Humanities & Social Sciences

89 items

Studies in Self-Access Learning Journal Sep 28, 2026
Surveying 172 students at a Japanese national university and analyzing responses with descriptive statistics, paired-samples t-tests, and content analysis, the study compared learners' perceptions of the usefulness, limitations, and future roles of self-access language learning (SALL) services and AI chatbots for English learning, finding that SALL services were rated significantly higher for speaking development, motivation, personalized advice and feedback, writing, and overall contribution, while chatbots were valued for convenience and accessibility, and that learners saw the two as complementary rather than competing resources.
Surveying 172 students at a Japanese national university and analyzing responses with descriptive statistics, paired-samples t-tests, and content analysis, the study compared learners' perceptions of the usefulness, limitations, and future roles of self-access language learning (SALL) services and AI chatbots for English learning, finding that SALL services were rated significantly higher for speaking development, motivation, personalized advice and feedback, writing, and overall contribution, while chatbots were valued for convenience and accessibility, and that learners saw the two as complementary rather than competing resources.
172 Students at a Japanese National University Rated Self-Access Language Learning Services Above AI Chatbots for Speaking, Motivation, and Personalized Guidance, Seeing the Two as Complementary
Surveying 172 students at a Japanese national university and analyzing responses with descriptive statistics, paired-samples t-tests, and content analysis, the study compared learners' perceptions of the usefulness, limitations, and future roles of self-access language learning (SALL) services and AI chatbots for English learning, finding that SALL services were rated significantly higher for speaking development, motivation, personalized advice and feedback, writing, and overall contribution, while chatbots were valued for convenience and accessibility, and that learners saw the two as complementary rather than competing resources.
Surveying 172 students at a Japanese national university and analyzing responses with descriptive statistics, paired-samples t-tests, and content analysis, the study compared learners' perceptions of the usefulness, limitations, and future roles of self-access language learning (SALL) services and AI chatbots for English learning, finding that SALL services were rated significantly higher for speaking development, motivation, personalized advice and feedback, writing, and overall contribution, while chatbots were valued for convenience and accessibility, and that learners saw the two as complementary rather than competing resources.
arXiv Sep 28, 2026
Limbu and Chounta used a 2x2 between-subjects design in which 20 right-handed German-native university students copied text on a WACOM tablet while touch sensitivity (glove use) and kinaesthetic intensity (increased writing pressure) were manipulated, measuring outcomes with a 10-item immediate recall test, dual-task reaction time, and NASA-TLX; Bayesian binomial regression showed about 85–88% probability that increased pressure reduced recall (Bayes factors 5.83 and 7.1, moderate evidence), glove use alone showed no clear effect, and Bayesian mediation analysis found no strong evidence that mental effort or perceived workload mediated these effects (all 95% credible intervals included zero).
Limbu and Chounta used a 2x2 between-subjects design in which 20 right-handed German-native university students copied text on a WACOM tablet while touch sensitivity (glove use) and kinaesthetic intensity (increased writing pressure) were manipulated, measuring outcomes with a 10-item immediate recall test, dual-task reaction time, and NASA-TLX; Bayesian binomial regression showed about 85–88% probability that increased pressure reduced recall (Bayes factors 5.83 and 7.1, moderate evidence), glove use alone showed no clear effect, and Bayesian mediation analysis found no strong evidence that mental effort or perceived workload mediated these effects (all 95% credible intervals included zero).
Among 20 university students, writing with a glove showed no clear effect on immediate recall, while increased writing pressure reduced recall with about 85–88% probability
Limbu and Chounta used a 2x2 between-subjects design in which 20 right-handed German-native university students copied text on a WACOM tablet while touch sensitivity (glove use) and kinaesthetic intensity (increased writing pressure) were manipulated, measuring outcomes with a 10-item immediate recall test, dual-task reaction time, and NASA-TLX; Bayesian binomial regression showed about 85–88% probability that increased pressure reduced recall (Bayes factors 5.83 and 7.1, moderate evidence), glove use alone showed no clear effect, and Bayesian mediation analysis found no strong evidence that mental effort or perceived workload mediated these effects (all 95% credible intervals included zero).
