## 3 Proposed Methods
### Online Prediction
#### 3.4.1 Course Suggestion  
The goal of this process is to retrieve the courses of potential interest using the personality patterns. Conceptually, this process is based on learners with similar personality patterns producing similar learning performances. Therefore, the key challenge for this process is taking advantage of patterns to suggest appropriate courses. Algorithm 1 shows the algorithm for course suggestion. First, the active user's personality is found from the personality database \(PDB\) in Step 1. In Step 2, the system performs the operation of matching rules. The matching rule is defined in Definition 2. ```
0: A set of unique courses \(C\), a personality database \(PDB\), a personality rule database \(PRDB=\cup r_{j}\), an active user \(a\); //\(P_{i}\) indicate the \(i\)th user's personality set and \(r_{j}\) indicates the \(j\)th rule;
0: A suggested course list;
0:\(Course\_Suggestion()\)
1 retrieve the personality set \(R_{a}\) of the active user \(a\);
2 find the matching rule set \(MR\) to \(a\) from \(PRDB\);
3:for each \(course_{c}\in C\)do
4:for each \(r\subseteq MR\)do
5:if\(course_{c}\) in \(r\) and \(r.course_{c}.degree\)\(>\)2 then
6:\(pd_{c}=pd_{c}+r.conf\)// \(pd_{c}\) indicates the accumulated confidence of a positive course and \(r.conf\) indicates the rule confidence;
7:else
8:\(nd_{c}=nd_{c}+r.conf\)// \(nd_{c}\) indicates the accumulated confidence of a negative course and \(r.conf\) indicates the rule confidence;
9:endif
10:endfor
11:if (\(pd_{c}\)-\(nd_{c}\)) \(>\) 0 then
12:\(SC=SC+\)\(course_{c}\);//SC is the suggested course list. 13:endfor
14:return\(SC\). ```  
**Algorithm 1** Algorithm of course suggestion. **Definition 2**. : _Given a rule \(r\) and a personality \(p\), the matching rule is defined as: \(r\subseteq p\)._  
Note, in this part, the whole idea is oriented from an aspect [3; 4; 41; 42]: the personalities represented by three bipolar dimensions and five senses are related to the preferences, and the preferences will further impact the achievement. That is, high preferences will result in high performances. Hence, the main scientific intent behind this aspect is to mine the rules between the personalities and the achievements, where the personalities indicate the adopted personalities and the achievements indicate the course degrees. If a rule implies a high degree, the related course can be viewed as a positive course to suggest. For mining rules, the major idea is motivated from data mining theory [43; 44; 45; 46; 47; 48] that, an association rule A = \(>\) B indicates that transactions in the database containing an item set A tend to contain an item set B, and the related confidence exceeds the threshold. From another viewpoint, a rule can be viewed as a high confident rule. In this paper, the association rules are mined by the well-known algorithm Apriori [43; 48], which is a level-wise scan for frequent itemsets. It uses frequent \(k\)-itemsets to generate candidate (\(k+1\))-itemsets and prunes non-promising candidates to speed up the frequent itemsets generation. Based on this idea, in our proposed method, a rule \(<\) personality = \(>\) course, degree \(>\) indicates a personality set with a higher frequency larger than the threshold implies a course degree. According to these mined rules, a course confidence is the one accumulated by matching rules' confidences, where the matching rules with higher frequencies are larger than the threshold. That is, in the proposed system, the discovered rules are confident because the related frequencies should be greater than the threshold. That is, the suggested courses are confident by using the confident rules. In Algorithm 1, the suggested course list is generated in Steps 3-13. In these steps, for each unique course in the database, the related rules are determined first. Then, for each related rule, if the course degree is greater than 2, the course is identified as a positive course and the related rule confidence is accumulated as a positive confidence.