mdpi-ai/citelens huggingface dataset:
OLTW-TEC: online learning with sliding windows for text classifier ensembles
Field: AI

In the digital age, rapid dissemination of information has elevated the challenge of distinguishing between authentic news and disinformation. This challenge is particularly acute in regions experiencing geopolitical tensions, where information plays a pivotal role in shaping public perception and policy. The prevalence of disinformation in the Ukrainian-language information space, intensified by the hybrid war with russia, necessitates the development of sophisticated tools for its detection and mitigation. Our study introduces the “Online Learning with Sliding Windows for Text Classifier Ensembles” (OLTW-TEC) method, designed to address this urgent need. This research aims to develop and validate an advanced machine learning method capable of dynamically adapting to evolving disinformation tactics. The focus is on creating a highly accurate, flexible, and efficient system for detecting disinformation in Ukrainian-language texts. The OLTW-TEC method leverages an ensemble of classifiers combined with a sliding window technique to continuously update the model with the most recent data, enhancing its adaptability and accuracy over time. A unique dataset comprising both authentic and fake news items was used to evaluate the method’s performance. Advanced metrics, including precision, recall, and F1-score, facilitated a comprehensive analysis of its effectiveness. The OLTW-TEC method demonstrated exceptional performance, achieving a classification accuracy of 93%. The integration of the sliding window technique with a classifier ensemble significantly contributed to the system’s ability to accurately identify disinformation, making it a robust tool in the ongoing battle against fake news in the Ukrainian context. The application of the OLTW-TEC method highlights its potential as a versatile and effective solution for disinformation detection. Its adaptability to the specifics of the Ukrainian language and the dynamic nature of information warfare offers valuable insights into the development of similar tools for other languages and regions. OLTW-TEC represents a significant advancement in the detection of disinformation within the Ukrainian-language information space. Its development and successful implementation underscore the importance of innovative machine learning techniques in combating fake news, paving the way for further research and application in the field of digital information integrity.
Navigating virtual selves: validation of the German version of the presentation of online self scale
Field: PSYCHOLOGY

The Presentation of Online Self Scale for Adults (POSSA), originally developed by Strimbu et al. is a well-regarded instrument for assessing online self-presentation. This study evaluated the factorial structure, reliability, and validity of the German adaptation of POSSA. A CFA analysis confirmed a satisfactory fit for the proposed three-factor model, as evidenced by a CFI of 0.919, a TLI of 0.902 and a RSMEA of 0.075. The subscales of the German POSSA demonstrated high internal consistency. Additionally, convergent validity was established through significant correlations with the Impostor-Profile 30 (IPP), affirming the interpretive accuracy of the subscale scores. Specifically, the Adaptable Self and Freedom of Self Online subscales positively correlated with IPP measures of Alienation and Other-Self-Divergence, whereas the Authentic Self subscale inversely correlated with these measures. Moreover, the German POSSA scores accounted for variance in the number of Instagram followers, surpassing the predictive power of self-esteem alone. Notably, the Adaptable Self factor was positively associated with the follower count, while the Freedom of Self Online factor displayed a negative association. Collectively, these findings underscore the DE-POSSA as a robust tool for assessing self-presentation behaviors in German-speaking populations and highlight its potential for cross-cultural research in online interpersonal interactions.
Development of Chinese college students’ perception teacher differential behavior scale and its reliability and validity test
Field: PSYCHOLOGY

To compile a scale of Chinese college students’ perception of teachers’ differential behavior and to provide a reference for college students to establish correct life values, promote college students’ physical and mental health, and reduce teachers’ differential treatment. Open-ended questionnaires and expert interviews were used to conduct interviews and correspondence with 58 college students, ten psychologists, and six psychologists to form an initial questionnaire. Then, the scale’s exploratory factor analysis, confirmatory factor analysis, and reliability and validity test were conducted on 7053 college students from 18 universities in 6 provinces (municipalities directly under the Central Government). The Chinese college students’ perception of teachers’ differential behavior scale has two dimensions: teacher prejudice and preference. Each dimension includes three aspects: emotional feedback, behavior orientation, and opportunity privilege, and each aspect have a total of 4 items. The consistency test coefficients of each dimension and each factor of the prepared scale are all above 0.7, and the split-half reliability is above 0.6. Confirmatory factor analysis shows that the six-factor structural model fits well (χ2/df = 4.287, RMSEA = 0.066, CFI = 0.950, TLI = 0.919). Using the generalized anxiety disorder scale and the patient health questionaire-9items as empirical criteria, each factor in the scale demonstrated significant correlations with both the GAD scale and the patient health questionaire-9items. The Chinese college students’ perception of teachers’ differential behavior scale has a two-dimensional six-factor structure and has good reliability and validity. It can be used as an effective tool to measure Chinese college students’ perceived teacher differential behavior.
No significant difference in salivary cortisol response on the Trier Social Stress Test-Online based on coffee consumption habits
Field: PSYCHOLOGY