Limbu and Chounta used a 2x2 between-subjects design in which 20 right-handed German-native university students copied text on a WACOM tablet while touch sensitivity (glove use) and kinaesthetic intensity (increased writing pressure) were manipulated, measuring outcomes with a 10-item immediate recall test, dual-task reaction time, and NASA-TLX; Bayesian binomial regression showed about 85–88% probability that increased pressure reduced recall (Bayes factors 5.83 and 7.1, moderate evidence), glove use alone showed no clear effect, and Bayesian mediation analysis found no strong evidence that mental effort or perceived workload mediated these effects (all 95% credible intervals included zero).
Studies in Self-Access Learning Journal Sep 28, 2026
This editorial introduction presents the September 2026 issue of Studies in Self-Access Learning Journal (Vol. 17, No. 3, pp. 268–272), with authors based in Indonesia, Japan, Oman, Thailand, the United Kingdom, and Vietnam, organizing nine papers into four themes: self-access connections with wider learning spaces and communities (Kashiwa; Martinho et al.), the lives and well-being of people within self-access communities (Phelps; Pemberton et al.), self-regulation and learning processes in self-access (Nakanishi et al.; Anggoro et al.), and the growing role of generative AI in self-access language learning (Ubukata & Marzin; Andersson; Al Ghaithi & Behforouz), plus two book reviews on self-regulated and self-directed learning (Nguyen; Tomiyama).
This editorial introduction presents the September 2026 issue of Studies in Self-Access Learning Journal (Vol. 17, No. 3, pp. 268–272), with authors based in Indonesia, Japan, Oman, Thailand, the United Kingdom, and Vietnam, organizing nine papers into four themes: self-access connections with wider learning spaces and communities (Kashiwa; Martinho et al.), the lives and well-being of people within self-access communities (Phelps; Pemberton et al.), self-regulation and learning processes in self-access (Nakanishi et al.; Anggoro et al.), and the growing role of generative AI in self-access language learning (Ubukata & Marzin; Andersson; Al Ghaithi & Behforouz), plus two book reviews on self-regulated and self-directed learning (Nguyen; Tomiyama).
Mynard and Ambinintsoa introduce the September 2026 SiSAL Journal issue, weaving nine papers and two book reviews into a four-theme narrative on self-access learning
This editorial introduction presents the September 2026 issue of Studies in Self-Access Learning Journal (Vol. 17, No. 3, pp. 268–272), with authors based in Indonesia, Japan, Oman, Thailand, the United Kingdom, and Vietnam, organizing nine papers into four themes: self-access connections with wider learning spaces and communities (Kashiwa; Martinho et al.), the lives and well-being of people within self-access communities (Phelps; Pemberton et al.), self-regulation and learning processes in self-access (Nakanishi et al.; Anggoro et al.), and the growing role of generative AI in self-access language learning (Ubukata & Marzin; Andersson; Al Ghaithi & Behforouz), plus two book reviews on self-regulated and self-directed learning (Nguyen; Tomiyama).
This editorial introduction presents the September 2026 issue of Studies in Self-Access Learning Journal (Vol. 17, No. 3, pp. 268–272), with authors based in Indonesia, Japan, Oman, Thailand, the United Kingdom, and Vietnam, organizing nine papers into four themes: self-access connections with wider learning spaces and communities (Kashiwa; Martinho et al.), the lives and well-being of people within self-access communities (Phelps; Pemberton et al.), self-regulation and learning processes in self-access (Nakanishi et al.; Anggoro et al.), and the growing role of generative AI in self-access language learning (Ubukata & Marzin; Andersson; Al Ghaithi & Behforouz), plus two book reviews on self-regulated and self-directed learning (Nguyen; Tomiyama).
Journal of General Education and Humanities Sep 28, 2026
The study surveyed 78 students enrolled in the Educational Economics course in the Economic Education Study Program at Universitas Jambi during the 2025/2026 academic year, using total sampling and a 16-item four-point Likert-scale questionnaire across five dimensions (ease of use, knowledge, satisfaction, motivation, activity), and found generally positive perceptions with ease of use scoring highest (M = 3.12), followed by knowledge (M = 3.02), satisfaction (M = 2.94), motivation (M = 2.83), and activity (M = 2.81), while perceptions were comparatively lower for response accuracy, critical thinking, and motivation for academic writing.
