Although some researchers have suggested that video games such as Minecraft have the potential to foster student creativity, little is known regarding how educational games should be designed to effectively promote creativity. To address this gap, this study aimed to develop an educational game by modifying Minecraft and evaluate the effects of two game design features, challenge and autonomy, on the in-game creative performance of middle school students. A creativity support game intervention was implemented with 88 sixth-grade students. The results revealed that games incorporating challenge and autonomy had a positive influence on students' in-game creative performance. This impact was observed to be mediated by two factors: creative selfefficacy and engagement. Findings of the current study suggest that designing educational games with specific features can enhance students' creative performance within the game environment.

ABSTRACT Wallas' model of creativity describes a continuous process by which an individual obtains relevant information (preparation), unconsciously processes said information (incubation), has a flash of insight (illumination), and builds upon an idea (verification or implementation). Per this model, technologies or methodologies looking to develop creative skills in individuals must find a way to nurture this four‐step process. Regarding technologies, creativity research has shown that games and simulations meant to provide the tools and context needed to promote creative thinking may have significant developmental value. The goal of this paper is to detail a methodology for the design of games which promote the development of creative skills through the facilitation of all four phases of Wallas' model. The example we will be providing involves creative skills in a general chemistry context, but the design principles at play should apply to other domains as well.

The increasing adoption of artificial intelligence (AI) in education has accelerated the development of intelligent tutoring systems capable of providing adaptive instructional support. However, many existing systems remain constrained by limited personalization, weak reasoning transparency, and insufficient integration of cognitive-developmental learning principles. Most approaches rely on either neural conversational intelligence or symbolic pedagogical reasoning independently, restricting their ability to provide explainable and developmentally aligned educational support. This study proposes and empirically evaluates an adaptive neuro-symbolic learning companion framework designed to support explainable cognitive scaffolding through integrated learner modeling and pedagogical reasoning. Using the Khan Academy Tutoring Accuracy Dataset, a neuro-symbolic analytical framework was developed that combines transformer-based conversational analysis, symbolic tutoring inference, emotional state detection, scaffolding evaluation, and explainability assessment. The framework operationalizes scaffolding strategies grounded in Vygotsky’s Zone of Proximal Development (ZPD), including adaptive hint escalation, conceptual reinforcement, reflective prompting, and meta-cognitive support. Statistical analyses, including independent samples t-tests and ANOVA, were conducted to examine relationships between scaffolding strategies and learning effectiveness. Results indicate that existing tutoring interactions are predominantly procedural and direct-answer oriented, with only six explicit meta-cognitive prompts identified across 2,022 tutoring interactions. Although conversational coherence remained high (BERTScore F1 = 0.815), reflective scaffolding was largely absent. The proposed framework successfully identified interpretable pedagogical behaviors and demonstrated measurable explainability through symbolic reasoning structures. The study contributes a novel neuro-symbolic approach to adaptive learner modeling, explainable educational AI, and cognitively aligned tutoring systems, advancing the development of transparent and personalized AI learning companions.

Assessing artistic creativity is foundational to creativity research and arts education, yet manual scoring (e.g., Torrance Tests of Creative Thinking) is labor-intensive at scale. Prior machine-learning approaches show promise for visual creativity scoring, but many rely mainly on image features and provide limited or no explanatory feedback. We propose a framework for automated creativity assessment of human paintings by fine-tuning the vision-language model Qwen2-VL-7B with multi-task learning. Our dataset contains 1000 human-created paintings scored on a 1-100 scale and paired with a short human-written description (content or artist explanation). Two expert raters evaluated each work using a five-dimension rubric (originality, color, texture, composition, content) and provided written critiques; we use an 80/20 train-test split. We add a lightweight regression head on the visual encoder output so the model can predict a numerical score and generate rubric-aligned feedback in a single forward pass. By embedding the structured rubric and the artwork description in the system prompt, we constrain the generated text to match the quantitative prediction. Experiments show strong accuracy, achieving Pearson r > 0.97 and MAE about 3.95 on the 100-point scale. Qualitative evaluation indicates the generated feedback is semantically close to expert critiques (average SBERT cosine similarity = 0.798). The proposed approach bridges computer vision and art assessment and offers a scalable tool for creativity research and classroom feedback.

