## 3 Methodology  
### Background  
With rapid progress in sensing and communication technologies, rich classroom interactions with smart devices has become more and more popular. Blended learning, also known as hybrid learning or mixed-mode instruction, appears as a new education approach that combines e-learning and traditional classroom learning. Compared to e-learning focused on individual learning, blended learning is \"a combination of traditional face-to-face modes of instruction with online modes of learning, drawing on technology-mediated instruction\" [28], which can be applied to a group of students with especially designed teaching methods. Compared to cameras, motion sensors, and other devices, accepting and intending to use mobile devices in the classroom are topics of growing interest in the field of blended learning. A mobile-based assessment acceptance model [29] investigated the perceived ease of use and perceived usefulness, the constructs of facilitating conditions, social influence, mobile device anxiety, personal innovativeness, mobile-self-efficacy, perceived trust, content, cognitive feedback, user interface and perceived ubiquity value and investigates their impact on the behavioral intention. A key problem lies in the fact that smartphones in classroom teaching may enhance the learning but also become an interference [30]; findings have shown that this is a challenging task, and that the proper rules of using smartphones in class for students to abide to should be established before teaching. Clicker Assessment and Feedback (CAF) [31] assesses and investigates students' perceptions of CAF tools, examines the effects of university professors' CAF development, and then investigates the impact of professors' CAF methods on student learning and engagement. An exploratory study [1] compared the number of correct, incorrect, and missing responses from students who responded to in-class polling questions using clickers or mobile devices. In one of two classes, students using mobile devices had a greater number of missing responses and fewer correct responses than students using clickers, but there were no differences in the final grades. In the other class, there were no differences in these measures. In-class phone usage in college students was found to be negatively correlated with student grades [32], in which students use their phones for more than 25% of effective class duration, and phone distractions occur every 3-4 min for over a minute in duration. A research synthesis [33] investigated the effects of integrated mobile devices in teaching and learning, including 110 experimental and quasi-experimental journal articles, and found a moderate mean effect size of 0.523 for the application of mobile devices to education. In addition, mobile pedagogies [34] or mobile learning [35] are not discussed in this study, which belong to the extensions of e-learning systems for reducing or replacing the teaching activities of human teachers. In this study, we proposed a target-oriented teaching path optimization scheme as cloud computing-assisted instructions for blended learning. In a typical classroom teaching environment, e.g., one teacher and a group of students having certain lessons, and the main assumptions of our scheme are listed as follows:  
1. The teacher and all students have smartphones or pads with an especially designed APP, which supports in-class question-answer and can access cloud computing services. 2. All teaching contents, including in-class questions, homework, exams, are converted into some knowledge points, or some combinations of knowledge points. 3. All the responses of students, including in-class answers, homework, exam answers, are recorded by cloud computing services for teaching path optimization. 4. All teaching targets, including the passing rate, average score, and so on, can be defined as the mastering of knowledge points by students, e.g., the results of teaching path optimization.