Background: Coffee is widely consumed around the world. In Japan, it is a type of “Shikohin” (consumed for flavor, not nutrition). Several medical studies have reported the beneficial effects of coffee consumption, whereas others suggest that these beneficial effects on psychological aspects are marginal. The habit of consuming large amounts of caffeine through coffee may improve short-term resilience in stressful situations and may exhaust individuals in the long term. We hypothesized that people who habitually drink high amounts of coffee would have lower resilience scores and higher acute stress responses. Methods: Adult Japanese men completed a questionnaire that included a resilience scale and Shikohin consumption habits. Experimental participants were recruited from the survey respondents and classified into three groups based on their coffee consumption per day: No Coffee, Low Coffee, and High Coffee. All participants were asked to join the Trier Social Stress Test-Online (TSST-OL). Subjective stress and salivary cortisol concentrations was measured at eight time points during the experiment. There were 16 participants in each group for the analysis (mean age = 46.10 years, SD = 12.58). Results: Statistical analysis showed that both subjective stress and salivary cortisol concentrations significantly increased following TSST-OL exposure. However, there were no significant differences among the groups, and the hypotheses were not supported. Conclusions: This study demonstrated the effectiveness and stability of the TSST-OL. Additionally, coffee consumption habits were not significantly related to resilience scale scores or acute stress responses.
A systematic review of student learning outcomes in CLIL in LOTE
Field: EDUCATION

Introduction: This paper aims to provide a first systematic research overview of student learning outcomes in programs teaching school subjects through languages other than English (LOTE) which are not the mother tongue of the students, according to school- or researcher-administered assessments and stakeholder perspectives, following the PRISMA statement. For brevity, we shall refer to these types of programs as CLIL in LOTE, though we have also included programs which use other labels, such as bilingual education or immersion, due to their similarities with those labeled “content and language integrated learning” (CLIL).Methods: The selected studies, published between November 1994 and December 2023, were identified through the search of SCOPUS and EBSCO. In determining which studies to include in the review, we employed the following selection criteria: (1) articles focusing on children and youth (ages 5–17 years), (2) articles focusing on CLIL programs in LOTE, (3) articles focusing on student achievement, (4) articles focusing on studies that have collected primary data, and (5) studies that used school−/researcher-administered assessments (objective) or self/ hetero-reported measures (subjective). The screening of titles, abstracts and keywords left a final sample of n = 29 scientific papers, which were then read exhaustively and assessed for methodological quality.Results: Most studies (26 of 29) addressed academic and/or linguistic outcomes, with some studies additionally addressing social/cultural outcomes, behavioral/affective outcomes, and/or (meta) cognitive outcomes. Of the learning outcomes reported, 25 (53%) were positive, five (11%) were negative, four (9%) were neutral, eight (17%) were mixed and four (9%) identified factors influencing outcomes.Discussion: Theoretically, the study contributes to establishing more general theories about the specific role of CLIL in LOTE in students’ learning. Empirically, the study outlines pathways for future research on CLIL in LOTE. In practice, the study presents challenges identified by stakeholders to suggest pathways forward in CLIL teaching/learning.Systematic review registration: Open Science Framework (OSF):
Associations between motivational factors and burnout syndrome among elite skiers
Field: PSYCHOLOGY

The present research investigated the association between a series of motivational factors and burnout syndrome among elite skiers at the contextual level within the Hierarchical Model of Intrinsic and Extrinsic Motivation (HMIEM). There are 352 subjects (258 males, 94 females, aged 18 to 25 years) across five skiing events from three sport universities in this study. Four psychological scales related to motivational factors and burnout syndrome were completed by subjects. Overall, the result showed that a task-involving climate had a positive relationship with basic psychological needs, eliciting a positive pathway to autonomous motivation, and thus negatively affecting burnout syndromes. On the other hand, an ego-involving climate had a negative relationship with basic psychological needs, eliciting a negative pathway to amotivation, and then positively affecting burnout syndromes. The results underscore the intricate associations between a variety of motivational factors and athletes’ burnout syndrome, supporting the need to incorporate burnout syndrome elements into the outcomes of HMIEM sequence.
Collaborative AI-enhanced digital mind-mapping as a tool for stimulating creative thinking in inclusive education for students with neurodevelopmental disorders
Field: PSYCHOLOGY