Preservice teachers’ beliefs play an important role in shaping their future instructional practices. While creativity is increasingly recognized as a key element in education, little is known about how preservice teachers perceive creative versus good teachers. The current study explored preservice teachers’ views on the characteristics associated with creative and good teachers, identifying both shared and unique attributes. A total of 438 preservice teachers participated in this study. The results revealed that preservice teachers value relationship building, professional competence, and creativity in both categories. Notably, the emphasis on creativity in both creative and good teachers suggests an evolving perspective on teaching that highlights the significance of fostering creativity in classrooms. Additionally, good teachers were associated with a wider range of social and personality traits, whereas creative teachers were characterized by a stronger focus on adaptability and leadership. The findings underscore the need for teacher education programs to adopt an integrative approach that values both teaching effectiveness and creativity, ensuring that future educators are well-prepared to meet the demands of 21st-century education.

Psychometric network analysis (PNA) has been gaining great popularity over the past decade. As a promising dimensionality assessment method, existing research has shown that PNA is able to outperform traditional methods such as exploratory factor analysis in examining the internal structure of a latent construct, and various R packages have been developed to carry out PNA. Yet, PNA has not been widely used in various fields due to researchers’ lack of familiarization with this method and the available R packages. Therefore, this study aims to briefly review the PNA method, compare different R packages, and provide step-by-step guidance on how to use these R packages to conduct PNA using a personality dataset.

Assessing artistic creativity has long been a challenge. Traditional tests are widely used but often require time-consuming manual scoring. Thus, researchers are exploring a new way, such as machine learning (ML), to automate the assessment. Recent research on visual artistic creativity assessment has demonstrated that ML methods are effective but constrained by their reliance on visual data alone. This study integrates textual descriptions alongside visual data for a more holistic assessment of paintings’ creativity, which is more sophisticated to measure than simple sketches. The multimodal model was fine-tuned and leveraged both visual and textual inputs. It achieved approximately 95.3% accuracy in predicting the painting creativity scores, demonstrating a strong positive correlation (Pearson r = 0.96) with expert ratings. The study allows a text-image evaluation of paintings’ creativity to better align with human interpretations.

Assessing artistic creativity has long challenged researchers, with traditional methods proving time-consuming. Recent studies have applied machine learning to evaluate creativity in drawings, but not paintings. Our research addresses this gap by developing a CNN model to automatically assess the creativity of human paintings. Using a dataset of six hundred paintings by professionals and children, our model achieved 90 evaluation times than human raters. This approach demonstrates the potential of machine learning in advancing artistic creativity assessment, offering a more efficient alternative to traditional methods.

Creativity has been well studied over the past several decades. However, our understanding regarding the nature of creativity remains limited, partially due to all kinds of methodological challenges that researchers have been facing. For example, the widely used factor-analytic techniques require researchers to make decisions about rotation methods, the number of factors to retain, and the interpretation of factor loadings. As a result, achieving a consensus on the factor structure of a latent construct is often difficult, especially when there is a lack of strong theoretical guidance in developing items. Recent trends in creativity research have shown the need and efforts to reduce subjectivity and increase the accuracy of creativity measurements. Hence, this study provides an example of how to estimate the number of dimensions underlying multivariate creativity data using psychometric network analysis (PNA). This new approach to dimensionality assessment can estimate the factor structure of a latent construct without extensive decision making from researchers. Step-by-step instructions on how to conduct the analysis in R using the Self-perceptions of Creativity data are presented. The interpretation of outputs and recommendations for beginners are also discussed.

