This study aimed to find the Classical Test Theory (CTT) and Modern Test Theory (MTT) item parameters of the Differential Aptitude Test (DAT) and examined their comparability of them. The item parameters being studied are difficulty level and discrimination index. 5.024 data of the result sub-test DAT were documented by the Department of Psychology and Guidance and Counselling bureau. The parameter of classical and modern test items was estimated and correlated by examining the comparability between parameters. The results show that there is a significant correlation between item parameter estimates. The Rasch and IRT 1-PL models have the highest correlation toward CTT regarding the item difficulty level. In contrast, model 2-PL has the highest correlation toward CTT in the item discrimination index. Overall, the study concluded that CTT and MTT were comparable in estimating item parameters of DAT and thus could be used independently or complementary in developing DAT.

This article examines the influence of the university environment and learning programs on students' entrepreneurial intentions and attitudes, considering the moderating roles of entrepreneurial self-efficacy and locus of control. The empirical analysis employs a multilevel (hierarchical) linear model, utilizing responses from 713 students across 30 universities in Kazakhstan who participated in the GUESSS 2021 project survey. Our findings reveal that students' entrepreneurial intentions are directly influenced by entrepreneurial self-efficacy and locus of control. However, the locus of control is also indirectly influenced by the university environment and learning program. The learning program's effect on both entrepreneurial aspirations and attitudes is mediated by self-efficacy. Locus of control, conversely, negatively affects both entrepreneurial attitudes and intentions in program learning. The study's results underscore that student entrepreneurship is shaped by personal factors such as self-efficacy and locus of control, alongside the university context. Interestingly, the findings also indicate interdependencies between these factors, further influencing students' entrepreneurial intentions and attitudes.

The objective of this study is to create a tool for evaluating teachers’ effectiveness in boarding schools. Planning and preparation stages, instrument testing, and measurements were used in this study to implement research on the creation of the Mardapi model instrument. In order to generate instrument items, the planning and preparation stage tasks included a literature assessment of teacher performance appraisal manuals. Data were gathered utilizing documentation approaches, and descriptive and qualitative analysis was performed. Six specialists validated the built instruments, which were subsequently put through limited testing at two boarding schools and extensive trials at nine boarding schools in Surakarta residency area. The Aiken formula was used to examine the expert’s evaluation data, and confirmatory factor analysis was used to analyze the test results. The results of this study indicate that the instrument for assessing teacher performance in boarding schools meets the validity criteria. This is indicated by the existence of loading factor values ranging from .51 to .72 (>.4) and t-sign values ranging from 4.75 to 9.25 (>1.96) and meeting the requirements of a fit model since Chi-square = 1307.95 < 2524 (2*df), p-value = .17956 (>.05), and RSMEA value = .014 (<.08). The instrument items are reliable assessment packages, and this is shown by the reliability value of omega .967 > .70. Because there is a guarantee of validity and reliability, the test can be used to further assess teacher performance in boarding schools.

University students may encounter situations where they perform poorly in a course and contemplate dropping out. This intention to drop out of a course manifests not only in thoughts or ideas but also in a cognitive self-evaluation of their performance and skills, enabling them to reflect on the possibility of dropping out. In this sense, there is a shortage of instruments that evaluate the intention to drop out of a course, so the aim was to develop and validate the Course Dropout Intention Scale (CDIS). Data from two samples (N1 = 198; N2 = 675) were used; the first was for the EFA, and the second was for the CFA, GRM, and SEM. The one-factor model was derived from the EFA and confirmed in the second sample, exhibiting appropriate goodness-of-fit indices. Similarly, the GRM obtained adequate fit indices; all items discriminated adequately, and the difficulty parameter had a monotonic increase. The SEM model of the effect of satisfaction with studies on the CDIS showed a negative and statistically significant effect. Thus, it was demonstrated that the CDIS is a robust instrument in its psychometric properties and empirical evidence with other variables.

This study arises from the need to scientifically investigate how university students deal with their emotions in school situations. Therefore, the objective was to develop an instrument that measured university students' level of emotional regulation during academic activities and evaluate its validity and reliability. After a thorough literature review of the definitions of the constructs, the Emotional Regulation Scale in Academic Activities (ERAAS) was developed. The first version of ERAAS consisted of 18 Likert-type items. A total of 1975 university students in various departments responded to the instrument. Two groups of experts evaluated its content validity. Validity and reliability analysis was performed. According to the EFA, three factors were found: emotional regulation, psychologic inadequate emotional regulation, and physiological insufficient emotional regulation. The final version of the scale consisted of 11 items, the validity and reliability of which could be demonstrated for further research purposes.

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.

This research investigated the validity of measurement and causal model of online scam protection behavior (OSPB) among at risk Thai students. The sample comprised 286 high school students from three demonstration schools under the University. Data were analyzed using descriptive statistics, confirmatory factor analysis (CFA), and structural equation modeling (SEM). The factor loadings for all items satisfied the standard criteria with scores ranging from .40 to .80, item-total correlations ranging from .405 to .718, and Cronbach’s alpha coefficients ranging from .773 to .928. The modified model demonstrated a better fit with the empirical data (χ² = 47.62, df = 37, p = .113,  χ²/df = 1.287, RMSEA = .032, SRMR = .028, GFI = .97, CFI = 1.00, NFI = .99). All factors: a) awareness of online risks, b) inhibitory control, c) game-based learning, d) social support, and e) motivation to prevent online scams can predict 81% of OSPB. The motivation to prevent online scams strongly influenced OSPB, with an effect size of .60. Additionally, all factors can predict 88% of the motivation for online scam prevention, suggesting that Protection Motivation Theory (PMT) is a suitable framework for understanding and evaluating Thai students' preventive behaviors in online deception scenarios. This newly developed instrument is highly reliable and can be effectively used by researchers and educators to assess the risk of online fraud victimization among high school students.

Artificial intelligence (AI) has revolutionized higher education. The rapid adoption of artificial intelligence in education (AIED) tools has significantly transformed educational management, specifically in self-directed learning (SDL). This study examines the factors influencing Indonesian higher education students' intention to adopt AIED tools for self-directed learning using a combination of the Theory of Planned Behavior (TPB) with additional theories. A total of 322 university students from diverse academic backgrounds participated in the structured survey. This study utilized machine learning it was Artificial Neural Networks (ANN) to analyze nine factors, including attitude (AT), subjective norms (SN), perceived behavioral control (PBC), optimism (OP), user innovativeness (UI), perceived usefulness (PUF), facilitating conditions (FC), perception towards ai (PTA), and intention (IT) with a total of 41 items in the questionnaire. The model demonstrated high predictive accuracy, with SN emerging as the most significant factor to IT, followed by AT, PBC, PUF, FC, OP, and PTA. User innovativeness was the least influential factor due to the lowest accuracy. This study provides actionable insights for educators, policymakers, and technology developers by highlighting the critical roles of social influence, supportive infrastructure, and student beliefs in shaping AIED adoption for self-directed learning (SDL). This research not only fills an important gap in the literature but also offers a roadmap for designing inclusive, student-centered AI learning environments that empower learners and support the future of SDL in digital education.