Situational interest is an important factor that has a great influence on learning success in both in-school and out-of-school learning situations. Although there has been extensive research on interest in its diverse forms for decades, an evaluated measurement instrument for situational interest that covers the three theoretically defined components of interest (emotional, cognitive, value-related) is still missing. Therefore, in this study, based on person-object theory of interest, a short scale was developed that can be used in a variety of learning programs independent of content or methods. In study 1, eight suitable items were selected and their structure was examined using exploratory methods. In study 2, the results of study 1 were verified using confirmatory factor analyses. Study 3 shows an example of a practical application of the newly developed scale in two different learning settings. The findings provide evidence that the scale developed here is a practical instrument to measure situational interest taking into account all its components. On the one hand, the scale can help teachers evaluate their educational programs; on the other hand, it can be used by researchers to empirically investigate the construct of interest. Thus, the scale makes an important contribution to research and practice.

Service-learning (SL) emphasizes the fusion and integration of community service and academic learning, while Physical Education (PE) is oriented towards community health and well-being, aligning with Sustainable Development Goals. The study's primary objective is to discern the benefits perceived by PE students when engaged in SL courses and to test the correlation between benefits and the motivational climate they experience. Additionally, the research explores potential gender-specific variations in students' attitudes. The research was conducted with a survey on a sample of 58 students responding to a Motivational Climate in Physical Education Scale (MPCES) and the SErvice LEarning Benefit Scale (SELEB), which assesses the motivation of students and their perceptions of SL benefits. Descriptive statistics was used to analyse the data. Pearson's correlation coefficient was used to test the relationship between the core constructs - SL benefits and student motivation, and t-tests to evaluate gender differences in SL benefits. Key findings underscore that students primarily perceived the greatest advantages from SL in the domain of knowledge application, with comparatively lower scores in general life skills. Notably, female students, on the whole, exhibited higher ratings in both SL benefits and student motivation compared to their male counterparts. Nonetheless, specific exceptions emerged in certain sub-scales, including social relatedness and ego climate within the MPCES scale, as well as critical thinking within the SELEB scale. According to the results, it is suggested that cultivating a student-centered learning environment can augment engagement and motivation in the realm of physical education (PE). Furthermore, the delineated gender differences offer valuable insights for educators, highlighting the need for tailored approaches to accommodate the diverse preferences and requirements of male and female students.

It is known that the COVID-19 pandemic led to learning losses among students both domestically and internationally. Therefore, situational and casual factors were examined to discover and understand them so that learning loss could be reduced or recovered from. This research aimed to: (a) study learning loss situation; and (b) develop and examine the causal model of learning loss among lower secondary school students affected by the pandemic. The sample included 650 Grade 7-9 students selected by multi-stage random sampling. The data was collected using a self-developing questionnaire as a research instrument. The data was analyzed using descriptive statistics, independent samples t-test, ANOVA, and structural equation modeling (SEM) through the LISREL program. The findings were: (a) Lower secondary school students had an average academic achievement learning loss at the moderate level with the highest mean of learning loss in mathematics (M=3.012, SD=1.074), and an average learning characteristics learning loss at the medium level (M=2.824, SD=0.842). Several situational factors had a different effect depending on the school size with a statistical significance of .05.; and (b) the causal model showed the learning loss of grade 7-9 students was consistent with the empirical data (χ2=46.885, df=34, p= .069, GFI=0.991, AGFI=0.964, CFI=0.999, RMSEA=0.024, SRMR=0.014).

Since psychological satisfaction is influenced by the interaction between individuals and their environment, it is necessary to create a cooperative climate at the organizational level and strengthen collective innovativeness at the individual level to improve teachers' job satisfaction. Therefore, the study investigated whether collective innovativeness can be mediated by the school climate to enhance teacher job satisfaction. This study extensively examined survey data with a sample of 3,976 teachers in Shanghai through Structural Equation Modeling, obtained from Teaching and Learning International Survey (TALIS). The findings revealed that teachers' collective innovativeness served as a significant mediator between school climate and job satisfaction. Furthermore, higher levels of collective innovativeness among teachers amplified the influence of school climate on their job satisfaction. These findings show that schools should strive to foster a collaborative school climate and provide support for teachers in implementing innovative and collaborative teaching activities with the aim of enhancing their job satisfaction. Above all, efforts are needed to support teachers' active and cooperative practice capabilities in building teacher-student relationships.