The study surveyed 78 students enrolled in the Educational Economics course in the Economic Education Study Program at Universitas Jambi during the 2025/2026 academic year, using total sampling and a 16-item four-point Likert-scale questionnaire across five dimensions (ease of use, knowledge, satisfaction, motivation, activity), and found generally positive perceptions with ease of use scoring highest (M = 3.12), followed by knowledge (M = 3.02), satisfaction (M = 2.94), motivation (M = 2.83), and activity (M = 2.81), while perceptions were comparatively lower for response accuracy, critical thinking, and motivation for academic writing.
Survey of 78 economics-education students at Universitas Jambi finds ChatGPT ease of use scores highest (M = 3.12) while accuracy and critical thinking rate lower
The study surveyed 78 students enrolled in the Educational Economics course in the Economic Education Study Program at Universitas Jambi during the 2025/2026 academic year, using total sampling and a 16-item four-point Likert-scale questionnaire across five dimensions (ease of use, knowledge, satisfaction, motivation, activity), and found generally positive perceptions with ease of use scoring highest (M = 3.12), followed by knowledge (M = 3.02), satisfaction (M = 2.94), motivation (M = 2.83), and activity (M = 2.81), while perceptions were comparatively lower for response accuracy, critical thinking, and motivation for academic writing.
The study surveyed 78 students enrolled in the Educational Economics course in the Economic Education Study Program at Universitas Jambi during the 2025/2026 academic year, using total sampling and a 16-item four-point Likert-scale questionnaire across five dimensions (ease of use, knowledge, satisfaction, motivation, activity), and found generally positive perceptions with ease of use scoring highest (M = 3.12), followed by knowledge (M = 3.02), satisfaction (M = 2.94), motivation (M = 2.83), and activity (M = 2.81), while perceptions were comparatively lower for response accuracy, critical thinking, and motivation for academic writing.
JOURNAL OF DIGITAL LEARNING AND DISTANCE EDUCATION Sep 28, 2026
This study develops a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model that predicts student academic performance and failure risk from weekly interaction patterns in a Learning Management System (LMS), using a dataset of interaction logs from 1,250 distance education students over one semester with features such as material access frequency, forum participation, and assignment submission timing, and reports 91.4% accuracy, 89.2% precision, and 92.6% recall as early as Week 6 of the course, enabling educators to implement timely pedagogical interventions to reduce dropout rates in digital and distance learning environments.
This study develops a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model that predicts student academic performance and failure risk from weekly interaction patterns in a Learning Management System (LMS), using a dataset of interaction logs from 1,250 distance education students over one semester with features such as material access frequency, forum participation, and assignment submission timing, and reports 91.4% accuracy, 89.2% precision, and 92.6% recall as early as Week 6 of the course, enabling educators to implement timely pedagogical interventions to reduce dropout rates in digital and distance learning environments.
BiLSTM model predicts distance-education students' academic failure risk by Week 6 from LMS weekly interaction logs, reporting 91.4% accuracy and 92.6% recall
This study develops a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model that predicts student academic performance and failure risk from weekly interaction patterns in a Learning Management System (LMS), using a dataset of interaction logs from 1,250 distance education students over one semester with features such as material access frequency, forum participation, and assignment submission timing, and reports 91.4% accuracy, 89.2% precision, and 92.6% recall as early as Week 6 of the course, enabling educators to implement timely pedagogical interventions to reduce dropout rates in digital and distance learning environments.
This study develops a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model that predicts student academic performance and failure risk from weekly interaction patterns in a Learning Management System (LMS), using a dataset of interaction logs from 1,250 distance education students over one semester with features such as material access frequency, forum participation, and assignment submission timing, and reports 91.4% accuracy, 89.2% precision, and 92.6% recall as early as Week 6 of the course, enabling educators to implement timely pedagogical interventions to reduce dropout rates in digital and distance learning environments.
