We applied latent class analysis and the rule space model to verify the cumulative characteristic of conceptual change by developing a learning progression for buoyancy. For this study, we first abstracted seven attributes of buoyancy and then developed a hypothesized learning progression for buoyancy. A 14-item buoyancy instrument was administered to 1089 8 th grade students to verify and refine the learning progression. The results suggest four levels of progression during conceptual change when 8 th grade students understand buoyancy. Students at level 0 can only master Density. When students progress to level 1, they can grasp Direction, Identification, Submerged volume and Relative density on the basis of the prior level. Then, students gradually master Archimedes' theory as they reach level 2. The most advanced students can further grasp Relation with motion and arrive at level 3. In addition, this 4-level learning progression can be accounted for by the Qualitative-Quantitative-Integrative explanatory model.

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.

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.

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