Nowadays, inclusive education is becoming an increasingly important method in the education of people with various types of disabilities. This study is aimed at investigating the effectiveness of utilizing collaborative digital mind-mapping techniques in the practical work of students in inclusive educational groups, as well as examining how the use of AI-provided prompts influences the development of creative skills. The study involved 163 participants, among whom 28 had neurodevelopmental disorders. The application of the proposed methodology resulted in an improvement in the indicators of creative thinking as measured by the Torrance Figural Creativity Test, specifically in terms of Fluency, Originality, Elaboration, and overall creativity score; the observed increase was statistically significant according to the Wilcoxon signed-rank test (p = 0.05). This increase in indicators was observed both in students with neurodevelopmental disorders and in students without developmental disorders, with a notably stronger impact observed on students with neurodevelopmental disorders. Furthermore, a slightly higher effectiveness of the applied methodology was recorded when AI prompts were used for both categories of students. Students with neurodevelopmental disorders largely perceived the usefulness of the prompts they received subjectively. The present research may contribute to further study of various creativity development methodologies in inclusive education, as well as regarding the influence of AI utilization on creative skills. The obtained results can be utilized in the development of educational programs for students in higher education institutions that support inclusive forms of learning.
The Effects of Invented Spelling Instruction on Literacy Achievement and Writing Motivation
Field: EDUCATION

Early writing performance strongly predicts long-term literacy performance. It follows that early underachievement in writing is highly correlated with early underachievement in reading. One strategy teachers and students can use to approach writing in the kindergarten classroom is invented spelling. Invented spelling is children’s spontaneous or self-directed attempts to represent words in print by matching sounds to known letters or phonics patterns. A quasi-experimental study was used to evaluate the impact of invented spelling on foundational literacy skills and writing motivation in 63 kindergarten students at a rural school in the Mid-South. The research questions focused on the impact of invented spelling instruction on a variety of literacy outcomes, including foundational skills, spelling, and motivation. The results indicate the significant main effects of invented spelling instruction on students’ invented spelling (p < 0.001), conventional spelling (p < 0.001), complex vocabulary use (p < 0.001, writing motivation (p = 0.040), and writing achievement (p < 0.001). Other outcomes as well as implications and future directions are reported. The invented spelling intervention encouraged low-stake risk taking when writing and removed barriers to writing entry. Allowing time and space for invented spellings means students can focus on communicating their ideas in print without being hindered by the expectation to conform to conventional spellings.
Probabilistic Ensemble Framework for Injury Narrative Classification
Field: AI

In this research, we analyzed narratives from the National Electronic Injury Surveillance System (NEISS) dataset to predict the top two injury codes using a comparative study of ensemble machine learning (ML) models. Four ensemble models were evaluated: Random Forest (RF) combined with Logistic Regression (LR), K-Nearest Neighbor (KNN) paired with RF, LR combined with KNN, and a model integrating LR, RF, and KNN, all utilizing a probabilistic likelihood-based approach to improve decision-making across different classifiers. The combined KNN + LR ensemble achieved an accuracy of 90.47% for the top one prediction, while the KNN + RF + LR model excelled in predicting the top two injury codes with a very high accuracy of 99.50%. These results demonstrate the significant potential of ensemble models to enhance unstructured narrative classification accuracy, particularly in addressing underrepresented cases, and the potential of the proposed probabilistic ensemble framework ML models in improving decision-making in public health and safety, providing a foundation for future research in automated clinical narrative classification and predictive modeling, especially in scenarios with imbalanced data.
Transarterial chemoembolization combined with sintilimab and lenvatinib for the treatment of unresectable hepatocellular carcinoma: a retrospective study
Field: ONCOLOGY