Recent research has identified factors influencing the choice of a special education career; however, it has not thoroughly examined their connection with personality traits and identity status. Thus, the present study was designed to explore how different personality traits and identity statuses correlate with the motives and perceptions associated with the choice to teach in special education. The study involved 209 pre-service special education teachers. The NEO-Five Factor Inventory was used to measure the Big 5 personality traits. The Ego Identity Process Questionnaire assessed identity commitment and exploration. The Factors Influencing Teaching Choice Scale was used to evaluate motivations and perceptions about teaching. Regarding the factors that influenced the decision to pursue a career in special education, intrinsic value, shaping children’s future, social equity, making social contributions, working with children, task demands, and job satisfaction were highly rated. Additionally, extraversion, openness to experience, conscientiousness, and identity statuses were identified as positive predictors for certain factors influencing the choice of a teaching career in special education. Finally, the study identified two distinct groups of students: "Identity Achievers" characterized by high positive personality traits, and identity commitment, and "Identity Explorers" characterized by lower positive personality traits and higher identity exploration. Differences were observed between the groups in their motives and perceptions concerning teaching in special education. In conclusion, this study highlights the relationships between personality, identity status, and career decision factors, offering insights into the factors that influence this critical career decision among future special educators. Directions for future research are discussed.

Given curiosity’s fundamental role in motivation and learning and considering the widespread use of digital stories as educational tools from the preschool age, we pursued measuring preschoolers’ curiosity when interacting with digital stories. Using 129 toddlers and preschoolers as a sample, three groups (one for each class) were given different versions of the same digital story to listen to: interactive, non-interactive, and animated. Toddlers' verbal and nonverbal behaviors were utilized to quantify curiosity as a condition brought on by the app. The participants' verbal and nonverbal behaviors were recorded during the digital reading aloud. Every child's data was encoded at one-minute intervals to examine concurrent behavior, and the results were then compiled. The findings show that interactive presentation formats encourage more touching and language use but less noise production and that interaction and the creative use of hot spots in digital illustrations are key elements in piquing viewers' curiosity while contributing to the strengthening of the engagement to the activity and the cultivation of critical thinking, creativity, and imagination.

In this article, we present a study that examined the effects of visual art activities on the realization of aspects of empowerment in a blind ninth-grade elementary school female student with minimal residual vision. We focused on three key aspects: well-being, positive self-image, and empowerment of strengths and weaknesses. In addition to the student, her mother and a personal assistant who accompanies the student during each activity in art class participated in the study. Based on initial interviews with all three participants, we developed ten visual art activities that address at least one of the listed aspects in different ways. Based on interviews, rating scales, observations and analysis of the visual art solutions, we found that visual art activities have a significant impact on improving a person's well-being, helping to strengthen a good self-image, reinforcing strong areas and developing weaker ones. In the future, we could broaden the range of psychological aspects that could be influenced by artistic activities, and we would also obtain more interesting and reliable results if more people with different special needs were included in the research.

The integration of AI tools in education is reshaping how students view and interact with their learning experiences. As AI usage continues to grow, it becomes increasingly important to understand how students' perceptions of AI technology impact their academic performance and learning behaviours. To investigate these effects, we conducted a correlational study with a sample of 44 students to examine the relationship between students' perceptions of ChatGPT’s utility—focusing on usage frequency, perceived usefulness, accuracy, reliability, and time efficiency—and key academic outcomes, including content mastery, confidence in knowledge, and grade improvement. Additionally, we explored how these perceptions influence student behaviours, such as reliance on ChatGPT, procrastination tendencies, and the potential risk of plagiarism. The canonical correlation analysis revealed a statistically significant relationship between students' perceptions of ChatGPT's utility and their academic outcomes. Students who viewed ChatGPT as reliable and efficient tended to report higher grades, improved understanding of the material, and greater confidence in their knowledge. Furthermore, the bivariate correlation analysis revealed a significant relationship between dependency on ChatGPT and procrastination (r = 0.546, p < .001), indicating that a higher reliance on AI tools may contribute to increased procrastination. No statistically significant association was identified between ChatGPT dependency and the risk of plagiarism. Future research should prioritize the development of strategies that promote the effective use of AI while minimizing the risk of over-reliance. Such efforts can enhance academic integrity and support independent learning. Educators play a critical role in this process by guiding students to balance the advantages of AI with the cultivation of critical thinking skills and adherence to ethical academic practices.

This study examines the factors influencing English language achievement among non-English major undergraduate students in Thailand, with a specific focus on the differences between high-achieving and low-achieving learners. Conducted at Rajamangala University of Technology Lanna, this research adopts a mixed-methods approach, combining quantitative data from questionnaires and qualitative insights from semi-structured interviews. Three primary influencing factors were identified: student-related factors (e.g., motivation and self-regulated learning), teacher-related factors (e.g., pedagogical practices and teacher-student interactions), and environmental factors (e.g., availability of learning resources). Student motivation and self-regulation emerged as the strongest predictors of success, while teacher-related factors unexpectedly showed a negative influence, suggesting a misalignment between teaching strategies and student needs. Environmental factors, though positively perceived, had a less direct impact on outcomes. Practical implications include enhancing intrinsic motivation, adopting tailored teaching strategies to meet diverse learner needs, and strengthening teacher-student relationships to support low-achieving students. Policymakers are encouraged to address resource disparities and develop targeted interventions to enhance English language proficiency among students.

