Yahya Maroofi","doi":"10.1016/j.chbr.2024.100554","DOIUrl":"10.1016/j.chbr.2024.100554","url":null,"abstract":"<div><div>The concept of technological, pedagogical, and content knowledge is influenced by various factors and can be assessed using self-assessment scales. However, relying solely on these scales raises concerns. This study comparing two teacher recruitment and training methods found that longer-term training resulted in better pedagogical and technological knowledge, while the short-term training group excelled in content knowledge. However, there were no significant differences in other TPACK components. The study also emphasized that teachers often overestimate their knowledge, underscoring the importance of comprehensive training programs and multiple assessment methods to evaluate teacher skills and knowledge.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"17 ","pages":"Article 100554"},"PeriodicalIF":4.9,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.chbr.2024.100551
Nanda van der Stap , Theo van den Bogaart , Ebrahim Rahimi , Stan van Ginkel , Tobey Nelson , Johan Versendaal
Previous studies have revealed numerous benefits of computer-mediated learning, yet the design of such learning programmes are not always supportive for adult-learners. Studies show that higher education adult-learners are wary of the digital environment in computer-mediated courses, and hence refrain from online interaction resulting in lower learning outcomes and lower perceived learning satisfaction. Without an instructional model that supports adult-learners’ online interaction, teachers may fall back on traditional in-class teaching in an effort to make up for missed learning opportunities. To remedy this, design principles, specifically supportive for adult-learners, were co-designed into an instructional model reflecting the concentric dynamics that strengthen said programmes. In order to validate and improve the quality of the model a Delphi study was carried out among experts (n = 8) to evaluate the various components of the model, including its overall supportiveness for adult-learners in computer-mediated learning. The experts were selected based on their expertise and research in blended learning. Two evaluative rounds through surveys in the programme Crowdtech took place until full alignment of the experts’ opinions was had. The resulting model is a concentric one, illustrating how activities are best distributed across the online environment and the in-class environment, and how social presence can be evoked. This study provides insights into the design of computer-mediated learning programmes tailored for higher education adult-learners as it closes the gap in literature on instructional models and blended learning design. This validated instructional model may help course designers in designing or improving computer-mediated learning programmes.
{"title":"Supporting adult-learners’ online interaction in computer-mediated learning","authors":"Nanda van der Stap , Theo van den Bogaart , Ebrahim Rahimi , Stan van Ginkel , Tobey Nelson , Johan Versendaal","doi":"10.1016/j.chbr.2024.100551","DOIUrl":"10.1016/j.chbr.2024.100551","url":null,"abstract":"<div><div>Previous studies have revealed numerous benefits of computer-mediated learning, yet the design of such learning programmes are not always supportive for adult-learners. Studies show that higher education adult-learners are wary of the digital environment in computer-mediated courses, and hence refrain from online interaction resulting in lower learning outcomes and lower perceived learning satisfaction. Without an instructional model that supports adult-learners’ online interaction, teachers may fall back on traditional in-class teaching in an effort to make up for missed learning opportunities. To remedy this, design principles, specifically supportive for adult-learners, were co-designed into an instructional model reflecting the concentric dynamics that strengthen said programmes. In order to validate and improve the quality of the model a Delphi study was carried out among experts (<em>n</em> = 8) to evaluate the various components of the model, including its overall supportiveness for adult-learners in computer-mediated learning. The experts were selected based on their expertise and research in blended learning. Two evaluative rounds through surveys in the programme Crowdtech took place until full alignment of the experts’ opinions was had. The resulting model is a concentric one, illustrating how activities are best distributed across the online environment and the in-class environment, and how social presence can be evoked. This study provides insights into the design of computer-mediated learning programmes tailored for higher education adult-learners as it closes the gap in literature on instructional models and blended learning design. This validated instructional model may help course designers in designing or improving computer-mediated learning programmes.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"16 ","pages":"Article 100551"},"PeriodicalIF":4.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.chbr.2024.100525
Yvonne M. Fromm , Dirk Ifenthaler
Adaptive learning environments (ALE) provide great potential for personalizing and supporting learning processes in continuing education (CE). However, valid frameworks for designing ALE for CE have been missing so far. For example, user-centered and empirically-based guidelines for selecting indicators (i.e., information about learners and their contexts that should be collected and analyzed by ALE) and interventions for personalizing and supporting learning processes have not been established yet. Therefore, this paper aims to develop a framework of indicators and interventions for ALE for CE by investigating the perspectives of different stakeholders (i.e., learners, CE specialists, and educational technology specialists). We first conducted an interview study (N = 37) to identify indicators for ALE for CE. Subsequently, we conducted focus group interviews (N = 19) and an online survey (N = 72) to specify and evaluate possible interventions. Several indicators related to internal (e.g., prior knowledge) and external (e.g., time available for learning) conditions of learning as well as corresponding interventions (e.g., adaptation of the general difficulty level and thematic focus, recommendation of timely suitable learning resources) were identified. We developed a framework classifying interventions based on indicators and adaptivity type and providing evaluations of learners’ willingness to use these interventions, perceived learning support, and implementation effort. This framework can be used by researchers, system designers, as well as CE and educational technology specialists to design and implement user-centered and trustworthy ALE for CE.
