Engagement is critical in learning, including computer-supported collaborative learning (CSCL). Previous studies have mainly measured engagement using students' self-reports which usually do not capture the learning process or the interactions between group members. Therefore, researchers advocated developing new and innovative engagement…

Background: Both learning and problem solving are major goals of complex problem solving in engineering education. The order of knowledge construction and problem solving in learning through problem solving, however, has not been explained in current literature. Objectives: To understand their relationships, this study compared the effects of…

The growing use of generative AI (GenAI) has sparked discussions regarding integrating these tools into educational settings to enrich the learning experience of teachers and students. Self-regulated learning (SRL) research is pivotal in addressing this inquiry. One prevalent manifestation of GenAI is the large-language model (LLM) chatbot,…

Background: Problem-solving is a multidimensional and dynamic process that requires and interlinks cognitive, metacognitive, and affective dimensions of learning. However, current approaches practiced in computing education research (CER) are not sufficient to capture information beyond the basic programming process data (i.e., IDE-log data).…

Collaborative concept mapping, as one of the widely used computer-supported collaborative learning (CSCL) modes, has been used to foster students' meaning making, problem solving, and knowledge construction. Previous empirical research has used varied instructional scaffoldings and has reported different effects of those scaffoldings on…

Background: Instructor scaffolding is proved to be an effective means to improve collaborative learning quality, but empirical research indicates discrepancies about the effect of instructor scaffoldings on collaborative programming. Few studies have used multimodal learning analytics (MMLA) to comprehensively analyze the collaborative programming…

Self-regulated learning (SRL) is a critical component of mathematics problem solving. Students skilled in SRL are more likely to effectively set goals, search for information, and direct their attention and cognitive process so that they align their efforts with their objectives. An influential framework for SRL, the SMART model, proposes that…

Self-regulated learning (SRL) is a critical component of mathematics problem-solving. Students skilled in SRL are more likely to effectively set goals, search for information, and direct their attention and cognitive process so that they align their efforts with their objectives. An influential framework for SRL, the SMART model (Winne, 2017),…

To understand how learner metacognition and goal orientation affect learner problem-solving in a Serious Game (SG) environment, this study examined 12 undergraduate students' metacognition, goal orientations, and problem-solving performances and processes while playing a SG that adopts problem-based learning pedagogy to teach space science.…

The integration of computational modelling in science classrooms provides a unique opportunity to promote key 21st century skills including computational thinking (CT) and collaboration. The open-ended, problem-solving nature of the task requires groups to grapple with the combination of two domains (science and computing) as they collaboratively…

The aim of this research is to examine the relationships between students' community of inquiry, academic self-efficacy, reflective thinking skills, problem-solving skills, and metacognitive awareness in a flipped learning environment supported by personalized recommendation and guidance messages based on learning analytics. For this purpose,…