Terence Tao blog RSS Sep 28, 2026
Using a hypothetical scenario discussed at her department's September 17 faculty retreat—an OpenAI five-sigma dark-energy detection—University of Washington Astronomy chair Jess Werk argues that generative AI makes productivity easy to manufacture while academic incentives have long rewarded output over understanding, and proposes three sets of department-level measures (PhD processes, faculty reward systems, and department community) that together form a "cognitive sanctuary" to protect doctoral training and human judgment.
Using a hypothetical scenario discussed at her department's September 17 faculty retreat—an OpenAI five-sigma dark-energy detection—University of Washington Astronomy chair Jess Werk argues that generative AI makes productivity easy to manufacture while academic incentives have long rewarded output over understanding, and proposes three sets of department-level measures (PhD processes, faculty reward systems, and department community) that together form a "cognitive sanctuary" to protect doctoral training and human judgment.
UW Astronomy chair Jess Werk proposes "cognitive sanctuaries," arguing department-level reforms are needed to protect PhD training in the AI era
Using a hypothetical scenario discussed at her department's September 17 faculty retreat—an OpenAI five-sigma dark-energy detection—University of Washington Astronomy chair Jess Werk argues that generative AI makes productivity easy to manufacture while academic incentives have long rewarded output over understanding, and proposes three sets of department-level measures (PhD processes, faculty reward systems, and department community) that together form a "cognitive sanctuary" to protect doctoral training and human judgment.
Using a hypothetical scenario discussed at her department's September 17 faculty retreat—an OpenAI five-sigma dark-energy detection—University of Washington Astronomy chair Jess Werk argues that generative AI makes productivity easy to manufacture while academic incentives have long rewarded output over understanding, and proposes three sets of department-level measures (PhD processes, faculty reward systems, and department community) that together form a "cognitive sanctuary" to protect doctoral training and human judgment.
Nature News Sep 28, 2026
An as-yet un-peer-reviewed MetaArXiv preprint manually scraped about 1,000 online advertisements for 'freelance' peer-reviewer jobs from LinkedIn, company websites and other recruitment platforms and interviewed five working and retired researchers in India, Spain and the United Kingdom, finding a market in which third-party firms offer paid reviewing services to scholarly publishers; interviewees often had 2–3 days or even 24 hours to assess a manuscript and most received €30–40 per review, while one company's website listed more than 10,000 registered freelance peer reviewers, including 5,669 marked 'active' who had each reviewed up to 50 manuscripts, though the study does not provide conclusive evidence about which journals use these services or how widespread the practice is.
An as-yet un-peer-reviewed MetaArXiv preprint manually scraped about 1,000 online advertisements for 'freelance' peer-reviewer jobs from LinkedIn, company websites and other recruitment platforms and interviewed five working and retired researchers in India, Spain and the United Kingdom, finding a market in which third-party firms offer paid reviewing services to scholarly publishers; interviewees often had 2–3 days or even 24 hours to assess a manuscript and most received €30–40 per review, while one company's website listed more than 10,000 registered freelance peer reviewers, including 5,669 marked 'active' who had each reviewed up to 50 manuscripts, though the study does not provide conclusive evidence about which journals use these services or how widespread the practice is.
Third-party firms openly recruit paid reviewers: about 1,000 job ads and five interviewees reveal a market for outsourced journal peer review
An as-yet un-peer-reviewed MetaArXiv preprint manually scraped about 1,000 online advertisements for 'freelance' peer-reviewer jobs from LinkedIn, company websites and other recruitment platforms and interviewed five working and retired researchers in India, Spain and the United Kingdom, finding a market in which third-party firms offer paid reviewing services to scholarly publishers; interviewees often had 2–3 days or even 24 hours to assess a manuscript and most received €30–40 per review, while one company's website listed more than 10,000 registered freelance peer reviewers, including 5,669 marked 'active' who had each reviewed up to 50 manuscripts, though the study does not provide conclusive evidence about which journals use these services or how widespread the practice is.