This study explores the impact of artificial intelligence (AI) integration on students' educational experiences. It investigates student perceptions of AI across various academic aspects, such as module outlines, learning outcomes, curriculum design, instructional activities, assessments, and feedback mechanisms. It evaluates the impact of AI on students' learning experiences, critical thinking, self-assessment, cognitive development, and academic integrity. This research used a structured survey distributed to 300 students through Microsoft Forms 365, yet the response rate was 29.67%. A structured survey and thematic analysis were employed to gather insights from 89 students. Thematic analysis is a qualitative method for identifying and analysing patterns or themes within data, providing insights into key ideas and trends. The limited response rate may be attributed to learners' cultural backgrounds, as not all students are interested in research or familiar with AI tools. The survey questions are about AI integration in different academic areas. Thematic analysis was used to identify patterns and themes within the data. Benefits such as enhanced critical thinking, timely feedback, and personalised learning experiences are prevalent. AI tools like Turnitin supported academic integrity, and platforms like ChatGPT and Grammarly were particularly valued for their utility in academic tasks. The study acknowledges limitations linked to the small sample size and a focus on undergraduate learners only. The findings suggest that AI can significantly improve educational experiences. AI provides tailored support and promotes ethical practices. This study recommends continued and expanded use of AI technologies in education while addressing potential implementation challenges.

This study addresses global concerns surrounding elementary students' science performance following the COVID-19, as a result of international tests such as Trends in International Mathematics and Science Study (TIMSS) highlight the ongoing challenges that urge the exploration of innovative educational approaches to improve science learning. This research employed gamification-assisted instruction and explored its impact on enhancing the understanding of science concepts and attitudes toward science class among fourth graders. The study adopted a quasi-experimental design and included an experimental group (ExG) that was taught using a gamification strategy and a control group (CoG) that was taught using a traditional method with a sample of 38 female elementary students from a public school in Jordan. Data were gathered using valid and reliable tools: the developed scientific concepts test and the Attitude Towards Science class measures. The ANCOVA analysis revealed that gamification significantly improves the acquisition of scientific concepts (η2=.208) and boosts a positive attitude toward science classes among elementary students (η2=.626). These findings encourage decision-makers to incorporate gamification into science teaching practices and methods.

Integrating generative artificial intelligence (GenAI) in education has gained significant attention, particularly in flexible learning environments (FLE). This study investigates how students’ voluntary adoption of GenAI influences their perceived usefulness (PU), perceived ease of use (PEU), learning engagement (LE), and student-teacher interaction (STI). This study employed a structural equation modeling (SEM) approach, using data from 480 students across multiple academic levels. The findings confirm that voluntary GenAI adoption significantly enhances PU and PEU, reinforcing established technology acceptance models (TAM). However, PU did not directly impact LE at the latent level—an unexpected finding that underscores students’ engagement’s complex and multidimensional nature in AI-enriched settings. Conversely, PEU positively influenced LE, which in turn significantly predicted STI. These findings suggest that usability, rather than perceived utility alone, drives deeper engagement and interaction in autonomous learning contexts. This research advances existing knowledge of GenAI adoption by proposing a structural model that integrates voluntary use, learner engagement, and teacher presence. Future research should incorporate variables such as digital literacy, self-regulation, and trust and apply longitudinal approaches to better understand the evolving role of GenAI inequitable, human-centered education.

The present study examined how empathy (affective and cognitive), basic psychological need satisfaction (autonomy, competence, and relatedness), and demographic factors (gender and academic achievement) jointly predict prosocial behavior during emerging adulthood. Grounded in Self-Determination Theory, this research explored whether relatedness need satisfaction mediates the relationship between empathy and prosocial tendencies. A total of N=889 undergraduate students from a large public university in the southeastern United States completed self-report measures assessing empathy, psychological needs, and prosocial behavior. Path analysis revealed that affective empathy and relatedness satisfaction were significant predictors of prosocial behavior. Relatedness also partially mediated the link between empathy and helping actions. Furthermore, gender and GPA contributed to prosocial outcomes, with female students and those with higher academic achievement reporting greater prosocial tendencies. These findings suggest that fostering emotional engagement and supporting students’ psychological needs—particularly the need for relatedness—may be key mechanisms for promoting prosocial development in educational settings during the critical stage of emerging adulthood.