In this study, we analyzed the descriptive knowledge and mental models of the phenomenon of tides manifested by 111 preservice primary teachers. The instrument employed is an open-ended questionnaire, analyzed by means of an approach that explores the descriptions, explanations, and predictions in respect of this phenomenon by our subjects. First,…

This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined 1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance (i.e. posttest math knowledge scores) prediction and 2)…

One of the goals of science education is to help students make sense of the world around them. To that end, it is critical that students understand the central ideas in each discipline like, in chemistry, energy and interactions. These ideas are of particular importance because they are directly related to one another and are relevant across other…

Active learning is widely recognized as superior to traditional passive, lecture-based techniques for fostering learning in STEM courses. Interactive, hands-on learning where students interact with their peers and physical systems is an effective type of active learning. As the need for scientists and engineers continues to grow, understanding and…

This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined: (1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance (i.e. posttest math knowledge scores) prediction; and…

In the present study, we tested the hypotheses that: a) there are individual differences in secondary students' conceptual and procedural fraction knowledge, and b) these differences are predicted by students' approach (deep vs. surface) to mathematics learning. We used two instruments developed and evaluated for the purposes of the study which…

This paper demonstrates how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. We examined: (1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance prediction; and (2) what types of in-game features were associated with student…

Small private online courses (SPOCs) have influenced teaching and learning in China's higher education. Learning management systems (LMSs) are important components in SPOCs. They can collect various data related to student behavior and support pedagogical interventions. This research used feature engineering and nearest neighbor smoothing models…

Utilizing facial recognition technology, the current study has attempted to predict the likelihood of student conceptual change with decision tree models based on the facial micro-expression states (FMES) students exhibited when they experience conceptual conflict. While conceptual change through conceptual conflicts in science education is a…

Comprehensive understanding of complex biological systems necessitates the use of computational models because they facilitate visualisation and interrogation of system dynamics and data-driven analysis. Computational model-based (CMB) activities have demonstrated effectiveness in improving students' understanding and their ability to use and…

The cognitive conflict hypothesis asserts that information that directly contradicts a prior conception is 1 of the prerequisites for conceptual change and other forms of nonmonotonic learning. There have been numerous attempts to support this hypothesis by adding a conflict intervention to learning scenarios with weak outcomes. Outcomes have been…

Scientific learning in schools requires not only students' ability to understand concept, but also critical thinking abilities of the students. However, the current scientific learning process is still focused on only cognitive aspects. Therefore, a teaching model or strategy that is able to support students to understand concept as well as…

Longitudinal studies can provide significant insights into how students develop competence in a topic or subject area over time. However, there are many barriers, such as retention of students in the study and the complexity of data analysis, that make these studies rare. Here, we present how a statistical framework, discrete-time survival…

Concept learning is challenging in part because the meanings of many concepts depend on their relationships to other concepts. Learning these concepts in isolation can be difficult, but we present a model that discovers entire systems of related concepts. These systems can be viewed as simple theories that specify the concepts that exist in a…

This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space--a concept language of logical rules. This article compares the model predictions to human generalization judgments in several…