An as-yet un-peer-reviewed MetaArXiv preprint manually scraped about 1,000 online advertisements for 'freelance' peer-reviewer jobs from LinkedIn, company websites and other recruitment platforms and interviewed five working and retired researchers in India, Spain and the United Kingdom, finding a market in which third-party firms offer paid reviewing services to scholarly publishers; interviewees often had 2–3 days or even 24 hours to assess a manuscript and most received €30–40 per review, while one company's website listed more than 10,000 registered freelance peer reviewers, including 5,669 marked 'active' who had each reviewed up to 50 manuscripts, though the study does not provide conclusive evidence about which journals use these services or how widespread the practice is.
IEEE Spectrum Sep 27, 2026
This short piece presents 'Poetry for Engineers: The UI Designer's Dream,' a poem by Ralph Earle, a former technical editor and later senior software engineer at the IBM Software Lab, in which writing code and building the internet pixel by pixel is framed as translating dry code into surpassing beauty, and the speaker dreams of translating holy visions into a communication protocol of universal wonderment and letting shreds of light fall like diamonds on the endless mountains of the Web; an accompanying biography notes that Earle joined the IBM Software Lab in Raleigh, N.C., in 1990, retired as a senior software engineer in 2017, coauthored Enterprise Computing with Objects, and after retirement published two poetry collections and was nominated for the Pushcart Prize three times.
This short piece presents 'Poetry for Engineers: The UI Designer's Dream,' a poem by Ralph Earle, a former technical editor and later senior software engineer at the IBM Software Lab, in which writing code and building the internet pixel by pixel is framed as translating dry code into surpassing beauty, and the speaker dreams of translating holy visions into a communication protocol of universal wonderment and letting shreds of light fall like diamonds on the endless mountains of the Web; an accompanying biography notes that Earle joined the IBM Software Lab in Raleigh, N.C., in 1990, retired as a senior software engineer in 2017, coauthored Enterprise Computing with Objects, and after retirement published two poetry collections and was nominated for the Pushcart Prize three times.
Retired IBM software engineer Ralph Earle turns code into interface beauty in the poem 'Poetry for Engineers'
This short piece presents 'Poetry for Engineers: The UI Designer's Dream,' a poem by Ralph Earle, a former technical editor and later senior software engineer at the IBM Software Lab, in which writing code and building the internet pixel by pixel is framed as translating dry code into surpassing beauty, and the speaker dreams of translating holy visions into a communication protocol of universal wonderment and letting shreds of light fall like diamonds on the endless mountains of the Web; an accompanying biography notes that Earle joined the IBM Software Lab in Raleigh, N.C., in 1990, retired as a senior software engineer in 2017, coauthored Enterprise Computing with Objects, and after retirement published two poetry collections and was nominated for the Pushcart Prize three times.
This short piece presents 'Poetry for Engineers: The UI Designer's Dream,' a poem by Ralph Earle, a former technical editor and later senior software engineer at the IBM Software Lab, in which writing code and building the internet pixel by pixel is framed as translating dry code into surpassing beauty, and the speaker dreams of translating holy visions into a communication protocol of universal wonderment and letting shreds of light fall like diamonds on the endless mountains of the Web; an accompanying biography notes that Earle joined the IBM Software Lab in Raleigh, N.C., in 1990, retired as a senior software engineer in 2017, coauthored Enterprise Computing with Objects, and after retirement published two poetry collections and was nominated for the Pushcart Prize three times.
International Journal of Modern Science and Research Technology Sep 27, 2026
This narrative review searched peer-reviewed GenAI studies published through September 05, 2026 alongside foundational cognitive-offloading research and found that evidence does not support a simple positive or negative effect of GenAI on human cognition: GenAI-supported offloading of functions such as memory retrieval, processing, and learning can reduce required cognitive capacity and support high performance across a variety of tasks, yet the same offloading can in some cases reduce the quality of subsequent encoding, and recent GenAI studies show a consistent performance-learning dissociation in which gains from support available at the time typically carry over to later unsupported performance only slightly, not at all, or even negatively.
This narrative review searched peer-reviewed GenAI studies published through September 05, 2026 alongside foundational cognitive-offloading research and found that evidence does not support a simple positive or negative effect of GenAI on human cognition: GenAI-supported offloading of functions such as memory retrieval, processing, and learning can reduce required cognitive capacity and support high performance across a variety of tasks, yet the same offloading can in some cases reduce the quality of subsequent encoding, and recent GenAI studies show a consistent performance-learning dissociation in which gains from support available at the time typically carry over to later unsupported performance only slightly, not at all, or even negatively.
