## 1 Introduction  
Advances in artificial intelligence (AI) have allowed the world to be automatic, intelligent, and interconnected. Therefore, people's careers might be changed, for example, investment portfolios can automatically be predicted without manual costs [1]. These changes have a serious impact on learning. Typically, as shown in Figure 1, learning can be decomposed into three stages: before learning, learning, and after learning. In these three stages, three main issues include what to learn, how to learn, and how to apply, respectively. The attention in most recent e-learning systems is focused on how to learn and how to apply. Although a number of works have contributed to how to learn and how to apply, for a student, knowing what courses they might prefer is also an important issue in addition to how to learn and how to apply [2]. A junior student may not understand or know their learning interests. Therefore, a guideline or suggestions about what to learn is necessary. Basically, the major idea relies on three factors, namely, personality, preference, and achievement. For \"personality and preference\", [PERSON] et al. [3] presented that a relationship exists between personality and preference. For \"personality and achievement\", [PERSON] et al. [4] focused on the effect of personality on learning styles of students and also briefly discusses the relationship between learning styles and culture. That is, different personalities will yield different learning styles, and then different learning styles will yield different learning results. Based on the educational points, our intent is to find the implicit associations among personalities, preferences, and achievements. To address this intent, in this paper, we propose an effective system, called a personality-driven course decision assistant, to provide helpful information for courses of potential interest. Learning is easier and more effective when a student takes courses in which they are interested.  
To increase the effectiveness of learning, the main contributions of this paper can be summarized as follows.  
* From an educational point of view: in fact, a good learning result is strongly related to a strong interest in the subject matter [4; 5]. Therefore, in this paper, an intelligent course assistant is proposed to bridge the gap between learning interest and learning direction. Through this assistant, the main goal of this study is to help the students clarify what courses they potentially prefer, thereby enhancing learning motivation and improving learning results.
* From a technical point of view, to identify the courses of potential interested, in this study, the learners (also called \"users\" in this paper) are modeled and filtered based on the personalities. In terms of learner modeling, the learning results and learner personalities are associated using association mining technology. With the discovered associations, the relationships between learning results and learner personalities can be clarified for researchers from a psychological viewpoint. In terms of filtering learners, the learners with relevant personalities are filtered to predict the potential learning degrees. With the potential learning degrees, the learners' course decisions are directed toward the area of potential interest.  
To evaluate the proposed idea, a number of experiments were conducted using the online course assistant system constructed in this work. Through the real system constructed, the objective and subjective evaluation results revealed that the proposed method is effective and reliable.  
The remainder of this paper is organized as follows. A brief review of past studies is outlined in Section 2. In Section 3, the proposed method of knowledge discovery for course decision support is presented in detail. The experimental results are provided in Section 4 and the conclusions are outlined in Section 5.