Research shows that learning enjoyment in specific socio-scientific issues (SSI) plays an important role in predicting grade 10 students’ learning interest and learning enjoyment (i.e., genuine interest) in SSI subjects generally. However, it remains unexplored whether learning enjoyment also mediates a predictive effect of learning interest in a Big Science SSI of pressing contemporary global concern—COVID-19—on grade 12 high school students’ learning interest in SSI generally. The purpose of this study is to investigate how learning enjoyment may mediate the predictive effect of learning interest in the specific Big Science SSI of COVID-19 specifically on students’ learning interest in SSI subjects generally. Latent variable modeling using data collected from grade 12 students (N = 691) showed personal perceptions of learning enjoyment in SSI partially mediated the predictive effect of learning interest in the SSI of COVID-19 on learning interest in other Big Science SSI subjects. Implications for promoting among science educators and policy specialists the active development of students’ individual interests and involvement in other 21st century Big Science SSI challenges are forwarded. 

Scaffolding dialogue is a concept in learning that refers to the support or assistance given to individuals during the dialogue process. The main objective of this research is to create a basic structure of dialogue to help and support students during the learning process in improving their algebraic reasoning skills. Algebraic reasoning is a process in which students generalize mathematical ideas from a certain set of examples, establish these generalizations through argumentative discourse, and express them in a formal and age-appropriate way. The study was designed using the grounded theory qualitative model method, which used three sequential steps: open coding, selective coding, and theoretical coding. The research was conducted on students of the mathematics education department at Universitas Islam Sultan Agung. Data collection methods include algebraic reasoning ability tests, questionnaires, and interviews. Data analysis in grounded theory is an iterative and non-linear process that requires researchers to constantly move back and forth between data collection and analysis. This process aims to produce a theory that is valid and can explain phenomena well based on empirical data obtained during research. The dialogue scaffolding strategy framework in improving students' algebraic reasoning abilities includes instructing, locating, identifying, modeling, advocating, exploring, reformulating, challenging, and evaluating.