Educational leadership is a multifaceted area of study. Unquestionably, leadership is the most deliberate field within the social sciences. Still, administrators have evaded the notions of leadership concept like a haunting tune. This study has focused particularly on the significance of varied leadership styles in teaching to sustain academic excellence at the secondary school level. The quantitative research method was used. Data was collected through the scale of diverse leadership styles (strategic, cultural, instructional leaderships and sustaining academic excellence) from 103 secondary schools in Punjab, Pakistan. The sample consisted of 540 teachers who were enacting as teachers presently. Based on research objectives and questions, two hypotheses were formed and tested using mean analysis to determine the average ranking of leadership styles. Pearson correlation to know the statistically significant relationship between each leadership styles, and overall scales with sustaining academic excellence. The results revealed that most teachers give preference strategic Leadership, then instructional leadership, and finally cultural leadership in their teaching to sustain academic excellence. Moreover, the findings also indicated that a statistically strong positive relationship among diverse leadership styles in teaching and sustaining academic excellence with the value (r = 0.752). Based on the findings, it has been concluded that when teachers increase their effort in the use of strategic, instructional, and cultural leadership styles, academic excellence may also sustain and improve.

At present, research on computational thinking in universities is gaining interest, and more attention is being paid to the cultivation and teaching of computational thinking. However, there is a lack of computational thinking assessment tools for college students, which makes it difficult to understand the current status and development of their computational thinking. In this study, computational thinking is regarded as the ability to solve practical problems. By analyzing the relevant literature, we identified five dimensions of computational thinking – decomposition, generalization, abstraction, algorithm and evaluation – and described their operational definitions. Referring to the Bebras and the problem situations in Google computational thinking education, we set up a life-based situation that college students are familiar with. Based on the life story situation, we developed a multidimensional assessment for college students’ computational thinking. This assessment tool contains 14 items, all of which are multiple-choice questions, and the structure and quality of the tool are verified by multidimensional item response theory. The results show that the assessment tool has good internal validity and can discriminate different disciplines of college students. The college students’ computational thinking test developed in this study can be used as an effective tool to assess college students’ computational thinking.

Globally, as we progress toward a more sustainable future, the sustainable development goals (SDGs) must be incorporated into various sectors, including education. This article explores the significant impact of inventive leadership styles on SDGs in education at the secondary school level in Pakistan. Based on this study, four hypotheses were developed using leadership variables such as empowerment of leadership, inspiring learners, resilient visions, and lead transformation. A quantitative survey method was utilized in this study. The researchers selected convenient sampling to collect data from 288 teachers, including heads of schools and administrators from 89 institutions. The data collection process included using a survey questionnaire to collect data on leadership styles and their relationship regarding SDGs within the field of education. In order to analyze the given data, two software programs were used, namely SPSS and Smart PLS 4.0. Several statistical methods were used to examine the collected data, such as confirmatory factor analysis (CFA) and structural equation modeling (SEM), which were implemented to confirm the measurement model. Additionally, path analysis was conducted to determine the relationship between independent and dependent variables. The findings revealed that overall leadership styles have a great positive impact on SDGs in education. Based on results, it has been found that it would be beneficial if leaders/teachers enhance their struggle using inventive leadership styles for achieving SDGs, fostering transformative education, and ensuring that learners and societies benefit from a more equitable and resilient future. Additionally, the need for more resources, resistance to change, and a need for leaders’/teachers’ professional development have been identified as potential obstacles. To overcome these challenges, a collaborative approach among stakeholders, policy support, and innovative practices are recommended.

A Q-matrix, which reflects how attributes are measured for each item, is necessary when applying a cognitive diagnosis model to an assessment. In most cases, the Q-matrix is constructed by experts in the field and may be subjective and incorrect. One efficient method to refine the Q-matrix is to employ a suitable statistic that is calculated using response data. However, this approach is limited by its need to estimate all items in the Q-matrix even if only some are incorrect. To address this challenge, this study proposes an item fit statistic root mean square error approximation (RMSEA) for validating a Q-matrix with the deterministic inputs, noisy, “and” (DINA) model. Using a search algorithm, two simulation studies were performed to evaluate the effectiveness and efficiency of the proposed method at recovering Q-matrices. Results showed that using RMSEA can help define attributes in a Q-matrix. A comparison with the existing Delta method and residual sum of squares (RSS) method revealed that the proposed method had higher mean recovery rates and can be used to identify and correct Q-matrix misspecifications. When no error exists in the Q-matrix, the proposed method does not modify the correct Q-matrix.

This paper explores Knowledge Tracing (KT) and its role in modeling students’ knowledge progression over time. While traditional approaches such as Bayesian Knowledge Tracing and Deep Knowledge Tracing have established a solid foundation in this field, previous research often overlooked the complex interactions between problem features and learner behaviors. To address this gap, this paper introduces the Knowledge Tracking Model for Integrating Question Information and Learning Behaviour (IQILB-KT), a novel framework that enhances the precision of knowledge tracing in online educational systems. By integrating the effects of question characteristics and student behaviour via a channel attention mechanism, the model captures deeper insights into both question information and the learning process. Furthermore, the IQILB-KT model employs a Bottleneck Attention Module (BAM) to calculate learning behaviour synergies, replacing the previous reliance on covariance statistics. This enhances the stability and accuracy of the model when dealing with learning behaviour synergy data. Finally, the efficacy of the IQILB-KT model was validated through rigorous testing on two real-world datasets from the ASSISTments educational platform. The results demonstrate that the model outperforms all existing methods in predicting students’ future performance and tracking their knowledge states in real time.