Narrative review: GenAI cognitive offloading can boost immediate performance but shows a performance-learning dissociation, and which functions are offloaded matters more than whether AI is used
This narrative review searched peer-reviewed GenAI studies published through September 05, 2026 alongside foundational cognitive-offloading research and found that evidence does not support a simple positive or negative effect of GenAI on human cognition: GenAI-supported offloading of functions such as memory retrieval, processing, and learning can reduce required cognitive capacity and support high performance across a variety of tasks, yet the same offloading can in some cases reduce the quality of subsequent encoding, and recent GenAI studies show a consistent performance-learning dissociation in which gains from support available at the time typically carry over to later unsupported performance only slightly, not at all, or even negatively.
This narrative review searched peer-reviewed GenAI studies published through September 05, 2026 alongside foundational cognitive-offloading research and found that evidence does not support a simple positive or negative effect of GenAI on human cognition: GenAI-supported offloading of functions such as memory retrieval, processing, and learning can reduce required cognitive capacity and support high performance across a variety of tasks, yet the same offloading can in some cases reduce the quality of subsequent encoding, and recent GenAI studies show a consistent performance-learning dissociation in which gains from support available at the time typically carry over to later unsupported performance only slightly, not at all, or even negatively.
arXiv Sep 27, 2026
The work introduces Cultural Divergence Preservation (CDP), a reference-light diagnostic based on a one-time human calibration that flags reduced cross-country divergence as cultural flattening and increased divergence as cultural caricature, and evaluates it across four LLM backbones, three persona-based prompting methods, and two survey domains, the World Values Survey (WVS) and the Big Five Personality Test; results reveal a systematic discrepancy between conventional fidelity metrics and CDP, with controlled experiments showing CDP changes monotonically as cross-country divergence is attenuated or amplified while corresponding JSD changes remain relatively small, and an audit of real LLM generations showing DeepPersona-Inspired prompting is frequently favored by conventional fidelity m
The work introduces Cultural Divergence Preservation (CDP), a reference-light diagnostic based on a one-time human calibration that flags reduced cross-country divergence as cultural flattening and increased divergence as cultural caricature, and evaluates it across four LLM backbones, three persona-based prompting methods, and two survey domains, the World Values Survey (WVS) and the Big Five Personality Test; results reveal a systematic discrepancy between conventional fidelity metrics and CDP, with controlled experiments showing CDP changes monotonically as cross-country divergence is attenuated or amplified while corresponding JSD changes remain relatively small, and an audit of real LLM generations showing DeepPersona-Inspired prompting is frequently favored by conventional fidelity m
A new CDP diagnostic finds DeepPersona-Inspired prompting shows the strongest cultural flattening in every model-domain block across four backbones and two survey domains
The work introduces Cultural Divergence Preservation (CDP), a reference-light diagnostic based on a one-time human calibration that flags reduced cross-country divergence as cultural flattening and increased divergence as cultural caricature, and evaluates it across four LLM backbones, three persona-based prompting methods, and two survey domains, the World Values Survey (WVS) and the Big Five Personality Test; results reveal a systematic discrepancy between conventional fidelity metrics and CDP, with controlled experiments showing CDP changes monotonically as cross-country divergence is attenuated or amplified while corresponding JSD changes remain relatively small, and an audit of real LLM generations showing DeepPersona-Inspired prompting is frequently favored by conventional fidelity m
The work introduces Cultural Divergence Preservation (CDP), a reference-light diagnostic based on a one-time human calibration that flags reduced cross-country divergence as cultural flattening and increased divergence as cultural caricature, and evaluates it across four LLM backbones, three persona-based prompting methods, and two survey domains, the World Values Survey (WVS) and the Big Five Personality Test; results reveal a systematic discrepancy between conventional fidelity metrics and CDP, with controlled experiments showing CDP changes monotonically as cross-country divergence is attenuated or amplified while corresponding JSD changes remain relatively small, and an audit of real LLM generations showing DeepPersona-Inspired prompting is frequently favored by conventional fidelity m

Page 4 · showing 10