Technology has been playing an increasingly important role in teaching and learning because of its capacity to increase students' motivation and provide them with opportunities to explore and acquire new knowledge and skills. However, little is known regarding the effectiveness of using the digital interactive case-based instruction (CBI) approach to support teaching and learning. Hence, this study investigated the effectiveness of digital interactive CBI in supporting learning of behaviourism among 234 preservice teachers. The results indicated that digital interactive CBI was a useful tool for enhancing learning. Preservice teachers not only found the activity engaging, but also believed that it was helpful in deepening their understanding towards operant conditioning and improving their abilities to analyse and solve real-world problems. The detailed findings will provide helpful insights to teacher educators who are considering implementing CBI in teacher education programmes.

Creativity can be assessed from four different perspectives – creative product, process, person, and environment. Popular methods of assessing creative products in education, psychology, and business range from rater-based techniques to self-reported measures. Regarding creative process, the Remote Associates Test has been used to test individuals’ ability to build remote connections among seemingly unrelated items, and divergent thinking tests such as the Torrance Tests of Creative Thinking have been widely employed to examine individuals’ divergent thinking ability. A number of creativity instruments concerning individual characteristics of creators have been developed. Both home and work environment influence creativity. Existing measures of the creative work environment include KEYS to Creativity and Innovation and the Creative Environment Perceptions scale.

The demographic landscape in the United States is changing rapidly, and early childhood programs are experiencing an increase in the enrollment of Dual Language Learners (DLLs). The current study employed a mixed-methods case study design to explore the impact of creative drama on Head Start DLLs’ social and emotional development. Six DLLs enrolled in a Head Start center participated in the 9-week creative drama intervention. Quantitative data showed that participants’ social and emotional skills improved significantly after the intervention. Qualitative data further revealed that participants demonstrated improvements in social interactions, including increased confidence, enhanced cooperation skills, and better emotion management. Overall, the findings from the current study suggest that creative drama is a promising strategy for Head Start DLLs to increase their social and emotional competence.

This mixed-method study explores current innovative methods towards technology integration in a teacher preparation programme at a mid-sized university in the Southeastern United States. The authors collected and analysed qualitative descriptive data from instructors of a foundational technology course. Using the recently developed Technology Education Technology Competencies (TETCs), they surveyed 17 faculty members and conducted follow-up interviews to understand their perspectives about the importance of the TETCs, and their belief of whether their current instructional technology integration aligned with them. Key findings suggest policies and initiatives to increase faculty technology integration did not tightly align with practice; teacher educators believed the TETCs were important, but most perceived their practices with teacher candidates were weak, and teacher educators with a higher proficiency with technology scored higher on their beliefs and practices with technology integration. The authors use their findings to propose ways to redesign teacher education programmes to increase faculty technology integration practices.

ABSTRACTCreativity has been well studied in the past several decades, and numerous measures have been developed to assess creativity. However, validity evidence associated with each measure is often mixed. In particular, the social consequence aspect of validity has received little attention. This is partly due to the difficulty of testing for differential item functioning (DIF) within the traditional classical test theory framework, which still remains the most popular approach to assessing creativity. Hence, this study provides an example of examining differential item functioning using multilevel explanatory item response theory models. The Creative Thinking Scale was tested for DIF in a sample of 1043 10th–12th graders. Results revealed significant uniform and non‐uniform DIF for some items. Differentially functioning items are able to produce measurement bias and should be either deleted or modeled. The detailed implications for researchers and practitioners are discussed.

微课教学作为一种新鲜的教学模式,可以破除课堂环境的呆板枯燥,有效激发出学生的主动学习热情,为学生的语文学习营造良好氛围.在高中语文课堂教学时,教师可以采取微课教学方式,构建高效课堂.本文就微课在高中语文课堂教学中的有效运用进行分析.

Creativity, as one of the key 21st century skills, has become increasingly important. Yet despite the huge volume of research on creativity in the past 60 years, a fundamental debate about the nature of creativity still remains unsolved: Is creativity domain specific or domain general? In the present study, multilevel explanatory item response theory models were used to analyze 359 undergraduate and graduate students' participation and achievements in a wide range of creative activities in six distinct domains. Results suggested that creativity was relatively domain general rather than domain specific. The findings will provide valuable insights for researchers and educators in issues related to creativity assessment and efforts to fulfill students' creative potential in schools.