Background The treatment of unresectable hepatocellular carcinoma (uHCC) challenging due to unfulfilled clinical requirements. Objective To evaluate the safety and efficacy of combining transarterial chemoembolization (TACE) with sintilimab and lenvatinib in the treatment of uHCC. Methods We retrospectively analyzed the data of patients with uHCC who were treated with a combination of TACE, sintilimab, and lenvatinib between May 2019 and December 2021 at the Chinese PLA General Hospital. Systemic treatment was started 1 week after TACE was performed. Sintilimab was administered intravenously at a dosage of 200 mg every three weeks, and lenvatinib was given orally at dosages of 8 mg or 12 mg daily, contingent upon the weight of the patients. The primary endpoint was the objective response rate (ORR) as per the mRECIST. Secondary endpoints were disease control rate (DCR), progression-free survival (PFS), overall survival (OS) and treatment-related adverse events (tr-AEs). Results A total of 32 patients were enrolled in the study. Among them, 9 patients were classified as Barcelona Clinic Liver Cancer-B (BCLC-B), 23 patients were classified as BCLC-C, 14 patients diagnosed with portal vein tumors, and 12 patients were diagnosed with extra hepatic metastases. The ORR and DCR were 75% and 90.6% respectively, with 4 patients exhibiting (12.5%) complete response, 20 patients exhibiting (62.5%) partial response, 5 patients exhibiting (15.6%) stable disease, and 3 patients exhibiting (9.4%) progressive disease. With a median follow-up time of 19.6 months, the median PFS was 9.9 months, and the median OS was 33.3 months. A total of 31 patients experienced different degrees of tr-AEs, of which 2 were grade 3 tr-AEs. Conclusion The combination therapy of TACE, sintilimab, and lenvatinib demonstrates satisfactory efficacy in the treatment of uHCC with manageable tr-AEs.
Implementing a Social Presence-Based Teaching Strategy in Online Lecture Learning
Field: PSYCHOLOGY

Previous studies have focused on the design of video lectures to improve students’ social presence by enhancing instructor presence for learners in lecture-based online courses; however, there has been limited emphasis on the peer presence in which learning from video lectures takes place. This study’s first objective is to develop a social presence (SP)-based teaching strategy to design online learning activities aimed at improving students’ social presence by providing social clues about peer presence and encouraging peer communication. The second objective is to compare students’ social presence, social interaction, and academic performance from lecture-based online learning supported by either a conventional teaching strategy or an SP-based teaching strategy. Using a quasi-experiment, we selected 81 Chinese university students to participate in a ten-week online course. The participants were randomly assigned to either an experimental group (EG) (N = 43) or a control group (CG) (N = 38). This study revealed that the SP-based strategy enhanced EG members’ social presence in online learning and that EG members achieved better academic performance than CG members. A significant correlation was found between the EG members’ academic performance and their social presence. The researchers also identified more concentrated social network sociograms with more cohesive subgroups in the EG members’ online interactions. The results indicate the necessity of applying an SP-based teaching strategy in lecture-based online courses to promote students’ social presence, social interaction, and academic performance.
Improving Distantly Supervised Relation Extraction with Multi-Level Noise Reduction
Field: AI

Background: Distantly supervised relation extraction (DSRE) aims to identify semantic relations in large-scale texts automatically labeled via knowledge base alignment. It has garnered significant attention due to its high efficiency, but existing methods are plagued by noise at both the word and sentence level and fail to address these issues adequately. The former level of noise arises from the large proportion of irrelevant words within sentences, while noise at the latter level is caused by inaccurate relation labels for various sentences. Method: We propose a novel multi-level noise reduction neural network (MLNRNN) to tackle both issues by mitigating the impact of multi-level noise. We first build an iterative keyword semantic aggregator (IKSA) to remove noisy words, and capture distinctive features of sentences by aggregating the information of keywords. Next, we implement multi-objective multi-instance learning (MOMIL) to reduce the impact of incorrect labels in sentences by identifying the cluster of correctly labeled instances. Meanwhile, we leverage mislabeled sentences with cross-level contrastive learning (CCL) to further enhance the classification capability of the extractor. Results: Comprehensive experimental results on two DSRE benchmark datasets demonstrated that the MLNRNN outperformed state-of-the-art methods for distantly supervised relation extraction in almost all cases. Conclusions: The proposed MLNRNN effectively addresses both word- and sentence-level noise, providing a significant improvement in relation extraction performance under distant supervision.
Governing AI in Southeast Asia: ASEAN’s way forward
Field: AI

Despite the rapid development of AI, ASEAN has not been able to devise a regional governance framework to address relevant existing and future challenges. This is concerning, considering the potential of AI to accelerate GDP among ASEAN member states in the coming years. This qualitative inquiry discusses AI governance in Southeast Asia in the past 5 years and what regulatory policies ASEAN can explore to better modulate its use among its member states. It considers the unique political landscape of the region, defined by the adoption of unique norms such as non-interference and priority over dialog, commonly termed the ASEAN Way. The following measures are concluded as potential regional governance frameworks: (1) Elevation of the topic’s importance in ASEAN’s intra and inter-regional forums to formulate collective regional agreements on AI, (2) adoption of AI governance measures in the field of education, specifically, reskilling and upskilling strategies to respond to future transformation of the working landscape, and (3) establishment of an ASEAN working group to bridge knowledge gaps among member states, caused by the disparity of AI-readiness in the region.