This study aims to develop and understand the effect of a mobile game based learning (M-GBL) implementation on student learning outcomes associated with student learning independence. The research was conducted in three schools at 58 Jakarta High School, 77 Jakarta High School, and 78 Jakarta...

The aim of this research was to develop the sustainability competencies of preservice chemistry teachers’ through the use of a project-based learning model. Preservice chemistry teachers were engaged in a summer course program in collaboration with national and international universities. The summer course program was conducted online due to the COVID-19 pandemic. The research involved 26 preservice chemistry teachers from a pedagogical university in Jakarta, Indonesia, which joined with other university students from other universities in Indonesia, America, Thailand, and Malaysia. We used a qualitative methodology. Data were collected through interviews, questionnaires, observations, preservice chemistry teachers’ portfolios, and reflective journals. The data were coded into themes and interpreted to reveal that all students engaged successfully in developing their sustainability perspectives, environmental awareness, project development engagement, communication, and collaboration skills. Meanwhile, the preservice chemistry teachers engaged in developing their project in an online summer course program within the framework of sustainability.

In this study, we sought to investigate the effect of a 2-week Technological Pedagogical Content Knowledge (TPACK)-based training program on how the pre-service teachers’ TPACK was perceived. To achieve the goal, we used four TPACK activities; Explore-Engage-Reflection-Transformation. The study employed mixed methods with a TPACK questionnaire and open questions. Increased self-efficacy perceptions of TPACK was evaluated using a one-group pre-post-post research design. The sample was 30 pre-service teachers (5 male; 25 female) at a public university in Indonesia. Paired t-test was used to examine the significant difference between pretest and posttest scores and Cohen’s d was calculated to check the strength of the effect size. As a result, the paired t-test showed that the posttest scores were significantly higher than the pretest scores for all TPACK dimensions with large effect sizes. In addition, qualitative data from students’ reflections were analyzed. It found that pre-service teachers understand the role of technology integration in the TPACK framework for creating meaningful learning experiences. Participants reflected on the role as assessment for, and as learning. Professional learning from the New Zealand education system was introduced within a holistic framework to stimulate students to be creative future-directed teachers and agents of change. This indicates that the present training program increased the pre-service teachers’ perceptions of TPACK at the end of the training. We suggest that TPACK training programs that facilitate how pre-service teachers integrate technology, content, and pedagogy to create effective technology-enriched learning environments in their subjects should be included in teacher education programs.

The university admission process plays a pivotal role in shaping the future of aspiring students. However, traditional methods of admission decisions often fall short in capturing the holistic capabilities of individuals and may introduce bias. This study aims to improve the admission process by developing and evaluating machine learning approach to predict the likelihood of university admission. Using a dataset of previous applicants' information, advanced algorithms such as K-Nearest Neighbors, Random Forest, Support Vector Regression, and XGBoost are employed. These algorithms are applied, and their performance is compared to determine the best model to predict university admission. Among the models evaluated, the Random Forest algorithm emerged as the most reliable and effective in predicting admission outcomes. Through comprehensive analysis and evaluation, the Random Forest model demonstrated its superior performance, consistency, and dependability. The results show the importance of variables such as academic performance and provide insights into the accuracy and reliability of the model. This research has the potential to empower aspiring applicants and bring positive changes to the university admission process.

This paper explores the potential of generative artificial intelligence (AI) to transform higher education. Generative AI is a technology that can create new content, like text, images, and code, by learning patterns from existing data. As generative AI tools become more popular, there is growing interest in how AI can improve teaching, learning, and research. Higher education faces many challenges, such as meeting diverse learning needs and preparing students for fast-changing careers. Generative AI offers solutions by personalizing learning experiences, making education more engaging, and supporting skill development through adaptive content. It can also help researchers by automating tasks like data analysis and hypothesis generation, making research faster and more efficient. Moreover, generative AI can streamline administrative tasks, improving efficiency across institutions. However, using AI also raises concerns about privacy, bias, academic integrity, and equal access. To address these issues, institutions must establish clear ethical guidelines, ensure data security, and promote fairness in AI use. Training for faculty and AI literacy for students are essential to maximize benefits while minimizing risks. The paper suggests a strategic framework for integrating AI in higher education, focusing on infrastructure, ethical practices, and continuous learning. By adopting AI responsibly, higher education can become more inclusive, engaging, and practical, preparing students for the demands of a technology-driven world.

This study aims to determine students scientific literacy skills after learning with a contextual-flipped classroom model. In flipped classroom-contextual learning, students study the lesson material through contextual videos outside the classroom, then conduct discussions in class and lab work. This study used a qualitative method with 36 students in class XI at senior high school in Jakarta. Data collection were carried out through scientific literacy tests, observation, interviews, and reflective journal of students. The students' scientific literacy skills in this study refer to the scientific literacy of PISA. The results showed that 16.7% students had achieved scientific literacy at level 6; 38.9% at level 5; 11.1% at level 4; 27.8% at level 3; 5.6% at level 2 and 1. In addition, the results of the study show that aspects of scientific literacy have also been mastered by students. Mastery of scientific literacy aspects, concerning: aspect of the scientific context is 83.92%, aspect of science knowledge is 84.36%, and aspect of scientific competence is 84.06%. Thus, it can be concluded that acid-base learning with a contextual-flipped classroom model can improve students scientific literacy skills.