Two new instruments were created to assess secondary students’ (ages 14-18) spatial learning attitudes and their interest in science and technology, related careers ideas and perceptions about geospatial technologies. These instruments were designed to evaluate the outcomes of a geospatial learning curriculum project. During a two-year period, we explored the use of these instruments during the prototype testing and pilot testing of a series of socio-environmental science investigations. The instruments were implemented with 664 ninth grade urban students from a population traditionally underrepresented in STEM-related fields. Both classical and Rasch analyses were conducted each year to optimize the instruments. The resulting 24-item Student Interest in Science, Technology and Geospatial Technology (STEM-GEO) measure and 9-item Spatial Learning Attitudes (SLA) measure had high internal consistency reliabilities (Cronbach’s Alpha) as well as acceptable Rasch reliabilities. Content validity and construct validity evidence were also summarized and discussed.

This research analyzed the involvement of teachers, parents, and school committees in improving scientific attitudes in science learning using Rasch model analysis. A survey method was used in this quantitative study. Participants in the study were selected using a purposive sample of 174 teachers, parents, and school committees in Sleman and Kebumen Regencies, Indonesia. A questionnaire was used in data collection to determine the involvement of teachers, parents, and school committees in improving scientific attitudes toward science learning. The questionnaires were completed using a Likert scale of 1-4, and the data were then analyzed using the Rasch model. The result showed that all participants were the average logit items (+1.03 logit). The reliability was 0.89, indicating a positive response to improving students' scientific attitudes. The results of the Rasch model analysis suggested that the involvement of parents, teachers, and school committees in improving scientific attitudes differed according to their roles. Each instrument element was analyzed in more detail in the Rasch model. Participants' roles were reflected in the specific involvements of teachers in learning, parents at home with children, and school committees participating in school policy-making.

The role of artificial intelligence (AI) in education remains incompletely understood, demanding further evaluation and the creation of robust assessment tools. Despite previous attempts to measure AI's impact in education, existing studies have limitations. This research aimed to develop and validate an assessment instrument for gauging AI effects in higher education. Employing various analytical methods, including Exploratory Factor Analysis, Confirmatory Factor Analysis, and Rasch Analysis, the initial 70-item instrument covered seven constructs. Administered to 635 students at Nueva Ecija University of Science and Technology – Gabaldon campus, content validity was assessed using the Lawshe method. After eliminating 19 items through EFA and CFA, Rasch analysis confirmed the construct validity and led to the removal of three more items. The final 48-item instrument, categorized into learning experiences, academic performance, career guidance, motivation, self-reliance, social interactions, and AI dependency, emerged as a valid and reliable tool for assessing AI's impact on higher education, especially among college students.