The Development of Blended Learning through Learning by Teaching for Mathematical Literacy in General Education Program on Higher Education

Abstract

Blended learning is a kind of classroom learning which is suitable to help students for promoting their learning with the 21st century skills. When the collaborative learning is integrated into classroom techniques, blended learning is a way for students to learn their basic knowledge with online technology before they come to classroom, and spend most of their time in the classroom performing team activities to solve new or complex problems. This study proposes a model for teaching and learning in order to develop the blended learning through learning by teaching. Based on the gradual release of responsibility model, rainbow talk is used to encourage students to take responsibility for collaborative learning combined with flipped learning which students are assigned to study lessons and take tests before they come to classroom. This aims at studying effectiveness of the proposed model in order to improve mathematical achievement in mathematical courses of general education program for higher education. The study is a practical research that provides statistical analysis of mathematical learning results of students from the proposed learning model. The results of this study indicate that mathematical achievement of students is attained in accordance with the learning outcomes of the course at a satisfactory level and is satisfied with the learning and teaching at a good level.

Measurement, Evaluation, and Model Construction of Mathematical Literacy Based on IoT and PISA

“…e security hidden danger of the Internet will pose corresponding challenges to the security of the IoT [11]. e perception layer of the IoT is mainly used for data acquisition and object communication, and it transmits information wirelessly, which is easy to be illegally monitored and stolen [12]. Firstly, the mathematical statistical model is used to quantitatively describe the relationship between the data feature vector and the data attribute value, and the resource data with similar feature vectors are summarized into the same classification plane.…”