{"title":"Designing adaptive learning environments for continuing education: Stakeholders’ perspectives on indicators and interventions","authors":"Yvonne M. Fromm , Dirk Ifenthaler","doi":"10.1016/j.chbr.2024.100525","DOIUrl":"10.1016/j.chbr.2024.100525","url":null,"abstract":"<div><div>Adaptive learning environments (ALE) provide great potential for personalizing and supporting learning processes in continuing education (CE). However, valid frameworks for designing ALE for CE have been missing so far. For example, user-centered and empirically-based guidelines for selecting indicators (i.e., information about learners and their contexts that should be collected and analyzed by ALE) and interventions for personalizing and supporting learning processes have not been established yet. Therefore, this paper aims to develop a framework of indicators and interventions for ALE for CE by investigating the perspectives of different stakeholders (i.e., learners, CE specialists, and educational technology specialists). We first conducted an interview study (<em>N</em> = 37) to identify indicators for ALE for CE. Subsequently, we conducted focus group interviews (<em>N</em> = 19) and an online survey (<em>N</em> = 72) to specify and evaluate possible interventions. Several indicators related to internal (e.g., prior knowledge) and external (e.g., time available for learning) conditions of learning as well as corresponding interventions (e.g., adaptation of the general difficulty level and thematic focus, recommendation of timely suitable learning resources) were identified. We developed a framework classifying interventions based on indicators and adaptivity type and providing evaluations of learners’ willingness to use these interventions, perceived learning support, and implementation effort. This framework can be used by researchers, system designers, as well as CE and educational technology specialists to design and implement user-centered and trustworthy ALE for CE.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"16 ","pages":"Article 100525"},"PeriodicalIF":4.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.chbr.2024.100538
Alexander Diel , Tania Lalgi , Isabel Carolin Schröter , Karl F. MacDorman , Martin Teufel , Alexander Bäuerle
Deepfakes are AI-generated media designed to look real, often with the intent to deceive. Deepfakes threaten public and personal safety by facilitating disinformation, propaganda, and identity theft. Though research has been conducted on human performance in deepfake detection, the results have not yet been synthesized. This systematic review and meta-analysis investigates human deepfake detection accuracy. Searches in PubMed, ScienceGov, JSTOR, Google Scholar, and paper references, conducted in June and October 2024, identified empirical studies measuring human detection of high-quality deepfakes. After pooling accuracy, odds-ratio, and sensitivity (d') effect sizes (k = 137 effects) from 56 papers involving 86,155 participants, we analyzed 1) overall deepfake detection performance, 2) performance across stimulus types (audio, image, text, and video), and 3) the effects of detection-improvement strategies. Overall deepfake detection rates (sensitivity) were not significantly above chance because 95% confidence intervals crossed 50%. Total deepfake detection accuracy was 55.54% (95% CI [48.87, 62.10], k = 67). For audio, accuracy was 62.08% [38.23, 83.18], k = 8; for images, 53.16% [42.12, 64.64], k = 18; for text, 52.00% [37.42, 65.88], k = 15; and for video, 57.31% [47.80, 66.57], k = 26. Odds ratios were 0.64 [0.52, 0.79], k = 62, indicating 39% detection accuracy, below chance (audio 45%, image 35%, text 40%, video 40%). Moreover, d' values show no significant difference from chance. However, strategies like feedback training, AI support, and deepfake caricaturization improved detection performance above chance levels (65.14% [55.21, 74.46], k = 15), especially for video stimuli.
{"title":"Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers","authors":"Alexander Diel , Tania Lalgi , Isabel Carolin Schröter , Karl F. MacDorman , Martin Teufel , Alexander Bäuerle","doi":"10.1016/j.chbr.2024.100538","DOIUrl":"10.1016/j.chbr.2024.100538","url":null,"abstract":"<div><div><em>Deepfakes</em> are AI-generated media designed to look real, often with the intent to deceive. Deepfakes threaten public and personal safety by facilitating disinformation, propaganda, and identity theft. Though research has been conducted on human performance in deepfake detection, the results have not yet been synthesized. This systematic review and meta-analysis investigates human deepfake detection accuracy. Searches in PubMed, ScienceGov, JSTOR, Google Scholar, and paper references, conducted in June and October 2024, identified empirical studies measuring human detection of high-quality deepfakes. After pooling accuracy, odds-ratio, and sensitivity (<em>d'</em>) effect sizes (<em>k</em> = 137 effects) from 56 papers involving 86,155 participants, we analyzed 1) overall deepfake detection performance, 2) performance across stimulus types (audio, image, text, and video), and 3) the effects of detection-improvement strategies. Overall deepfake detection rates (<em>sensitivity</em>) were not significantly above chance because 95% confidence intervals crossed 50%. Total deepfake detection accuracy was 55.54% (95% CI [48.87, 62.10], <em>k</em> = 67). For audio, accuracy was 62.08% [38.23, 83.18], <em>k</em> = 8; for images, 53.16% [42.12, 64.64], <em>k</em> = 18; for text, 52.00% [37.42, 65.88], <em>k</em> = 15; and for video, 57.31% [47.80, 66.57], <em>k</em> = 26. Odds ratios were 0.64 [0.52, 0.79], <em>k</em> = 62, indicating 39% detection accuracy, below chance (audio 45%, image 35%, text 40%, video 40%). Moreover, <em>d'</em> values show no significant difference from chance. However, strategies like feedback training, AI support, and deepfake caricaturization improved detection performance above chance levels (65.14% [55.21, 74.46], <em>k</em> = 15), especially for video stimuli.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"16 ","pages":"Article 100538"},"PeriodicalIF":4.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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